000202711 001__ 202711
000202711 005__ 20190317000036.0
000202711 0247_ $$2doi$$a10.3141/2422-01
000202711 022__ $$a0361-1981
000202711 02470 $$2ISI$$a000343640500002
000202711 037__ $$aARTICLE
000202711 245__ $$aEmpirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-Scale Systems
000202711 269__ $$a2014
000202711 260__ $$bNational Academy of Sciences$$c2014$$aWashington
000202711 300__ $$a11
000202711 336__ $$aJournal Articles
000202711 500__ $$aGreenshield's prize, 2014
000202711 520__ $$aResearch on congestion propagation in large urban networks has been based mainly on microsimulations of link-level traffic dynamics. However, both the unpredictability of travel behavior and the complexity of accurate physical modeling present challenges, and simulation results may be time-consuming and unrealistic. This paper explores empirical data from large-scale urban networks to identify hidden information in the process of congestion formation. Specifically, the spatiotemporal relation of congested links is studied, congestion propagation is observed from a macroscopic perspective, and critical congestion regimes are identified to aid in the design of peripheral control strategies. To achieve these goals, the maximum connected component of congested links is used to capture congestion propagation in the city. A data set of 20,000 taxis with global positioning system (GPS) data from Shenzhen, China, is used. Empirical macroscopic fundamental diagrams of congested regions observed during propagation are presented, and the critical congestion regimes are quantified. The findings show that the proposed methodology can effectively distinguish congestion pockets from the rest of the network and efficiently track congestion evolution in linear time O(n).
000202711 700__ $$0243524$$g192416$$aJi, Yuxuan
000202711 700__ $$aLuo, Jun
000202711 700__ $$aGeroliminis, Nikolaos$$g196675$$0243522
000202711 773__ $$j2422$$tTransportation Research Record$$k2$$q1-11
000202711 8564_ $$uhttp://trb.metapress.com/content/2w0nn18h642qx1t6/?p=7260c96ac1954fa48a11911c8f4a7be8&pi=0$$zURL
000202711 8564_ $$uhttps://infoscience.epfl.ch/record/202711/files/YJ_JL_NG_TRR_2014.pdf$$zPreprint$$s5169227$$yPreprint
000202711 909C0 $$xU12124$$0252222$$pLUTS
000202711 909CO $$qGLOBAL_SET$$particle$$ooai:infoscience.tind.io:202711$$pENAC
000202711 917Z8 $$x196675
000202711 917Z8 $$x196675
000202711 937__ $$aEPFL-ARTICLE-202711
000202711 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000202711 980__ $$aARTICLE