Microscopic Simulation Model for Pedestrian Flow at Signalized Crosswalks

Signalized crosswalks are considered an important pedestrian facility, where the pedestrian flow possesses unique characteristics. This study used an improved cellular automaton model, which incorporated social forces to describe the interactions between pedestrians, to model the bidirectional pedestrian flow at a crosswalk. The simulation model was capable of estimating crossing time for various levels of pedestrian demand. The observation field data extracted from the video record validated the model's ability to estimate average crossing speed. It also found that pedestrian crossing time and speed are correlated to pedestrian demand. In addition, the proposed model presented the characteristics of pedestrian flow at the crosswalk visually and showed that the interaction between the conflicting flows contributed to an increase in crossing time and a reduction in crossing speed. The benefits of this model can be used to assess the design of signalized crosswalks, including their geometry and pedestrian cycle length.

[1]  William H. K. Lam,et al.  Simulating pedestrian movements at signalized crosswalks in Hong Kong , 2008 .

[2]  Hao Yue,et al.  Simulation of pedestrian flow on square lattice based on cellular automata model , 2007 .

[3]  Mark R Virkler.  Scramble and Crosswalk Signal Timing , 1998 .

[4]  Michael Batty,et al.  Agent-based pedestrian modelling , 2003 .

[5]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[6]  Dirk Helbing,et al.  Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data , 2007, Adv. Complex Syst..

[7]  Jun Zhang,et al.  k-Nearest-Neighbor interaction induced self-organized pedestrian counter flow , 2010 .

[8]  M. Batty,et al.  Local movement: agent-based models of pedestrian flows , 1998 .

[9]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  李翔,et al.  Modeling and Simulation of Pedestrian Counter Flow on a Crosswalk , 2012 .

[11]  Serge P. Hoogendoorn,et al.  Gas-Kinetic Modeling and Simulation of Pedestrian Flows , 2000 .

[12]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[13]  Maria Davidich,et al.  Waiting zones for realistic modelling of pedestrian dynamics: A case study using two major German railway stations as examples , 2013 .

[14]  Dirk Helbing,et al.  Dynamics of crowd disasters: an empirical study. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Anais Golani,et al.  Model for Estimating Crossing Times at High-Occupancy Crosswalks , 2007 .

[16]  L. F. Henderson.  On the fluid mechanics of human crowd motion , 1974 .

[17]  Victor J. Blue,et al.  Modeling Four-Directional Pedestrian Flows , 2000 .

[18]  Wael K.M. Alhajyaseen,et al.  Methodology for Modeling Pedestrian Platoon Discharge and Crossing Times at Signalized Crosswalks , 2009 .

[19]  Mordechai Haklay,et al.  STREETS: an agent-based pedestrian model , 1999 .

[20]  V. J. Blue,et al.  Cellular Automata Model Of Emergent Collective Bi-Directional Pedestrian Dynamics , 2000 .

[21]  Franziska Klügl-Frohnmeyer,et al.  Large-Scale Agent-Based Pedestrian Simulation , 2007, MATES.

[22]  L. F. Henderson,et al.  The Statistics of Crowd Fluids , 1971, Nature.

[23]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .