Vehicle Occupancy Data Collection Methods

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2 Final Report Veicle Occupancy Data Collection Metods Contract No. BD Prepared by: Albert Gan, P.D., Associate Professor Rax Jung, P.D., Researc Associate Kaiyu Liu, Senior Software Engineer Xin Li, Graduate Researc Assistant Diego Sandoval, Graduate Researc Assistant Leman Center for Transportation Researc Department of Civil and Environmental Engineering Florida International University West Flagler Street, EC 3680 Miami, Florida Pone: (305) Fax: (305) in cooperation wit te State of Florida Department of Transportation 605 Suwannee Street, MS 30 Tallaassee, FL Te opinions, findings and conclusions expressed in tis publication are tose of te autors and not necessarily tose of te State of Florida Department of Transportation February 2005

3 1. Report No. Final Report for BD Tecnical Report Documentation Page 2. Government Accession No. 3. Recipient's Catalog No. 4. Title and Subtitle Veicle Occupancy Data Collection Metods 7. Autor(s) Albert Gan, Rax Jung, Kaiyu Liu, Xin Li, and Diego Sandoval 5. Report Date February Performing Organization Code 8. Performing Organization Report No. 9. Performing Organization Name and Address Leman Center for Transportation Researc Florida International University W. Flagler Street, EC 3680, Miami, FL Sponsoring Agency Name and Address 10. Work Unit No. (TRAIS) 11. Contract or Grant No. BD Type of Report and Period Covered Final Report October 2003 February 2005 Office of Researc and Development State of Florida Department of Transportation 605 Suwannee Street, MS 30, Tallaassee, FL Sponsoring Agency Code Supplementary Notes Mr. Gordon Morgan of te Office of Transportation Statistics at te Florida Department of Transportation served as te project manager for tis project. 16. Abstract As congestion management strategies begin to empasize more person movements tan veicle movements, veicle occupancy data are becoming increasingly important. Wit tis increasing need for occupancy data comes te need to examine and reexamine te ways in wic tese data ave been, and will be, collected. Tis project reviews te existing metods of veicle occupancy data collection, examines issues related to geograpic, temporal, and veicle coverage design of occupancy data collection, and develops guidelines for performing occupancy data collection as well as analyzing occupancy data. Appropriate sampling plans for site-specific, corridor, and regional studies are presented. Potential new metods for collecting occupancy data are discussed. A user-friendly software system tat can estimate occupancy rates from multiple years of cras records on te Florida state roadway system was developed as part of tis study. Te system can estimate occupancy rates for select roadway segment, corridor, or regional level for specific time periods for different types of veicles. Te system also includes a stand-alone GIS interface to facilitate te selection of geograpic features and display of occupancy rate estimates. Also developed is a Pocket PC application tat can facilitate field data collection based on te commonly used windsield metod. A companion program for tis Pocket PC application was also developed to compute te average veicle occupancy rates and related statistics from te field data. 17. Key Word Veicle Occupancy, Data Collection Metods, Sampling, Accident Data Applications 18. Distribution Statement 19. Security Classif. (of tis report) Unclassified 20. Security Classif. (of tis page) Unclassified 21. No. of Pages Price Form DOT F (8-72) Reproduction of completed page autorized i

4 ACKNOWLEDGEMENTS Tis researc was funded by te Researc and Development Center of te Florida Department of Transportation (FDOT). Te autors are grateful to te project manager, Mr. Gordon Morgan, of te FDOT Transportation Statistics Office for is guidance and assistance. Ms. Yujing Xie, a Graduate Researc Assistant wit te Leman Center for Transportation Researc (LCTR), served as te tester for te FAVORITE software system. Special tanks are due to Mr. David R. Rae, Vice President of URS Corporation, for providing te veicle occupancy data used in Capters 4 and 8. ii

5 TABLE OF CONTENTS ACKNOWLEDGEMENTS... ii TABLE OF CONTENTS...iii EXECUTIVE SUMMARY... vi CHAPTER 1. INTRODUCTION Researc Needs Objectives Report Organizations... 2 CHAPTER 2. EXISTING METHODS AND STUDIES Veicle Occupancy Collection Metods Field Data Collection Metods Roadside Windsield Metod Carousel Metod Video Surveillance Metod Computer Vision Metod Existing Data Metods Cras Records Houseold Survey Veicle Intercept Survey Census Transportation Planning Package (CTPP) Nationwide Personal Transportation Survey (NPTS) Transit Occupancy Existing Studies... 6 CHAPTER 3. STUDY DESIGN CONSIDERATIONS Geograpic Coverage Geograpic Units Geograpic Density Temporal Coverage Time-of-Day Day-of-Week Mont/Season-of-Year Temporal Trends Facility Types Data Collection Cycles Data Collection Locations Veicle Types Veicle Percentages iii

6 CHAPTER 4. STUDY PROCEDURE Defining Objectives and Selecting Data Collection Metod Establising Sampling Plan Field Observational Metods Major Factors Affecting AVO Simple Collection Stratified Collection Mail-Out Questionnaires Cras Records Comparing Costs Sampling Randomly Computing AVO Field Observational Metods Mail-Out Questionnaires Cras Records CHAPTER 5. EXTRACTION OF VEHICLE OCCUPANCY RATES FROM ACCIDENT RECORDS Overview Installation Input Specifications Accident Data Years Location Selection Filters Output Options Table Display Cart Display GIS Display Variable Re-categorization Validations Reasonableness Cecks Field Data Comparisons CHAPTER 6. AUTOMATED FIELD DATA COLLECTION SYSTEM Overview Installation Main Screen General Screen Create a New File Open a Existing File Input Location Information Data Entry Screen Editing Data Table Import and Export Database Import from Desktop to Windows CE Export an ADOCE Database to a Desktop iv

7 6.8. Post-Processing Program CHAPTER 7. POTENTIAL DATA COLLECTION METHODS Potograpic Detection In-Veicle Detection In-Veicle Systems Information Transmission Remarks CHAPTER 8. STUDY GUIDELINES General Determining Time Periods Determining Sampling Plan Number of Observation Sessions Sampling Procedure Field Operation Design Locate Counting Sites Work Plan Personnel Training Data Collection Tool Data Collection Plan Data Analysis CHAPTER 9. SUMMARY AND RECOMMENDATIONS REFERENCES v

8 EXECUTIVE SUMMARY Introduction Te Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 expanded te role of veicle occupancy data by requiring continued monitoring of congestion management strategies, many of wic empasize more person movements tan veicle movements. Wit tis increasing need for veicle occupancy data comes te need to examine and reexamine te ways in wic tese data ave been, and will be, collected. Tis report reviewed te existing metods of veicle occupancy data collection, examined issues related to geograpic, temporal, and veicle coverage design of occupancy data collection, and presented study guidelines for performing occupancy data collection as well as analyzing occupancy data. Occupancy Data Collection Metods Existing metods for veicle occupancy data can be grouped into tose tat are collected in te field for te sole purpose of computing veicle occupancy rates and tose tat are collected for oter purposes, but may be used to estimate veicle occupancy rates. In general, te field collection metods are more suitable for collecting data at te site-specific or corridor level, wile te existing data metods are more suitable for area-wide or regional studies. Field data collection metods are more commonly used because tey can be tailored to specific application needs in terms of location, sample size and accuracy, etc. Current field collection metods include roadway windsield, carousel, and video surveillance. Existing databases tat can be used to generate veicle occupancy include cras records, veicle and ouseold surveys, Census Transportation Planning Package (CTPP), Nationwide Personal Transportation Survey (NPTS), etc. Te accuracy and scope of applications of existing data are constrained to wat ave been collected. Study Design Considerations Important design considerations for veicle occupancy data collection include geograpic and temporal coverages, facility types, collection cycles, locations, and veicle types. Te general geograpic units include site-specific, corridor, and area-wide. Temporal variability in AVO is a common issue across all data collection metods. Existing studies sow significant variations in veicle occupancy rates by time-of-day, day-of-week, and season-of-year. Different types of roads typically ave different occupancy levels. For example, roadways of te iger functional ierarcy would typically be expected to ave lower AVO. It is important to tus sample all roadway types in order to generate a representative estimate of regional veicle occupancy. AVOs can also differ from one location to anoter. Te spatial variations of AVO are related to te distribution of ouseold types and work places. It is tus necessary to select many different locations in order to measure AVO variability adequately. Te veicle types included in a data collection are determined by te purpose for wic te data are to be collected. Different study purposes may utilize different criteria for interpreting AVO. vi

9 In most veicle occupancy studies, only data from passenger veicles or ligt veicles (private passenger automobiles, pickups, vans, recreational veicles and motorcycles) are usually counted. Buses are typically excluded or counted separately because it is difficult to count all te occupants using te roadside windsield metod or te carousel metod. Trucks are generally excluded because tey are used mainly for goods movement and ave little to do wit people mobility. Study Procedure A typical procedure for veicle occupancy studies include defining study objectives, selecting a data collection metod, establising a sampling plan, comparing costs, performing random sampling, and computing te average veicle occupancy (AVO). Te first step in conducting a veicle occupancy study is to define te study objectives, wic form te basis for furter study planning and design. Once te objectives are defined, inappropriate collection metods can be screened out. Te remaining metods can ten be considered based on cost comparisons. After te data collection metod is selected, te actual survey procedures and te sampling plan can be designed. Appropriate sampling plans for site-specific, corridor, and regional studies were presented and discussed in detail. Statistical sound collection tecniques are a major concern and sould be properly designed. A sound sampling procedure is needed to ensure tat te AVO estimates meet te desired precision wit a certain level of confidence. In tis study, standard deviations for several factors identified by Ferlis (1981) were derived from veicle occupancy data collected in a Florida statewide study from 1996 to1999. In te absence of standard deviations from local data, tese standard deviations are recommended for use in determining te appropriate sample sizes for corridor and area-wide studies. Software System for Estimating Veicle Occupancy from Cras Records As part of tis study, a user-friendly software system called FAVORITE (Florida Accident Veicle Occupancy Rate InformaTion Estimator) was developed to estimate te occupancy rates from multiple years of cras records on te Florida state roadway system. Te system can estimate occupancy rates for select roadway segments, corridors, or regions for specific time periods for different types of veicles. FAVORITE comes wit te accident data and includes passengers for up to two veicles for eac accident. In addition, te database also includes a number of variables tat can be used for various analyses, including district, county, our of day, day of week, mont of year, veicle type, facility type, area type, and cras severity. Because te system makes use of a compreensive statewide database, it can potentially be a igly cost-effective means for monitoring statewide, regional, and site-specific veicle occupancy trends. Wile a preliminary assessment of te system sow outputs tat are consistent wit te expected data trends, an enanced version of te system would require additional researc tat takes into account over- and under-involvement of certain types of accidents, e.g., young drivers and iger-occupancy veicles are more prone to traffic accidents. Failing to correct for tese factors could produce biased AVO estimates. vii

