Coordinated Highways Action Response Team

Transcription

1 Performance Evaluation and Benefit Analysis for CHART in Year 29 Coordinated Highways Action Response Team ( F i n a l r e p o r t ) Prepared by I Gang-Len Chang, Professor Department of Civil and Environmental Engineering University of Maryland, College Park Steven Rochon, Project Manager Office of CHART ITS Development, State Highway Administration of Maryland

2 Performance Evaluation of CHART - The Real-Time Incident Management System (Year 29 )

3 Table of Contents ACHNOWLWDEMENTS... ii EXECUTIVE SUMMARY... iii CHAPTER 1. INTRODUCTION...1 CHAPTER 2. DATA QUALITY ASSESSMENT Analysis of Data Availability Analysis of Data Quality...4 CHAPTER 3. ANALYSIS OF DATA CHARACTERISTICS Distribution of Incidents and Disabled Vehicles by Day and Time Distribution of Incidents and Disabled Vehicles by Road and Location Distribution of Incidents and Disabled Vehicles by Lane Blockage Type Distribution of Incidents and Disabled Vehicles by Blockage Duration...24 CHAPTER 4. ANALYSIS OF DATA CHARACTERISTICS Evaluation of Detection Efficiency and Effectiveness Analysis of Response Efficiency Distribution Reduction in Incident Duration...33 CHAPTER 5. ANALYSIS OF DATA CHARACTERISTICS Assistance to Drivers Estimated Benefits due to Efficient Removal of Stationary Vehicles Direct Benefits to Highway Users...38 CHAPTER 6. ANALYSIS OF DATA CHARACTERISTICS Distribution of Average Incident Durations by Nature Distribution of Average Incident Durations by County Distribution of Average Incident Durations by Weekdays/Ends, Peak/Off-Peak Hours, CHART Involvement and Roads...5 CHAPTER 7. ANALYSIS OF DATA CHARACTERISTICS Conclusions Recommendations for Further Developments...54 REFERENCES...56 APPENDIX...58 i

4 A C K N O W L E D G M E N T S The authors would like to thank Mr. Greg Welker, Mr. Thomas Hicks, and Mr. Michael Zezeski for their constant encouragement and constructive comments throughout the entire research period. This study would be incomplete without their strong support. We are certainly indebted to State Highway Administration senior managers for their suggestions regarding the report organization and presentation. We would also like to extend our appreciation to Mr. Howard Simons from the Maryland Department of Transportation, the technical staff of the Coordinated Highways Action Response Team, the Office of Traffic and Safety, and the operators of the Statewide Operations Center and the two other satellite Traffic Operations Centers. Their efforts in documenting the 29 incident response data for this study are greatly appreciated. ii

5 E X E C U T I V E S U M M A R Y Objectives This report presents the performance evaluation study of the Coordinated Highways Action Response Team (CHART) for the Year 29, including its operational efficiency and resulting benefits. The research team at the Civil Engineering Department of the University of Maryland, College Park (UM), has conducted the annual CHART performance analysis over the past eleven years for the Maryland State Highway Administration (MSHA). Similar to previous studies, the focus of this task was to evaluate the effectiveness of Maryland CHART s ability to detect and manage incidents on major freeways and highways. Assessing the benefits resulting from incident management was equally essential. In addition, this annual report has extended the analysis of incident duration distributions on major highways for better understanding of the incident characteristics and management. The study consisted of two phases. Phase 1 focused on defining objectives, identifying the available data, and developing the methodology. The core of the second phase involved assessing the efficiency of the incident management program and estimating the resulting benefits using the 29 CHART incident operations data. As some information essential for efficiency and benefit assessment was not available in the CHART-II database, this study presents only those evaluation results that can be directly computed from the incident management data or derived with statistical methods. Available Data for Analysis Upon a request made by MSHA, COSMIS began evaluating CHART operations performance in During the initial evaluation, the 1994 incident management data from the Traffic Operations Center (TOC) were reviewed but, for various reasons, not used. Thus, the conclusions drawn were based mostly on information either from other states or from nationwide averaged data published by the Federal Highway Administration. To ensure better evaluation quality and also in view of the Statewide Operations Center (SOC) having opened in August of 1995, those associated with the evaluation study concluded that the analysis should be based on actual performance data from the CHART iii

6 program. Hence, in 1996, the UM (Chang and Point-Du-Jour, 1998) was contracted to work jointly with MSHA staff to collect, and subsequently to analyze incident management data. This original study and evaluation analysis inevitably faced the difficulty of having insufficient information for analysis, since this was the first time CHART had to collect all previous performance records for a scrupulous evaluation. The 1997 CHART performance evaluation had the advantage of having relatively substantial information. The collected information comprised incident management records from the Statewide Operations Center (SOC), TOC-3 (positioned in the proximity of the Capital Beltway), and TOC-4 (sited near the Baltimore Beltway) over the entire year, as well as 1997 Accident Report Data from the Maryland State Police (MSP) for secondary incident analysis. Unlike previous studies, the quality and quantity of data available for performance evaluation has increased considerably since This results from CHART realizing the need to keep an extensive operational record in order to justify its costs and to evaluate the benefits of the emergency response operations. Due to CHART s efficient data collection, documentation of lane-closure-related incidents increased from 2,567 in 1997 to 23,585 in 29. The table below shows the total number of emergency response operations that have been assiduously documented from 23 to 29: Incidents only 18,68 19,127 2,515 21,55 21,236 21,586 23,585 Total 38,523 4,539 41,196 44,43 42,321 56,2 55,563 It should be noticed that CHART may have responded to more emergency service requests than those reported in the database. This may be due to insufficient recording of incidents by control center operators, which should be tackled with the implementation of the upgraded CHART information system. iv

7 Evolution of the Evaluation Work CHART has consistently worked on improving its data recording for both major and minor incidents over the past eleven years, which accounts for the substantial improvements in data quality and quantity. The evaluation work has also been advanced by the improved availability of data. It has also become imperative to assess the quality of data used and to only use reliable data in the benefit analysis. Thus, from 1999, the performance evaluation reports have included data quality analysis. This aims at ensuring continued advancement in the quality of incident-related data so as to reliably estimate all potential benefits of CHART operations. From February 21, all incident requests for emergency assistance have been recorded in the CHART-II information system, irrespective of whether CHART responded or not, and this has significantly enriched the available data. In the current CHART database system, most incident-related data can be generated directly for computer processing, except that incident-location-related information remains documented in a text format which cannot be processed automatically with a data analysis program. Distribution of Incidents The evaluation methodology was created to utilize all available data sets that are considered to be of acceptable quality. An analysis of incident characteristics by incident duration and number of blocked lanes was initially conducted. The analysis results indicate that 3,474 severe incidents in Year 29 resulted in onelane blockage, 4,16 severe incidents caused two-lane closures, and 2,812 severe incidents blocked more than two lanes. In addition, either disabled vehicles or minor incidents caused a total of 35,69 shoulder incidents. A comparison of lane-blockage incident data over the past seven years is summarized below: v

8 List of Incidents by Lane Blockage Type from 23 to Shoulder 23,399 24,518 23,748 25,631 23,94 36,861 35,69 1 lane 3,15 3,11 3,94 2,989 2,937 3,32 3,474 2 lanes* 2,443 2,891 3,193 3,659 3,824 3,579 4,16 3 lanes* ,78 1,245 1,331 1,238 1,486 4 lanes* ,127 1,33 1,356 1,185 1,326 * Counts the shoulder lane as one lane Most of those incidents were distributed along six major commuting corridors: I- 495/95, which experienced a total of 6,929 incidents in 29; I-695, I-95, US-5, MD-295, and I-27 with 7,159, 16,472, 3,214, 1,57, and 2,865 incidents, respectively. CHART managed an average of 45 emergency requests per day on I-95 alone, and 19, 2, 9, 4, and 8 responses per day for I-495/95, I-695, US-5, MD-295, and I-27, respectively. The distribution of incidents on those major commuting corridors between 23 and 29 is tabulated below: Summary of Incident Distributions on Major Freeway Corridors I-495/95 9,936 1,288 9,84 7,881 7,667 6,147 6,929 I-695 8,938 9,277 9,536 1,9 7,592 7,359 7,159 I-27 1,582 1, ,536 2,168 2,417 2,865 I-95 4,568 5,852 5,629 4,24 4,84 17,794 16,472 US-5 1,984 2,55 3,285 4,273 5,197 5,343 3,214 MD-295 1,591 1,462 1,858 1,417 1,418 2,239 1,57 vi

