Stability analysis for urban traffic evolution process using temporal traffic state patterns
Abstract
Recognizing the stability of the traffic evolution process of urban traffic networks has been an important consideration in traffic evolution research. However, little work has been conducted on identifying and associating temporal Traffic State Pattern (TSP) with the traffic evolution process. By clustering multi-dimensional traffic time series, we attempted to map the traffic evolution process into massive transitions of consecutive TSPs. Through the statistics of the time distribution of the transitions, we then defined the stability coefficient to conduct a quantitative analysis of the traffic evolution process. An empirical study using 30 days of traffic flow rate data of multiple road sections from the network of Nanshan District (Shenzhen, China) was carried out. Numerical results indicated that the traffic evolution process experienced obvious nonlinear changes at different periods of the day, but presented a regular cycle characteristic from morning till night. Further, with consideration of different travel purposes and traffic features on weekday and weekend, more traffic dynamics was extracted, which would be conducive to understand the complex behaviour of traffic evolution process.
Keyword : traffic flow, stability analysis, evolution process, traffic state, urban traffic
How to Cite
Wang, L., Wang, Y., & Wang, L. (2022). Stability analysis for urban traffic evolution process using temporal traffic state patterns. Transport, 37(5), 310–317. https://doi.org/10.3846/transport.2022.17955
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Anbaroglu, B.; Heydecker, B.; Cheng, T. 2014. Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks, Transportation Research Part C: Emerging Technologies 48: 47–65. https://doi.org/https://doi.org/10.1016/j.trc.2014.08.002
Andrienko, G.; Andrienko, N.; Bremm, S.; Schreck, T.; Von Landesberger, T.; Bak, P.; Keim, D. 2010. Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns, Computer Graphics Forum 29(3): 913–922. https://doi.org/10.1111/j.1467-8659.2009.01664.x
Banaei-Kashani, F.; Shahabi, C.; Pan, B. 2011. Discovering patterns in traffic sensor data, in IWGS’11: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming, 1 November 2011, Chicago, IL US, 10–16. https://doi.org/10.1145/2064959.2064963
Beaudoin, J.; Lin Lawell, C.-Y. C. 2018. The effects of public transit supply on the demand for automobile travel, Journal of Environmental Economics and Management 88: 447–467. https://doi.org/10.1016/j.jeem.2018.01.007
Chen, Y.; Zhang, Y.; Hu, J. 2008. Multi-dimensional traffic flow time series analysis with self-organizing maps, Tsinghua Science and Technology 13(2): 220–228. https://doi.org/10.1016/s1007-0214(08)70036-1
Chiou, Y.-C.; Lan, L. W.; Tseng, C.-M. 2014. A novel method to predict traffic features based on rolling self-structured traffic patterns, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 18(4): 352–366. https://doi.org/10.1080/15472450.2013.806764
Daganzo, C. F. 1995. Requiem for second-order fluid approximations of traffic flow, Transportation Research Part B: Methodological 29(4): 277–286. https://doi.org/10.1016/0191-2615(95)00007-z
Daganzo, C. F.; Geroliminis, N. 2008. An analytical approximation for the macroscopic fundamental diagram of urban traffic, Transportation Research Part B: Methodological 42(9): 771–781. https://doi.org/10.1016/j.trb.2008.06.008
García-Rois, J.; Burguillo, J. C. 2017. Topology-based analysis of self-organizing maps for time series prediction, Soft Computing 21(6): 1601–1618. https://doi.org/10.1007/s00500-015-1872-5
Ghadami, A.; Doering, C.; Drake, J. M.; Rohani, P.; Epureanu, B. 2022. Stability and resilience of transportation systems: is a traffic jam about to occur?, IEEE Transactions on Intelligent Transportation Systems 23(8): 10803–10814. https://doi.org/10.1109/TITS.2021.3095897
Gu, Y.; Wang, Y.; Dong, S. 2020. Public traffic congestion estimation using an artificial neural network, ISPRS International Journal of Geo-Information 9(3): 152. https://doi.org/10.3390/ijgi9030152
Kim, Y.; Keller, H. 2008. Analysis of characteristics of the dynamic flow-density relation and its application to traffic flow models, Transportation Planning and Technology 31(4): 369–397. https://doi.org/10.1080/03081060802334995
Kohonen, T. 1982. Self-organized formation of topologically correct feature maps, Biological Cybernetics 43(1): 59–69. https://doi.org/10.1007/bf00337288
Lan, L. W.; Sheu, J.-B.; Huang, Y.-S. 2008. Investigation of temporal freeway traffic patterns in reconstructed state spaces, Transportation Research Part C: Emerging Technologies 16(1): 116–136. https://doi.org/10.1016/j.trc.2007.06.006
Li, Q.-L.; Jiang, R.; Ding, Z.-J.; Wang, B.-H. 2019. Spatiotemporal evolution characteristics and phase diagrams of traffic dynamics at a crossroads, Journal of Statistical Mechanics: Theory and Experiment 2019: 113403. https://doi.org/10.1088/1742-5468/ab417b
Lighthill, M. J.; Whitham, G. B. 1955. On kinematic waves II. A theory of traffic flow on long crowded roads, Proceedings of the Royal Society A: Mathematical, Physical and Engineeering Sciences 229(1178): 317–345. https://doi.org/10.1098/rspa.1955.0089
Payne, H. J. 1971. Models of freeway traffic and control, Mathematical Models of Public Systems 1: 51–61.
Richards, P. I. 1956. Shock waves on the highway, Operations Research 4(1): 42–51. https://doi.org/10.1287/opre.4.1.42
Shen, W.; Zhang, H. M. 2009. On the morning commute problem in a corridor network with multiple bottlenecks: Its system-optimal traffic flow patterns and the realizing tolling scheme, Transportation Research Part B: Methodological 43(3): 267–284. https://doi.org/10.1016/j.trb.2008.07.004
TRB. 2010. Highway Capacity Manual. Transportation Research Board (TRB) Washington, DC, US. 1650 p.
Treiber, M.; Kesting, A. 2012. Validation of traffic flow models with respect to the spatiotemporal evolution of congested traffic patterns, Transportation Research Part C: Emerging Technologies 21(1): 31–41. https://doi.org/10.1016/j.trc.2011.09.002
Wang, L.; Chen, H.; Li, Y. 2014. Transition characteristic analysis of traffic evolution process for urban traffic network, The Scientific World Journal 2014: 603274. https://doi.org/10.1155/2014/603274
Yang, Y.; Cao, J.; Qin, Y.; Jia, L.; Dong, H.; Zhang, A. 2018. Spatial correlation analysis of urban traffic state under a perspective of community detection, International Journal of Modern Physics B 32(12): 1850150. https://doi.org/10.1142/s0217979218501503
Zhang, H. M. 1998. A theory of nonequilibrium traffic flow, Transportation Research Part B: Methodological 32(7): 485–498. https://doi.org/10.1016/s0191-2615(98)00014-9
Zhang, Z.; He, Q.; Tong, H.; Gou, J.; Li, X. 2016. Spatial-temporal traffic flow pattern identification and anomaly detection with dictionary-based compression theory in a large-scale urban network, Transportation Research Part C: Emerging Technologies 71: 284–302. https://doi.org/10.1016/j.trc.2016.08.006
Zhu, G.; Song, K.; Zhang, P.; Wang, L. 2016. A traffic flow state transition model for urban road network based on hidden Markov model, Neurocomputing 214: 567–574. https://doi.org/10.1016/j.neucom.2016.06.044