10 Field Data Collection Tool To facilitate field data collection and processing, an automated field data collection tool designed for use wit a andeld Pocket PC was developed as part of tis study. Te tool eliminates te need for manual data post-processing by allowing te user to make use of te touc-screen interface on a Pocket PC to record te number of passengers for different types of veicles and different lane numbers. In addition, a companion program tat can calculate te average occupancy rates from te data collected from te automated tool was also developed. Te researc team first investigated te possibility of applying voice recognition tecnology in lieu of screen input on a Pocket PC. Tis was found to be impractical due to te lack of an applicable commercial voice recognition system tat could work well wit te Windows CE operating system used by Pocket PCs. It was also found tat traffic noise in te field could interfere wit voice recording, resulting in erroneous data. Potential Metods for Occupancy Data Collection Current researc into new metods for collecting veicle occupancy as mainly been motivated by te needs for automated enforcement of ig occupancy and managed lanes. Tese metods can be divided into potograpic and in-veicle detection. Wit advances in image processing and pattern recognition, a number of researcers ave explored te use of potograpic systems primarily for automated enforcement of HOV lanes. Wile potograpic systems ave acieved some success in counting veicle occupants and ave been sown to ave some potential for furter improvements, an operational, cost-effective system for occupancy data collection does not currently exist. Te use of different types of in-veicle electronics in combination wit wireless communications can be a future source of occupancy data. Tese include systems tat can detect te presence and weigt of a passenger for safer deployment of air bags; in-veicle cameras to detect passenger location and position (also for safer deployment of air bags); automated detection of seat belt usage by eac passenger (already implemented for te driver); etc., all of wic old potential to provide more accurate veicle occupancy information tan any of te existing metods. Study Guidelines for Occupancy Studies One finding of tis study was tat fully automated metods of veicle occupancy data collection are still largely a distant reality due to tecnological, cost, and institutional barriers. Metods wit a lesser degree of automation tat record and analyze occupancy data electronically remain te current metods of coice. As part of tis study, a set of study guidelines was developed for te manual counting metods for corridor and area-wide studies. Te guidelines address design issues related to time periods, sampling plan, field operation, work plan, personnel training, use of data collection tool and equipment, data collection plan, and data analysis. viii

11 CHAPTER 1 INTRODUCTION 1.1. Veicle Occupancy Rates Traditionally, veicle occupancy rates ave been used to convert person trips to veicle trips in te four-step travel demand forecasting process and to determine te required parking spaces for fixed-seat facilities suc as sporting facilities and performing centers. Te Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 expanded tis traditional role of veicle occupancy rates by requiring continued monitoring of congestion management strategies, many of wic empasize person movements rater tan veicle movements. Today, traffic engineers use veicle occupancy data to compute person delays; transportation planners use veicle occupancy rates to derive person-miles traveled and to set policies for ig-occupancy and managed lanes; transit planners use transit occupancy rates to identify routes tat need service expansion; etc. More applications of veicle occupancy data can be expected. For example, transit advocates are proposing person volumes as te basis for traffic signal warrants, transit signal priority, and transit preferential lanes Researc Needs Wit te increasing need for veicle occupancy data comes te need to examine and reexamine te ways in wic tese data ave been, and will be, collected. Unlike counting veicles, wic can be automatically recorded wen veicles run over pneumatic road tubes, counting te number of persons in a veicle in te field remains largely te task of uman observers. In te face of budget reductions, agencies must find better ways and define acceptable practices of collecting veicle occupancy data tat not only meet te needed accuracy, but also te limits of a restricted budget. A somewat limited number of studies ave been found to examine metods and issues related to collection of veicle occupancy data. Te most compreensive study to date as been performed by Heidtman et al. (1997) for te Federal Higway Administration. Te study evaluated various data collection metods wit field data and provided a relatively detailed comparison of te metods. In Florida, te Florida Department of Transportation (FDOT) undertook a statewide pilot study in 1996 and 1997 to examine alternative metods of veicle occupancy data collection, observation locations, field procedures, treatments of commercial veicles, etc. (Liu and Desai 1998; Reed et al. 1998). As part of te Florida study, over 2,000 ours of veicle occupancy data from 21 sites covering different types of roadways trougout te state were collected. Wile tese prior studies ave been relatively compreensive, muc remain to be done. No formal guidelines ave been developed to assist users in selecting te proper metods and te associated geograpic, temporal, and veicle coverage for specific applications. In addition, it is necessary to explore te use of new tecnology to improve existing metods, to investigate alternative metods, and to develop tools tat can ease data collection, processing, and analysis. 1

12 1.3. Objectives Tis project was initiated and funded by te Florida Department of Transportation (FDOT) to accomplis te following objectives: 1. To researc and evaluate existing metods of veicle occupancy data collection wit respect to teir geograpic, temporal, and veicle coverage design. 2. To identify new metods as a potential source of veicle occupancy data. 3. To present recommended practices as Florida s guidelines for te collection of veicle occupancy data. 4. To develop tools to facilitate te collection, processing, and analysis of veicle occupancy data Report Organizations Te rest of tis report is organized as follows. Capter 2 summarizes te existing metods for veicle occupancy data collection. Existing studies on veicle occupancy collection metods were also summarized. Capter 3 discusses various factors tat need to be considered in occupancy data collection studies. Capter 4 presents a study procedure for veicle occupancy collection. Capter 5 introduces an automated system developed as part of tis study to extract average veicle occupancy rates from traffic crases on Florida s state roadway system. Capter 6 describes an automated tool, also developed as part of tis study, for collecting veicle occupancy data in te field. A post-processing program tat can compute te average veicle occupancy rates and te related statistics from data collected from te automated tool is also presented. Capter 7 reviews potential metods for veicle occupancy data collection. Based on findings from te previous capters, Capter 8 presents study guidelines for veicle occupancy data collection and analysis. Finally, Capter 9 summarizes tis study and recommends furter researc. 2

13 2.1. Veicle Occupancy Collection Metods CHAPTER 2 EXISTING METHODS AND STUDIES Existing metods for veicle occupancy data can be grouped into tose tat are collected in te field for te sole purpose of computing veicle occupancy rates and tose tat are collected for oter purposes, but may be used to estimate veicle occupancy rates. In general, te field collection metods are more suitable for collecting data at te site-specific or corridor level, wile te existing data metods are more suitable for area-wide or regional studies. Tis is obviously because existing data were, by nature, collected for larger areas and wit relatively small sample sizes Field Data Collection Metods Field data collection metods are more commonly used because it can be tailored to specific application needs in terms of location, sample size and accuracy, etc. However, tey are also more costly, tus sampling is a major design issue for tis metod. Current field collection metods include roadway windsield, carousel, and video surveillance Roadside Windsield Metod Roadside windsield observation is te most widely used metod for collecting veicle occupancy data. It involves stationing observer(s) along te roadside to perform pysical counts of veicles and occupants on different lanes. Data are manually recorded using paper forms or manual count boards. To reduce potential errors and te time required for data entry and analysis, electronic counter boards or laptop computers ave been used. In general, tis metod is labor intensive and sampling metods are normally used to keep cost at a reasonable level. Because data collected by te windsield observers are often affected by suc factors as uman fatigue, weater conditions, amount of dayligt, veicle mix, and traffic volume and speed, te sampling procedure needs to accommodate for tese factors by taking measures suc as sceduling periodic breaks and possibly sampling only a portion of te traffic stream Carousel Metod Te carousel metod positions observers in veicles traveling on multi-lane igways to collect veicle occupancy data on neigboring veicles. During data collection, te observer veicle drives sligtly slower tan te general traffic, resulting in te continuous flow of traffic by te observation veicle. Te observer may use an electronic counter or a laptop computer to record te veicle occupancy data. Te observer veicle begins a cycle traveling in one direction along te survey route, ten turn around to drive te same route in te opposite direction to te beginning point on te roadway segment before anoter run is started. Te average traffic volumes and speeds along te selected roadway segment are examined in eac study to calculate te number of observers and observer veicles needed to collect an adequate sample size. 3

14 Wit te carousel metod, altoug fewer veicles are actually surveyed (about one-fort of tose observed by te windsield metod), te ability to discern te number of persons including small cildren in eac passing veicle is greater because te survey veicle travels along wit te veicles under observation. Obviously, more observed veicles can be captured by deploying more survey veicles. However, tis may require careful planning and coordination among te survey veicles to avoid double counting of veicles Video Surveillance Metod Tis metod uses video cameras mounted on overpasses or along te side of a roadway to capture passing veicles and ten uses a uman observer to view te capture videos to extract occupancy data. Because tis metod allows te observers te needed time to make a better judgment about occupancy, te counts can be more accurate tan te direct field observation metods. In addition, video segments skipped for te purpose of sampling or fast-forwarded to skip large veicle gaps can provide significant time savings especially for low-volume roadways. Te video surveillance metod is preferred wen it is impractical to station a field observer during te entire exercise because of pysical conditions, or tere is a need for a large amount of data to be collected continuously. Te cost from equipment purcase and setup can be justified if te existing video cameras installed for traffic surveillance and management purposes allow te collection of multiple data types from te monitoring period. Oter tan te ig initial implementation cost, tis metod as some limitations. Te windsield glare from observed veicles or veicles wit tinted windows could prevent veicle occupants from being accurately counted. Tere may also be difficulty recording data in poor weater conditions or during darkness Computer Vision Metod Tis metod applies computer vision tecniques to automatically recognize people in a veicle from captured images. It offers a potential solution to te time-consuming task of viewing video images to count veicle occupancy. Altoug a few researcers ave attempted tis metod, an operational system is not currently available. More information related to current advances in tis metod is presented in Capter 7 of tis report Existing Data Metods Te accuracy and scope of applications of veicle occupancy data extracted from te existing data are constrained to wat ave been collected. Obviously, te sampling metod and size are not a design issue wit existing data. Te objective of te users in tis case is mainly to extract te maximum amount of information at te igest level of details possible. In addition, te accuracy of estimation tat can be acieved wit te existing data needs to be determined. Te following subsections introduce several sources of data tat can be used to extract veicle occupancy information Cras Records Tis metod extracts veicle occupancy estimates from police cras records for a particular road 4

