The Influence of Spillback Modelling when Assessing Consequences of Blockings in a Road Network

Robustness of a network is a main objective for road network managers these days, and has therefore become an important study area for transportation scientists. This article discusses one specific aspect in assessing road network robustness: the consequences of the closure of a link. These spillback effects have been examined in a dedicated traffic simulator in which the representation of spillback can be switched on and off. The impacts are studied in a simulation study of a road network of a regional size in which sequentially links are blocked. Two scenarios for route choice are considered: a fixed route choice based on a daily congestion pattern and a route choice adapted to the actual congestion caused by the closure. The study has also shown the influence of information which makes travellers adapt their routes. Road network robustness and characteristics of vulnerable links are evaluated for both spillback and non-spillback cases. It is found that a valid spillback modelling is a prerequisite for correctly analysing the robustness of the network as a whole, as well as for correctly indicating the locations in the network where a closure causes the largest delays. Furthermore, without simulating spillback, it is not possible to identify correctly the most vulnerable links for the network performance.

[1]  J. Lebacque THE GODUNOV SCHEME AND WHAT IT MEANS FOR FIRST ORDER TRAFFIC FLOW MODELS , 1996 .

[2]  M. Andersson Marginal Railway Infrastructure Costs in a Dynamic Context , 2007 .

[3]  D. Ngoduy Macroscopic discontinuity modeling for multiclass multilane traffic flow operations , 2006 .

[4]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[5]  Michael G.H. Bell,et al.  Risk-averse user equilibrium traffic assignment: an application of game theory , 2002 .

[6]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .

[7]  H. J. Van Zuylen,et al.  Comparison of link-level robustness indicators , 2007 .

[8]  Serge P. Hoogendoorn,et al.  Quantification of the impact of spillback modeling in assessing network reliability , 2007 .

[9]  Serge P. Hoogendoorn,et al.  Approach to Critical Link Analysis of Robustness for Dynamical Road Networks , 2007 .

[10]  M J Lighthill,et al.  ON KINEMATIC WAVES.. , 1955 .

[11]  Henry X. Liu,et al.  Uncovering the contribution of travel time reliability to dynamic route choice using real-time loop data , 2004 .

[12]  J. Bates,et al.  The valuation of reliability for personal travel , 2001 .

[13]  Henk J. van Zuylen COMBINING DTA APPROACHES FOR STUDYING ROAD NETWORK ROBUSTNESS , 2006 .

[14]  P. Varaiya,et al.  Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand , 2006 .

[15]  Marco Schreuder,et al.  Vulnerability of a National Road Network , 2008 .

[16]  P. I. Richards Shock Waves on the Highway , 1956 .

[17]  Michael G.H. Bell,et al.  A game theory approach to measuring the performance reliability of transport networks , 2000 .

[18]  Serge P. Hoogendoorn,et al.  Joint Modeling of Advanced Travel Information Service, Habit, and Learning Impacts on Route Choice by Laboratory Simulator Experiments , 2005 .

[19]  Ben Immers,et al.  Methodology for Identifying Vulnerable Sections in a National Road Network , 2006 .

[20]  TU Huizhao,et al.  REAL-TIME MODELING TRAVEL TIME RELIABILITY ON FREEWAY , 2005 .