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Global Navigation Satellite System (GNSS) data is an inexpensive and ubiquitous source of activity data. Global Positioning System (GPS) is an example of such data. Although there have been several studies about inferring device activity using GPS data from a consumer device, freight GPS data presents unique challenges for example having low and variable frequency, long transmission gaps, and frequent and unpredictable device ID resetting for preserving privacy. This study aims to provide an end-to-end, generic data analytical framework to infer multiple aspects of truck activity such as stops, trips, and tours. We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage. We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data. We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods. Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference, but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification. We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.
Akter, T., Hernandez, S., Diaz, K.C., Ngo, C., 2018. Leveraging opensource GIS tools to determine freight activity patterns from anonymous GPS data. In: American Association of State Highway and Transportation Officials (AASHTO) GIS for Transportation Symposium, 55–69.
Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory. New York: ACM, 144–152.
Camargo, P., Hong, S., Livshits, V., 2017. Expanding the uses of truck GPS data in freight modeling and planning activities. Transportation Research Record. 2646, 68–76.
Chankaew, N., Sumalee, A., Treerapot, S., Threepak, T., Ho, H. W., Lam, W. H. K., 2018. Freight traffic analytics from national truck GPS data in Thailand. Transp Res Procedia. 34, 123–130.
Choudhry, A., 2022. Smart mobility. XRDS Crossroads ACM Mag Stud, 28, 14–19.
DE JONG, G., Gunn, H., Walker, W., 2004. National and international freight transport models: An overview and ideas for future development. Transp Rev, 24, 103–124.
Ester, M., Kriegel, H. P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–231.
Gingerich, K., Maoh, H., Anderson, W., 2016. Classifying the purpose of stopped truck events: An application of entropy to GPS data. Transp Res C Emerg Technol, 64, 17–27.
Holguín-Veras, J., Encarnación, T., Pérez-Guzmán, S., Yang, X. S., 2020. Mechanistic identification of freight activity stops from global positioning system data. Transportation Research Record, 2674, 235–246.
Karam, A., Illemann, T. M., Reinau, K. H., Vuk, G., Hansen, C. O., 2020. Towards deriving freight traffic measures from truck movement data for state road planning: A proposed system framework. ISPRS Int J Geo Inf, 9, 606.
Liu, Y., Zhang, Q., Lyu, C., Liu, Z., 2021. Modelling the energy consumption of electric vehicles under uncertain and small data conditions. Transp Res A Policy Pract, 154, 313–328.
Luo, T., Zheng, X., Xu, G., Fu, K., Ren, W., 2017. An improved DBSCAN algorithm to detect stops in individual trajectories. ISPRS Int J Geo Inf, 6, 63.
Ma, X., Wang, Y., McCormack, E., Wang, Y., 2016. Understanding freight trip-chaining behavior using a spatial data-mining approach with GPS data. Transportation Research Record, 2596, 44–54.
Qu, X., Zeng, Z., Wang, K., Wang, S., 2022. Replacing urban trucks via ground–air cooperation. Commun Transp Res, 2, 100080.
Rabiner, L., Juang, B., 1986. An introduction to hidden Markov models. IEEE ASSP Mag, 3, 4–16.
Sharman, B. W., Roorda, M. J., 2011. Analysis of freight global positioning system data. Transportation Research Record, 2246, 83–91.
Siripirote, T., Sumalee, A., Ho, H. W., 2020. Statistical estimation of freight activity analytics from Global Positioning System data of trucks. Transp Res E Logist Transp Rev, 140, 101986.
Thakur, A., Pinjari, A. R., Zanjani, A. B., Short, J., Mysore, V., Tabatabaee, S. F., 2015. Development of algorithms to convert large streams of truck GPS data into truck trips. Transportation Research Record, 2529, 66–73.
Thierry, B., Chaix, B., Kestens, Y., 2013. Detecting activity locations from raw GPS data: A novel kernel-based algorithm. Int J Health Geogr, 12, 14.[PubMed]
Yang, X., Sun, Z., Ban, X. J., Holguín-Veras, J., 2014. Urban freight delivery stop identification with GPS data. Transportation Research Record, 2411, 55–61.
Yang, Y., Jia, B., Yan, X. Y., Jiang, R., Ji, H., Gao, Z., 2022. Identifying intracity freight trip ends from heavy truck GPS trajectories. Transp Res C Emerg Technol, 136, 103564.
Yang, Y., Jia, B., Yan, X. Y., Li, J., Yang, Z., Gao, Z., 2022b. Identifying intercity freight trip ends of heavy trucks from GPS data. Transp Res E Logist Transp Rev, 157, 102590.
You, S. I., Ritchie, S. G., 2018. A GPS data processing framework for analysis of drayage truck Tours. KSCE J Civ Eng, 22, 1454–1465.
This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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