Agent-Based Implementation of Particle Hopping Traffic Model With Stochastic and Queuing Elements
Camilla Champion, Cody Champion

TL;DR
This paper presents an agent-based traffic model incorporating stochastic, queuing, and multi-lane elements to analyze traffic flow, showing that automation and passing rules significantly improve system capacity and stability.
Contribution
It introduces a multifaceted traffic simulation model with stochastic human factors, queuing, and passing conventions, extending existing models to better reflect real-world traffic dynamics.
Findings
Automating traffic systems increases critical density by 160%.
Passing conventions like REP improve maximum system capacity.
Accidents cause local jams but do not trigger global congestion.
Abstract
Lagging or halted traffic is bothersome. As such, it is desirable to have a model that can begin to determine the efficiency of various traffic standardizations. Our model intended to create a multifaceted realistic simulation of traffic flow while considering several factors. These factors included: passing conventions, e.g., right except to pass (REP) rule, system perturbation caused by insertion of an accident into the system, accessible number of lanes available with the REP, various human factors such as variation of individual maximum speed and likelihood to pass. A succession of models were created from a variation on an existing single-lane traffic model and adding extra dimensionality to the lattice to include multiple lanes, passing conventions, stochastic elements for individuality, and queuing rules to movement algorithms. We found that the REP is an effective means of…
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Taxonomy
TopicsTraffic control and management · Transportation Planning and Optimization · Simulation Techniques and Applications
