Vehicle travel path recognition in urban dense road network environments by using mobile phone data

References

• Bassolas, A., J. J. Ramasco, R. Herranz, and O. G. Cantú-Ros. 2019. “Mobile Phone Records to Feed Activity-Based Travel Demand Models: MATSim for Studying a Cordon Toll Policy in Barcelona.” Transportation Research Part A: Policy and Practice 121 (3): 56–74. doi:10.1016/j.tra.2018.12.024.
   Google Scholar
• Bierlaire, M., J. Chen, and J. Newman. 2013. “A Probabilistic Map Matching Method for Smartphone GPS Data.” Transportation Research Part C: Emerging Technologies 26: 78–98. doi:10.1016/j.trc.2012.08.001.
   Google Scholar
• Bin Ran. 2013. “Application of Cellphone Data in Travel Survey and Transportation Planning.” Urban Transportation of China 11 (1): 38+78–38+87. doi:10.13813/j.cn11-5141/u.2013.01.010.
   Google Scholar
• Chen, C., L. Bian, and J. Ma. 2014. “From Traces to Trajectories: How Well Can We Guess Activity Locations from Mobile Phone Traces.” Transportation Research Part C: Emerging Technologies 46: 326–337. doi:10.1016/j.trc.2014.07.001.
   Google Scholar
• Chen, C., J. Ma, Y. Susilo, Y. Liu, and M. Wang. 2016. “The Promises of Big Data and Small Data for Travel Behavior (Aka Human Mobility) Analysis.” Transportation Research Part C: Emerging Technologies 68: 285–299. doi:10.1016/j.trc.2016.04.005.
   Google Scholar
• Chen, G., A. C. Viana, and C. Sarraute. 2017. “Towards an Adaptive Completion of Sparse Call Detail Records for Mobility Analysis.” Paper presented at the 2017 IEEE International Conference on Pervasive Computing and Communications: Workshops (PerCom Workshops), Kona, 302-305.
   Google Scholar
• Deng, M., Z. Fan, Q. Liu, and J. Gong. 2016. “A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets.” ISPRS International Journal of Geo-Information 5 (2): 13–14. doi:10.3390/ijgi5020013.
   Google Scholar
• Fan, J., C. Fu, K. Stewart, and L. Zhang. 2019. “Using Big GPS Trajectory Data Analytics for Vehicle Miles Traveled Estimation.” Transportation Research Part C: Emerging Technologies 103: 298–307. doi:10.1016/j.trc.2019.04.019.
   Google Scholar
• Fan, Z., T. Pei, T. Ma, Y. Du, C. Song, Z. Liu, and C. Zhou. 2015. “Estimation of Urban Crowd Flux Based on Mobile Phone Location Data: A Case Study of Beijing, China.” Computers, Environment and Urban Systems 69: 114–123. doi:10.1016/j.compenvurbsys.2018.01.005.
   Google Scholar
• Ficek, M., and L. Kencl. 2012. “Inter-Call Mobility Model: A Spatio-Temporal Refinement of Call Data Records Using a Gaussian Mixture Model.” Paper presented at the IEEE INFOCOM 2012 - IEEE Conference on Computer Communications, Orlando, FL, USA, Mar 469-477.
   Google Scholar
• Hoteit, S., S. Secci, S. Sobolevsky, C. Ratti, and G. Pujolle. 2014. “Estimating Human Trajectories and Hotspots Through Mobile Phone Data.” Computer Networks 64: 296–307. doi:10.1016/j.comnet.2014.02.011.
   Google Scholar
• Huang, Z., X. Ling, P. Wang, F. Zhang, Y. Mao, T. Lin, and F.-Y. Wang. 2018. “Modeling Real-Time Human Mobility Based on Mobile Phone and Transportation Data Fusion.” Transportation Research Part C: Emerging Technologies 96: 251–269. doi:10.1016/bs.atpp.2019.08.001.
   Google Scholar
• Li, M., S. Gao, F. Lu, and H. Zhang. 2019. “Reconstruction of Human Movement Trajectories from Large-Scale Low-Frequency Mobile Phone Data.” Computers, Environment and Urban Systems 77: 101346. doi:10.1016/j.compenvurbsys.2019.101346.
   Google Scholar
• Liu, K., S. Gao, and F. Lu. 2019. “Identifying Spatial Interaction Patterns of Vehicle Movements on Urban Road Networks by Topic Modelling.” Computers, Environment and Urban Systems 74: 50–61. doi:10.1016/j.compenvurbsys.2018.12.001.
