: Developing Freight Performance Measures Using GPS Truck Data

Transcription

1 -: Developing Freight Performance Measures Using GPS Truck Data Zach Johnson Department of Civil Engineering, University of Memphis, TN, Engineering Administration Bldg., Central Avenue, Memphis, TN,, USA, Memphis, TN,, Ioannis Psarros Department of Civil Engineering, University of Memphis, Engineering Administration Bldg., Central Avenue, Memphis, TN,, USA, Mihalis Golias (corresponding author) Department of Civil Engineering, University of Memphis, B Engineering Science Bldg, Central Avenue, Memphis, TN,, USA, Office: + () -, Fax: + () -, mgkolias@memphis.com Sabyasachee Mishra Department of Civil Engineering, University of Memphis, D Engineering Science Bldg, Central Avenue, Memphis, TN,, USA, Office: + () -, Fax: + () -, smishra@memphis.com Word Count: Figures & Tables: x = Total Words: Submitted for consideration for presentation at the rd Annual Meeting of the Transportation Research Board TRB Annual Meeting

2 Johnson et al. ABSTRACT Freight transportation planning is largely limited by the amount, quality, and detailed truck trip data. Most truck movement data are reported at the inter-county level and represented as aggregated tonnages that must be broken down into truck trips. Additionally, intra-county flows can be largely under-represented and commercially available commodity flow databases are prohibitively expensive. Surveying truck drivers (such as at truck stops or at terminal gates) and following trucks from terminals are time-consuming and require a great amount of labor to process the survey data. Truck trip traffic generated from these aforementioned sources relies on outdated and insufficient traffic generation data while truck trip generation and assignment models are seldom validated or calibrated. In this study, we present a case study of how GPS truck data can be applied to determine a number of freight performance indicators. This research has the potential to address difficulties encountered by a number of state DOTs in effectively integrating freight transportation into the long-range transportation planning process. TRB Annual Meeting

3 Johnson et al. INTRODUCTION Freight movement is a significant and unquestionably important aspect of both the transportation planning and economic success of a region. Therefore, it is imperative that appropriate measures are taken for the improvement of freight movement. To accomplish this task, methods of measuring performance are required. This can be rather difficult because of the private nature of freight operations in the United States (and around the world). However, with the ever increasing presence of freight movement on roadway facilities, the impact of trucks can no longer be overlooked. The Federal Highway Administration (FHWA) has developed a set of Performance Measures (PMs) that can be used to assess a road system that is utilized by personal vehicles; however, development of PMs that focus on freight is limited. Freight Performance Measures (FPMs) are a relatively new area of interest for transportation research (considering both the academia and the public sector) and there is limited knowledge regarding the effects of freight on commuter and personal vehicle traffic. The National Cooperative Freight Research Program Report-(NCFRP ) is currently underway in assessing how the Highway Capacity Manual (HCM ) incorporates freight flows into traffic planning and engineering. Initially, the report found that HCM only incorporates freight trucks as they affect other traffic on roadways and it lacks the necessary tools to accurately account for and evaluate freight flows (). With the surface transportation reauthorization, Moving Ahead for Progress in the st Century (MAP-), new incentives are in place for state Departments of Transportation (DOTs) to integrate FPMs into their transportation planning and operations. Several states have already begun the process of gathering and sharing freight data including freight corridors, temporal variation, pavement deterioration, etc. (). Several studies have analyzed the use of Global Positioning System (GPS) technology for the purpose of developing FPMs (; ), but have been limited by the expense of gathering and difficulty in handling the extensive databases. The American Transportation Research Institute (ATRI) is working closely with FHWA to collect data regarding truck movements in the United States (U.S). ATRI has gathered millions of GPS location data points for tens of thousands of trucks (). By using these data and developing FPMs, federal, state and local agencies can have additional tools for more effective transportation planning. The intention of this research is to demonstrate how available tuck GPS data can be used to support transportation decisions that will facilitate freight flows through the development of FPMs. In this paper a case study is presented utilizing GPS truck data to develop two FPMs. The first one includes the analysis of network flows and the second focuses on the operations at intermodal freight terminals. The study area for this research is Memphis, TN and Nashville, TN due to location relativity and data availability. Through this research we show the usefulness of GPS truck data in developing FPMs. The remainder of this paper is organized as follows. The next section presents a detailed literature review on FPMs and GPS technology use in freight transportation research. Data and case study area description are presented in the third section followed by results. The summary of findings and future recommendations are discussed in the conclusion section. LITERATURE REVIEW The development of FPMs is an important aspect of transportation planning enabling a more robust and efficient transportation system; however, research on this topic has only recently become a focus of attention from policy makers. GPS technology will enable the development TRB Annual Meeting

