Spatial Agent-Based Simulation of Connected and Autonomus Vehicles to Assess Impacts on Traffic Conditions

Traffic congestion and its effect on aging transportation infrastructure have been a significant issue in many cities. Various policies, such as fast-track lanes, have been applied to optimize traffic on roadways. However, the increasing adoption of connected and autonomous vehicles (CAVs) raises the question of whether they can reduce traffic congestion.

CAVs are a radical evolution of regular vehicles, which are driven by humans. A reduction in manufacturing costs, a growing willingness to pay amongst Americans, and more welcoming changes in governmental policies towards CAV adoption will result in a higher market share of CAVs soon.

This study aimed to evaluate the integration of CAVs into existing transportation networks, comprising highways and urban roads. To quantify their impact, agent-based simulation models were developed and validated.


Pedestrian detection
Pedestrian detection. (a) State chart representing the logic for pedestrian detection, (b) CAV agent with front sensors to detect pedestrians

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