Practical approach to determining traffic congestion propagation boundary due to traffic incidents

Abstract

A practical approach for predicting the congestion boundary due to traffic incidents was proposed. Based on the kinematic wave theory and Van Aerde single-regime flow model, a model for estimating the congestion propagation speed for the basic road segment was developed. Historical traffic flow data were used to analyze the time variant characteristics of the urban traffic flow for each road type. Then, the saturation flow rate was used for analyzing the impact of the traffic incident on the traversing traffic flow at the congestion area. The base congestion propagation speed for each road type was calculated based on field data, which were provided by the remote traffic microwave sensors (RTMS), floating car data (FCD) system and screen line survey. According to a comparative analysis of the congestion propagation speed, it is found that the expressway, major arterial, minor arterial and collector are decreasingly influenced by the traffic incident. Subsequently, the impact of turning movements at intersections on the congestion propagation was considered. The turning ratio was adopted to represent the impact of turning movements, and afterward the corresponding propagation pattern at intersections was analyzed. Finally, an implementation system was designed on a geographic information system (GIS) platform to display the characteristics of the congestion propagation over the network. The validation results show that the proposed approach is able to capture the congestion propagation properties in the actual road network.

Keywords

kinematic wave model / Van Aerde model / traffic incident / congestion propagation on network

References

[1] EdieL C. Car-following and steady-state theory for non-congested traffic [J]. Operations Research, 1966, 9(1): 66-76

[2] LiX-s, ZhengX-l, GuoW-w, RenY-y, WangY-n, YangMeng. Traffic accident propagation properties and control measures for urban links based on cellular automata [J]. Advances in Mechanical Engineering, 2013, 2013: 1-8

[3] RobergP, AbbessC R, WrightC. Traffic jam simulation [J]. Journal of Maps, 2007, 3(1): 107-121

[4] WrightC, RobergP. The conceptual structure of traffic jams [J]. Transport Policy, 1998, 5(1): 23-35

[5] DaganzoC. The cell transmission model: Network traffic [J]. Transportation Research Part B: Methodological, 1995, 29(2): 79-93

[6] LighthillM J, WhithamG B. On kinematic waves II: A theory of traffic flow on long crowded roads [C]//. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1955London, UKThe Royal Society317-345

[7] RichardsP I. Shock waves on the highway [J]. Operations Research, 1956, 4(1): 42-51

[8] XieB, XuM, HarriJ, ChenY-wen. A traffic light extension to cell transmission model for estimating urban traffic jam [C]//. Personal Indoor and Mobile Radio Communications (PIMRC), IEEE, 20132566-2570

[9] LongJ-c, GaoZ-y, RenH-l, LianA-ping. Urban traffic congestion propagation and bottleneck identification [J]. Science in China Series F: Information Sciences, 2008, 51(7): 948-964

[10] LongJ-c, GaoZ-y, OrensteinP, RenH-ling. Control strategies for dispersing incident-based traffic jams in two-way grid networks [J]. IEEE Transportation on Intelligent Transportation Systems, 2012, 13(2): 469-481

[11] ZhangA-m-h, GaoZ-y, RenH-ling. Incident-based traffic congestion control strategy [J]. Technological Sciences, 2011, 54(5): 1338-1344

[12] LiS-b, FuB-b, ZhengJ-feng. Dynamic analysis of traffic state and congestion propagation on bidirectional grid network [J]. Discrete Dynamics in Nature and Society, 2013, 2013: 1-7

[13] QiH-s, WangD-h, ChenP, BieY-ming. Traffic jam in signalized road network [J]. Journal of Central South University, 2014, 21: 832-842

[14] QiH-s, WangD-h, ChenPeng. Formation and propagation of local traffic jam [J]. Discrete Dynamics in Nature and Society, 2013, 2013: 1-12

[15] LecueF, TuckerR, BicerV, TommasiP, DiotalleviS T, SbodioM. Predicting severity of road traffic congestion using semantic web technologies [C]//. European Semantic Web Conference, 2014Crete, GreeceSpringer International Publishing611-627

[16] ZhangX-l, PengHu. An intelligent-based prediction system for incident induced congestion spreading [C]//. 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), 20112021-2025

[17] MaX-l, YuH-y, WangY-p, WangY-hai. Large-Scale Transportation network congestion evolution prediction using deep learning theory [J]. PloS One, 2015, 10(3): 1-17

[18] ChengT, TanaksaranondG, BrunsdonC, HaworthJ. Exploratory visualisation of congestion evolutions on urban transport networks [J]. Transportation Research Part C: Emerging Technologies, 2013, 36: 296-306

[19] AnwarA, NagelT, RattiC. Traffic origins: A simple visualization technique to support traffic incident analysis [C]//. Pacific Visualization Symposium (PacificVis), 2014316-319

[20] MichalopoulosP G, PisharodyV B. Derivation of delays based on improved macroscopic traffic models [J]. Transportation Research Part B: Methodological, 1981, 15(5): 299-317

[21] RakhaH, CrowtherB. Comparison of the Greenshields, Pipes, and Van Aerde car-following and traffic stream models [J]. Transportation Research Record: Journal of Transportation Research Board, 2002, 2143: 248-262

[22] ZhaoN-l, YuL, ZhaoH, GuoJ-f, WenH-min. Analysis of traffic flow characteristics on ring-road expressways in Beijing [J]. Journal of the Transportation Research Board, 2009, 2124: 178-185

[23] ZhuLinTheory and method studies on traffic situation assessment for urban expressways [D], 2013Beijng, ChinaBeijing Jiaotong University

[24] YangX-b, ZhangN, HuangL-bing. Lane changing model in road capacity simulation [J]. Journal of Highway and Transportation Research and Development, 2007, 24(5): 109-113

[25] HESHAM R. Spd_cal [EB/OL]. [2015–4-23] http://filebox.vt.edu/ users/hrakha/software.htm#Integration.