4 Johnson et al. and analysis of FPMs easier and more effective. The literature review will focus on current FPMs research as well as on how GPS data can be used for developing FPMs by researchers and policy makers. Freight Performance Measure Development Interest in FPMs is a relatively recent concern for researchers in the transportation field. Congestion along transportation networks is believed to have a significant negative impact on the U.S. economy costing an estimated $ billion per year (). In, FHWA created the Freight Performance Measure Initiative to address the lack of data on freight movements and its impact on congestion at road networks. The initiative focused on collecting travel time data for freight significant corridors and delay times at border crossings combined with urban congestion data to provide a more detailed summary of surface transportation network conditions (). Travel time reliability and delay at border crossing were important measures of freight performance, but over time, as freight research progressed and the needs of both the public and the industry became more apparent, further PMs were developed. These PMs focused on areas of concern such as safety, efficiency, and security. In, the National Cooperative Freight Research Program (NCFRP) produced a report defining PMs related to freight (). The research team analyzed available data sources, current measures, and both the public and private perspectives of importance on freight performance to develop a possible framework from which FPMs could be analyzed. The report consisted of a comprehensive analysis of the highest freight movement in the U.S. which was accomplished by different transportation modes including truck, rail, and inland waterway (barge). However, the study excluded air freight which accounts for a significant proportion of freight movement in Memphis, TN due to the existence of FedEx headquarters. The report recommended the use of FPMs such as average speeds, travel time, link delay, miles of congested roadway, travel rate (travel time divided by travel distances), cost-permile, driver wages, fuel cost, number of crashes, cost of crashes, etc. These FPMs could be determined using different sources of data. Traditionally, data on truck freight movements have been collected through surveys such as roadside/intercept surveys, combined telephone/mail-back surveys, telephone surveys, personal interviews, trip diary surveys and stakeholder interviews (). This, however, could be expensive, time consuming and limited in reach. Other methods relied on license plate matching (manual and electronic) and more recently, GPS tracking data. FHWA collected and published the results of the Commodity Flow Survey taken every five years since called the Freight Analysis Framework (FAF) (). This database provided detailed information on the tonnages of many different types of commodities by all modes (land, sea, rail, and air). This, however, was a broad overview of freight movement in the U.S. and did not provide enough information regarding single vehicle movements on specific road networks, which is required for many FPMs. GPS technology is a relatively new source of data for use by FPMs. It can provide detailed freight movement data for specific trucks that are outfitted with a GPS device. This can provide detailed route choice data and other information that were not possible with the traditional freight movement data collecting practices. GPS Data Use in Freight Performance Measurement TRB Annual Meeting

5 Johnson et al. One of the principal problems with the potentially unlimited amount of data that can be collected with GPS technology is the ability to analyze and manipulate these data to suit the needs of the researcher. Stopher et al. () used GPS data to develop trip tables for passenger vehicles. This could organize the massive amounts of data into a more usable format for use alongside household travel surveys (HTS). Authors concluded that it was possible to develop rules for GPS data that would coincide with the HTS with a % accuracy rate. The automation of this process reduced necessary effort on behalf of the researchers by -%. Freight GPS data analysis is an area that has not been examined nearly as much as household travel data via GPS devices, but requires a similar approach for data manipulation and analysis. Greaves and Figliozzi () focused on the development of urban freight movement characteristics. One of the major obstacles in freight GPS data is the prosperity for a truck to make several stops during a typical trip, making analysis of trip origin-destination to be rather difficult. This type of trip has become known as a tour. Researchers found that given the amounts of data, issues of low signal strength, and trip end time, the developed algorithms to process the data must be efficient and intelligent to accurately capture the information. However, the authors further stated that GPS technology would not replace traffic counts and roadside interviews due to the proprietary nature of truck operations. McCormack and Hallenbeck () compared the use of in-truck transponders that would record a timestamp at weigh stations with GPS units recording location data every -seconds that were installed in trucks. The authors found that technologies were practical for developing performance benchmarks and measures. The GPS units allowed for more precise analysis, showing route choice changes between origin-destination pairs. However, the key to accurate reporting these measures was the volume of instrumented vehicles that utilized the segment of roadway of concern. McCormack et al. () studied the collection and processing of more robust sources of freight GPS data for the purpose of building an FPM program. The researchers found that it was easier to obtain data directly from a GPS vendor. This data consisted of less frequent GPS reading, but comprised of a large number of trucks. The authors evaluated whether this type of information could be accurately used to determine travel time and speeds and found that it was sufficient in determining travel times and speeds during free flow. However, a greater quantity of data would be required to measure travel time and speed during congested periods. More important was the discovery of the limitations that were inherent with this type of data. The location data were recorded every - minutes which caused issues when analyzing the data in an urban area. The limited readings would cause inconsistency in the development of travel times. The researchers suggested that these data were more suitable for heavily congested roadways and for over long periods of time to accurately represent typical operations. Research by the American Transportation Research Institute (ATRI) has been analyzing freight GPS data for several years. In, ATRI began working with the FHWA to develop FPM. Jones et al. () developed methods for capturing and analyzing these data to develop travel time measures along freight significant corridors. Since then, ATRI has worked in collaboration with the FHWA and several state DOTs and universities on various reports to develop methods of analyzing a large database of freight GPS data. Some of the research includes bottleneck analysis (; ) and the National Corridor Analysis and Speed Tool (N- CAST). The Indiana Department of Transportation (INDOT) used data provided by ATRI to develop an improved truck trip table. Bernardin et al. () discovered that the GPS dataset provided a significant step up for the production of truck trip tables. However inequities in the TRB Annual Meeting