15 segment, corridor, or metropolitan area for specified time periods. Cras reports typically record information on type of veicle involved, number of occupants in eac veicle, and time and location of te cras. Te makes it possible to compute average veicle occupancy (AVO) tat is stratified by tese variables. Also, te continuous nature of cras records allows analysis of trends on veicle occupancy. However, adjustments may be needed to account for overinvolvement of certain subpopulations in terms of driver gender or age in crases. In addition, delays in compiling cras records prevent te metod from providing up-to-date AVO. An automated system developed as part of tis study to extract average veicle occupancy rates from traffic crases on Florida s state roadway system is presented in Capter Houseold Survey Houseold survey is conducted by randomly sampling on te general resident population of te urban transportation study area. Compreensive information is obtained regarding socioeconomic data, trip purposes, and modes of travel. Houseold surveys are generally conducted troug telepone interview, personal interview, or questionnaire mail-out/mail-back procedures. A common type of ouseold survey is origin-destination (O-D) survey, wic collects not only trip origins and destinations, but also veicle occupancy Veicle Intercept Survey Veicle intercept survey collects origin-destination and basic caracteristics of te trips based on roadside interview or postal questionnaire. Te veicles intercepted for survey can be randomly or systematically selected. Roadside interview surveys can be unsafe and expensive to conduct in many situations. Were practical, owever, interview surveys will yield iger and less biased response rates for a set of limited data items. On facilities wit ig volume and were traffic cannot be stopped long enoug for an interview, postal questionnaire can be used instead. Typically, pre-paid mail-back questionnaires are distributed to drivers in veicle stopped at traffic signals or toll boots to reduce delay to traffic. However, te typical low response rates (20-35%) of tis metod may produce biased estimates (Pietrzyk, 1996) Census Transportation Planning Package (CTPP) Census Transportation Planning Package (CTPP) is a set of compreensive data extracted from te decennial census. Te journey-to-work data collected as part of CTPP reports travel beavior and caracteristics for work trips trougout te country. Tese data are compiled and reported for every Metropolitan Statistical Area (MSA) in te United States. It may serve as a good source of data for examining te relationsip between veicle occupancy and demograpic caracteristics suc as te number of ours worked a week, sex, age, income, and veicle available. Because CTPP is available only on a decennial basis, te data do not allow annual monitoring of trends. In addition, census data provide veicle occupancy information only for journey-to-work trips. Wit te relative decline of ome-based work trips, journey-to-work data may not be adequate to measure system performance. 5

16 Nationwide Personal Transportation Survey (NPTS) Nationwide Personal Transportation Survey (NPTS) is a national ouseold survey tat is based on a random telepone survey designed to collect ouseold information on te daily trips made by ouseold members. NPTS can provide veicle occupancy information by trip purpose, ouseold caracteristics, and oter factors. However, it cannot provide information on veicle occupancy by type of igway or time of day Transit Occupancy Transit occupancy data is difficult to collect wit eiter te windsield or te carousal metod. Also, traffic cras data do not usually contain a sufficient number of bus crases. An approximation metod tat as been used is to determine if a bus is empty (only te driver), onequarter full (or about 10 occupants), one-alf full (or about 20 occupants), and full (or about 40 occupants). Variation on te associated passenger estimates can be made depending on typical bus sizes in te study area (Levine and Wacs, 1994). Existing data sources for estimating transit occupancy data include: 1. Manual Cecker or Automatic Passenger Counter (APC) Data: Transit agencies regularly use uman cecker to count passengers on select transit veicles on a sampling basis to estimate passenger trips and passenger miles. More recently, APC tecnology as been deployed to count passengers automatically as tey board transit veicles. Veicle occupancy rates (i.e., passenger loads) may be obtained at any particular point on a transit route from tese data. Currently, te Jacksonville Transportation Autority (JTA) is te only agency known to ave some of its buses equipped wit APCs; owever, most transit agencies are expected to eventually ave APCs on all of teir buses. 2. National Transit Database (NTD): NTD is te most compreensive database collected by and for te transit industry and can be used to estimate only te system-wide transit occupancy rate Existing Studies Various studies ave been conducted to collect veicle occupancy data for different purposes. For example, Ruterford et al. (1990) reviewed te metods used by agencies for sort-term and long-term monitoring of ig-occupancy veicle (HOV) lane violations. Levine and Wacs (1994) presented a metodology for conducting regional and corridor-level veicle occupancy surveys. Heidtman et al. (1997) compared various veicle occupancy data collection metodologies based on field studies and cost consideration. A number of concerns and issues reported in tese studies include: Observation locations and days were cosen arbitrarily, rater tan sampled randomly in order to represent veicle occupancy accurately for a particular purpose. Locations wit iger traffic volume are more likely to be cosen to te exclusion of lower volume locations. 6

17 Sites near central business districts (CBDs) or major trip attraction areas were congestion is assumed to be igest were also cosen frequently. Many agencies appear to acquire AVO estimates using te simple mean calculation procedure. Veicle occupancy observations taken under tese conditions will create biased estimates and in turn unreliable and inaccurate findings would be drawn. Table 2-1 provides a cronological summary of studies conducted in terms of study objectives, study metodology, and use of temporal and spatial coverages. It can be concluded from tis summary tat: Roadside windsield is te most commonly used metod. Tere is a trend to save data entry time by switcing recording from manual count boards to laptop computers or electronic counter boards. Measurements were usually taken in AM peak period, or wit PM peak period. Observers generally counted te sort periods rater tan continuously counting witin one our. Observation were typically taken in te middle of te week (Tuesdays, Wednesdays, and Tursdays) to avoid unusual conditions of recreational travel prevailing on Mondays, Fridays, and weekends. 7

18 Table 2-1. Summary of Veicle Occupancy Collection Practices in Different Study Areas City/State Sponsoring Report Study Purpose Metodology Used Spatial Coverage Temporal Coverage Agency Year Detroit, MI Souteast Micigan Council of Governments 1980 To test Ferlis sampling procedure Roadside windsield observation wit random sampling Area-wide: - 69 counting sessions stratified by geograpic area, igway class - 15-minute count wit 5- minute break - 2-our AM peak, 2-our midday, and 4-our PM peak - Weekday - Te first tree weeks of Atlanta, GA Georgia DOT 1980 To test Ferlis sampling procedure Wasington, D.C. FHWA Office of Researc and Development 1981 To test metodology to measure veicle occupancy by prototype camera system Portland Oregon DOT 1982 To determine te effectiveness of HOV lanes Roadside windsield observation wit random sampling Potograpic Surveillance Roadside windsield observation wit traffic counter board Area-wide: - 64 counting sessions stratified by geograpic area type and igway class Corridor: - 22 locations at freeways and arterials crossing te railroad cordon - 5 locations along te igway adjacent to proposed park-and-ride lots Corridor: - Two HOV lanes and a ramp on Interstate Higway Site-specific: - Location on freeway wit HOV lanes May - 45-minute count wit 15- minute break - 12-our dayligt period - April and May - times of day - 9 days - Different weater condition - 10 minutes in te peak direction and 5 minutes in non-peak direction in eac our - 3-our peak period - Tuesday troug Tursday - During 2nd week of te mont 8

19 Table 2-1. Summary of Veicle Occupancy Collection Practices in Different Study Areas (Cont.) City/State Sponsoring Report Study Purpose Metodology Used Spatial Coverage Temporal Coverage Agency Year George Wasington Bridge, NJ New Jersey DOT Roadside windsield observation wit traffic counter Site-specific: - 3 toll plazas on te bridge wit HOV lane New York City, NY Puget Sound Area, WA Puget Sound Area, WA New York City and surrounding areas Poenix, AZ New York State DOT Wasington State DOT Wasington State DOT for Puget Sound Council of Governments New York Metropolitan Transportation Autority Maricopa Association of Governments 1987 To evaluate te impact of expansion te bus-only lane into a longer bus-carpool lane 1988 To determine impacts of proposed development scenarios for a route reconstruction 1988 To test metodology for long-range VOC program planning 1989 To update travel demand model for veicle occupancy program planning 1989 To support transit agency planning 1989 To calibrate te sared-ride component of te mode coice model Mail-back questionnaire and associated roadside windsield observation Roadside windsield observation Roadside windsield observation wit random sampling Telepone survey of random, stratified sample on travel pattern Roadside windsield observation and associated Mail-back questionnaire wit random sampling Corridor: - Cordon line sounding te impact zones Area-wide: - 10 locations including employment sites, arterials, and freeways. Area-wide: - 54 sampling sites Area-wide: - New York City and surrounding areas Area-wide: - 36 locations stratified by area type, facility type - 33 parking lots and garages - 20-minute period eac alf-our - 3-our AM peak - Tuesday troug Tursday - During 3rd week of te mont - Once a mont - 2-our AM peak - Monday, Tuesday, and Tursday - November - 15-minute periods wit 15- minute breaks and 30- minute periods wit 15- minute breaks - 3-our AM, 3-our PM peak, and 3-our evening - Weekdays and weekends - AM and PM peak - One year period - Over 24-our day - Spring - 2-our AM, 5-our midday, 2-our PM peak, 1- our evening - Weekdays - February and Marc 9

20 Table 2-1. Summary of Veicle Occupancy Collection Practices in Different Study Areas (Cont.) City/State Sponsoring Report Study Purpose Metodology Used Spatial Coverage Temporal Coverage Agency Year Oklaoma, Canadian, and Cleveland Counties Association of Oklaoma Governments 1991 To update survey metodology to better meet planning needs including CAAA requirements Roadside windsield observation wit clustering tecnique Area-wide: - 20 stations of different facility types in tree counties - 1-our AM peak, 1-our midday, and 1-our PM peak - Monday, Tuesday, and Tursday - Two seasons Elmira, NY Providence, RI Selected counties in New Jersey and Pennsylvania Minnesota Dallas-Fort Wort, TX Executive Transportation committee for Cemung County Rode island DOT Division of Planning Delaware Valley Regional Planning Commission Minnesota DOT 1991 To support general planning and development strategies 1991 To study traffic patterns to alleviate congestion on an Interstate Higway 1992 To design statistically valid telepone survey metod to measure veicle occupancy To conduct an annual Metropolitan Occupancy Data Collection program - To monitor veicle occupancy on bot te HOV and mixflow lanes O-D survey and pullover roadside interview License plate sample and mail-back postcard survey of sample Telepone interview wit random sampling Direct observation field counts from overpasses and elevated barricade positions Corridor: - 13 point-of-entry sites at ig volume roads bisecting cordon line Corridor: - two ig volume facilities bisecting te corridor Area-wide: - 4,800 interview across 13 counties Statewide: - 18 count locations stratified by facility type and area type Corridor: - 6 count locations along an Interstate igway wit HOV lane Texas DOT 1993 Area-wide: - 26 locations stratified by area type - Two years in a row - AM peak - six weekdays - two-week period - AM peak - Weekday - 3-our AM and 3-our PM peak - Tuesday troug Tursday - Twice a year during te spring (April and May) and fall (September and October) - once a year 10