9 However, it should be mentioned that most incidents on the major commuting freeways did not block traffic for more than one hour. For instance, the ratio of disabled vehicles to incidents which had durations shorter than 3 minutes was about 81 percent in 29. This observation can be attributed to the nature of the incidents and, more probably, to the efficient response of CHART. The distribution of incidents/disabled vehicle duration from 23 to 29 is summarized below: Distribution of Incidents/Disabled Vehicle Duration from 23 to 29 Duration(Hrs) D <.5 79% 8% 81% 8% 78% 83% 81%.5 D < 1 11% 11% 11% 11% 13% 1% 11% 1 D < 2 5% 5% 4% 4% 5% 4% 5% 2 D 5% 5% 4% 5% 4% 3% 3% In brief, it is apparent that the highway networks served by CHART remain plagued by a high frequency of incidents with durations ranging from 1 to over 12 minutes. Those incidents were the primary contributors to traffic congestion in the entire region, especially on major commuting highway corridors, such as I-95, I-27, I-495/95, and I-695. Efficiency of Operations Detection, response, and traffic recovery are the three vital features associated with the efficiency of an incident management program. Unfortunately, data needed for the execution of detection and response time analysis are not yet available under the CHART data system. MSHA patrols and MSP remain the main sources of incident detection and response data. The average response time is the average duration elapsing from the reception of an emergency request to the arrival of the emergency response unit. The 29 data show the average response times of 11.41, 14.41, 3.5, 7.87, 12.83, 6.4 and 5.81 minutes for TOC-3, TOC-4, TOC-5, TOC-6, TOC-7, and SOC, respectively. As the table below shows, CHART has demonstrated a steady improvement in its response times over the past eleven vii

10 years: Evolution of Response Times by Center from 25 to 29 Response time (mins) TOC TOC TOC TOC TOC-7 N/A SOC AOC OTHER Weighted Average To better understand the contribution of the incident management program, the study compared the average duration of incidents to which CHART responded and those managed by other agencies. For instance, for two-lane-blockage incidents to which the SHA Patrol did not respond, the average duration (7.38 minutes) was about 28.4 minutes longer than these to which they responded (42.34 minutes). The duration of incidents managed by CHART response units in 29 averaged minutes, shorter than the average duration of minutes for those incidents managed by other agencies. On average, CHART operations in Year 29 reduced the average incident duration by about 31 percent. Performance improvement of CHART operations from years 23 to 29 is summarized below: viii

11 Comparison of Average Incident Duration with and without CHART Response Year With CHART (mins) Without CHART (mins) Resulting Benefits The benefits attributed to CHART operations that were estimated directly from the available data include assistance to drivers and reductions in driver delay times, fuel consumption, and emissions. CHART responded to a total of 23,585 lane blockage incidents in 29, and assisted 31,978 highway drivers who may otherwise have caused incidents or rubbernecking delays to highway traffic. CHART s contribution to shortening incident durations also reduced potential secondary incidents. In addition, efficient removal of stationary vehicles and large debris from travel lanes by CHART patrol units may have prevented 571 potential lane-changing-related collisions in 29, as approaching vehicles under those conditions would have been forced to perform unsafe mandatory lane changes. CORSIM, a traffic simulation program produced by the Federal Highway Administration (FHWA), was used to estimate the direct benefits of reductions in delay time and fuel consumption. Analysis determined that CHART s services in 29 reduced delays by million vehicle-hours, as well as reducing fuel consumption by 6.23 million gallons. A comparison of direct benefits from 23 to 29 is summarized below: ix

12 Comparison of Direct Benefits from 23 to 29 Total Direct Benefits (million) # of Incidents/Disabled Vehicles 23 $ , $ , $864.31* 41, $1,92.35* 44,43 27 $1,118.55** 42, $981.6** 56,2 29 $1,6.5*** 55,563 * Results based on the U.S Census Bureau data (25) ** Results based on the U.S Census Bureau data (26) and the Energy Information Administration (27) *** Results based on the U.S Census Bureau data (26) and the Energy Information Administration (27) Analysis of Incident Durations For effective and efficient traffic management after incidents, responsible agencies can convey the information to travelers by updating the variable message signs; they can also estimate the resulting queue length and assess the need to implement detour operations and any other control strategies to mitigate congestion. This study conducted a statistical analysis of incident durations, which provides fundamental insight into the characteristics of incident durations under various conditions. In this analysis, the distributions of average incident duration are identified by a range of categories, including Nature, County, County and Nature, Weekdays and Weekends, Peak and Off-Peak Hours, CHART Involvement, and Roads. The average duration if incidents involving fatalities was 27 minutes, while incidents with property damage and personal injuries lasted, on average, were 33 and 59 minutes, respectively. The average duration of disabled vehicle incidents was 2 minutes, much shorter than that of any other incident natures (e.g., debris, vehicles on fire, police activities, etc.) classified as Others category. The average incident x

13 duration of the Others was approximately 73 minutes. Conclusions and Recommendations Building from the earlier research, this study has conducted a rigorous evaluation of CHART s performance in 29 and its resulting benefits under the constraints of data availability and quality. Overall, CHART has made significant progress in recording more reliable incident reports, especially after implementation of the CHART-II Database. However, much remains to be done in terms of collecting more data and extending operations to major local arterials, if resources are available to do so. For example, data regarding the potential impacts of major incidents on local streets have not been collected by CHART. Without such information, one may substantially underestimate the benefits of CHART operations, as most incidents causing lane blockages on major commuting freeways are likely to spill congestion back to neighboring local arterials if the speed of traffic queue formation is faster than the pace of the incident clearance progress. By the same token, a failure to respond to major accidents on local arterials, such as MD-355, may also significantly degrade traffic conditions on I-27. Effectively coordinating with county agencies on both incident management and operational data collection is one of the major tasks to be done by CHART. With respect to performance, CHART has maintained nearly the same level of efficiency in responding to incidents and driver assistance requests in recent years. The average response time in Year 29 was minutes. In view of the worsening congestion and the increasing number of incidents in the Washington-Baltimore region, it is commendable that CHART can maintain its performance efficiency with approximately the same level of resources. This study s main recommendations, based on the performance of CHART in 29, are listed below: Allocate more resources to CHART for incident response and traffic management to improve the performance of the response teams so that they can effectively contend with the ever-increasing congestion and accompanied incidents. xi

14 Coordinate with county traffic agencies to extend CHART operations to major local routes and include data collection as well as performance benefits in the annual CHART review. Make CHART s quality evaluation report available to the operators to facilitate continuous improvement of their response operations. Implement training sessions to instruct operators on how to record critical performance-related data effectively. Improve the data structure used in the CHART-II system for recording incident locations to eliminate the current laborious, complex procedures. Reduce average response times by increasing freeway service patrols and assigning patrol locations based on both the spatial distribution of incidents along freeway segments and the probability of an incident occurring. Integrate police accident data efficiently into the CHART-II incident response database in order to have a complete representation of statewide incident records. Incorporate, in the CHART benefit evaluation, the delay and fuel consumption benefits from reducing potential secondary incidents. Please note that comprehensive evaluation results of CHART performance over the past six years are available on the Web site ( xii

15 C H A P T E R 1 I N T R O D U C T I O N CHART (Coordinated Highways Action Response Team) is the highway incident management system of the Maryland State Highway Administration (MSHA). Initiated in the mid-8s as The Reach the Beach Program, it was subsequently expanded as a statewide program. The Statewide Operations Center (SOC), an integrated traffic control center for the state of Maryland, has its headquarters in Hanover, Maryland. The SOC is supported by three satellite Traffic Operations Centers (TOCs), of which one is seasonal. CHART s current network coverage consists of statewide freeways and major arterials. CHART has four major functions: traffic monitoring, incident response, traveler information, and traffic management. Incident response and traveler information systems have received increasing attention from the general public, media, and transportation experts. In 1996, incident data were collected and used in the pilot evaluation analysis conducted by the University of Maryland in conjunction with MSHA staff (Chang and Point-Du-Jour, 1998). As this was the first time that previous records were to be analyzed, researchers were inevitably faced with the difficulty of having a database with insufficient information. The 1997 CHART performance evaluation was far more extensive. The researchers were able to obtain a relatively richer set of data, obtained from incident management reports gathered over 12 months from the SOC, TOC-3 (located near the Capital Beltway), and TOC-4 (situated near the Baltimore Beltway). In addition to these data, accident reports from the Maryland State Police (MSP) were also available for secondary incident analysis. The data used for the evaluations have improved incredibly since This has occurred because CHART recognized the need to keep an extensive operational record in order to justify the costs and to evaluate the benefits of the emergency response operation. The data available for analysis of lane closure incidents increased from 5, reports in 1999 to 23,585 reports in 29. A summary of total emergency response operations 1