6 Johnson et al. percentages of external-external movements produced concern over whether the types of vehicles that were included in the GPS data provided by ATRI are sufficient for this type of analysis. Future research could focus on using the dataset to revise and improve the current state commodity flow and local truck models. The literature review suggests that FPM is an evolving topic and the researchers are still in the process of providing conclusive outcomes from FPM. Nevertheless, it is quite evident that use of GPS data for FPM determination has been a trend that is adapted by a number of states and regional agencies. The objective of this paper is to investigate the use of GPS truck data to examine FPM trends in network flows and turn-times. DATA DESCRIPTION In this section we present the dataset characteristics and the case study area. Analysis was based on a comprehensive dataset provided by the American Trucking Research Institute (ATRI). The database included Global Positioning System (GPS) data, which tracked the route and trip characteristics of each truck operating within the borders of the case study area. The dataset captured % to % of the total truck population and covered two months period from September st, until October st, with a total of,, observations. Network Flows The Interstate (I-) road network between Memphis and Nashville, Tennessee was selected as the case study area. The specific area was selected mainly because of data availability. Figure highlights the section of highway for analysis. FIGURE Case Study Area Source: maps.google.com TRB Annual Meeting

7 Johnson et al. Facility Turn-Times Memphis, TN comprises of hundreds of freight facilities that aid in the steady movement of goods through the mid-south given the strategic location and infrastructure surrounding the city. It facilitates five Class railroads, as well as major North-South (I-) and East-West (I-) truck freight routes. This makes Memphis an ideal location to study freight movements, focusing on freight facilities. Figure shows the area of interest for the freight facility s turn-time analysis. FIGURE Memphis Study Area. In order to get a reasonable snapshot of the daily freight operations the facilities were categorized by type. The rationale was that different types of facilities should exhibit different turn type characteristics and thus different pattern recognition models should be developed for each one of them. The process yielded four facility types: Intermodal, Warehouse, Private Warehouse and Distribution Centers. Table outlines the differences in the facility types. TABLE Description of Facility Types Facility Type Intermodal Public Warehouse Private Warehouse Distribution Center Description Expedites the transfer of goods between modes of transportation (road/rail). Provide warehousing capabilities for several companies. Could include cross-dock services. Facility operated and used by single company. Reduces delay caused by unfamiliar routine. Similar to the operations of a private warehouse, however, goods being moved tend to be of similar size and weight limiting delay. TRB Annual Meeting

