Discovering vehicle usage patterns on the basis of daily mobility profiles derived from floating car data

References

• Altintasi, O., H. Tuydes-Yaman, and K. Tuncay. 2017. “Detection of Urban Traffic Patterns from Floating Car Data (FCD).” Transportation Research Procedia 22: 382–391. doi:10.1016/j.trpro.2017.03.057.
   Google Scholar
• Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022.
   Web of Science ®Google Scholar
• Brockfeld, E., S. Lorkowski, P. Mieth, and P. Wagner, 2007. “Benefits and Limits of Recent Floating Car Data Technology–an Evaluation Study.” 11th WCTR Conference, Berkeley, USA.
   Google Scholar
• Chen, H., C. Yang, and X. Xu. 2017. “Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data.” Journal of Advanced Transportation 2017: 1-14. doi:10.1155/2017/1738085.
   Web of Science ®Google Scholar
• Ciscal-Terry, W., M. Dell’Amico, N. S. Hadjidimitriou, and M. Iori. 2016. “An Analysis of Drivers Route Choice Behaviour Using GPS Data and Optimal Alternatives.” Journal of Transport Geography 51: 119–129. doi:10.1016/j.jtrangeo.2015.12.003.
   Web of Science ®Google Scholar
• de Fabritiis, C., R. Ragona, and G. Valenti, 2008. “Traffic Estimation And Prediction Based On Real Time Floating Car Data.” 2008 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, 2008. 197–203. doi:10.1109/ITSC.2008.4732534
   Google Scholar
• Découpage Morphologique d’Île-de-France. 2017. “[WWW Document].” Accessed 19 Feburary 2020. https://data-iau-idf.opendata.arcgis.com/datasets/d%C3%A9coupage-morphologique-d%C3%AEle-de-france.
   Google Scholar
• Fusco, G., C. Colombaroni, and N. Isaenko. 2016. “Short-term Speed Predictions Exploiting Big Data on Large Urban Road Networks.” Transportation Research Part C: Emerging Technologies 73: 183–201. doi:10.1016/j.trc.2016.10.019.
   Web of Science ®Google Scholar
• Gong, L., T. Morikawa, T. Yamamoto, and H. Sato. 2014. “Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies.” Procedia-Social and Behavioral Sciences 138: 557–565. doi:10.1016/j.sbspro.2014.07.239.
   Google Scholar
• Gong, L., H. Sato, T. Yamamoto, T. Miwa, and T. Morikawa. 2015. “Identification of Activity Stop Locations in GPS Trajectories by Density-based Clustering Method Combined with Support Vector Machines.” Journal of Modern Transportation 23: 202–213. doi:10.1007/s40534-015-0079-x.
   Web of Science ®Google Scholar
• Hoffman, M., F. R. Bach, and D. M. Blei. 2010. “Online Learning for Latent Dirichlet Allocation.” Advances in Neural Information Processing Systems 23: 856–864.
   Google Scholar
• Hunter, T., R. Herring, P. Abbeel, and A. Bayen. 2009. “Path and Travel Time Inference from GPS Probe Vehicle Data.” NIPS Analyzing Networks and Learning with Graphs 12: 1–8.
   Google Scholar
• Jenelius, E., and H. N. Koutsopoulos. 2013. “Travel Time Estimation for Urban Road Networks Using Low Frequency Probe Vehicle Data.” Transportation Research Part B: Methodological 53: 64–81. doi:10.1016/j.trb.2013.03.008.
   Web of Science ®Google Scholar
• Lin, K., Z. Xu, M. Qiu, X. Wang, and T. Han, 2016. “Noise Filtering, Trajectory Compression and Trajectory Segmentation on GPS Data.” 2016 11th International Conference on Computer Science & Education (ICCSE), Nagoya, 2016. IEEE, 490–495.
   Google Scholar
• Liu, X., L. Gong, Y. Gong, and Y. Liu. 2015. “Revealing Travel Patterns and City Structure with Taxi Trip Data.” Journal of Transport Geography 43: 78–90. doi:10.1016/j.jtrangeo.2015.01.016.
   Web of Science ®Google Scholar
• Nguyen, T. T., J. Armoogum, J. L. Madre, and T. H. T. Pham, 2017. “GPS and Travel Diary: Two Recordings of the Same Mobility.” ISCTSC, 11th International Conference on Transport Survey Methods, Esterel, Canada. 13.
   Google Scholar
• Proulhac, L. 2019. Qui se cache derrière la baisse de la mobilité automobile en Île-de-France? Une analyse typologique des pratiques modales des actifs occupés franciliens. Cybergeo: European Journal of Geography.