21 Table 2-1. Summary of Veicle Occupancy Collection Practices in Different Study Areas (Cont.) City/State Sponsoring Report Study Purpose Metodology Used Spatial Coverage Temporal Coverage Agency Year Souteastern area, VA Virginia DOT 1993 To regularly monitor veicle occupancy 2 locations on a HOV system Wasington, D.C. Nationwide New York Wasington, D.C. Council of Government U.S. DOT National Higway Traffic safety Administration New York State DOT Roadside windsield observation wit intersection turning movement recorder 1994 Roadside windsield observation wit laptop computers 1994 To propose metod to measure level of seatbelt use nationwide 1996 To investigate te capacity of traffic accident database to predict AVOs Florida Florida DOT 1997 To determine efficient procedures for te collection, analysis and use of veicle occupancy data Connecticut Kansas City, MO Connecticut Public Transportation Commission Mid-America Regional Council 1998 To monitor te efficiency of roadway system 2002 To monitor impacts of increasing occupancy and compare te results wit assumptions used in te longrange transportation planning process Roadside windsield observation wit clustering and stratification tecniques Traffic accident records Roadside windsield observation Traffic Accident Database Roadside windsield observation Corridor: - Freeways and principal arterial igways bisecting cordon lines Nationwide: - 50 primary sampling units tracts - 4,000 sites Statewide - Accident on interstates Statewide: - 21 sites stratified by geograpic area and facility type Statewide: - Accidents on interstates Regional: - 44 sites stratified by geograpic area (inner and outer urbanized areas) and four functional classifications of roadways - 3-our AM and 3-our PM peak - four times a year - Spring and fall To be probability determined - Can be by our of te day, week, season, or year - 3-years period - 12-our dayligt period - Five consecutive weekdays - Two weekdays in eac mont for twelve consecutive monts Can be by our of te day, week, season, or year our AM peak and 2- our PM peak - Tuesday, Wednesday, and Tursday 11

22 CHAPTER 3 STUDY DESIGN CONSIDERATIONS Tis capter reviews several design considerations for veicle occupancy data collection, including geograpic and temporal coverages, facility types, collection cycles, locations, and veicle types and percentages Geograpic Coverage Geograpic Units Geograpic units used to evaluate veicle occupancy may include te entire state, air quality non-attainment area, urban area, subarea, region, corridor, activity center, functional class, and external cordon line (Heidtman et al. 1997). Depending on te specific study objectives, one or more of tese units may be appropriate. Te traditional interest as been to estimate veicle occupancy at eiter site-specific (e.g., factory entrance and critical roadway section) or area-wide (e.g., metropolitan and county) level. Te more recent focus, owever, as been on te intermediate level of geograpic coverage, wic is referred to as te corridor level, e.g., at cutline and CBD cordon line Geograpic Density Geograpic density can be minimized to reduce cost if clusters of geograpic features can be identified. Spatial analysis using a GIS can elp identify clusters and detect canges in veicle occupancy rates across geograpic features. Due to its compreensive nature, cras records may be used to evaluate spatial canges in veicle occupancy rates Temporal Coverage Temporal variability in AVO is a common issue across all data collection metods. Existing studies sow significant variations in veicle occupancy rates by time-of-day, day-of-week, and season-of-year Time-of-Day Te AM peak period tends to be eavily dominated by ome-based work trips, primarily wit single-occupant veicles. As te day progresses, increasing variety of oter trip purposes occur wic are likely to sift veicle occupancy upward. In te PM peak period, veicle occupancy may be iger tan te AM peak period due to a multitude of different trip purposes, many of wic involve more tan one person Day-of-Week Veicle occupancy levels vary from day to day, wit Saturday typically being iger. However, studies did not find a consistent day-of-week pattern for variations in AVO. In general, it is a common practice to exclude Monday, Friday, and weekend counts because tese days are 12

23 assumed to contain atypical and non-recurrent trips. Measurements were typically taken on Tuesday, Wednesday, and Tursday only, as tese days are considered most representative of average weekday travel beavior and commute conditions Mont/Season-of-Year Veicle occupancy rates ave been found to vary wit mont and season. It is tus important tat observations be taken trougout te year (and not restricted to any one season) in order to properly represent average veicle occupancy (Heidtman et al., 1997) Temporal Trends Te following temporal trends in AVO ave been observed in various studies (Roac and Lester, 1978; URS Corporation, 1997): Weekday AM peak AVO is normally lower tan weekday midday and PM peak AVOs. Weekend AVO is normally iger tan weekday AVO. Off-peak AVO is normally iger tan AM and PM peak AVOs. Summer AVO is iger tan winter AVO for te nortern cities. Based on data from Florida, Liu and Desai (1998) made te following general recommendations wit regards to temporal coverage: Te data collection period sould desirably be witin te period of interest (e.g., peak ours, etc.). Counts of one to two ours would produce data wit sufficient accuracy and precision for most purposes. Tuesdays and Wednesdays are normally adequate for data collection and te best days for collecting data are Tursdays. Mondays and Fridays sould be avoided. Time of day cosen for data collection is important. As a rule of tumb, mid-morning to mid-afternoon counts are adequate for most purposes. However, if te 5-6 pm period is cosen for performance monitoring, adjustments would be necessary to derive te AVO for te day Facility Types Different types of roads typically ave different occupancy levels. Roads of te iger functional ierarcy would typically be expected to ave lower AVO. Freeways and major arterials are eavily used for ome-based work trips and commercial trips, particularly in te AM and PM peak periods. Tese two trip purposes tend to ave lower AVO. Conversely, minor arterials, collectors, and local streets are utilized more extensively for trips of ome-based sop, ome-based scool, and social/recreational, resulting in iger AVO. In addition, freeway segments wit ig occupancy veicle (HOV) lanes will ave iger AVO tan mix-flow freeway segments. However, tese penomena are subject to large variations in different study 13

24 areas. It is tus important to sample all roadway types in order to generate a representative estimate of regional veicle occupancy for an area-wide estimate of AVO Data Collection Cycles A literature searc did not reveal specific practices or guidelines for data collection cycles. It is, owever, an important design issue tat needs to be addressed as it as a major impact on te cost of data collection. Given te fact tat veicle occupancy data are rater stable and tat te cost of collecting tem are currently ig, a collection cycle of one year or less would generally be bot unnecessary and unfeasible. Especially for an area-wide level, it is not necessary to collect data every year but to establis it wit sufficient precision to produce a reliable indicator of regional road efficiency. An initial area-wide study sould ence be conducted to establis a statistical sound baseline. Once te initial study as been conducted, area-wide studies can be conducted every couple of years to monitor trends in veicle occupancy. However, cycles greater tan five years sould be avoided if te data are to be included in te FDOT s Roadway Caracteristics Inventory (RCI) database, wic as a maximum collection cycle of five years Data Collection Locations AVO can differ from one location to anoter. Te spatial variations of AVO are related to te distribution of ouseold types and work places. It is necessary to select many different locations in order to measure variability adequately. Site selection for traffic data collection is dependent on te purposes and te overall expectation of te study (Liu and Desai, 1998). To ensure tat a representative sample of te population will be selected, data collection locations sould be sampled randomly from all possible igway segments for area-wide studies. If an initial study as been performed, te entire random selection procedure sould be repeated again for subsequent studies. Te same sampled locations sould not be cosen unless tey are randomly selected. In contrast to te randomly selected locations for area-wide studies, locations are judgmentally selected due to te nature of corridor/site-specific studies. Terefore, it would be appropriate to use te same locations for every collection cycle. Tis ensures tat te data can be used for continued monitoring, tat any canges in te data trend may be attributed to factors oter tan location. Tis is consistent wit te state s practice for telemetry and portable traffic monitoring sites, wic uses te same locations from year to year Veicle Types Te veicle types included in a data collection are determined by te purpose for wic te data are to be collected. Different study purposes may utilize different criteria for interpreting AVO. If te purpose is to evaluate te overall efficiency of a road system, ten all veicles sould be counted regardless of veicle types in order to assess te entire load on te road system. On te oter and, if te purpose is to measure te impact of a ridesaring program, for example, may be only passenger veicles sould be included because tey are most sensitive to ridesaring incentives. In most veicle occupancy studies, only data from passenger veicles or ligt veicles (private passenger automobiles, pickups, vans, recreational veicles and motorcycles) are usually 14

25 counted. Buses are typically excluded or counted separately because it is difficult to count all te occupants using te roadside windsield metod and carousel metod. Trucks are generally excluded because tey are used mainly for goods movement and ave little to do wit people mobility. Te definition of veicle type remains an issue in veicle occupancy data collection. A simple breakdown of passenger veicles, buses, and trucks are generally sufficient for veicle occupancy studies. However, tere is no consistency in definition across different regions or different transportation models. Terefore, te veicle type definition sould be determined by te data collection objective and purpose, as well as te consistency wit existing traditional transportation model requirements. For example, if te veicle occupancy data were used to validate te modal split in FSTUMS, te veicle type definition of veicle occupancy data sould be consistent wit te veicle type definition used in FSTUMS Veicle Percentages Instead of recording data from all veicles traversing a link during a selected time period, a subset of veicles tat are assumed to reflect te same caracteristics as te target population is generally observed. Systematic sort-count procedure, in wic observations are made for a fixed interval in eac our of te day, can be used to produce relatively accurate daily estimates wile conserving manpower resources. Te following tree basic types of sort-count procedures were suggested by Ferlis (1981): Position one or more observers to count all veicles tat pass by during a fixed interval witin eac our (e.g., count for 45 minutes and rest for 15 minutes, tus representing a 75 percent systematic sample). Position one observer to count veicles tat pass by on eac lane during a fixed interval witin eac our (e.g., count eac of tree lanes during successive 15-minute periods and rest for 15 minutes witin eac our, tus representing a 25 percent systematic sample). Position one or more observers to systematically observe two or more locations concurrently by counting all veicles passing a particular location during te same time interval witin eac our (e.g., count veicles at one location from 7:00 to 7:15, 8:00 to 8:15, etc., and at anoter location from 7:30 to 7:45, 8:30 to 8:45, etc., tus representing a 25 percent systematic sample). 15