8 Johnson et al. RESULTS This section presents results from the network flows and facility turn-times analysis and model development. Network Flow Results The data from each day was averaged into weekday volumes and travel time graphs. Figure shows the volumes (GPS data sample was %-% of total truck population) for each day of the week after averaging the corresponding values of the two month period of data. When viewed side-by-side, a noticeable pattern emerged. During the early hours of the day (-AM), truck volumes were higher than during the morning period (AM-PM). This could have been affected by the number of trucks that were immobilized (rest periods) at rest areas along the link during earlier hours of the day. As the morning hours progressed, truck volumes decreased as the trucks re-entered the network and later cleared the case study segment. During afternoon hours (-PM), there was a large spike in the number of trucks operating along I- between Memphis, TN and Nashville, TN. This would suggest that the majority of truck operations occurred during this period of the day as trucks departed from origin points in Memphis and arrived at destinations in Nashville. After PM, on most weekdays, truck volumes drastically decreased suggesting that few new trucks entered the case study links at this time period. Similarly with truck travel times, a pattern emerged when the data were averaged into week day graphs. Figure presents average daily trip times for trucks for each week s day. During the early morning hours (-AM) trip times were significantly higher, compared with the daytime hours. This could be caused due to the fact that early morning hours could be used as rest and break periods for a large number of truck drivers, something which could significantly affect the corresponding trip times. During the day time hours (AM- PM), trip times averaged between - hours. This was the typical truck trip time between Memphis and Nashville area without significant rest or break periods. Additionally, a slight decline in the average trip times as the day progressed towards the late afternoon hours was observed. One cause of such a decrease might be the need for truck drivers to accomplish a delivery within specific time windows; however, this decline was very gradual and might merely be the result of data quality. In the late evening hours (PM-AM), the travel time spiked. This was similar to what was observed in the early hours of the morning. Drivers might include rest periods in their schedules during this time causing the trip time between Memphis and Nashville to increase. A notable difference at this time period of the day occurred on Fridays. The Friday graph showed that the average trip time during the late night hours was much lower than the other days of the week. One cause of this phenomenon could be regarding the type of driver that operated during the weekdays versus the weekends. Different companies have different policies on the operational hours of truck drivers. A number of companies only operate during the weekdays which would mean that come Friday night many of these drivers are off the road. This could potentially explain the differences in late night trip times observed on Fridays. TRB Annual Meeting

9 Johnson et al. Volume (Trucks) Monday : : : : : : : : : : : : Wednesday Volume (Trucks) Tuesday : : : : : : : : : : : : Thursday Volume (Trucks) : : : : : : : : : : : : Volume (Trucks) : : : : : : : : : : : : Friday Volume (Trucks) : : : : : : : : : : : : FIGURE Weekday Truck Volumes. TRB Annual Meeting

10 Johnson et al. Travel Time (Hours) Travel Time (Hours) : : : : : : : Monday : : : Wednesday : : : : : : Travel Time (Hours) : : : : : : : : : : : : Travel Time (Hours) Travel Time (Hours) Friday : : : FIGURE Weekday Truck Travel Times. : Tuesday : : : : : : : : : : : : Thursday : : : : : : : : : : : : : : : : Facilities Turn-Times Statistics FIGURE displays the outcome of the analysis for the facility types studied. Results showed a skewing to the left with the frequency of turn-time decreasing drastically the longer the truck is TRB Annual Meeting

11 Frequency Johnson et al. within the facility. Warehouse and Private Warehouse facilities have a larger percentage of the turn-time occurring within the - minutes time window whereas Distribution Centers and Intermodal facilities have lower truck turn-times with a majority occurring within the - minute time window. Increased turn times can be attributed to increased movement complexity within each facility type. Intermodal facilities handle large containers of standard size making movements predictable and standard. Distribution facilities typically manage goods that are similar, if not identical, in size and shape making the loading and unloading processes faster. This is contrary to Warehouses and Private Warehouses where the type of product and the loading configuration can change with each truck load. The large frequency of trucks remaining in the facilities (mainly Warehouses) for longer than minutes is an indicator of efficiency issues, but also of how a specific facility type is used by the operators. The Private Warehouse facilities have a significantly higher percentage of trucks remaining within the facility. This could be attributed to the storage of vehicles overnight and/or the lengthened loading/unloading process that occurred at these types of facilities. Warehouses had the second highest percent of trucks remaining within the facility longer than minutes followed by Distribution Centers. All three of these facilities required a loading/unloading process that involved removing and loading goods from/to the containers, which could take time. The Intermodal facilities, however, had very few vehicles that remained within the facility for longer than minutes. The key difference between Intermodal facilities and the other facility types was the handling of goods. Intermodal facilities operated strictly in a container handling roll transferring containers between modes (rail-truck-sea). They did not offer services such as cross docking and distribution in which goods within the containers were sorted/handled or transferred between the same mode (truck-truck). % % % % % % % % % Warehouse Private Warehouse Distribution Intermodal % % + Turn Time (minutes) FIGURE Turn-Times Distribution. Facilities Turn-Times Prediction Model Using data from ATRI had the potential to provide a unique understanding of truck turn-times that was unavailable through any other means of data collection. These data provided accurate TRB Annual Meeting