   Google Scholar
• Rahmani, M., H. N. Koutsopoulos, and E. Jenelius. 2017. “Travel Time Estimation from Sparse Floating Car Data with Consistent Path Inference: A Fixed Point Approach.” Transportation Research Part C: Emerging Technologies 85: 628–643. doi:10.1016/j.trc.2017.10.012.
   Web of Science ®Google Scholar
• Rempe, F., P. Franeck, U. Fastenrath, and K. Bogenberger. 2017. “A Phase-based Smoothing Method for Accurate Traffic Speed Estimation with Floating Car Data.” Transportation Research Part C: Emerging Technologies 85: 644–663. doi:10.1016/j.trc.2017.10.015.
   Web of Science ®Google Scholar
• Ren, K., A. M. Kim, and K. Kuhn. 2018. “Exploration of the Evolution of Airport Ground Delay Programs.” Transportation Research Record 2672: 71–81. doi:10.1177/0361198118782272.
   Web of Science ®Google Scholar
• Rousseeuw, P. J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
   Web of Science ®Google Scholar
• Sarti, L., L. Bravi, F. Sambo, L. Taccari, M. Simoncini, S. Salti, and A. Lori, 2017. “Stop Purpose Classification from Gps Data of Commercial Vehicle Fleets.” 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017. IEEE, 280–287.
   Google Scholar
• Simoncini, M., F. Sambo, L. Taccari, L. Bravi, S. Salti, and A. Lori, 2016. “Vehicle Classification from Low Frequency GPS Data.” 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, 2016. IEEE, 1159–1166. doi:10.1109/ICDMW.2016.0167
   Google Scholar
• Simoncini, M., L. Taccari, F. Sambo, L. Bravi, S. Salti, and A. Lori. 2018. “Vehicle Classification from Low-frequency GPS Data with Recurrent Neural Networks.” Transportation Research Part C: Emerging Technologies 91: 176–191. doi:10.1016/j.trc.2018.03.024.
   Web of Science ®Google Scholar
• Sun, D., F. Leurent, and X. Xie. 2020. “Floating Car Data Mining: Identifying Vehicle Types on the Basis of Daily Usage Patterns.” Transportation Research Procedia 47: 147–154. doi:10.1016/j.trpro.2020.03.087.
   Google Scholar
• Sun, D., C. Zhang, L. Zhang, F. Chen, and Z.-R. Peng. 2014. “Urban Travel Behavior Analyses and Route Prediction Based on Floating Car Data.” Transportation Letters 6: 118–125. doi:10.1179/1942787514Y.0000000017.
   Web of Science ®Google Scholar
• Sun, Z., and X. Ban. 2013. “Vehicle Classification Using GPS Data.” Transportation Research Part C: Emerging Technologies 37: 102–117. doi:10.1016/j.trc.2013.09.015.
   Web of Science ®Google Scholar
• Survey of the use of road freight vehicles (TRM). 2018. “WWW Document.” Accessed 16 April 2019. https://www.statistiques.developpement-durable.gouv.fr/enquete-sur-lutilisation-des-vehicules-de-transport-routier-de-marchandises-trm.
   Google Scholar
• Thorndike, R. L. 1953. “Who Belongs in the Family?” Psychometrika 18: 267–276. doi:10.1007/BF02289263.
   Google Scholar
• Von Eye, A. 2003. Configural Frequency Analysis: Methods, Models, and Applications. Psychology Press, Mahwah, NJ.
   Google Scholar
• Yin, B., 2019. “Car Trips Clustering Based on EGT 2010.”
   Google Scholar
• Yuan, N. J., Y. Zheng, X. Xie, Y. Wang, K. Zheng, and H. Xiong. 2014. “Discovering Urban Functional Zones Using Latent Activity Trajectories.” IEEE Transactions on Knowledge and Data Engineering 27: 712–725. doi:10.1109/TKDE.2014.2345405.
   Web of Science ®Google Scholar
• Zhao, Y., X. Zhu, W. Guo, B. She, H. Yue, and M. Li. 2019. “Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China.” Sustainability 11: 6152. doi:10.3390/su11216152.
   Web of Science ®Google Scholar
• Zhu, S., and D. Levinson. 2015. “Do People Use the Shortest Path? an Empirical Test of Wardrop’s First Principle.” PloS One 10: e0134322. doi:10.1371/journal.pone.0134322.
   PubMed Web of Science ®Google Scholar