Speed-Volume Relationship Model for Speed Estimation on Urban Roads in Intelligent Transportation Systems

Estimating average speed on roads is required by many applications in Intelligent Transportation Systems. In spite of abundant researches done on speed estimation on highways, there is only limited effort made in urban traffic networks. Current reserach methodology is to apply highway models to urban roads directly or under trivial modification. In this paper, we propose a novel speed estimation model tailored for urban roads. The contribution of this work includes the following two aspects: 1)we demonstrate that application of modified highway models to urban roads is not always an effective methodology; 2)we propose a speed-volume relationship model tailored for speed estimation on urban roads by incorporating the impedance effect of exit intersection of a concerned road. We have applied the model to estimate the speed in Cologne, Germany, compared the accuracy between the proposed model and a slightly modified Greenshield’s Model, and confirmed its effectivity as well as superiority.

[1]  Zilu Liang,et al.  Real-time urban traffic amount prediction models for dynamic route guidance systems , 2014, EURASIP Journal on Wireless Communications and Networking.

[2]  Carlos F. Daganzo,et al.  Queue Spillovers in Transportation Networks with a Route Choice , 1998, Transp. Sci..

[3]  Anthony Chen,et al.  A DYNAMIC TRAFFIC ASSIGNMENT MODEL WITH TRAFFIC-FLOW RELATIONSHIPS , 1995 .

[4]  J. M. D. Castillo Three new models for the flow–density relationship: derivation and testing for freeway and urban data , 2012 .

[5]  Zilu Liang,et al.  City traffic prediction based on real-time traffic information for Intelligent Transport Systems , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[6]  H. Greenberg An Analysis of Traffic Flow , 1959 .

[7]  Rob J. Hyndman,et al.  Another Look at Forecast Accuracy Metrics for Intermittent Demand , 2006 .

[8]  Mohan M. Trivedi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Integrated Lane and Vehicle Detection, Localization, , 2022 .

[9]  Martin L. Hazelton Estimating Vehicle Speed from Traffic Count and Occupancy Data , 2004 .

[10]  Ramachandran Balakrishna,et al.  Calibration of the demand simulator in a dynamic traffic assignment system , 2002 .

[11]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[12]  Satish Chandra,et al.  Speed Prediction Models for Urban Arterials Under Mixed Traffic Conditions , 2013 .

[13]  Agachai Sumalee,et al.  Short-Term Traffic State Prediction Based on Temporal–Spatial Correlation , 2013, IEEE Transactions on Intelligent Transportation Systems.

[14]  Hesham Rakha,et al.  Comparison of Greenshields, Pipes, and Van Aerde Car-Following and Traffic Stream Models , 2002 .

[15]  Karine Zeitouni,et al.  Proactive Vehicular Traffic Rerouting for Lower Travel Time , 2013, IEEE Transactions on Vehicular Technology.

[16]  D. Gazis Optimum Control of a System of Oversaturated Intersections , 1964 .

[17]  Benito E. Flores,et al.  A pragmatic view of accuracy measurement in forecasting , 1986 .

[18]  Jung-Min Choi,et al.  Multi-touch Based Standard UI Design of Car Navigation System for Providing Information of Surrounding Areas , 2013, HCI.