Lane Selection Model for Urban Intersections

A lane choice model for urban arterial intersections is presented. This model replaces the rule-based heuristics to assign vehicles in their subsequent lanes used in state-of-the-art microscopic traffic simulators. The lane choice at intersections is modeled as a two-step process: target lane choice and immediate lane selection based on the selected target lane. The target lane is one that the driver perceives as the best to be in, considering a wide range of factors and goals. These include path plan considerations and lane-specific attributes and can vary with the planning capability and aggressiveness of the driver. However, a maneuver to the target lane may not be possible immediately. The observed trajectories consist only of the immediate lane choices of the drivers. The choice of immediate lane is conditional on the target lane selection and affected by maneuverability considerations and aggressiveness of the driver. The parameters of the target lane and immediate lane choice models are jointly estimated with detailed vehicle trajectories. The heterogeneities of the driver population, for both planning capability and aggressiveness, are explicitly taken into account in the model formulation. The estimated model is compared with a single-level intersection lane choice model to demonstrate the improvements in the goodness of fit. The improvements are further strengthened by validation studies within the microscopic traffic simulator MITSIMLab, where the simulation results using the proposed model yield better matches with observed data compared to the rule-based models.

[1]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[2]  Jiang Li,et al.  Microscopic simulation of traffic flow at signalized intersection based on cellular automata , 1999, Proceedings of the IEEE International Vehicle Electronics Conference (IVEC'99) (Cat. No.99EX257).

[3]  Peter Hidas,et al.  Microscopic simulation of lane changing under incident conditions , 1999 .

[4]  Charisma Farheen Choudhury,et al.  Modeling driving decisions with latent plans , 2007 .

[5]  K. Ahmed.  Modeling drivers' acceleration and lane changing behavior , 1999 .

[6]  Qiang Li,et al.  Observation-Based Lane-Vehicle Assignment Hierarchy: Microscopic Simulation on Urban Street Network , 2000 .

[7]  H. Akaike.  A new look at the statistical model identification , 1974 .

[8]  Peter Hidas,et al.  MODELLING LANE CHANGING AND MERGING IN MICROSCOPIC TRAFFIC SIMULATION , 2002 .

[9]  Joe Lee,et al.  Characterizing and Modeling Observed Lane-Changing Behavior: Lane-Vehicle-Based Microscopic Simulation on Urban Street Network , 2000 .

[10]  Yunlong Zhang,et al.  Multiregime Approach for Microscopic Traffic Simulation , 1998 .

[11]  Moshe Ben-Akiva,et al.  MODELS OF FREEWAY LANE CHANGING AND GAP ACCEPTANCE BEHAVIOR , 1996 .

[12]  Harilaos N. Koutsopoulos,et al.  A microscopic traffic simulator for evaluation of dynamic traffic management systems , 1996 .

[13]  Varun Ramanujam,et al.  Lane changing models for arterial traffic , 2007 .

[14]  安川 文明,et al.  予防歯科プログラムの選考に関する Discrete Choice Analysis , 2002 .

[15]  Moshe Ben-Akiva,et al.  Extended Lane-Changing Model to Capture Lane-Change Duration , 2008 .

[16]  Moshe Ben-Akiva,et al.  STRUCTURE OF PASSENGER TRAVEL DEMAND MODELS , 1974 .

[17]  S Walker,et al.  CORSIM - CORRIDOR TRAFFIC SIMULATION MODEL , 1997 .

[18]  Haris N. Koutsopoulos,et al.  Modeling Integrated Lane-Changing Behavior , 2003 .

[19]  Moshe Ben-Akiva,et al.  Lane-Changing Model with Explicit Target Lane Choice , 2005 .

[20]  P. G. Gipps,et al.  A MODEL FOR THE STRUCTURE OF LANE-CHANGING DECISIONS , 1986 .