   Google Scholar
• Lv, M., L. Chen, Y. Shen, and G. Chen. 2015. “Measuring Cell-Id Trajectory Similarity for Mobile Phone Route Classification.” Knowledge-Based Systems 89: 181–191. doi:10.1016/j.knosys.2015.07.002.
   Google Scholar
• Massaro, E., D. Kondor, and C. Ratti. 2019. “Assessing the Interplay Between Human Mobility and Mosquito Borne Diseases in Urban Environments.” Scientific Reports 9 (1): 1–13. doi:10.1038/s41598-019-53127-z.
   Google Scholar
• Miwa, T., D. Kiuchi, T. Yamamoto, and T. Morikawa. 2012. “Development of map Matching Algorithm for low Frequency Probe Data.” Transportation Research Part C: Emerging Technologies 22: 132–145. doi:10.1016/j.trc.2012.01.005.
   Google Scholar
• Qiao, S., D. Shen, X. Wang, N. Han, and W. Zhu. 2015. “A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models.” IEEE Transactions on Intelligent Transportation Systems 16 (1): 284–296. doi:10.1109/TITS.2014.2331758.
   Google Scholar
• Qiao, S., C. Tang, H. Jin, T. Long, S. Dai, Y. Ku, and M. Chau. 2010. “PutMode: Prediction of Uncertain Trajectories in Moving Objects Databases.” Applied Intelligence 33 (3): 370–386. doi:10.1007/s10489-009-0173-z.
   Google Scholar
• Tettamanti, T., H. Demeter, and I. Varga. 2012. “Route Choice Estimation Based on Cellular Signaling Data.” Acta Polytechnica Hungarica 9 (4): 207–220. doi:10.1016/j.proeng.2012.07.044.
   Google Scholar
• Tettamanti, T., and I. Varga. 2014. “Mobile Phone Location Area Based Traffic Flow Estimation in Urban Road Traffic.” Advances in Civil and Environmental Engineering 1 (1): 1–15.
   Google Scholar
• Wang, Y., G. H. de A. Correia, and B. van Arem. 2019. “Chapter Four - Relationships Between Mobile Phone Usage and Activity-Travel Behavior: A Review of the Literature and an Example.” Advances in Transport Policy and Planning, Academic Press 3: 81–105. doi:10.1016/bs.atpp.2019.08.001.
   Google Scholar
• Wang, Z., S. Y. He, and Y. Leung. 2018. “Applying Mobile Phone Data to Travel Behaviour Research: A Literature Review.” Travel Behaviour and Society 11: 141–155. doi:10.1016/j.tbs.2017.02.005.
   Google Scholar
• White, C. E., D. Bernstein, and A. L. Kornhauser. 2000. “Some map Matching Algorithms for Personal Navigation Assistants.” Transportation Research Part C: Emerging Technologies 8 (1-6): 91–108. doi:10.1016/S0968-090X(00)00026-7.
   Google Scholar
• Yang, C.-S., S.-P. Kao, F.-B. Lee, and P.-S. Hung. 2004. “Twelve Different Interpolation Methods: A Case Study of Surfer 8.0.” Proceedings of the XXth ISPRS Congress 35: 778–785.
   Google Scholar
• Yang, Fei, Lilei Wang, Fan Zuo, Yudong Guo, and Xin Yue*. 2020. “Adjacency Matrix Modeling for Urban Subway Passenger Travel Information Extraction Based on Cell Phone Data.” Paper presented at the 99th Annual Meeting of Transportation Research Board, Washington, D.C.
   Google Scholar
• Yang, F., Z. Yao, P. J. Jin, and D. Yang. 2016. “Performance Evaluation of Handoff-Based Cellular Traffic Monitoring Systems Using Combined Wireless and Traffic Simulation Platform.” Journal of Intelligent Transportation Systems 20 (2): 113–124. doi:10.1080/15472450.2014.1001027.
   Google Scholar
• Yue, Y., T. Lan, A. G. O. Yeh, and Q.-Q. Li. 2014. “Zooming Into Individuals to Understand the Collective: A Review of Trajectory-Based Travel Behaviour Studies.” Travel Behaviour and Society 1 (2): 69–78. doi:10.1016/j.tbs.2013.12.002.
   Google Scholar
• Zhong, G., T. Yin, J. Zhang, S. He, and B. Ran. 2020. “Characteristics Analysis for Travel Behavior of Transportation Hub Passengers Using Mobile Phone Data.” Transportation 46 (5): 1713–1736. doi:10.1007/s11116-018-9876-5.
   Google Scholar
• Zilske, M., and K. Nagel. 2015. “A Simulation-Based Approach for Constructing All-Day Travel Chains from Mobile Phone Data.” Procedia Computer Science 52: 468–475. doi:10.1016/j.procs.2015.05.017.
   Google Scholar