16 documented from 22 to 29 is presented in Table 1.1. Table 1.1 Total Number of Emergency Response Operations Records Incidents 13,752 18,68 19,127 2,515 21,55 21,236 21,586 23,585 Disabled Vehicles 19,62 2,455 21,412 2,681 22,988 21,85 34,614 31,978 Total 32,814 38,523 4,539 41,196 44,43 42,321 56,2 55,563 The objective of this study is to evaluate the effectiveness of CHART s incident detection, response, and traffic management operations on interstate freeways and major arterials. This assessment also includes an estimation of CHART benefits an essential part of the study, since support of MSHA programs from the general public and state policymakers largely depends on the benefits the state obtains from its ongoing programs. In order to conduct a comprehensive analysis using available data to ensure the reliability of the evaluation results, the evaluation study has been divided into the following three principal tasks: Task 1: Assessment of Data Sources and Data Quality involves identifying data sources, evaluating their quality, analyzing available data, and classifying missing parameters. Task 2: Statistical Analysis and Comparison entails performing comparisons based on data available in 28 and 29, with an emphasis on these target areas: incident characteristics, incident detection efficiency, distribution of detection sources, incident response efficiency, and effectiveness of incident traffic management. Task 3: Benefits Analysis entails the analysis of the reduction in total delay times, fuel consumption, emissions, and secondary incidents due to CHART/SHA operations, as well as the reduction in potential accidents due to efficient removal of stationary vehicles in travel lanes by the CHART/SHA response team. The subsequent chapters are structured as follows: Chapter 2 assesses the quality of data available for the 29 CHART performance evaluation. This includes total available incident reports, percentage of missing data for 2

17 each critical performance parameter, and a comparison of 29 data quality with that of 28. Chapter 3 outlines the statistical analysis of incident data characteristics, such as distributions of incidents and disabled vehicles by road name, by location on road, by weekday and weekend, by lane-blockage type, and by lane-blockage duration. The analysis also includes a comparison of the average incident duration caused by different types of incidents. Chapter 4 provides a detailed report on the efficiency and effectiveness of incident detection. Issues discussed are detection rate, distribution of detection sources for various types of incidents, and driver requests for assistance. The chapter also touches on an evaluation of incident response efficiency. The efficiency rate is based on the difference between incident report time and the arrival time of emergency response units. Also, the assessment of incident clearance efficiency is based on the difference between the arrival time of the emergency response units and the incident clearance time. Chapter 5 estimates the direct benefits associated with CHART s operations. Parameters used for the estimates are the reductions in fuel consumption, delays, emissions, secondary incidents, and potential accidents. CHART patrol units also respond to a significant number of driver assistance requests, and these services provide direct benefits to drivers and minimize potential rubbernecking delays on highways. Chapter 6 performs a statistical analysis of incident durations, which provides fundamental insight into the characteristics of incident durations under various conditions. In this analysis, the distributions of average incident duration are identified by a range of categories, including Nature, County, County and Nature, Weekdays and Weekends, Peak and Off-Peak Hours, CHART Involvement, and Roads. Finally, Chapter 7 offers some concluding comments and makes recommendations for future evaluations. 3

18 C H A P T E R 2 D A T A Q U A L I T Y A S S E S S M E N T This chapter assesses the quality of data available for the CHART 29 performance evaluation and compares it with the data from CHART Analysis of Data Availability In 29, CHART recorded a total of 55,563 emergency response cases. These are categorized into two groups: incidents and disabled vehicles. A summary of the total available incident reports for the years 27, 28, and 29 is shown in Table 2.1. Table 2.1 Comparison of Available Data for 27, 28, and 29 Available Records Records Total (%) Records Total (%) Records Total (%) CHART II Database Disabled 21, , , Vehicles Incidents 21, , , Total 42, ,2 1 55, Analysis of Data Quality More than 1 million records in 24 tables from the CHART II database have been filtered to obtain key statistics for a detailed evaluation of the data quality. Figures 2.1 and 2.2 illustrate the comparison of the quality of data recorded in 28 and 29. 4

19 1% 9% 8% 7% 6% 99.51% 97.73% 99.71% 97.49% 78.42% 78.5% 1% 1% 99.98% 1% 1% 99.99% 58.31% 62.67% 5% 4% 3% 2% 1% % COUNTY SOURCE NATURE DIRECTION TYPE LOCATION LANE BLOCKAGE Year 29 Year 28 Figure 2.1 Summary of Data Quality for Critical Indicators 1% 9% 8% 7% 1% 96.6% 1% 92.67% 86.92% 85.11% 79.67% 85.1% 6% 5% 4% 46.25% 46.64% 3% 2% 1% % RECEIVED TIME CONFIRMED TIME DISPATCHED TIME ARRIVAL TIME CLEARED TIME Year29 Year 28 Figure 2.2 Summary of Data Quality for Time Indicators 5

20 Nature of Incidents/Disabled Vehicles Data was classified based on the nature of the incidents, such as vehicle on fire, collision-personal injury, and collision-fatality. CHART s records for disabled vehicles are also categorized as abandoned vehicles, tire changes, and gas shortage. As shown in Figure 2.1, about 8 percent of emergency responses reported in 29 recorded the nature of incidents. Detection Sources As Figure 2.1 shows, about 98 percent of all emergency responses recorded in 29 contained the source of detection, which is slightly more than the previous year s data. In 29, about 95 percent of incidents reported and 1 percent of the disabled vehicles reported had a definite detection source. Operational Time-Related Information To evaluate the efficiency and effectiveness of emergency response operations, CHART, in 29, used five time parameters for performance measurements: Received Time, Dispatched Time, Arrival Time, Cleared Time, and Confirmed Time. Figure 2.2 illustrates the data quality analysis with respect to these performance parameters. The figure indicates that the quality of data for Received Time and Confirmed Time are sufficient for reliable analysis, while the data of Dispatched Time, Arrival Time, and Cleared Time still require improvement for reliable analysis. Type of Reports The total number of incidents/disabled vehicles managed by each operation center in 29 is summarized in Table 2.2. Overall, CHART responded to a total of 23,585 incidents in 29. Over the same period, the response team also attended to 31,978 disabled vehicle requests. 6

21 Table 2.2 Emergency Assistance Reported in 29 Operation Center SOC TOC3 TOC4 TOC5 TOC6 TOC7 AOC OTHER TOTAL Disabled Vehicles 1,4 7,256 7, ,987 6,214 7,995 31,978 (34,614) Incidents 3,216 6,99 4, ,128 5,544 1,6 Total 4,616 13,355 11, ,115 11,758 9,595 23,585 (21,586) 55,563 (56,2) Note: numbers in the parenthesis are the corresponding data from 28 Location and Road Name Associated with Each Response Operation The location and road name information associated with each emergency response operation was used to analyze the spatial distribution of incidents/disabled vehicles and to identify freeway segments that experience frequent incidents. As shown in Figure 2.1, all incident response reports have documented location information. This feature has always been properly recorded over the years. However, the location information associated with each response operation is structured in a descriptive text format that cannot be processed automatically with a computer program. Hence, road names and highway segments must be manually located and inputted into the evaluation system. Table 2.3 shows the percentage of data with valid road names and highway segment location information (i.e., exit numbers) for incidents and disabled vehicles in the CHART II Database for 29. Note that road names can be identified only for 82 percent of incidents in which the database specifies the highway segments where the incidents occurred. For the remaining 18 percent of incident, road names are either unclear or not specified, and therefore, cannot be used for reliable performance analysis. 7

22 Table 2.3 Data Quality Analysis with Respect to Road and Location Data Quality Incident Disabled Vehicles Total Road 81.59% 93.38% 88.38% Location 99.98% 1% 99.99% Lane/Shoulder Blockage Information To compute additional delays and fuel consumption costs caused by each incident requires knowing the number of lanes (including shoulder lanes) blocked as a result of the incident. The analysis of all available data in 29 shows that up to percent of emergency response reports involved a lane/shoulder blockage. This value is slightly higher than the percent in 28. In summary, in 29, improvements have been made in documenting CHART s performance and recording operations-related information. The use of the CHART II Database has had a noticeable positive impact on data quality improvement, but room for improvement still exists, as shown in the above statistics evaluating data quality. Finally, CHART operators should be made aware of their contribution to mitigation of traffic congestion, driver assistance, and the overall improvement of the driving environment. 8

23 C H A P T E R 3 A N A L Y S I S O F D A T A C H A R A C T E R I S T I C S The evaluation study started with a comprehensive analysis of the spatial distribution of incidents/disabled vehicles and their key characteristics to improve the efficiency of incident management. 3.1 Distribution of Incidents and Disabled Vehicles by Day and Time The research team analyzed the differences between the distribution of incidents/disabled vehicles during weekdays and weekends. As shown in Table 3.1, a large number (about 85 percent) of incidents/disabled vehicles in 29 occurred on weekdays. Thus, more resources and personnel are required on weekdays than on weekends to manage the incidents/disabled vehicles more effectively. Table 3.1 Distribution of Incidents/Disabled Vehicles by Day Center TOC3 TOC4 TOC5 TOC6 TOC7 Year Weekdays 98% 99% 99% 1% 78% 3% 92% 1% 98% 1% Weekends 2% 1% 1% % 22% 7% 8% % 2% % Center SOC AOC Other* Total Year Weekdays 6% 89% 73% 72% 34% 24% 85% 86% Weekends 4% 11% 27% 28% 66% 76% 15% 14% * Includes RAVENS TOC and REDSKINS TOC As defined by the 1999 CHART performance evaluation, peak hours in this study are from 7: AM to 9:3 AM and from 4: PM to 6:3 PM. Table 3.2 illustrates that 35 percent of incidents/disabled vehicles reported in 29 occurred 9