26 CHAPTER 4 STUDY PROCEDURE Tis capter describes te procedure for conducting a veicle occupancy study. A significant portion of te materials presented was taken from Ferlis (1981) and Heidtman et al. (1997). Te procedure include defining study objectives, selecting a data collection metod, establising a sampling plan, comparing costs, performing random sampling, and computing te average veicle occupancy (AVO) Defining Objectives and Selecting Data Collection Metod Te first step in conducting a veicle occupancy study is to define te study objectives, wic form te basis for furter study planning and design. Once te objectives are defined, inappropriate collection metods can be screened out. Te remaining metods can ten be considered based on cost comparisons. After te data collection metod is selected, te actual survey procedures and te sampling plan can be designed. A sample of objectives for veicle occupancy estimates may include: Evaluate te effectiveness of te transportation system management (TSM) program. Verify compliance wit state regulation. Analyze transportation related air quality and energy efficiency measures. Validate te adequacy of urban transportation planning models. Assess te impact of a new transportation system. Monitor te general trends in travel caracteristics. Formulate transportation strategies. Based on te desired objective, te target collection population can be defined in terms of geograpic scope, temporal coverage, facility types, etc. Stratification of collection population may also be needed to provide estimates at a finer level. For example, if te estimates of AVO are desired for te central business district (CBD) and suburban area as well as for te entire region, a stratified sampling plan will ten be required. Depending on te geograpic and temporal coverages for data collection, collection metods tat are not suited for te intended collection design can be discarded from te list. Te general applicability in terms of geograpic scope for different occupancy data collection metods are summarized in Table 4-1. Te level of precision is viewed as an acceptable range of error about te mean AVO estimated from te data collection. Te minimum sample size and collection cost will be directly affected by te need for AVO estimates at predetermined levels of precision. Table 4-1. Applicability of Occupancy Data Collection Metods by Geograpic Scope Metod Geograpic Scope Area-Wide Corridor Site-Specific Roadside Windsield Carousel Observation Video Surveillance Mail-Out Questionnaires Cras Records 16

27 4.2. Establising Sampling Plan After te objectives are determined and te metod of data collection selected, a proper sampling procedure can be used to construct a sampling plan, wic applies te sampling teory to determine te sample size needed to obtain te desired level of precision in te AVO estimates. Te sample size required to attain a certain level of precision depends on te degree of variation in AVO measures, wic is usually measured in standard deviation. For example, a common approac is to set te tolerance level for an estimate to be witin ±0.03 of te true AVO wit 95 percent confidence, i.e., tere is a 95 percent likeliood tat te estimated AVO will fall witin a range of ±0.03 from te true population average. Tolerance is defined as te acceptable difference between te estimated AVO and te true AVO, wile te level of confidence represents te probability tat te sample estimate will fall witin tis range. Tus, a more precise estimate will require a iger sample size. Once te sample size is determined, te sample units are randomly selected from a frame were all possible individuals are listed. Te random sampling of sample unit will result in an unbiased estimate of AVO. Different sampling procedures are required depending on weter te AVO estimates are site-specific, corridor, or area-wide. Multiple objectives requiring different levels of AVO measures are often of interest to transportation agencies. Te coice of te area-wide veicle occupancy study is recommended to acieve te multiple objectives troug a single collection effort. In addition, later uses of occupancy data for different purposes wen selecting a data collection metod sould be considered Field Observational Metods Te sampling process tat is conventionally used in te field observational metods includes simple collection and stratified collection. Simple collection involves selection of sample units at random from te entire population so tat eac sample unit as an equal probability of being selected. Stratified collection, on te oter and, begins by dividing te entire sample frame into mutually exclusive strata, ten selecting sample units from eac stratum using simple collection. Te following sections begin wit te introduction of key factors related to te computation of variance in AVO. Te estimation of minimum sample size on te basis of te derived variance for two types of collection metods can ten be establised Major Factors Affecting AVO A number of factors described in Capter 3 could affect te variation in te AVO estimates. Due to limited resources available for tis study, it is not possible to examine te effect of eac of tese actors. However, it is possible to examine major factors tat are believed to ave a major influence on veicle occupancy. Te sampling sceme developed by Ferlis (1981) is to estimate te number of sampling units on te basis of spatial and temporal variation and te precision required. To account for te impact of spatial and temporal factors upon te accuracy of AVO estimates, Ferlis (1981) suggested te following primary sources of variation: Te variation across link-days witin a season (σ L ) Te variation from day to day witin a season (σ D ) 17

28 Te variation from season to season (σ S ) Te variation from time interval to time interval during a day as a result of sort-counts (σ W ) Additional sources of variations ave been suggested by oter studies. Ulberg and McCormack (1988) examined some potential sources of error and concluded tat observer counting error (σ O ) sould be included. In addition, te degree of variation in AVO derived in Ferlis study may not be reflective of te traffic caracteristics today. Wit a relatively small variance derived from is study, te required sample size would be too small to adequately represent te regional total. Te variability of AVO obtained from local data sould be used instead. A study was conducted in 1997 by URS Corporation for te Florida Department of Transportation to collect veicle occupancy data from 21 individual sites trougout te State of Florida. Tese sites were selected for roadways ranging from two-lane rural routes to four- and six-lane urban arterial routes, including interstate igways. Occupancy data were collected for two directions from 17 conventional sites for twelve ours during daytime on five consecutive weekdays. Accordingly, variations in occupancy data of private veicles due to time periods during a day resulting from te use of sort-counts, day of week, and mont of year could be establised. Four seasonal sites were observed twice eac mont over a one-year period to determine te montly and seasonal variation in veicle occupancy. Veicles were classified as passenger cars, vans, trucks, and buses. Vans and buses were not counted due to difficulty in getting an accurate passenger count of tese veicles, especially at ig speed. Since a very ig proportion of te veicles were passenger cars, variation in veicle occupancy attributed to oter veicle types was relatively insignificant. Table 4-2 sows te results computed wit local data for different sources of variation along wit te suggested values from studies by Ferlis (1981) and Ulberg and McCormack (1988). Te table sows tat te standard deviations observed in te data from te URS study is generally larger tan tose suggested by Ferlis. It sould be noted tat te values for te witinday term, σ W, are based on an assumed systematic sampling rate of 25 percent. Table 4-2. Sources of Variation in Average Occupancy Geograpic Scope Standard Variation Ferlis (1981) Area-wide Site-specific or Corridor σ L σ S σ W σ O σ D σ S σ W σ O Ulberg and McCormack (1988) Data from URS Study (1997)

29 19 Te formulas and computational procedures for te different standard deviations in Table 4-2 are given below: ( ) 2 1/ 2 2 = i i i i L i i L V V AVO P N σ = i i i i L V P AVO were N i = number of locations, P i = number of persons counted at location i, and V i = number of passenger veicles counted at location i. ( ) 2 1/ 2 2 = k k k k S k k S V V AVO P N σ = k k k k S V P AVO or ( ) ( ) 4 k k k k S V P Min V P MAX = σ were N k = number of seasons, P k = number of persons counted during season k, and V k = number of passenger veicles counted during season k.

30 σ W N = m m ( P AVO V ) m m V m 2 W m 2 1/ 2 AVO W = m m P V m m were N m = number of possible time intervals witin eac our, P m = number of persons counted during time interval m, and V m = number of passenger veicles counted during time interval m. σ D N P AVO V ( ) d d D d d = 2 d V d 2 1/2 AVO D = d d P V d d were N d = number of days, P d = number of persons counted on day d, and V d = number of passenger veicles counted on day d Simple Collection Simple collection can meet a wide range of needs for te area-wide and site-specific occupancy studies. Te composite standard deviation of AVO sould be estimated before te minimum sample size can be computed. Following te discussion in te previous section, Ferlis framework is modified by defining te composite standard deviation as: Area-wide Level: ( ) 1/ 2 σ = σ + σ + σ + σ Site-specific Level: ( ) 1/ 2 L σ = σ + σ + σ + σ D S S W W O O were σ = composite standard deviation of AVO, σ L = standard deviation of AVO across link-days witin a season, 20

31 σ D = standard deviation of AVO across days witin a season, σ S = standard deviation of AVO across seasons, σ W = standard deviation of AVO across time periods witin a day, and σ O = standard deviation of AVO due to observer error. Depending on te sampling procedure, some of tese sources of variance may not apply. Te link-day variation, σ L, reflects te fact tat different AVO measurements can vary by measured locations as well as by different days witin a season. For an area-wide study, σ L accounts for a muc greater variability tan te oter terms. Te daily variation term, σ D, sould be included for corridor and site-specific studies because it can vary substantially from one day to te next. Te seasonal variation, σ S, sould be included if te estimated occupancy is intended to represent more tan one season and te data collection is terefore extended trougout tis period. Similarly, te witin-day variation, σ W, sould be included only if a sort-count metod is used. For te first year, te results of a previous veicle occupancy survey can be used to estimate te individual standard deviation terms by using te formulas described above. If agencies ave not conducted prior studies or te standard deviations is difficult to estimate before te initial study is conducted, te values derived in tis study as sown in Table 4-2 may be used. Te default composite standard deviation for area-wide studies is For corridor and site-specific studies, te default value is For subsequent years, te composite standard deviation estimated from te first-year study can be used directly. Te basic sampling unit is a link-day, representing an estimate of te survey measures made for a particular link on a particular day for a specified time period. After te composite standard deviation is estimated, te number of link-days required to reliably estimate AVOs witin a desired tolerance and confidence level can be computed as follows: N 2 Z σ = T were N = number of link-days to be sampled, Z = normal variant for te specific level of confidence, and T = desired tolerance. Te Z values are and for 90% and 95% confidence levels, respectively. At te sitespecific level, te sample size indicates te number of days of data collection due to one location/link. Every location must be sampled on at least one day Stratified Collection Agencies often place empasis on collecting separate AVO estimates for various subsets of te area-wide transportation network (e.g., separate estimates for individual counties, freeways and arterials, and freeways by HOV and mix-flow lanes). In tis case, te collection population is stratified for separate sampling. Te set of links witin eac stratum is considered a separate population. On te oter and, a corridor-level study can also be treated wit stratified sampling plans in wic eac location represents a different stratum. Te minimum sample size of link- 21

