Microscopic Traffic Behaviour near Incidents

Much of the delays on road networks are caused by incidents. This is partially caused by blockage or closure of lanes, but also by the change of driving behaviour in the remaining lanes. This contribution analyses traffic flow conditions near an incident both microscopically and macroscopically. A theory is proposed to describe drivers’ behaviour, which is tested using traffic data of individual vehicles, collected using a helicopter. A bimodal headway distribution is observed, centred around two mean values, 2 seconds and 4 seconds. To understand the underlying mechanisms a car-following model is fitted to the drivers’ behaviour. The model parameters show that the reaction time is much higher than usual. Using this model-based analysis, we conclude that the incident distracts the drivers and less attention is paid to the driving process. The consequence is that the queue discharge rate for the unblocked lanes is 30% lower than the usual queue discharge rate per lane.

[1]  B. P. Murphy,et al.  Handbook of Methods of Applied Statistics , 1968 .

[2]  M. Baucus.  Transportation Research Board , 1982 .

[3]  Fred L. Hall,et al.  FREEWAY CAPACITY DROP AND THE DEFINITION OF CAPACITY , 1991 .

[4]  Piet H. L. Bovy,et al.  CAR-FOLLOWING UNDER NON-CONGESTED AND CONGESTED CONDITIONS , 1997 .

[5]  M. Cassidy,et al.  Some traffic features at freeway bottlenecks , 1999 .

[6]  Brian L. Smith,et al.  Characterization of accident capacity reduction , 2001 .

[7]  Ning Wu,et al.  A new approach for modeling of Fundamental Diagrams , 2002 .

[8]  S. P. Hoogendoorn,et al.  Microscopic Traffic Data Collection by Remote Sensing , 2003 .

[9]  Andrew Griffith,et al.  Research Implementation and Information Dissemination , 2004 .

[10]  Chris M.J. Tampère,et al.  Human-kinetic multiclass traffic flow theory and modelling. With application to Advanced Driver Assistance Systems in congestion , 2004 .

[11]  Serge P. Hoogendoorn,et al.  Interdriver Differences in Car-Following: A Vehicle Trajectory–Based Study , 2006 .

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

[13]  Mohammed Hadi,et al.  Modeling Reductions in Freeway Capacity due to Incidents in Microscopic Simulation Models , 2007 .

[14]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics Based on NGSIM Trajectory Data , 2007 .

[15]  Michael J. Cassidy,et al.  Relation between traffic density and capacity drop at three freeway bottlenecks , 2007 .

[16]  Haris N. Koutsopoulos,et al.  Estimation of Vehicle Trajectories with Locally Weighted Regression , 2007 .

[17]  S.J.L. Ossen,et al.  Longitudinal driving behavior: Theory and empirics , 2008 .

[18]  Nina Schaap,et al.  Drivers' behavioural reactions to unexpected events , 2008 .

[19]  Serge P. Hoogendoorn,et al.  Capacity Reduction at Incidents , 2008 .