An Intersection Importance Estimation Method Based on the Road Network Topology

This paper develops a method to quantify the intersections’ importance at a global level based on the road network topology, which is the location of the intersection in the road network and the structural characteristics of the intersection decided by the traffic movement. The priority order in traffic signal coordination is the sorting results of intersection’s importance. The proposed method consists of two consecutive algorithms. Firstly, the graph connectivity of network is defined based on the shortest path distance and spatial connectivity between adjacent intersections. Secondly, The Importance Estimation Model (IEM) is built, which is the function of the importance indexes of current intersection and its neighboring intersections. A simulated case of a six by eight grid network was employed to evaluate the effectiveness of the proposed method in TRANSYT.

[1]  P. Wagner,et al.  Sorting Model of Optimization Order in Traffic Signal Planning , 2014 .

[2]  Giovanni De Nunzio,et al.  A model-based eco-routing strategy for electric vehicles in large urban networks , 2016 .

[3]  Yang Xiaoguang Incidence Degree Model of Signalized Intersection Group Based on Routes , 2009 .

[4]  Mahdi Shariati,et al.  Assessment of high strength and light weight aggregate concrete properties using ultrasonic pulse velocity technique , 2011 .

[5]  Bill Hillier,et al.  Network and Psychological Effects in Urban Movement , 2005, COSIT.

[6]  H N Yagoda,et al.  SUBDIVISION OF SIGNAL SYSTEMS INTO CONTROL AREAS , 1973 .

[7]  Stephan Winter,et al.  Modified Betweenness Centrality for Predicting Traffic Flow , 2009 .

[8]  Nikolaos Geroliminis,et al.  Clustering of Heterogeneous Networks with Directional Flows Based on “Snake” Similarities , 2016 .

[9]  Mohd Zamin Jumaat,et al.  State-of-the-art review on the design and performance of steel pallet rack connections , 2016 .

[10]  Hu Jian-ming Dynamic subdivision of road network into coordinated control regions , 2009 .

[11]  R Akcelik,et al.  TRAFFIC SIGNALS: CAPACITY AND TIMING ANALYSIS , 1981 .

[12]  Li Juan Road network dynamic splitting based on time-space characteristic of traffic flow , 2010 .

[13]  Atsuyuki Okabe,et al.  A kernel density estimation method for networks, its computational method and a GIS‐based tool , 2009, Int. J. Geogr. Inf. Sci..

[14]  Yang Xiao-guang Method of intersection-group dynamic division considering OD path inroad network , 2010 .

[15]  Ting Lu Dynamic Network-Wide Traffic Signal Optimization , 2015 .

[16]  Meldi Suhatril,et al.  Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading , 2013 .

[17]  Pengxiang Zhao,et al.  A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China , 2017 .

[18]  N Wu AN APPROXIMATION FOR THE DISTRIBUTION OF QUEUE LENGTHS AT UNSIGNALISED INTERSECTIONS , 1994 .

[19]  S. Winter,et al.  Can Betweenness Centrality Explain Traffic Flow , 2009 .

[20]  A. Jalali,et al.  Buckling analysis of circular functionally graded plate under uniform radial compression including shear deformation with linear and quadratic thickness variation on the Pasternak elastic foundation , 2017 .