32 days needed to estimate AVO for eac stratum can ten be computed. A minimum sample size of one day per location is also required. Once te sample size for eac stratum is known, te level of precision tat te sampling is to acieve can be directed toward te level of eac stratum and te total estimate from all strata. Te samples of link-days are ten independently drawn at random from eac stratum population. In contrast to te simple estimating metod, te composite standard deviation is estimated for eac stratum as: Area-wide Level: ( ) 1/ 2 σ = σ + σ + σ + σ Corridor Level: ( ) 1/ 2 L D S S W σ = σ + σ + σ + σ were σ = composite standard deviation of AVO in stratum, σ L = standard deviation of AVO across link-days witin a season in stratum, σ D = standard deviation of AVO across days witin a season in stratum, σ S = standard deviation of AVO across seasons in stratum, σ W = standard deviation of AVO across time periods witin a day in stratum, and σ O = standard deviation of AVO due to observer error in stratum. Te definition and logic of tese variations are equivalent to tose described for te simple occupancy study except tey apply to individual strata. Te results of a prior study sould preferably be used for te initial study. Oterwise, te composite standard deviation can be judgmentally estimated from te default values given in Table 4-2. Te composite standard deviation computed from te first year s survey can ten be used in a later year. Te sample size of link-days required to estimate stratum AVO witin a desired tolerance can be computed as: W O O N Z σ = T 2 were N = number of link-days required for stratum, and T = desired tolerance for stratum. Alternatively, if te objective of a corridor study is to estimate te AVO across all locations witin a tolerance rater tan to obtain precise estimates for eac location, te total sample size of days for all locations and te sample size for eac location can be computed as follows: N = Z W T σ 2 22

33 and N W σ = N W σ were W is te proportion of total traffic volumes across all locations occurring in location Mail-Out Questionnaires For a mail-out survey, questionnaires are mailed to a particular subpopulation to inquire te residents typical trips about te number of occupants traveling wit tem to or from work. Mail-out questionnaires are most often used for area-wide studies. Te sampling of te region often involves stratifying te population into strata (e.g., zip code area), and to send out questionnaires in proportion to te driver population in eac stratum. Te sampling unit is te returned questionnaire. Te observation is te number of persons tat travel wit te respondent during te day on te trips described. Wit prior estimates of te standard deviation in veicle occupancy for eac stratum, te number of returned questionnaires needed to estimate te regional AVO witin a desired tolerance can be computed as follows: N = 2 2 Z D D σ D T + Z D σ were N = number of returned questionnaires required for stratum, Z = normal variant for te specific level of confidence, D = total driver population in te region, D = driver population in stratum, σ = composite standard deviation for stratum, and T = desired tolerance for stratum. If no stratification is needed or no variability in veicle occupancy patterns exists, te following formula can be used: N = 2 2 Z D σ D T + Z σ were N = number of returned questionnaires required, σ = composite standard deviation for te region, and T = desired tolerance. Because not all survey respondents return te surveys, it is important to acieve a maximum response rate, r, to reduce te no-response bias. Depending on te population and te lengt and 23

34 complexity of te questionnaire, it is reasonable to assume a 10 to 15% response rate. After te number of returned questionnaires N (or N) is determined, te number of questionnaires necessary to distribute N d (or N d ) can be calculated as follows: N N d = or r N d = N r Cras Records Instead of determining te sample size to be collected, cras records are used to determine if te database contains a sufficient number of records to make te required estimation. Te sampling unit is te veicle(s) involving in an accident. Based on te defined population of interest in terms of geograpic scope and temporal coverage, te number of veicles required to estimate AVO witin a specified precision level can be computed for eac stratum by using te variability in occupancy rates: N Z σ = T 2 were N = number of veicles required for stratum, σ = composite standard deviation for stratum, and T = desired tolerance for stratum. Te standard deviation estimates of veicle occupancy for different strata of crases can be obtained from existing studies. If te number of veicles is not enoug to provide estimation of AVO witin a specified level of precision, anoter metod of collecting veicle occupancy data sould be used Comparing Costs Costs are calculated and compared for te remaining metods tat are suited for te prospective data collection design. Heidtman et al. (1997) suggested te istorical practice of disaggregating costs by survey stage for cost comparison. Because factors affecting te total cost vary among different collection metods, a procedure for establising te relative costs was establised. Te costs associated wit eac metod are broken down into te five stages involved in a collection effort: survey planning, survey design, data collection/entry, data analysis, and reporting. Table 4-3 sows a cost breakdown by data collection metods and study activities. Te cost of eac activity is ranked in terms of te relative level of costs associated wit te given collection metod. Typically, te data collection/entry activity is te most costly stage, wic includes field personnel wages, data entry personnel wages, and equipment costs. Heidtman et al. (1997) suggested tat te final cost estimates to collect veicle occupancy data depend on a number of decisions made during te planning and design processes. Based on te specific study objectives, all metods but video surveillance may be te most cost-effective. 24

35 Table 4-3. Comparison of Occupancy Data Collection Costs by Metod and Activity (Heidtman et al., 1997) Metod Activity Roadside Windsield Carousel Observation Video Surveillance Mail-Out Surveys Cras Records Planning M M M-H H L Design L M H H L study design (e.g., site-specific vs. area-wide) and regional labor costs Collection/Entry greatly impact collection costs Analysis L M M L H Reporting L L L L-M M H = relatively iger cost; M = relatively medium cost; L = relatively lower cost 4.4. Sampling Randomly Te random sampling process sould be applied to select a representative subset of te population suc tat every individual in te population as te same probability of being drawn. Ferlis (1981) suggested tat te target population sould include all possible igway segments and every possible date on wic te sample could be randomly cosen. For area-wide studies, te locations and dates sould be selected based on random sampling. For corridor and sitespecific studies, te locations sould be selected judgmentally, but te selection of dates sould be randomly sampled. A link list and a date list are ten generated for te area-wide studies, wile corridor/site-specific studies only require a date list. In practice, tere is a tendency to arbitrarily select iger volume roads suc as freeways and major arterials, wic can result in biased area-wide AVO estimates and limited extrapolation of te finding outside te collection sites. Te sample of link-days can ten be drawn from te available lists using standard random selection tecniques. Te links are randomly selected first wit a probability proportional to veicle mile traveled (VMT), wic is a function of te traffic volume and te lengt of link. All links could furter be divided into multiple basic units wit equal lengt (e.g., te minimum link lengt of all links). As a result, tere is no difference in te weigt assigned to a link from traffic volume or VMT (Levine and Wacs, 1994). Consequently, te iger volume roads ave a greater probability of being selected over te lower volume road in tis weigted random sampling process. After te locations are decided, te dates witin a year for eac location are ten selected eiter randomly (data to be collected at several locations on same days migt occur) or systematically (data collection effort is spread out evenly trougout te study period). If a stratified sampling plan is used, a separate sample of link-days and days sould be selected for eac stratum of area-wide studies and corridor/site-specific studies, respectively Computing AVO Field Observational Metods If sort-count data collection procedures are used, an expansion factor, as defined below, sould be computed to expand person and veicle counts to total day estimates for specified time 25

36 period: f = ij N n i ij were f ij = expansion factor for lane j at location i, N i = number of possible count periods during a session at location i, and n i = number of actual count periods during a session on lane j at location i. Te simple (or unweigted) estimate of AVO can be computed by dividing te total number of occupants by te total number of veicles for a specified geograpic area and te time period. Tis calculation assumes tat te traffic volumes observed at eac location is proportional to te actual traffic flow at te corresponding location. Te simple estimate of AVO and its corresponding composite standard deviation can be computed as follows: N σ = AVO = i i i P V ( P AVO V ) i i V 2 i i i i 2 1/ 2 were Pi = factored number of persons counted in session i, and V i = factored number of veicles counted in session i. Tese same formulas can also be applied to data collected for eac stratum of a stratified data collection to compute te average veicle occupancy AVO and its composite standard deviation σ. After computing te composite standard deviation of te data collection result, te actual precision of te estimates derived from te survey can be assessed. Te actual precision (or tolerance) of te estimates obtained from te data collection can be computed as below: T = 2 Z 2 σ N 1/ 2 Te weigted mean of te strata AVOs by VMT (for area-wide studies) or traffic flow (for corridor studies) can be very important, especially if tere are significant differences between AVOs on ig- and low-volume roads. Weigted estimates are used to provide an overall measure representing multiple strata. If a particular location was only surveyed on one day, te composite standard deviation cannot be estimated from te survey results. Te assumed value of 26

37 te composite standard deviation sould be used to estimate precision. A weigted estimate of te AVO and corresponding tolerance can be computed as: AVO = W AVO 2 2 T = Z W σ N were W is te estimated proportion of total VMT of all te selected links occurring in stratum or of total traffic volumes across all selected locations occurring in location. A level C (e.g., 95%) confidence interval is calculated by: Mail-Out Questionnaires 1/ 2 Level C Confidence Interval of AVO = AVO ± T Since mail-out questionnaires are used frequently to collect area-wide information wit stratification tecnique, a stratified estimator of AVO is as follows: AVO N = j= 1 P N j were P j = number of occupants recorded on questionnaire j in stratum, and N = number of questionnaires returned in stratum. To estimate te regional AVO, te weigted estimate of AVO sould be calculated as follows rater tan using te simple mean procedure: AVO = D AVO D were D = driver population in stratum, and D = total driver population in te region Cras Records In calculating AVO estimates from cras records, an approac analogous to te simple mean procedure for estimating AVOs of field observational metod is normally applied. Tis metod is not applicable given te fact tat cras data are likely to be a biased sample of te population. To accommodate tese biases, some adjustments and filtering of te data as well as a weigted means procedure are required to remove biases inerent in tese types of data. 27