12 Johnson et al. location and time information that could be used to determine volumes within a facility as well as truck turn times. With this information, it was possible to develop a model to predict turn times utilizing facility volumes produced from the GPS data. However, as stated earlier, the data were only a small sample of the truck traffic population in the study area. Not knowing the exact number of trucks that were in each facility at the time the data were recorded, limited the prediction capabilities of the model. This was due to a level of uncertainty that was created when building a model based on sample data and unknown arrival frequency at each facility. To address this problem, significantly more data should be gathered from the facilities either by increasing the percentage of GPS data sampling or the information provided by the facility operators. Information directly from the facilities is typically proprietary and it is unlikely that it can be obtained. Variable selection was directed by the information that could be gathered from these data. This dataset could produce turn times and volumes within the facility. The volumes could be further categorized by entry volume, volume within the facility, and exit volume. Based on a report by the Tioga Group () truck volume was selected as the predictor for the regression models. The interval of time used to calculate the volume within the facility and the average turn-time during that period could also affect the accuracy of the model. For this reason, three different time intervals ( min, min, and min) were used to group the data. The variables selected for model development are listed as follows: Y: turn-time of the facility (min) x : percent daily volume per time interval Assuming that the truck volumes were representative of the facilities actual demand pattern, truck volumes were converted into a percentage of the total measured daily volume (x ). This helped to generalize these data compensating for the sample size of %-% of actual population. Due to the sparse availability of data within some of the facilities observed, a -fold Hold Out cross validation technique was used to produce more representative models (). Models were produced for average weekday operations within each facility type. TABLE Turn-Time Model Development for Facilities Facility Type Time Interval (min) Model (min) R Intermodal. Distribution. Private Warehouse. Public Warehouse. Table shows the time intervals that were the most successful for producing turn time models (second column). Intermodal facilities, followed by Private Warehouse facilities showed a better fit to the data comparing to the Distribution Centers or Public Warehouses where the low R -values suggested that the models produced an insignificant fit for the dataset. As mentioned in the previous section, the operations within an Intermodal facility were relatively less complex when compared to the operations at Warehouses and Distribution Centers which could explain the existence of a pattern. TRB Annual Meeting

13 Johnson et al. DISCUSSION AND CONCLUSION Current methods used to determine freight movement and network flows utilize FHWA s Freight Analysis Framework or TRANSEARCH, both of which only offer commodity flows with varying degrees of detail. Other methods include spot count data and roadside interviews which can provide insufficient information and can potentially be cost prohibitive. The ability to track singular vehicle movements can provide insights into specific route choices and activities that have, so far, required inference and assumptions limiting the accuracy of previous analysis. Further development of a freight network flows performance measure is a critical area of interest for researchers and policy makers alike. While there is relatively substantial research available on network flows for personal vehicles, development of freight network flows is still in its infancy. GPS technology can offer the ability to study route volumes and travel times as well as route choice. Data available through GPS devices could lead to more accurate freight transportation models that may be used in conjunction with current transportation models (especially if combined with trip information e.g. commodity moved). Other uses of these data enable the research of freight handling facilities that were once considered to be a difficult task due to lack of publicly available data. A unique FPM that can be assessed with these data is facility turn-times for many intermodal freight terminals. Normally a researcher would have to ask for the information (which is often proprietary) or survey the facility, which is time consuming, but with these data, detail truck movement in and out of the facility can be assessed. These data can then be used in developing more accurate miscro- or mesoscopic simulation models in areas with heavy intermodal freight traffic (). The research presented in this paper has used GPS location data to determine truck turntimes regression models, however, insufficient amount of data limited the validity of these models for certain facility types. As GPS systems become more widespread among truck companies, the ability and accuracy of suck prediction should become feasible. REFERENCES. Dowling, R. NCFRP : Incorporating Truck Analysis into the Highway Capacity Manual. TRB, National Research Council,.. Hall, J. P. Freight Data for State Transportation Agencies. In Transportation Research Board Electronic Circular, E-C, Transportation Research Board of the National Academies, Boston,.. McCormack, E. D., X. Ma, C. Klowcow, A. Currarei, and D. Wright. Developing a GPS- Based Truck Freight Performance Measure Platform. Seattle. Publication TNW -. Washington State Department of Transportation,.. Greaves, S. P. and M. A. Figliozzi. Commercial Vehicle Tour Data Collection Using Passive GPS Technology: Issues and Potential Applications. Institute of Transport and Logistics Studies Working Paper. Publication ITLS-WP--. Sydney,.. Federal Highway Administration and American Transportation Research Institute. FPMweb. June,. performance.org/fpmweb. Accessed June,.. U.S. Department of Transportation. National Strategy to Reduce Congestion on America's Transportation Network. May. Accessed July,. TRB Annual Meeting

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