Truck industry classification from anonymous mobile sensor data using machine learning
Tài liệu tham khảo
Abd Elrahman, 2013, A review of class imbalance problem, J. Network Innov. Comput., 1, 332
Adler, 1979, A theoretical and empirical model of trip chaining behavior, Transp. Res. Part B, 13, 243, 10.1016/0191-2615(79)90016-X
Akter, 2018, Leveraging Open-Source GIS Tools to Determine Freight Activity Patterns from Anonymous GPS Data
Allahviranloo, 2013, Daily activity pattern recognition by using support vector machines with multiple classes, Transp. Res. Part B: Methodol., 58, 16, 10.1016/j.trb.2013.09.008
Allahviranloo, 2017, Modeling the activity profiles of a population, Transportmetrica B: Transport Dyn., 5, 426
Alpaydin, 2014
ATRI, 2019. Freight Performance Measures. <https://truckingresearch.org/tag/freight-performance-measures/> (accessed November 25, 2019).
Aziz, 2016, Identifying and characterizing truck stops from GPS data, 168
Beagan, D., Tempesta, D. and Proussaloglou, K., 2019. Quick Response Freight Methods Third Edition (No. FHWA-HOP-19-057).
Biau, 2016, A random forest guided tour, Test, 25, 197, 10.1007/s11749-016-0481-7
Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324
Camargo, 2017, Expanding the uses of truck GPS data in freight modeling and planning activities, Transp. Res. Rec., 2646, 68, 10.3141/2646-08
Caruana, R. and Niculescu-Mizil, A., 2006. An empirical comparison of supervised learning algorithms. in: Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh (PA).
Chen, 2004, 24
Chow, 2010, State-of-the art of freight forecast modeling: lessons learned and the road ahead, Transportation, 37, 1011, 10.1007/s11116-010-9281-1
Corro, 2019, Comparison of overnight truck parking counts with GPS-derived counts for truck parking facility utilization analysis, Transp. Res. Rec., 2673, 377, 10.1177/0361198119843851
FHWA, 2014. All Road Network of Linear Referenced Data (ARNOLD) reference manual.
FHWA, 2018. Status of the Nation's Highways, Bridges, and Transit Conditions and Performance: 23rd Edition: Part III: Highway Freight Transportation - report to congress.
FHWA, 2019a. Freight Analysis Framework Version 4. <http://faf.ornl.gov/fafweb/>. (accessed November 18, 2019).
FHWA, 2019b. Freight Management and Operations - Freight Analysis Framework 3 User Guide. <https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf3/userguide/>. (accessed November 18, 2019).
Ghasri, 2017, Developing a disaggregate travel demand system of models using data mining techniques, Transp. Res. Part A: Policy Pract., 105, 138
Gingerich, 2016, Classifying the purpose of stopped truck events: an application of entropy to GPS data, Transp. Res. Part C: Emerg. Technol., 64, 17, 10.1016/j.trc.2016.01.002
Giovannini, L., 2011. A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis.
Gong, 2015, Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines, J. Modern Transp., 23, 202, 10.1007/s40534-015-0079-x
Grzybowski, 1997, Statistical methodology: III. Receiver operating characteristic (ROC) curves, Acad. Emerg. Med., 4, 818, 10.1111/j.1553-2712.1997.tb03793.x
Hess, 2015, Developing advanced route choice models for heavy goods vehicles using GPS data, Transp. Res. Part E: Logistics Transp. Rev., 77, 29, 10.1016/j.tre.2015.01.010
Holguín-Veras, 2020, Mechanistic identification of freight activity stops from global positioning system data, Transp. Res. Rec., 2674, 235, 10.1177/0361198120911922
Jiang, 2012, Clustering daily patterns of human activities in the city, Data Min. Knowl. Disc., 25, 478, 10.1007/s10618-012-0264-z
Jing, 2018
Kuppam, A., Lemp, J., Beagan, D., Livshits, V., Vallabhaneni, L. and Nippani, S., 2014. Development of a tour-based truck travel demand model using truck GPS data (No. 14-4293).
Kwok, 1990, Multiple decision trees, 327, 10.1016/B978-0-444-88650-7.50030-5
Laranjeiro, 2019, Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: the case of São Paulo, Brazil, J. Transp. Geogr., 76, 114, 10.1016/j.jtrangeo.2019.03.003
Li, 2017, Learning daily activity patterns with probabilistic grammars, Transportation, 44, 49, 10.1007/s11116-015-9622-1
Liao, 2009
Liu, 2014, Building a validation measure for activity-based transportation models based on mobile phone data, Expert Syst. Appl., 41, 6174, 10.1016/j.eswa.2014.03.054
Louppe, G., 2014. Understanding random forests: From theory to practice. arXiv preprint arXiv:1407.7502.
Ma, 2011, Processing commercial global positioning system data to develop a web-based truck performance measures program, Transp. Res. Rec., 2246, 92, 10.3141/2246-12
Mortazavi, 2016, Analysis of machine learning techniques for heart failure readmissions, Circulation: Cardiovasc. Qual. Outcomes, 9, 629
Pline, 1999
Quddus, 2015, Shortest path and vehicle trajectory aided map-matching for low frequency GPS data, Transp. Res. Part C: Emerg. Technol., 55, 328, 10.1016/j.trc.2015.02.017
SANDAG, 2018. Visualizing Truck Flows Based on Industry Data and Using Truck Visualization as a Planning Tool. <https://www.sandag.org/uploads/publicationid/publicationid_4542_24796.pdf>. (accessed April 20, 2021).
Sarti, 2017, Stop purpose classification from GPS data of commercial vehicle fleets, 280
Sharman, 2011, Analysis of freight global positioning system data: clustering approach for identifying trip destinations, Transp. Res. Rec., 2246, 83, 10.3141/2246-11
Shoval, 2007, Tracking tourists in the digital age, Ann. Tour. Res., 34, 141, 10.1016/j.annals.2006.07.007
Sun, 2013, Vehicle classification using GPS data, Transp. Res. Part C: Emerg. Technol., 37, 102, 10.1016/j.trc.2013.09.015
Thakur, 2015, Development of algorithms to convert large streams of truck GPS data into truck trips, Transp. Res. Rec., 2529, 66, 10.3141/2529-07
Xie, 2010, Kernel-based machine learning models for predicting daily truck volume at seaport terminals, J. Transp. Eng., 136, 1145, 10.1061/(ASCE)TE.1943-5436.0000186
Yang, 2014, Urban freight delivery stop identification with GPS data, Transp. Res. Rec., 2411, 55, 10.3141/2411-07
Yang, 2010, Trip chain's activity type recognition based on support vector machine, J. Transp. Syst. Eng. Inf. Technol., 10, 70
Zanjani, 2015, Estimation of statewide origin–destination truck flows from large streams of GPS data: application for Florida statewide model, Transp. Res. Rec., 2494, 87, 10.3141/2494-10