Dissecting Big Trajectory Data to Analyse Road Network Travel Efficiency
Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from trav-eling vehicles, such as taxis through installed global positioning sys-tem (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.
[1] Y. Djenouri, A. Belhadi, J. C.-W. Lin, D. Djenouri, and A. Cano, “A Survey on Urban Traffic Anomalies Detection Algorithms,” IEEE Access, vol. 7, pp. 12192–12205, 2019.
[2] J. D. Mazimpaka and S. Timpf, “Trajectory data mining: A review of methods and applications,” J. Spat. Inf. Sci., vol. 13, no. 13, pp. 61–99, 2016.
[3] Y. Zheng, Y. Liu, J. Yuan, and X. Xie, Urban Computing with Taxicabs. (NNLS), Quadric Programming (QP), and Linear programming 2011.
[4] A. Siffer, P. A. Fouque, A. Termier and C. Largouet, “Anomaly Detection in Streams with Extreme Value Theory,” 2017.
[5] L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, “On Mining Anomalous proposed LANCE is the novel approach in the area. Further, Patterns in Road Traffic Streams,”2011.
[6] L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, “On Detection of Emerging Anomalous Traffic Patterns Using GPS Data,” 2013.
[7] W. Kuang,S. An and H. Jiang, “Detecting traffic anomalies in urban areas using taxi GPS data.” Mathematical Problems in Engineering 2015.
[8] M. Xu, J. Wu, H. Wang, and M. Cao, “Anomaly Detection in Road Networks Using Sliding - Window Tensor Factorization,” 2019.
[9] T. V Duong, H. H. Bui, D. Q. Phung, and S. Venkatesh, “Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model,” 2005.
[10] R. Engle, “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics,” 2001.
[11] J.-G. Lee, J. Han, and X. Li, “Trajectory Outlier Detection: A Partition-and-Detect Framework,” 2008.
[12] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection,” 2016.
[13] J. Mao, T. Wang, C. Jin, and A. Zhou, “Feature grouping-based outlier detection upon streaming trajectories,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 12, pp. 2696–2709, Dec. 2017.
[14] S. Chawla, Y. Zheng, and J. Hu, “Inferring the root cause in road traffic anomalies,” Proc. IEEE Int. Conf. Data Mining, ICDM, pp. 141–150, 2012.
[15] F. Kelly, “The Mathematics of Traffic in Networks,” 2006.
[16] V. Lianty, “Finding the Shortest Path of Taxi pick-up location to Customers Using A * pathfinding Algorithm,” May, 2014.
[17] J. Dai, B. Yang, C. Guo, and Z. Ding, “Personalized route recommen-dation using big trajectory data,” Proc. Int. Conf. Data Eng., 2015-May, pp. 543—554, 2015.
[18] H. Wang, Y. Li, G. Liu, X. Wen, and X. Qie, “Accurate detection of road network anomaly by understanding crowd’s driving strategies from human mobility,” ACM Trans. Spat. Algorithms Syst., vol. 5, no. 2, pp. 1–17, Aug. 2019.
[19] S. Boyd and L. Vandenberghe, “Convex Optimization.” [Online]. Avail-able: http://www.cambridge.org. (Accessed: 21-Aug-2020).
[1] Y. Djenouri, A. Belhadi, J. C.-W. Lin, D. Djenouri, and A. Cano, “A Survey on Urban Traffic Anomalies Detection Algorithms,” IEEE Access, vol. 7, pp. 12192–12205, 2019.
[2] J. D. Mazimpaka and S. Timpf, “Trajectory data mining: A review of methods and applications,” J. Spat. Inf. Sci., vol. 13, no. 13, pp. 61–99, 2016.
[3] Y. Zheng, Y. Liu, J. Yuan, and X. Xie, Urban Computing with Taxicabs. (NNLS), Quadric Programming (QP), and Linear programming 2011.
[4] A. Siffer, P. A. Fouque, A. Termier and C. Largouet, “Anomaly Detection in Streams with Extreme Value Theory,” 2017.
[5] L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, “On Mining Anomalous proposed LANCE is the novel approach in the area. Further, Patterns in Road Traffic Streams,”2011.
[6] L. X. Pang, S. Chawla, W. Liu, and Y. Zheng, “On Detection of Emerging Anomalous Traffic Patterns Using GPS Data,” 2013.
[7] W. Kuang,S. An and H. Jiang, “Detecting traffic anomalies in urban areas using taxi GPS data.” Mathematical Problems in Engineering 2015.
[8] M. Xu, J. Wu, H. Wang, and M. Cao, “Anomaly Detection in Road Networks Using Sliding - Window Tensor Factorization,” 2019.
[9] T. V Duong, H. H. Bui, D. Q. Phung, and S. Venkatesh, “Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model,” 2005.
[10] R. Engle, “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics,” 2001.
[11] J.-G. Lee, J. Han, and X. Li, “Trajectory Outlier Detection: A Partition-and-Detect Framework,” 2008.
[12] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection,” 2016.
[13] J. Mao, T. Wang, C. Jin, and A. Zhou, “Feature grouping-based outlier detection upon streaming trajectories,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 12, pp. 2696–2709, Dec. 2017.
[14] S. Chawla, Y. Zheng, and J. Hu, “Inferring the root cause in road traffic anomalies,” Proc. IEEE Int. Conf. Data Mining, ICDM, pp. 141–150, 2012.
[15] F. Kelly, “The Mathematics of Traffic in Networks,” 2006.
[16] V. Lianty, “Finding the Shortest Path of Taxi pick-up location to Customers Using A * pathfinding Algorithm,” May, 2014.
[17] J. Dai, B. Yang, C. Guo, and Z. Ding, “Personalized route recommen-dation using big trajectory data,” Proc. Int. Conf. Data Eng., 2015-May, pp. 543—554, 2015.
[18] H. Wang, Y. Li, G. Liu, X. Wen, and X. Qie, “Accurate detection of road network anomaly by understanding crowd’s driving strategies from human mobility,” ACM Trans. Spat. Algorithms Syst., vol. 5, no. 2, pp. 1–17, Aug. 2019.
[19] S. Boyd and L. Vandenberghe, “Convex Optimization.” [Online]. Avail-able: http://www.cambridge.org. (Accessed: 21-Aug-2020).
@article{"International Journal of Information, Control and Computer Sciences:80583", author = "Rania Alshikhe and Vinita Jindal", title = "Dissecting Big Trajectory Data to Analyse Road Network Travel Efficiency", abstract = "Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from trav-eling vehicles, such as taxis through installed global positioning sys-tem (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.", keywords = "GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE", volume = "15", number = "11", pages = "451-8", }