24 during peak hours, which is slightly higher than the same observation made in 28. Table 3.2 Distribution of Incidents/Disabled Vehicles by Peak and Off-Peak Periods Center TOC3 TOC4 TOC5 TOC6 TOC7 Year Peak** 41% 41% 45% 45% 36% 18% 29% 35% 45% 45% Off-Peak 59% 59% 55% 55% 64% 82% 71% 65% 55% 55% Center SOC AOC Other* Total Year Peak** 21% 35% 26% 26% 1% 1% 35% 34% Off-Peak 79% 65% 74% 74% 9% 99% 65% 66% * Includes RAVENS TOC and REDSKINS TOC ** 7: AM ~ 9:3 AM and 4: PM ~ 6:3 PM Frequency Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles * Off-PkHR, AM-PkHR, and PM-PkHR stand for Off-Peak hours, AM-Peak hours, and PM-Peak hours, respectively. Figure 3.1 Distributions of Incidents/Disabled Vehicles by Time of Day in 29 1

25 I-95 I-495/I-95 I-27 I-7 MD-295 US-5 US-1 I-37 I-695 I-795 I-895 I-83 I-68 I-97 US-34 Others Figure 3.1 illustrates the distributions of incidents/disabled vehicles by time of day in more detail. The frequency of incidents in off-peak hours is much higher than in AM or PM peak hours, since there are many more such hours. More detailed information regarding distributions by time of day is presented in the Appendix. 3.2 Distribution of Incident and Disabled Vehicles by Road and Location Figure 3.2 compares the frequency distribution among roads between 29 and 28, and Figure 3.3 depicts the frequency distribution of incidents and disabled vehicles for 29. Frequency Figure 3.2 Distributions of Incidents/Disabled Vehicles by Road in Based on the statistics shown above, the roadways with high incident frequencies for 29 were I-95 (from the Delaware border to the Capital Beltway), I-695 (Baltimore Beltway), I-495/95 (Capital Beltway), US-5, I-27 and I-7. I-95 experienced a total of 16,472 incidents/disabled vehicles in 29, while I-695 had 7,159 incidents/disabled vehicles within the same period. I-495/95, US-5, I-27 and I-7 were plagued with 6,929, 3,214, 2,865, and 2,11 incidents/disabled vehicles, respectively. Figures 3.4 and 3.5 present comparisons of frequency distributions by time of day 11

26 I-95 I-495/I-95 I-27 I-7 MD-295 US-5 US-1 I-37 I-695 I-795 I-895 I-83 I-68 I-97 US-34 Others on major roads in Maryland for incidents and disabled vehicles. As shown in these figures, somewhat more incidents/disabled vehicles occurred during PM-peak hours than AM-peak hours. Frequency Incidents Disabled Vehicles Frequency 5 Figure 3.3 Distributions of Incidents/Disabled Vehicles by Road in I-95 I-495 I-27 I/MD-295 I-695 I-83 Off-PkHR AM-PkHR PM-PkHR Figure 3.4 Distributions of Incidents by Time of Day on Major Roads in 29 12

27 Frequency I-95 I-495 I-27 I/MD-295 I-695 I-83 Off-PkHR AM-PkHR PM-PkHR Figure 3.5 Distributions of Disabled Vehicles by Time of Day on Major Roads in 29 I-95, I-27, and US-5 are connected to I-495/95 and are the main contributors of traffic congestion on I-495 during commuting periods. Due to its high traffic volumes, any incident on I-495 is likely to cause a spillback of vehicles onto I-95, I-27, and US-5, causing congestion on those three freeways as well. The interdependent nature of incidents between the primary commuting freeways should be taken into account when prioritizing and implementing incident management strategies. To better allocate patrol vehicles and response units to hazardous highway segments, the distribution of incidents/disabled vehicles between two consecutive exits was employed as an indicator in the analysis. Figure 3.6 shows the distribution of incidents and disabled vehicles by location on I- 695 in 29, while Figure 3.7 compares these values with the 28 values. The high-incident segments are from Exits 44 to 1 and Exits 22 to 23 (165 and 15, respectively). Exits 44 to 1 are the segment on Francis Scott key bridge, and Exits 22 to 23 are closed to the interchange of I-695 and I-83. Exits 32 to 33 had the third largest number of incidents, and this segment is closed to the interchange of I-95 and I-695. The two high frequencies of disabled vehicles (513 cases) were recorded on the segments between Exits 22 and 23 and Exits 11 and 12, which are 13

28 Frequency close to the I-83 and I-95 interchanges, respectively Incidents Disabled Vehicles Figure 3.6 Distributions of Incidents/Disabled Vehicles by Location on I Exit 14

29 Frequency 4 I Frequency Exit Exit Figure 3.7 Comparisons of Incidents/Disabled Vehicles Distributions by Location on I-695 The subsequent figures present the comparison between 29 and 28 data, as well as the geographical distribution of incidents and disabled vehicles on I-495/95. 15

30 A comparison with the previous year s data is illustrated in Figure 3.9. From Figure 3.8, it can be observed that the highest frequency of incidents (281 cases) occurred between Exits 31 and 33 of I-495. The location with the highest frequency of disabled vehicles (346 cases) occurred between Exits 4 and 7. Frequency Incidents Disabled Vehicles Exit 4 41 Figure 3.8 Distributions of Incidents/Disabled Vehicles by Location on I-495/I-95 16

31 Frequency Figure 3.9 Comparisons of Incidents/Disabled Vehicles Distributions by Location on I-495/I Exit 4 41 Figure 3.1 shows the distribution of incidents and disabled vehicles by location on I- 95, and Figure 3.11 compares this distribution between data obtained in 29 and 28. As shown in Figure 3.1, the highest number of incidents occurred at the segment between Exits 55 and 56 (655 cases), which is close to the I-395 interchange. The segments between Exits 67 and 74 experienced a high number of disabled vehicles (2,215 cases). 17

32 N/A N/A N/A N/A N/A Frequency Incidents Disabled Vehicles Exits Figure 3.1 Distributions of Incidents/Disabled Vehicles by Location on I-95 Frequency Exits Figure 3.11 Comparisons of Incidents/Disabled Vehicles Distributions by Location on I-95 18

33 In 29, the incidents and disabled vehicles recorded for the I-95 segment between Exits 67 and 74 received the maximum number of incident responses, with a total frequency of 2,485. The segment on I-95 between Exits 55 and 56 sustained the second largest number of incidents/disabled vehicles requests (1,215) in 29. These trends are slightly different from those observed in 28. Also, in 29, unlike previous years, the distribution of incidents/disabled vehicles for the I-95 segment between Exits 8 and 19 is presented in details in Figure Figure 3.12 represents the spatial distribution of incidents/disabled vehicles data on I-27 for 29. Figure 3.13 makes a comparison between 29 and 28 data. Frequency I Incidents Disabled Vehicles Exits Figure 3.12 Distributions of Incidents/Disabled Vehicles by Location on I-27 19

34 Frequency I N/A N/A Exits Figure 3.13 Comparisons of Incidents/Disabled Vehicles Distributions by Location on I-27 The segment between Exits 26 and 31 on I-27 in Figure 3.12 experienced the highest numbers of incidents and disabled vehicles (115 and 162, respectively). In Figure 3.13, the 29 data recorded more incidents/disabled vehicles than in 28 except at the location between Exits 1 and 4, between Exits 1 and 11 and between Exits 13 and Distribution of Incidents and Disabled Vehicles by Lane Blockage Type Figure 3.14 illustrates the distribution of incidents by lane blockage in 29. A large portion of those incidents involved two-lane blockages. The comparison of 29 incidents/disabled vehicles distribution by lane blockage with 28 data is illustrated in Figure Note that all reported disabled vehicles are classified as shoulder lane blockages. 2

35 Frequency Same direction Opposite direction Both direction 1 Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** Note: *Not including Disabled Vehicles ** Also includes shoulder lane blockages Figure 3.14 Distributions of Incidents by Lane Blockage Frequency Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** 2 shoulders Note: * Disabled Vehicles are all classified as Shoulder Lane Blockages. ** Also includes Shoulder Lane Blockages. Figure 3.15 Comparisons of Incidents/Disabled Vehicles Distributions by Lane Blockage 21

36 Figures 3.16 and 3.17 depict a comparison of lane blockage incidents between 29 and 28 for major roads in the Washington Metropolitan and Baltimore areas. Frequency 14, 12, 12,922 Washington Region Year 29 1, 8, 6, 4, 2, 4,556 1, I-495/95 I-95 I-27 Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** Frequency 16, 14, 12, 1, 8, 14,239 Year 28 6, 4, 2, 4, , I-495/95 I-95 I-27 Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** Note: ** Also includes Shoulder Lane Blockages. Figure 3.16 Distributions of Lane Blockages Occurring on Major Freeways in the Washington Area 22

