Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks
Abstract
The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.
References
Ambrosini C, Routhier JL. Objectives, methods, and results of surveys carried out in the field of urban freight transport: An international comparison. Transport Reviews. 2004;24: 57-77.
De Jong G, Gunn H, Walker W. National and international freight transport models: An overview and ideas for future development. Transport Reviews. 2004;24: 103-124. doi: 10.1080/0144164032000122343.
Lomax T, Schrank D, Turner S, Margiotta R. Selecting travel time reliability measures. Federal Highway Administration, Washington DC; 2003. https://static.tti.tamu.edu/tti.tamu.edu/documents/TTI-2003-3.pdf [Accessed 11th July 2020].
Hadavi S, et al. Monitoring urban-freight transport based on GPS trajectories of heavy-goods vehicles. IEEE Transactions on Intelligent Transportation Systems. 2018;20(10): 3747-3758. doi: 10.1109/TITS.2018.2880949.
Quiroga CA, Bullock D. Travel time studies with global positioning system and geographic information systems: An integrated methodology. Transportation Research Part C. 1998;6(1-2): 101-127. doi: 10.1016/S0968-090X(98)00010-2.
Berzina L, Faghri A, Shourijeh MT, Li M. Evaluation of travel time data collection techniques: A statistical analysis. International Journal of Traffic and Transportation Engineering. 2013;2(6): 149-158. doi: 10.5923/j.ijtte.20130206.03.
Du JH, Aultman-Hall L. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues. Transportation Research Part A. 2007;41: 220-232. doi: 10.1016/j.tra.2006.05.001.
Huang J, et al. Mining freight truck’s trip patterns from GPS data. 17th International IEEE Conference on Intelligent Transportation Systems: 1988-1994; 2014.
Thakur A, et al. Development of algorithms to convert large streams of truck GPS data into truck trips. TRR: Journal of the Transportation Research Board. 2015;2529: 66-73. doi: 10.3141/2529-07.
McCormack E, Hallenbeck M. ITS devices used to collect truck data for performance benchmarks. TRR: Journal of the Transportation Research Board. 2006;1957: 43-50.
Sharman BW, Roorda MJ. Analysis of freight global positioning system data: Clustering approach for identifying trip destinations. TRR: Journal of the Transportation Research Board. 2011;2246: 83-91. doi: 10.3141/1957-07.
Greaves SP, Figliozzi M. Collecting commercial vehicle tour data with passive global positioning system and technology. TRR: Journal of the Transportation Research Board. 2008;2049: 158-166. doi: 10.3141/2049-19.
Zanjani AB, et al. Estimation of statewide origin-destination truck flows from large streams of GPS data: Application for Florida statewide model. TRR: Journal of the Transportation Research Board. 2015;2494: 87-96. doi: 10.3141/2494-10.
Flaskou M, et al. Analysis of freight corridors using GPS data on trucks. TRR: Journal of the Transportation Research Board. 2015;2478: 113-122. doi: 10.3141/2478-13.
Liao CF. Using archived truck GPS data for freight performance analysis on I-94/I-90 from the Twin Cities to Chicago. University of Minnesota. Report No. CTS 09-27, 2009.
Kamali M, Ermagun A, Viswanathan K, Pinjari AR. Deriving truck route choice from large GPS data streams. TRR: Journal of the Transportation Research Board. 2016;2563: 62-70. doi: 10.3141/2563-10.
Zhao WJ, McCormack E, Dailey DJ, Scharnhorst E. Using truck probe GPS data to identify and rank roadway bottlenecks. Journal of Transportation Engineering. 2012;139(1): 1-7. doi: 10.1061/(ASCE)TE.1943-5436.0000444.
Dulebenets MA, Pujats K, Deligiannis N. Development of tools for processing truck GPS data and analysis of freight transportation facilities. Transportation Research Board 96th Annual Meeting, 8-12 January 2017. Washington DC; 2017. Paper No. 17-00717.
Yang X, Sun Z, Ban XJ, Holguín-Veras J. Urban freight delivery stop identification with GPS data. TRR: Journal of the Transportation Research Board. 2014;2411: 55-61. doi: 10.3141/2411-07.