38 CHAPTER 5 EXTRACTION OF VEHICLE OCCUPANCY RATES FROM ACCIDENT RECORDS Tis capter describes a user-friendly system tat applies multiple years of Florida accident records to derive AVO estimates. Te system, named Florida Accident Veicle Occupancy Rate InformaTion Estimator, or FAVORITE, is able to generate AVO estimates at te district, county, and corridor levels Overview FAVORITE is a typical Windows program designed to run on Microsoft Windows operating systems. Te program is fully stand-alone and was developed using Visual Basic, Microsoft Access, and ESRI MapObjects Developer Library. Te database tat comes wit FAVORITE includes te accident data for te Florida state igway system. Te data include te number of passengers of eac accident for up to two veicles, wic are used to calculate te average veicle occupancy (AVO). In addition, te database also includes te following variables: District County Section, subsection, beginning and ending mileposts State road number Hour of day Day of week Mont of year Type of veicle Type of road Type of area Accident severity Te major functionalities of te current version of FAVORITE include: Calculate and display AVOs in a cross table of two variables, for example, te AVO for different days of a week for different FDOT districts. Te total AVOs are also provided for all rows and columns of te cross table. Calculate and display AVOs on line, bar, or pie carts. Calculate and display AVOs on a GIS map at te district, county, and corridor level. Export tables and carts to Excel. Apply filters for time of day, day of week, mont of year, type of veicle, type of road, type of area, and accident severity. Allow variables to be re-categorized. For example, you can define te Spring season by combining te monts of January, February, and Marc. 28

39 5.2. Installation Te FAVORITE setup was packaged using te InstallSields install software. To install FAVORITE, insert your FAVORITE CD and wait several seconds for te install to automatically start te setup program. You can ten follow te instructions on te screen to complete te installation. If an existing version of FAVORITE is detected on your computer, you will be prompted to remove it before you can install a new version Input Specifications Figure 5-1 sows te main screen of te FAVORITE program. Te screen allows you to make te following tree major selections: Accident data years Locations Filters In addition, te main screen also allows you to re-categorize te variables. Tese functions are furter described in te following sections. Figure 5-1. FAVORITE Main Screen 29

40 Accident Data Years Te current version of FAVORITE includes te complete accident records for Florida s State Roadway System. You can select any number of years of data to include in te analysis using te From and To dropdown lists on te main screen Location Selection Location selection can be made at tree different levels: district, county, and corridor. More tan one item in eac level can be selected. For example, you can select Districts 4 and 6 to cover te souteast Florida region. Wen a district is selected, all te counties in it will be listed under te County list box. Selection of counties is optional. If no counties are selected, te selection is considered to ave been made at te district level only, tat all counties in a selected district will be included. Wen a county is selected, only data for tat county is included. Te selection of districts or counties is made by cecking te appropriate ceckboxes. Alternatively, you can click te Map button beside te District or County list box to select by map. Selection by map is convenient wen you want to select a contiguous area suc as selecting two adjacent counties. Figure 5-2 sows a map screen in wic te Miami-Dade and Broward counties are selected. Figure 5-2. Select Locations by Map 30

41 In tis screen, you can: Select a county by clicking te county polygon on te map. Selected counties are sown in blue. Note tat only te counties in te selected districts can be selected. Te selectable counties are enclosed by red borders. Click a selected county to unselect it. Click to clear all selections. Click to display county or district names. Click,, and to zoom in, zoom out, or pan te map. Click to display te complete map. Coose a different selection metod suc as Rectangle to select multiple features at once by drawing a rectangle wit te mouse cursor. Click te On radio button to display information of a feature pointed by mouse cursor. Click OK to exit te map view and return to te main screen wit your selections. To select a specific corridor, you can eiter enter te county/section/subsection standard roadway ID and te beginning and ending mileposts, or you can also click te adjacent Map button to select a particular roadway by pointing and clicking on te map Filters In addition to accident data years and analysis locations, FAVORITE includes filters for te following variables: Accident severity Hour of day Mont of year Day of week Veicle Type Area type Road type Tis allows you to include only a subset of tese variables by cecking or uncecking te appropriate ceckboxes for te variable options listed for eac variable. By default, all options of a variable are included. At least one variable option must be selected Output Options Once te input specifications described in te previous section are completed, you can click te Table, Cart, or GIS button at te bottom of te main screen to start computing and displaying AVOs on a cross table, a cart, or a map, respectively. 31

42 Table Display Tis option allows you to display te AVOs on a cross table. Figure 5-3 sows a table tat is cross-classified by District and Day of Week. Te following functions are available: Te Var 1 and Var 2 dropdown lists allow you to select up to two variables to cross classify te AVO estimates. By default, AVO estimates are displayed wen te table is fist displayed. Click te Veicle and Occupant buttons to display te corresponding cross tables for number of veicles and number of occupants, respectively, by clicking te appropriate radio buttons. Click to export te current view to Excel. Click to swap te rows and columns of te cross table. Click to exit to te main screen. Unceck te Sow total option to exclude te summary column and row in te table. Ceck te Minimum number of veicles option and ten enter a tresold number to exclude cells tat do not meet te minimum veicle sample size. Ceck te Sow empty rows or columns option to sow rows and columns tat are empty (i.e., wit zero sample size for all cells across a row or a column). Select from te View X-Long or Sort or View Y-Long or Sort dropdown menu items to toggle between weter to sow te variable option name after eac option code. Figure 5-3. AVOs Cross-Classified by FDOT District and Day of Week 32

43 Cart Display Te Cart option allows you to display AVOs on a cart. Te same interface is sared by bot te Cross Table and Cart displays. Once you ave selected one of te two display options, you can simply click and to switc between te two displays. Figure 5-4 sows a cart for automobile AVOs for different FDOT districts for different days of a week. Te overall AVOs are also sown for eac district and all district combined. Figure 5-4. AVOs Cross-Classified by FDOT District and Day of Week GIS Display Wile tables and carts are able to display AVOs tat are cross-classified by variables, tey cannot sow te AVOs spatially. FAVORITE provides a GIS interface tat can display AVOs by district, county, and segment. Figure 5-5 sows te main interface of te GIS display. Te left side of te screen allows you to make various selections wile te rigt side of te screen displays a map view of te state. Te top-left corner lists te GIS layers and te corresponding colors used for display. Below tis layer box is a Variable dropdown box tat allows you to specify te variable options to include in te calculation of AVOs. Once a variable is selected, all options will be displayed at te box below it. You can select te options to include by cecking te appropriate ceckboxes. 33

44 Figure 5-5. Automobile AVOs Displayed by County Once a variable option is selected, te six specification boxes below te option list box will become active. Te first of tese boxes allows you to select a teme to display by county, district, or segment. By default, te Segment option is selected. Te Class dropdown list box allows you to select te number of classes for te teme. Te default number of classes is seven. Te first class is always assigned to 0.00 to 0.99, wic normally include features tat ave no accidents. Te last class includes any number above a certain tresold. All classes between tese two boundary classes are divided based on te increment specified to te rigt. By default, te increment is 0.1. Te Style option allows you to specify eiter to sow temes by color, line widt or a combination of bot. Te default option is to display by different colors. Obviously, te line widt option applies only to te Segment teme. Te Color option allows you to select a color sceme. By default, random colors are used. You may coose to use te Blue and Red color scemes, wic display features from gradual ligt to dark colors to indicate low to ig AVOs. Te GIS display is automatically refresed as soon as any one of tese specifications is canged. Figure 5-6 sows an example of AVOs displayed by segments. 34

45 Figure 5-6. Automobile AVOs Displayed by Segment A number of tool buttons are available on te GIS screen. Clicking te to te Print Layout screen sown in Figure 5-7. In tis screen, you can: button will bring you Single click any object on te screen and ten drag it to re-position te object. Single click on an object to igligt te object and ten drag te mouse cursor to enlarge or srink te object. Double click title or footnote to enter a title or a footnote, respectively. Click to print te map to te default printer. Click to toggle between te portrait and landscape page orientation. Click to copy te print layout to clipboard. Click to add a text box. Click to remove a text box. Click to add or remove te page border. 35

46 In addition to te Print button, you can: Figure 5-7. Print Layout Click to save te map as a new sape file. Click to return to te mouse pointer. Click to display county or district names. Click,, and to zoom in, zoom out, or pan te map. Click to display te complete map. Click to identify features by mouse cursor. Click to exit to te FAVORITE main screen. 36

47 5.5. Variable Re-categorization FAVORITE allows you to re-categorize te options of eac variable. To add a new category, select te Setup Add Category dropdown menu item from te main screen to invoke te screen sown in Figure 5-8. To create a new category, follow tese steps: Figure 5-8. Screen for Adding a Category Enter a name for te new category. Select an original variable to be re-categorized. Enter te names of groups under te Group Name column. Enter te code numbers to be included in eac group. Code numbers are separated by commas. Enter 1, 2, 3 if to assign January, February, and Marc from te Mont of Year variable. Alternatively, you can also enter 1-3. If you are not familiar wit te code numbers, click te cell for wic codes are to be entered and ten press te F2 function key. Tis will invoke te screen sown in Figure 5-9, wic lists all te codes and option names for te selected variable. Ceck te boxes to be included and click OK to return te selected options to te Add Category screen. Click te Save button to save te new category and exit to te FAVORITE main screen. 37

48 Figure 5-9. Screen for Specifying Options for Group To edit an existing category, select te Setup Edit Category dropdown menu item. Tis will bring up a screen similar to te one in Figure 5-8. Te screen allows you to specify an existing category to edit Validations Tis section attempts to validate te AVO estimates from FAVORITE via bot reasonableness cecks and field data comparisons Reasonableness Cecks Reasonableness cecks involve examining te AVO estimates to determine if tey matc expectations and known trends. A number of cases are presented below: Case 1: AVO Trends by Year and Mont Figure 5-10 sows tat, as expected, weekend AVOs for automobiles are iger tan weekday AVOs, wit Sunday aving te igest AVOs, tat Mondays and Fridays tend to ave sligtly iger AVOs tan Tuesdays, Wednesdays, and Tursdays. 38

49 Case 2: AVO Trends by Mont Figure AVO Trend by Year and Day of Week Figure 5-11 sows tat, as expected, te summer monts of July and August ave te igest AVOs wile te monts of September, October, and November ave te lowest. Overall, AVOs do not cange significantly over te year. Figure AVO Trend by Mont 39

50 Case 3: AVO Trends by Hours Figure 5-12 sows tat, as expected, AVOs during te morning peak ours are lower tan tat of te afternoon peak ours, tat te daytime AVOs are lower tan te nigttime AVOs. An interesting observation is tat te AVOs continue to drop over te period of 9:00 pm to 1:00 am, wit te our just past te mid-nigt aving a significantly lower AVO tan any oter ours. However, after 1:00 am, te AVOs increased significantly. Case 4: AVO Trends by Area Type Figure AVO Trend by Time of Day Figure 5-13 sows a GIS tematic map of AVO distribution by county. Te darker te color, te iger te AVO. Te map sows tat, as expected, te more rural counties ave iger AVOs tan tose of te more urbanized counties. 40