37 Frequency 6, 4,685 Baltimore Region Year 29 4, 2, Frequency 6, 1, I-695 I-7 I-83 Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** Year 28 5, 4,79 4, 3, 2, 1,672 1, I-695 I-7 I-83 Shoulder 1 Lane 2 Lanes** 3 Lanes** >=4 Lanes** Note: ** Also includes Shoulder Lane Blockages. Figure 3.17 Distributions of Lane Blockages Occurring on Major Highways in the Baltimore Region 23

38 (79%) 27.9 (92%) (89%) (82%) (85%) 83%) 38.9 (83%) (87%) (94%) (93%) (85%) (88%) (92%) 62.3 (96%) (96%) (82%) (92%) (86%) 83.9 (9%) 91.6 (95%) Note that disabled vehicles caused most of the shoulder lane blockages. Most of the disabled vehicles were recorded as a result of driver assistance requests due to flat tires, minor mechanical problems, or gas shortages. 3.4 Distribution of Incidents and Disabled Vehicles by Blockage Duration Lane blockage analysis naturally leads to the comparison of incident duration distribution. Figure 3.18 illustrates a relation between lane blockages and their average durations on each major freeway. Mins I-495/I-95 I-95 I-27 I-695 Shoulder 1 Lane 2 Lanes* 3 Lanes* >=4 Lanes Note: *Also includes Shoulder Lane Blockages. ** Numbers in each parenthesis show the percentage of data available. Figure 3.18 Distributions of Lane Blockages and Road It is quite obvious that CHART s highway network has experienced high incident frequencies ranging from ten minutes to more than one hour in duration. These incidents are 24

39 clearly primary contributors to traffic congestion in the entire region, especially on the major commuting highway corridors of I-495, I-695, I-27, and I-95, making it imperative, therefore, continuously improving traffic management and incident response systems. As shown below, most disabled vehicles did not block traffic for more than half an hour. About 8 percent of incidents and disabled vehicles had durations of less than 3 minutes. Frequency Duration = Cleared Time Received Time >=6 Incidents Disabled Vehicle Duration (Mins) Figure 3.19 Distributions of Incidents/Disabled Vehicles by Duration in 29 Although most incidents in 29 were not severe, their impacts were significant during peak hours. Clearing the blockages did not require special equipment, and the incident duration was highly dependent on the travel time of the incident response units. Figure 3.2 presents the distribution of records in 29 and its comparison with 28 data. About 16 percent, 19 percent, and 28 percent of reported incidents/disabled vehicles managed by TOC-3, TOC-4, and TOC-7, respectively, had blocked traffic lasting longer than 3 minutes. For SOC, about 58 percent of reported incidents lasted longer than 3 minutes. This implies that only 19 percent of reports to which CHART responded lasted more than 3 minutes in

40 Frequency <.5 >=.5<1 >=1<2 >=2 Total Frequency <.5 >=.5<1 >=1<2 >=2 Total TOC3 TOC4 TOC5 TOC6 TOC7 SOC AOC Total Figure 3.2 Comparisons of Incidents/Disabled Vehicles Distributions by Duration and Operation Center 26

41 CHAPTER 4 EVALUATION OF EFFICIENCY AND EFFECTIVENESS 4.1 Evaluation of Detection Efficiency and Effectiveness An automatic incident detection system has yet to be implemented by CHART. Therefore, CHART has no means of evaluating the detection and false-alarm rates. Also, at this point, CHART has no way to determine the time taken by the traffic control centers to detect an incident from various sources after its onset. Therefore, this evaluation of detection efficiency and effectiveness focuses only on the incident response rate and on distribution of detection sources. The response rate is defined as the ratio of the total number of traffic incidents reported to the CHART control center to those managed by the CHART/MSHA emergency response teams. Based on 29 incident management records, the overall response rate was about 93 percent. As in the previous year, existing incident reports did not specify the reasons for ignoring some requests. It appears that most of the ignored incidents happened during very light traffic periods or were not severe enough to cause any significant traffic blockage or delay. Notwithstanding the lack of an automated incident detection system, CHART has maintained an effective coordination system with state and municipal agencies that deal with traffic incidents and congestion. Figures 4.1, 4.2, 4.3 and 4.4 illustrate the distributions of Incidents/Disabled Vehicles by Detection Source for control centers TOC 3, TOC 4, TOC6 and TOC7, respectively. 27

42 Citizen,.8% [.9] No Info.,.% [.] Media, 1.% MCTMC, 2.1% [2.4] Other, [1.5] 5.7% [2.] CCTV, 1.6% [2.5] System Alarm,.% [.] SHA, 2.9% [3.] MDTA,.% [.] State Police, 21.8% [17.5] CHART, 62.7% [69.1] Local Police, 1.2% [1.] Note: Numbers in [ ] show the percentages from Year 28 Figure 4.1 Distributions of Incidents/Disabled Vehicles by Detection Source for TOC 3 Media,.2% [.3] MCTMC,.% No Info.,.% [.] [.] Citizen,.8% [.7] Other,.8% [.3] CCTV,.8% [.9] System Alarm,.% [.] SHA, 5.4% [5.1] MDTA,.% [.] State Police, 25.7% [28.] CHART, 57.3% [55.5] Local Police, 9.% [9.2] Note: Numbers in [ ] show the percentages from Year 28 Figure 4.2 Distributions of Incidents/Disabled Vehicles by Detection Source for TOC 4 28

43 Citizen,.% [.] CHART,.% [4.1] MCTMC,.% [.] Local Police, 19.2% [7.7] Media,.% [1.2] Other, 11.5% [5.3] No Info.,.8% [.] CCTV,.% [.] System Alarm,.% [.] State Police, 22.3% [7.7] SHA, 46.2% [74.] MDTA,.% [.] Note: Numbers in [ ] show the percentages from Year 28 Figure 4.3 Distributions of Incidents/Disabled Vehicles by Detection Source for TOC 6 Media,.% [.1] MCTMC,.% [.] Citizen,.5% [.3] No Info.,.% [.] Other, 3.6% [4.3] CCTV,.3% [.2] System Alarm,.% [.1] SHA, 3.7% [5.8] MDTA,.% [.] CHART, 44.4% [57.7] State Police, 46.9% [31.3] Local Police,.5% [.2] Note: Numbers in [ ] show the percentages from Year 28 Figure 4.4 Distributions of Incidents/Disabled Vehicles by Detection Source for TOC 7 29

44 With respect to the distribution of all detection sources, the statistics in Figure 4.5 clearly show that about 36 percent of incidents in 29 were detected by MSHA/CHART patrols, i.e., a lower percentage than in 28. About 17 percent were reported by the MSP, slightly higher than the 14.7 percent figure in 28. Note that the numbers in parentheses indicate the 28 statistics. MCTMC, 1% [.3] Citizen, 1% [.7] No Info.,.% [.2] Media, % [.65] Other, 2% [1.8] CCTV, 1% [1.2] System Alarm,.% [.] SHA, 3% [2.8] CHART, 36% [39] MDTA, 36% [34.5] Local Police, 4% [4.3] State Police, 17% [14.7] Note: Numbers in [ ] show the percentages from Year 28 Figure 4.5 Distributions of Incidents/Disabled Vehicles by Detection Source 4.2 Analysis of Response Efficiency The distributions of response times and incident durations were used to analyze the efficiency of incident responses. The response time is defined as the interval between the onset of an incident and the arrival of response units. Since the actual start time of an incident is unknown, the response time used in this analysis is based on the difference between the time the Response Center received a request and the time of arrival of response unit at the incident site. 3

45 The average response time for incidents in 29 is given in Figure 4.6. The average response time in 29 was 9.91 minutes, lower than that of 28 (9.99 minutes). Response Time = Arrival Time Received Time Response Time (mins) % % 39% % % % 16% 55% % 43% % 27% 29% 23% % 18% 34% 29% TOC3 TOC4 TOC5 TOC6 TOC7 SOC AOC Other Average Note: The percentage shows the amount of data available for computing the response time. Figure 4.6 Average Response Time Distributions Figure 4.7 compares response times by time of day in 29 and 28. Generally speaking, the response times during off-peak hours are longer than those during peak hours. In the case of peak hours, in 29, the response time during AM-peak hours was slightly longer than that during PM-peak hours for both incidents and disabled vehicles. The response times during off-peak hours were slightly shorter in 29 than in 28. More detailed information associated with response efficiency is provided in the Appendix. 31

46 Response Time (mins) 8 Year (11,136) (2,655) (2,91) (15,135) (3,34) (5,417) Off-PkHR AM-PkHR PM-PkHR Response Time (mins) Incidents Disabled Vehicles Year (11,868) 4.13 (2,82) 4.35 (3,246) (15,76) (3,656) 2 (5,76) 1 Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Note: 1. The number in each parenthesis shows the amount of data available for computing the response time. 2. Off-PkHR, AM-PkHR, and PM-PkHR stand for Off-Peak hours, AM-Peak hours, and PM-Peak hours, respectively. Figure 4.7 Average Response Time Distributions by Time of Day in 29 and 28 32