Gingerich K, Maoh H, Anderson W. Classifying the purpose of stopped truck events: An application of entropy to GPS data. Transportation Research Part C. 2016;64: 17-27. doi: 10.1016/j.trc.2016.01.002.
McCormack E. Developing transportation metrics from commercial GPS truck data. University of Washington. Report No. TNW2011-12, 2011.
Liao CF. Measure of truck delay and reliability at the corridor level. Minnesota Department of Transportation. Report No. MnDOT 2018-15, 2018.
Yang S, An CC, Wu YJ, Xia J. Origin-destination-based travel time reliability. TRR: Journal of the Transportation Research Board. 2017;2643: 139-159. doi: 10.3141/2643-16.
Clark SD, Watling DP. Modelling network travel time reliability under stochastic demand. Transportation Research Part B. 2005;39(2): 119-140. doi: 10.1016/j.trb.2003.10.006.
Doustmohammadi E, Sisiopiku VP, Sullivan A. Modeling freight truck trips in Birmingham using tour-based approach. Journal of Transportation Technologies. 2016;6: 436-448. doi: 10.4236/jtts.2016.65035.
Bassok A, McCormack ED, Outwater ML, Chilan T. Use of truck GPS data for freight forecasting. Transportation Research Board 90th Annual Meeting, Washington DC; 2011. Paper No. 11-3033.
Ma XL, McCormack E, Wang YH. Processing commercial global positioning system data to develop a web-based truck performance measures program. TRR: Journal of the Transportation Research Board. 2011;2246: 92-100. doi: 10.3141/2246-12.
Jackson E, Aultman-Hall L, Holmén BA, Du J. Evaluating the ability of global positioning system receivers to measure a real-world operating mode for emissions research. TRR: Journal of the Transportation Research Board. 2005;1941: 43-50. doi: 10.1177/0361198105194100106.
Li Y, Zhao L, Rilett LR. Driving performances assessment based on speed variation using dedicated route truck GPS data. IEEE Access. 2019;7: 51002-51013. doi: 10.1109/ACCESS.2019.2909572.
Nur Arifin Z. Route choice modeling based on GPS tracking data: The case of Jakarta. PhD thesis. ETH Zurich; 2012.
Rousseeuw P. Least median of squares regression. Journal of the American Statistical Association. 1984;79(388): 871-880. doi:10.1080/01621459.1984.10477105.
Zegeer J, et al. Incorporating travel time reliability into the Highway Capacity Manual. National Research Council. Report No. S2-L08-RW-1, 2014.
Texas Transportation Institute and Cambridge Systems Inc. Travel time reliability: making it there on time, all the time. US DOT. http://ops.fhwa.dot.gov/publications/tt_reliability/ [Accessed 26th June 2019].
Pu WJ. Analytic relationships between travel time reliability measures. TRR: Journal of the Transportation Research Board. 2011;2254: 122-130. doi: 10.3141/2254-13.
Cambridge Systematics Inc. Cost-effective performance measures for travel time delay, variation, and reliability. Transportation Research Board, Washington. NCHRP Report 618. DC, 2008.
Van Lint JW, Van Zuylen HJ, Tu H. Travel time unreliability on freeways: Why measures based on variance tell only half the story. Transportation Research Part A. 2008;42(1): 258-277. doi:10.1016/j.tra.2007.08.008.
Chen P, Tong R, Lu G, Wang Y. Exploring travel time distribution and variability patterns using probe vehicle data: Case study in Beijing. Journal of Advanced Transportation. 2018; ID 3747632. doi: 10.1155/2018/3747632.
Pu WJ, Meese AJ. Exploring travel time reliability under different circumstances: A case study. Transportation Research Board 89th Annual Meeting, Washington DC; 2010. Paper No. 10-0161.
Wang Z, Goodchild A, McCormack E. Measuring truck travel time reliability using truck probe GPS data. Journal of Intelligent Transportation Systems. 2016;20(2): 103-112. doi: 10.1080/15472450.2014.1000455.
Copyright (c) 2022 Li ZHAO, Ying LI
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