51 Case 5: AVO Trends by Veicle Type Figure AVO Trend by County Figure 5-14 sows te AVOs for different types of veicles. Te AVOs obtained appear to be logical compared to te expected AVOs for different veicle types. Some observations include: AVOs for passenger vans are iger tan tose for passenger cars. Buses ave a significantly iger AVO. Bicycles ave te lowest AVOs. Figure AVO Trend by Veicle Type 41

52 Field Data Comparisons Table 5-1 compares te AVO estimates from te field data collected in te 1998 study conducted by TEI Engineers and Planners and RSH, Inc. wit tose estimated from FAVORITE for te same locations. Te list includes only tose locations wit at least 100 counted veicles. In computing te AVOs in FAVORITE, accident from one mile upstream and one mile downstream of te field collection location for years 1998 and 1999 were included. Table 5-1. Comparisons of AVOs from Field Data and FAVORITE County Roadway Milepost Total Veicles Total Occupants AVO from Field Data AVO from FAVORITE Hillsboroug Hillsboroug Pinellas Hillsboroug Sarasota Duval Orange Orange Volusia Broward Broward Broward Dade Palm Beac Palm Beac Figure 5-15 plots te field estimates for te 15 locations against te FAVORITE estimates for te same locations. Wile te regression equation sows a positive relationsip between te two sets of AVO estimates, te R 2 value is not ig. Overall, te AVO estimates from te cras records tend to be iger tan te field estimates. Tis is expected since te windsield metod used in te field data collection was not able to capture all passengers, especially infants and cildren. 42

53 1.7 Estimates from te Field (windsied) y = x R 2 = Estimates from Cras Data Figure Field versus FAVORITE Estimates 43

54 CHAPTER 6 AUTOMATED FIELD DATA COLLECTION SYSTEM Tis capter describes an automated field data collection system designed for use wit a andeld Pocket PC. In addition, a post-processing program tat can calculate te average occupancy rates from te data collected from te automated data collection tool is described. It is noted tat te researc team investigated te possibility of applying voice recognition tecnology in lieu of screen input on a Pocket PC. Tis was found to be impractical for two reasons: (1) Lack of appropriate software: te researc team was not able to find a suitable voice recognition system tat can work wit te Windows CE operating system; and (2) unreliability: interference from traffic noise in te field can easily result in te recording of incorrect data Overview Te field data collection tool provides a touc-screen interface on a Pocket PC to record te number of passengers in veicles classified by veicle type and/or lane number. Te field collected data are ten processed by a desktop program to produce average occupancy rates and related statistics. Te field data collection tool is a typical Window installation designed to run on all Microsoft Windows CE operating systems. For successful installation and application of te tool, your system must ave: Minimum screen size: 3.5" A minimum of 48 MB memory storage space for a full installation For general elp on using Windows CE system suc as managing te environment and te file system, refer to te Microsoft Windows CE and Microsoft ActiveSync User s Guide Installation To install te field data collection tool, follow te following steps: 6. Set up te connection between te Pocket PC and te desktop computer. 6. Insert te field data collection tool CD into te CD-ROM drive. 6. Use Windows Explorer to find Setup.exe in te CD1 folder on your CD-ROM drive. Double click te Setup.exe icon. 6. Coose a destination location. 6. Continue to follow on-screen instructions until te sown screen in Figure 6-1 pops up. Clicking Yes will install files to te default storage in te Pocket PC directly. Clicking No will invoke te screen sown in Figure 6-2, wic allows you to select a storage option from te Save In list box. Click OK button to complete te installation. Note: If te Confirm File Replace box is sown on te screen of Pocket PC at te same time, select te No to All button to keep te existing files of te Pocket PC system. 44

55 Figure 6-1. Determine Application Storage Figure 6-2. Select Storage Option 6.3. Main Screen Te field data collection tool can be started by clicking te Start Programs menu from te Pocket PC. Te main screen of te program is sown in Figure 6-3. Te screen allows you to specify weter to differentiate your occupancy data by veicle type, lane number, or bot. Tese options are specified by cecking te appropriate ceckboxes on te screen General Screen Figure 6-3. Input Screen for Data Classification 45

56 After making te coices on classification, you can click te Enter button to bring up te screen sown in Figure 6-4, wic lists tree buttons on top of te screen: General and Data Entry. By default, te screen will display te General form. Tis allows you to select weter to create a new file or open an existing file, and to input te general roadway location information. Te Data Entry button allows you to input te number of passengers and, if applicable, veicle type and/or lane number Create a New File To create a new file, follow te steps below: Figure 6-4. Input Screen for General Information 1. On te General form screen, click te New File radio button to bring up te screen sown in Figure Enter te new file name in te text box. 3. Coose te folder for saving te new file from te Folder list box. 4. Coose a storage location from te Location list box. 5. Click OK to finis creating a new file. Te file pat and name will be sown in te text box under te radio buttons as sown in Figure Open a Existing File To open an existing file, follow te steps below: 1. Click te Open File radio button to bring up te screen sown in Figure

57 2. Select a folder name from te Folder list box. All te files wit te selected file type (*.cdb) in te folder will be displayed. 2. Double click a file name to open. Te file pat and name will be sown in te text box under te radio buttons on Figure 6-4. Figure 6-5. Create a New File Figure 6-6. Open an Existing File 47

58 Input Location Information Te general location information can be added or edited after creating a new file or opening an existing file. 5. Click te Name text box. 5. Click te icon on te rigt corner to bring up a keyboard to key in te name. 5. Repeat Steps 1 and 2 for Place, Roadway Name, and Roadway ID. 5. Click te Data Entry button to start collecting occupancy data Data Entry Screen Depending on te classification cosen, one of te following four screens will be displayed: 0. Witout veicle type and lane number 0. Wit veicle type only 0. Wit lane number only 0. Wit bot veicle type and lane number Te respective screens for eac of tese four classifications are sown in Figures 6-7 to 6-10, respectively. Figure 6-7. Data Entry Screen: Witout Veicle Type and Lane Number 48

59 Figure 6-8. Data Entry Screen: wit Veicle Type Only Figure 6-9. Data Entry Form wit Lane Number 49

60 Figure Data Entry Screen: wit Bot Veicle Type and Lane Number Te operations of tese four screens are similar: Click te number buttons to enter te veicle occupancy. Te number will be sown on te Occupancy text box. Click Save to save a new entry (for eac veicle). Click te Backspace to delete an entered occupancy number. Click te Lane (if applicable) to specify te lane number. You may set your own lane numbering sceme and ten use it consistently. Click a new lane number to replace an entered lane number. Click Veicle Type (if applicable) to specify te veicle type. Click a new veicle type to replace an entered veicle type. Click Clear to clear all entries, including occupancy, and if applicable, lane number and veicle type. Click Data Table to bring up screen sown in Figure Tis is furter described in te next section Editing Data Table Te data table allows you to make canges to te entered records. Figure An example is given in 50

61 Figure Data Table Click Edit to edit a record. Te Edit button will cange to te Save button. Click tis button to save canges to te record. Click Delete to delete te current record. Click Previous to retrieve te previous record. Click Next to retrieve te next record. Click Move First to retrieve te first record. Click Move Last to retrieve te last record. Click Main Menu to go back to te Data Entry screen Import and Export Database ActiveSync 3.1 uses file filters to automatically andle conversions between desktop computer file formats and Windows CE based device file formats. Te ADOCE control provides te file filter, Adofiltr.dll, to andle te conversion from te Microsoft Access file format (.mdb) to te Windows CE database file format (.cdb). Te procedures for manually importing and exporting databases to and from te desktop and Windows CE devices are described below Import from Desktop to Windows CE Te steps for importing a database file from a desktop computer to a Pocket PC are as follows: 1. Connect te Windows CE based device to te desktop computer. 2. Open ActiveSync 3.1 and coose Import Database Tables from te Tools menu. 3. Type te database pat and filename in te Open dialog box; click OK. 51

62 4. In te Import dialog box tat appears, specify te location on te target device were te database will be placed. Te default folder is "My Documents." 9. Ceck a table in te database view window to copy te table to te device. Eac table can be expanded and individual fields from tat table selected. 9. Selecting te Default button will reset te selections to teir original state, wic includes all tables and fields in a database. 9. Click OK to copy te entire database to te Databases folder on te device Export an ADOCE Database to a Desktop Te steps for exporting an ADOCE database file from a Pocket PC to a desktop computer are as follows: 5. Select Export Database Tables from te Tools menu in te Mobile Devices window. 5. Type te database pat and filename of te database to be placed on te desktop computer in te Location box. 5. Select all te tables to be copied in te Select te tables to copy box. 5. Select te box labeled Overwrite existing tables and/or data; click OK. Selecting tis option causes te converted table from te device to replace te table in te file. 5. Open te database file in Microsoft Access to see te exported database Post-Processing Program Tis section describes a post-processing program tat serves as a companion program to te AVO field data collection tool described above. Developed as a simple Microsoft Access application, te program can compute te average veicle occupancy rates and te related statistics from veicle occupancy data collected by te AVO field data collection tool. Te output from te application is a simple one-page report tat summarizes te number of veicles, number of occupants, average occupancy rates, standard deviation of average occupancy rates, and 95% confidence intervals for average occupancy rates. Figure 6-12 sows an example of te summary report. As can be seen, te output summary is stratified by veicle type and lane number. Eac summary table also includes a Total column and a Total row. Eac of te total values provides te aggregate statistics for eac veicle type and eac lane. Te common cell for te Total row and Total column gives te overall statistics for a data collection location. Te name of te application file is AVO REPORT.MDE, wic is a Microsoft Access executable file. To generate a summary report: 0. Find te AVO REPORT.MDE file and double click it to bring up te screen sown in Figure Click te Select Database File button to bring up te screen sown in Figure Specify a MDB data file tat contains data tables generated by te AVO field data collection tool. As soon as an MDB data file is selected, all te data tables in te file will be listed on te list box sown Figure

63 0. Higligt a data table on tis list and click OK to generate a summary report for te occupancy data in te selected data table. Figure Output Summary Table 53

64 Figure Screen for Selecting Database File and Data Tables Figure Screen for Specifying a Database File 54