47 4.3 Reduction in Incident Duration An essential performance indicator is the reduction in average incident duration due to the operations of CHART. Theoretically, a before-and-after analysis would be the most effective way to evaluate CHART s effects on incident duration. However, no incidentmanagement-related data prior to CHART exists to aid in any meaningful assessment. Due to this shortcoming, this study used the alternative of computing average incident clearance times in 29 for non-responded incidents and those to which CHART responded. Since CHART s incident management team responded to most incidents in 29, the data for CHART-ignored incidents are very limited. As shown in Table 4.1, the average durations for clearing an incident with and without the assistance of CHART were, respectively, about minutes and minutes. Note that incidents with durations less than one minute were excluded for the analysis. Also, incidents of Unknown Lane Blockage were redistributed into Shoulder and 1-Lane Blockage, since they showed a similar duration distribution. Based on the results shown in Table 4.1, it seems that the assistance of CHART/MSHA response units reduced the time it took to clear an incident. On average, CHART contributed to a reduction in blockage duration of about 31 percent, which has certainly contributed significantly to savings in travel times, fuel consumption, and related socioeconomic costs. Note that the statistics shown in Table 4.1 are likely to be biased, as only about 85 percent of incident reports contain all the information (reported received time and cleared time) required for incident duration computation. Data quality remains a critical issue to be addressed by CHART. 33

48 Table 4.1 Comparisons of Incident Durations for Various Types of Lane Blockages Duration= Cleared Time-Received Time Blockage With SHA Patrol Without SHA Patrol Duration (min) Frequency Duration (min) Frequency Shoulder lane lanes lanes >=4 lanes Weighted Average 28.35(24.95) 13,983(13,917) 41.12(34.56) 591(61) Unknown Note: 1. Incidents with durations of less than one minute were excluded from the analysis. 2. Cases of Unknown blockage were redistributed into Shoulder and one Lane Blockage. 3. The numbers in parentheses show the results from year 28 34

49 CHAPTER 5 BENEFITS FROM CHART S INCIDENT MANAGEMENT Due to the data availability, the benefit assessment for CHART has always been limited to those directly measurable or quantifiable based on incident reports. These direct benefits, both to roadway users and the entire community, are classified as the following categories: assistance to drivers; reduction in secondary incidents; reduction in driver delay time; reduction in vehicle operating hours; reduction in fuel consumption; and reduction in emissions. Some other intangible impacts, such as vitalizing the local economy and increasing network mobility, are not included in this benefit analysis. 5.1 Assistance to Drivers The public has expressed great appreciation for the timely assistance given to drivers by the CHART incident management units. Prompt responses by CHART have directly contributed to minimizing the potential effects of rubbernecking on the traffic flow, particularly during peak hours, where incidents can cause excessive delays. Thus, providing assists to drivers is undoubtedly a major direct benefit generated by the CHART program. The distributions of assistance to drivers (labeled Disabled Vehicles in the CHART II Database) by request type in Year 29 and Year 28 are depicted in Figure 5.1. For those assists offered by TOC-3, TOC-4 and TOC-7 are illustrated in Figure 5.2, Figure 5.3 and Figure 5.4, respectively. 35

50 Frequency Figure 5.1 Natures of Driver Assistance Requests in 29 and 28 Frequency Figure 5.2 Natures of Driver Assistance Requests for TOC 3 36

51 Frequency Figure 5.3 Natures of Driver Assistance Requests for TOC Figure 5.4 Natures of Driver Assistance Requests for TOC 7 37

52 The type of driver assistance accounted for in 29 includes flat tires, shortages of gas, or mechanical problems. Out of the 35,343 assistance requests, a total of 1,378 assists were related to out of gas and tire changes, i.e., fewer than the number in 28 (11,78 cases). 5.2 Estimated Benefits due to Efficient Removal of Stationary Vehicles Drivers are forced to perform undesirable lane-changing maneuvers because of lane blockages around incident sites. Considering that improper lane changing is a prime contributor to traffic accidents, prolonged obstruction removal certainly increases the risk of accidents. Thus, CHART/MSHA s prompt removal of stationary vehicles in travel lanes may directly alleviate potential lane-changing-related accidents around incident sites. The estimated results from potential incident reduction for selected freeways are reported in Table 5.1. Note that this estimation was made using peak period data. Off-peak data were omitted because it is known not to have any correlation with lane-changing maneuvers and accidents. A detailed description of the estimation methodology can be found in the previous CHART performance evaluation reports. Table 5.1 Reduction of Potential Incidents due to CHART Operations Road Name I- MD- US- I-95 I-27 I-695 I-7 I / Total Mileage No. Potential Incident Reduction * The analysis has excluded the outlier data (i.e., mean + 2 standard deviation) 5.3 Direct Benefits to Highway Users The benefits obtained as a result of reduced delays and fuel consumption are summarized in the following tables. Table 5.2 shows the benefits from delays calculated using the unit rates obtained from the U.S Census Bureau (28) and the Energy 38

53 Information Administration (29). To convert delays to monetary value for commercial vehicles, we multiply delays by the value of time factors ($27.37/hr for driver and $45.4/hr for cargo). Table 5.2 Total Direct Benefits to Highway Users in 29 Dollars Reduction due to CHART Amount Unit rate (million) $2.68/hr truck drivers' cost Truck 1.68 Delay (M veh-hrs) $45.4/hr (cargo's cost) Car 3.75 $27.37/hr (car driver's cost) Fuel Consumption (M gallons) $2.32/gal (gasoline) 1 $2.5/gal (diesel) HC 424. $6,7/ton Emission (tons) CO 4, $6,36/ton NO 23.7 $12,875/ton 37.6 CO ,98.97 $23/metric ton 3 Total (M dollars) 1,6.5 (981.6 in 28) Note: 1. The car driver s cost and fuel price are updated based on the information from the U.S Census Bureau in Year 28 and the Energy Information Administration in Year 29, respectively. 2. The fuel consumption was computed based on the rate of.156 gallons of gas per hour for passenger cars from the Ohio Air Quality Development Authority and the rate of.85 gallon per hour for trucks from the literature Heavy-Duty Truck Idling Characteristics-Results from a Nationwide Truck Survey by Lutsey et al. and Environmental Protection Agency (EPA). 3. This value is computed based on the unit rates of lbs CO 2 /gallon of gasoline and lbs CO 2 /gallon of diesel from Energy Information Administration and $23/metric ton of CO 2 from CBO (Congressional Budget Office) s cost estimate for S. 2191, America s Climate Security Act of 27. e.g. 4.8(million gallons) * (lbs CO 2 /gallon) / 224 (lbs/metric ton) * 23($/metric ton) Table 5.3 Comparison of Incident Duration Reduction in 29 and 28 With CHART (mins) W/O CHART (mins) Difference (mins) Ratio in Difference % % 39

54 *The number in the parenthesis shows the data from year 28 Figure 5.5 Reduction in Delay due to CHART in 29 Table 5.4 Average Increased Rates of AADT for Major Roads from 28 to 29 Road I95 I495 I27 I695 MD295 US5 US1 I7 I83 Average Increase Rate in % 3.46%.5%.55% 1.41%.27%.29%.99% 1.25% The evaluation for 29 has adopted different parameters for cars and trucks in conversion of delays to fuel consumption. The Note 2 in Table 5.2 states the references of those values in detail (Lutsey et al., 24). The estimated reductions in vehicle emissions for HC, CO, and NO were based on parameters provided by MDOT and the total delay reduction. Since CO 2 is recognized as a primary factor of global warming, we also included the estimated reduction of this emission based on the factor from Energy Information Administration. Using the cost parameters shown in Table 5.2 (DeCorla-Souza, 1998), the above reduction in emissions resulted in a total savings of 37.6 million dollars. Thus, CHART/MSHA s activities in Year 29 generated a total savings of 1,6.5 million dollars, more than the million dollars saved in 28. Figure 5.5 presents the estimated total delays with and without CHART operations, 4

55 which are million vehicle-hours and million vehicle-hours, respectively. This figure along with Table 5.3 indicates that the efficiency of CHART operations in reducing incident duration, based on the comparison with these managed by police alone, increased by about 3 percent, although the overall CHART performance with respect to both response time and average incident duration was inferior to last year. Besides, as shown in Table 5.4, the average increased rate of AADT for major roads between 28 and 29 is positive, which means that, on average, the AADT for those roads increased slightly during 29. Since these parameters have non-linear positive relationships with the direct benefit, the total benefits (in terms of delay, fuel consumption, and emissions) estimated in 29 is higher than the total benefits in 28. In addition to the above savings, a reduction in emissions due to reduced running time in the Baltimore and Washington regions have been computed. The results are summarized in Tables

56 Table 5.5(a) Delay and Emissions Reductions for Trucks Due to CHART/MSHA Operations for Washington and Baltimore Regions Truck Total by CHART Washington Region Baltimore Region Year 29 Year 28 Year 29 Year 28 Year 29 Year 28 Annual Delay Reduction hour 1,682,962 2,85,69 496,27 468,94 1,186,934 1,616,129 Daily Delay Reduction hour 6,473 8,19 1,98 1,84 4,565 6,216 Emission Reduction HC reduction CO reduction NO reduction CO 2 reduction ton/day $/day ton/day $/day 6, , , ,2.63 3, , ton/day $/day metric ton/day $/day , Total $/day 8, , , , ,23.9 6,

57 Table 5.5 (b) Delay and Emissions Reductions for Cars due to CHART/MSHA Operations for Washington and Baltimore Regions Car Annual Delay Reduction Daily Delay Reduction HC reduction CO reduction NO reduction CO 2 reduction Total by CHART Washington Region Baltimore Region Year 29 Year 28 Year 29 Year 28 Year 29 Year 28 hour 3,75,598 29,573,248 12,16,968 9,222,932 18,643,631 2,35,316 hour 118, ,743 46,565 35,473 71,76 78,27 Emission Reduction ton/day $/day 1, , , , , , ton/day $/day 11, , , , , , ton/day $/day 9, , , , , , metric ton/day $/day , , , Total $/day 134, , , , , , As shown in Tables 5.5 (a) and 5.5 (b), the daily delay reductions for the Washington region in 29 were 1,98 hours/day and 46,565 hours/day for trucks and cars, respectively, compared to the 1,84 hours/day for trucks and 35,473 hours/day for cars recorded in 28. The delay reduction for trucks in the Baltimore region decreased from 6,216 hours/day in 28 to 4,565 hours/day in 29, and decreased from 78,27 hours/day in 28 to 71,76 hours/day in 29 for passenger cars. The overall reductions in emissions (i.e., by cars and trucks) for the entire region were $142,525/day and $139,42/day for the years 29 and 28, respectively. 43

58 C H A P T E R 6 A N A L Y S I S O F I N C I D E N T D U R A T I O N S For effective and efficient traffic management after incidents, responsible agencies can convey information to travelers by updating variable message signs, estimate the resulting queue length, assess the need to implement detour operations, and perform any other control strategies to mitigate congestion. To maximize the effectiveness of these operational measures, reliably predicted/estimated incident durations will certainly play an essential role. This chapter presents the results from the statistical analysis of incident duration data; this analysis provides a fundamental insight into the characteristics of incident duration under various conditions. In this analysis, the distributions of average incident duration are identified by the following categories: Nature, County, County and Nature, Weekdays and Weekends, Peak and Off-Peak Hours, CHART Involvement, and Roads. 6.1 Distribution of Average Incident Durations by Nature In general, incidents are classified into two large groups, based on whether or not they involved collisions. The first group, incidents with collisions, consists of three types: collisions with fatalities (CFs), collisions with personal injuries (CPIs), and collisions with property damage (CPDs). The second group, incidents without collisions, includes incidents of various natures, such as disabled vehicles, debris in the roadway, vehicles on fire, police activities, etc. Table 6.1 summarizes the categories of incidents by nature used in the remaining analysis. 44

59 Table 6.1 Summary of Categories of Incident Natures Incidents With Collisions Without Collisions Collisions-Fatalities(CF) Collisions-Property Damage (CPD) Collisions-Personal Injuries (CPI) Disabled Vehicles Police Activities Off-Road Activities Others Emergency Roadwork Debris in Roadway Vehicles on Fire Note that Disabled Vehicles, one type of incident nature, are defined as those disabled vehicles that interrupt the normal traffic flow on the main lanes. In the category of incidents without collisions, most are Disabled Vehicles. In 29, about 4 percent of incidents without collisions were caused by Disabled Vehicles. A similar pattern was also observed in 28, when about 35 percent of non-collision incidents occurred due to Disabled Vehicles. In contrast, the other natures of non-collision incidents occurred in relatively low frequencies; therefore, the study classifies all such incident natures as one category, i.e., Others, as shown in Table 6.1. Figure 6.1 summarizes the average incident duration of each nature for the years 29 and 28. The statistical results indicate that the average incident duration for CFs is significantly higher than for the other incident natures. Statistically, an incident that has resulted in a fatality can last more than three hours on average. In contrast, incidents caused by Disabled Vehicles, on average, were much shorter in duration. 45

60 Avg. Inc. Duration (mins) (159) (161) (5317) (4392) (387) (2665) (3218) (4578) CF CPD CPI DisableVeh Others (4911) (4578) Figure 6.1 Distribution of Average Incident Duration by Nature 6.2 Distribution of Average Incident Durations by County The distribution of incident durations also varies between counties. Figure 6.2 demonstrates that the average incident durations in the Washington region decreased obviously in 29 compared to those in 28. As shown in Figures 6.2 to 6.5, incident durations in the Washington region were likely to be shorter than those in other regions. Figure 6.3 shows that especially the incidents around Baltimore City, including Baltimore County, had much shorter durations than incidents occurring in any other counties in the Baltimore region. Notably, incidents in Carroll were highly likely to last longer than 1.5 hours, on average. 46

61 Avg. Inc. Duration (mins) 14 -Washington Region Frederick County Montgomery County Prince George's County Washington DC N/A Washington Region Figure 6.2 Distribution of Average Incident Duration by County in Washington Region Avg. Inc. Duration (mins) Baltimore Region Anne Arundel County Baltimore City Baltimore County Carroll County Harford County Howard County Figure 6.3 Distribution of Average Incident Duration by County in Baltimore Region Incidents that occurred in counties of Western and Southern Maryland mostly resulted in relatively longer durations. Figure 6.4 shows that the average incident duration in these areas usually exceeds two hours. Allegany County had the shortest average incident duration on year 28 and 29. Still, those average incident durations were about one and half hours. 47

62 Avg. Inc. Duration (mins) Western Southern MD Allegany County Calvert County Charles County Garrett County Saint Mary's County Washington County Figure 6.4 Distribution of Average Incident Duration by County in Western and Southern Region Avg. Inc. Duration (mins) 6 5 -Eastern Shore Caroline County Cecil County Dorchester County Kent County Queen Anne's County Somerset County Talbot County Wicomico County Worcester County Figure 6.5 Distribution of Average Incident Duration by County on Eastern Shore 48

63 Similarly, the incidents occurring on the Eastern Shore (Figure 6.5) are highly likely to result in longer durations than those in any other area of Maryland. Most incidents on the Eastern Shore, except those in Cecil, and Queen Anne s, took more than two hours on average to recover. Avg. Inc. Duration (mins) Avg. Inc. Anne Arundel County Baltimore City Baltimore County Duration (mins) Figure 6.6 Distribution of Average Incident Duration by County and Nature Figure 6.6 compares incident durations by nature only for several major counties in Maryland. As shown in the figure, the average incident duration for CF in Baltimore City was much shorter than in any other area. On the other hand, CF-related incidents in Montgomery County mostly resulted in relatively long durations. Particularly those incidents with their nature classified as Others in Montgomery County had much longer durations than similar incidents in other areas Frederick County Montgomery County Prince George's County CF CPD CPI Disabled Veh. Others

64 6.3 Distribution of Average Incident Durations by Weekdays/Ends, Peak/Off-Peak Hours, CHART Involvement and Roads According to Table 6.2, although the average response times for weekdays and weekends in 29 were very similar, the average clearance time for weekends was approximately 1.5 times longer than that for weekdays. As a result, weekend incidents were highly likely to last longer than those occurring on weekdays. This is due mostly to the fact that fewer response teams are available during weekends than during weekdays; thus, it would take more time to clear the incident scene. Table 6.2 Distribution of Average Incident Duration by Weekday and Weekend Weekdays Weekends Frequency Avg. Response Time Avg. Clearance Time Avg. Incident Duration 29 14, , , , Table 6.3 Distribution of Average Incident Duration by Off-Peak and Peak Hours Off-Peak Peak* Frequency Avg. Response Avg. Clearance Avg. Incident Time Time Duration 29 11, , , , * 7: AM to 9:3 AM and 4: PM to 6:3 PM Table 6.3 shows that the average clearance time during off-peak hours was much longer than during peak hours. Consequently, the average duration for incidents occurring during off-peak hours was longer than for those during peak hours. 5

65 Table 6.4 Distribution of Average Incident Duration without and with CHART w/ CHART w/o CHART Frequency Avg. Response Avg. Clearance Avg. Incident Time Time Duration 29 13, , Whether or not CHART responded to an incident is another significant factor affecting the distribution of incident durations. When CHART was involved in the incident recovery task, the incident duration would likely be reduced significantly. This observation indicates that CHART played an efficient role in shortening incident durations, reducing the delay caused by non-recurrent congestion. Avg. Inc. Duration (mins) N/A I-27 I-495/95 I-695 I-95 CF CPD CPI Disabled Veh Others Figure 6.7 Distribution of Average Incident Duration by Road and Nature Figure 6.7 shows the distribution of average incident duration by road and nature. Notably, the average incident duration of CFs was much longer than for other incident 51

