Cellular Automaton Based Simulation of Large Pedestrian Facilities - A Case Study on the Staten Island Ferry Terminals

Current metropolises largely depend on a functioning transport infrastructure and the increasing demand can only be satisfied by a well organized mass transit. One example for a crucial mass transit system is New York City's Staten Island Ferry, connecting the two boroughs of Staten Island and Manhattan with a regular passenger service. Today's demand already exceeds 2500 passengers for a single cycle during peek hours, and future projections suggest that it will further increase. One way to appraise how the system will cope with future demand is by simulation. This contribution proposes an integrated simulation approach to evaluate the system performance with respect to future demand. The simulation relies on a multiscale modeling approach where the terminal buildings are simulated by a microscopic and quantitatively valid cellular automata (CA) and the journeys of the ferries themselves are modeled by a mesoscopic queue simulation approach. Based on the simulation results recommendations with respect to the future demand are given.

[1]  E. Cascetta A stochastic process approach to the analysis of temporal dynamics in transportation networks , 1989 .

[2]  Alexis Drogoul,et al.  A hybrid macro-micro pedestrians evacuation model to speed up simulation in road networks , 2011, AAMAS'11.

[3]  E. Morrow Efficiently Using Micro-Simulation to Inform Facility Design - A Case Study in Managing Complexity , 2011 .

[4]  Wilco Burghout,et al.  Hybrid Traffic Simulation with Adaptive Signal Control , 2007 .

[5]  A. Schadschneider,et al.  Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram , 2012 .

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

[7]  C. Gawron,et al.  An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model , 1998 .

[8]  Kai Nagel,et al.  The MATSim Network Flow Model for Traffic Simulation Adapted to Large-Scale Emergency Egress and an Application to the Evacuation of the Indonesian City of Padang in Case of a Tsunami Warning , 2009 .

[9]  Haris N. Koutsopoulos,et al.  Hybrid Mesoscopic-Microscopic Traffic Simulation , 2004 .

[10]  D. Manocha,et al.  Pedestrian Simulation Using Geometric Reasoning in Velocity Space , 2014 .

[11]  Armin Seyfried,et al.  Large scale and microscopic: a fast simulation approach for urban areas , 2014 .

[12]  Dirk Helbing A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[13]  Jean-Baptiste Lesort,et al.  Mixing Microscopic and Macroscopic Representations of Traffic Flow: Hybrid Model Based on Lighthill–Whitham–Richards Theory , 2003 .

[14]  Kai Nagel,et al.  The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations , 2010 .

[15]  Mohcine Chraibi,et al.  Collision-free speed model for pedestrian dynamics , 2015, 1512.05597.

[16]  D Gattuso,et al.  Hybrid Traffic Model Coupling Macro- and Behavioral Microsimulation , 2006 .

[17]  Kai Nagel,et al.  Simple queueing model applied to the city of Portland , 1998 .

[18]  Kai Nagel,et al.  Iterative route planning for large-scale modular transportation simulations , 2004, Future Gener. Comput. Syst..

[19]  Boris Pushkarev,et al.  CAPACITY OF WALKWAYS , 1975 .

[20]  J. Nash,et al.  NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[21]  Giuseppe Vizzari,et al.  Multi-scale Simulation for Crowd Management: A Case Study in an Urban Scenario , 2016, AAMAS Workshops.

[22]  Gunnar Flötteröd,et al.  A CA Model for Bidirectional Pedestrian Streams , 2015, ANT/SEIT.

[23]  Gregor Lämmel,et al.  Multidestination Pedestrian Flows in Equilibrium: A Cellular Automaton‐Based Approach , 2016, Comput. Aided Civ. Infrastructure Eng..

[24]  Dirk Helbing,et al.  Micro- and macro-simulation of freeway traffic , 2002 .

[25]  Mohcine Chraibi,et al.  Generalized centrifugal-force model for pedestrian dynamics. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[27]  Serge P. Hoogendoorn,et al.  Multiscale Traffic Flow Model Based on the Mesoscopic Lighthill–Whitham and Richards Models , 2015 .

[28]  J L Adler,et al.  Emergent Fundamental Pedestrian Flows from Cellular Automata Microsimulation , 1998 .

[29]  Gunnar Flötteröd,et al.  Bidirectional pedestrian fundamental diagram , 2015 .

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

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

[32]  Ulrich Weidmann,et al.  Transporttechnik der Fussgänger , 1992 .

[33]  Nitish Chooramun Implementing a hybrid spatial discretisation within an agent based evacuation model , 2011 .

[34]  Isabella von Sivers,et al.  How Stride Adaptation in Pedestrian Models Improves Navigation , 2014, ArXiv.

[35]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.