66 natures. Also, note that CF incidents occurring on I-495/95 seemed to exhibit the longest average duration (i.e., minutes). 52

67 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS 7.1 Conclusions Building on the previous research experience, this study has conducted a rigorous evaluation of CHART s performance in the year 29 and its resulting benefits under the constraints of data availability and quality. Overall, CHART has made significant progress in recording more reliable incident reports, especially after implementation of the CHART- II Database. However, much remains for CHART to do in terms of collecting more data and extending its operations to major local arterials if resources are available to do so. For example, data associated with the potential impacts of major incidents on local streets has not been collected by CHART. Without such information, one may substantially underestimate the benefits of CHART operations, as most incidents causing lane blockage on major commuting freeways are likely to spill their congestion back to neighboring local arterials if the speed of traffic queue formation is faster than the pace of incident clearance progress. By the same token, a failure to respond to major accidents on local arterials, such as MD-355, may also significantly degrade traffic conditions on I-27. Effectively coordinating with county agencies on both incident management and operational data collection is one of the major tasks to be done by CHART. With respect to its performance, CHART has maintained nearly the same level of efficiency in responding to incidents and driver assistance requests in recent years. The average response time in 29 was 9.91 minutes. In view of the worsening congestion and the increasing number of incidents in the Washington-Baltimore region, it is commendable that CHART can maintain its performance efficiency with approximately the same level of resources. In brief, CHART operations by MSHA in the Year 29 have yielded significant benefits by assisting drivers, and by reducing delay times and fuel consumption, as well as emissions. Other, indirect benefits could be estimated if appropriate data regarding traffic 53

68 conditions before and after incidents were collected during each operation. Such benefits include impacts related to secondary incidents, potential impacts on neighboring roadways, and reductions in driver stress on major commuting corridors. In addition, an in-depth analysis of the nature of incidents and their spatial distribution may offer insight into developing safety improvement measures for the highway networks covered by CHART. 7.2 Recommendations and Further Development listed below: The main recommendations, based on the performance of CHART in 29, are More resources should be allocated to CHART for incident response and traffic management to improve the performance of the response teams so that they can effectively contend with the ever-increasing congestion and accompanying incidents. CHART s quality evaluation report should be made available to the operators to facilitate their continuous improvement of response operations. CHART should coordinate with county traffic agencies to extend its operations to major local routes and to include the data collection, as well the performance benefit, in the annual CHART review. Training sessions should be implemented to instruct operators on how to effectively record critical data associated with incident response performance. The data structure used in the CHART-II system for recording incident location should be improved to eliminate the current laborious, complex procedures. The average response time should be reduced by increasing freeway service patrols and by assigning patrol locations based on both the spatial distribution of incidents along freeway segments and the probability of an incident occurring. Police accident data should be efficiently integrated into the CHART incident response database in order to have a complete representation of statewide incident records. 54

69 The benefits of reduced potential secondary incidents on delay and fuel consumption should be incorporated into the CHART benefit evaluation. 55

70 REFERENCES 1. Amos, G., Shakas, C., and Avery, M., Incident management systems lessons learned, the 2nd World Cogress of ITS, Yokohama, Carson, J. L., Legg, B., Mannering, F. L., Nam, D., and Nee, J., Area incident management programs effective Findings from Washington State, TRB, 78th annual meeting, Chang, G. L., and Point-du-Jour, J. Y., Performance evaluation of CHART the real time incident management system in Year 23, final report, March Chang, G. L., and Point-du-Jour, J. Y., Performance evaluation of CHART the real time incident management system in Year 22, final report, March Chang, G. L., and Point-du-Jour, J. Y., Performance evaluation of CHART the real time incident management system in Year 21, final report, March Chang, G. L., and Point-du-Jour, J. Y., Performance evaluation of CHART the real time incident management system in Year 2, final report, March Chang, G. L., and Point-du-Jour, J. Y., Performance evaluation of CHART, incident management program in 1999, final report, July Chang, G. L., and Point-du-Jour, J. Y., Performance and benefit evaluation for CHART, incident management program in 1997, final report, September Chang, G. L., and Point-du-Jour, J. Y., Performance and benefit evaluation for CHART, incident management program in 1996, final report, September CHART incident response evaluation report by COMSIS, Cuciti, P., and Janson, B., Courtesy patrol pilot program, final Report, Colorado Department of Tranportation, DeCorla-Souza, P., Cohen, H., Haling, D., and Hunt, J., Using STEAM for benefitcost analysis of transportation alternatives, Transportation Research Record 1649, DeCorla-Souza, P., Gardener, B., Culp, M., Everet, J., Ngo, C., and Hunt, J., Estimating costs and benefits of transportation corridor alternatives, Transportation Research Record 166, Fenno, D., W., and Ogden, M., A., Freeway service patrols, a state of the practice, Transportation Research Record 1634, Gilen, D., Li, J., Evaluation methodologies for ITS applications, California PATH. University of California, Institute of Transportation Studies, Berkley, CA, Gilen, D., Li, J., Dahgren, J., and Chang, E., Assessing the benefits and costs of ITS projects: volume 1. methodology, California PATH, University of California, Institute of Transportation Studies, Berkley, CA, March,

71 17. Incident reports for 1997 from statewide operation center, Traffic Operation Center 3 and 4, State Highway Administration, Maryland. 18. ITS benefits database, US Department of Transportation, September 3, Karimi, A., and Gupta, A., Incident management system for santa monica smart corridor, ITE 1993 Compendium of Technical Papers. 2. Lutsey, N., Brodrick, C-J., Sperling, D., and Oglesby, C., Heavy-Duty Truck Idling Characteristics-Results from a Nationwide Truck Survey, Transportation Research Record 188, Maryland State Police Accident Report in Maryland Wages by Occupation, Department of Business and Economic Development, Maryland. 23. Meyer, M., A toolbox for alleviating traffic congestion and enhancing mobility, ITE, Meyer, M., and Miler, E., Urban transportation planning: a decision oriented approach, 2nd edition, International Edition 22, McGraw-Hill, Rossi, P.H., and Freeman, H.E. Evaluation: a systematic approach, 5th edition, Sage Publications, Inc., Newbury Park, California, Skabardonis, A., Pety, K., Noeimi, H., Rydzewski, D., and Varaiy, P. P., I-88 field experiment: database development and incident delay estimation procedures, Transportation Research Record 1554, Evaluating safety and health impacts, TDM impacts on road safety, Personal security and public health, TDM Encyclopedia De Jong, Gerard, Value of Freight Travel-Time Savings, in Hensher, D.A. and K.J. Buton (eds.): Handbook of Transport Modeling, Elsevier, Levinson, D. and B. Smalkoski, Value of Time for Commercial Vehicle Operators in Minnesota, University of Minnesota, TRB International Symposium on Road Pricing, Federal Highway Administration, HERS-ST v2: Highway Economic Requirements System - State Version Technical Report. Federal Highway Administration, FHWA IF-2-6, Mackie, PJ et al, Value of Travel Time Savings in the UK - Summary Report. Institute for Transport Studies, University of Leeds, for the UK Department for Transport,

72 APPENDIX Frequency Off-PkHR AM-PkHR PM-PkHR Frequency 3 Figure A.1 Distributions of Incidents by Time of Day on I-95 in Year Off-PkHR AM-PkHR PM-PkHR Figure A.2 Distributions of Disabled Vehicles by Time of Day on I-95 in Year 29 58

73 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.3 Distributions of Incidents by Time of Day on I-495 in Year 29 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.4 Distributions of Disabled Vehicles by Time of Day on I-495 in Year 29 59

74 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.5 Distributions of Incidents by Time of Day on I-27 in Year 29 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.6 Distributions of Disabled Vehicles by Time of Day on I-27 in Year 29 6

75 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.7 Distributions of Incidents by Time of Day on I-695 in Year 29 Frequency Off-PkHR AM-PkHR PM-PkHR Figure A.8 Distributions of Disabled Vehicles by Time of Day on I-695 in Year 29 61

76 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.9 Distributions of Clearance Time by Time of Day in Year 29 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.1 Distributions of Incident Duration by Time of Day in Year 29 62

77 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.11 Distributions of Incident Duration by Time of Day on I-95 in Year 29 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.12 Distributions of Incident Duration by Time of Day on I-495 in Year 29 63

78 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.13 Distributions of Incident Duration by Time of Day on I-27 in Year 29 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.14 Distributions of Incident Duration by Time of Day on I-695 in Year 29 64

79 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.15 Distributions of Incident Duration by Time of Day on I-295 in Year 29 Minutes Off-PkHR AM-PkHR PM-PkHR Incidents Disabled Vehicles Figure A.16 Distributions of Incident Duration by Time of Day on I-83 in Year 29 65