Abstract
Previous studies on Connected and Automated Vehicles (CAVs) demonstrated the potential to coordinate the behaviors of multiple connected vehicles for traffic improvements. In this paper, we first propose a Conflict Duration Graph-based (CDG-based) coordination framework to resolve collisions and improve the traffic capacity of signal-free intersections. Secondly, a Speed Control-based Intersection Coordination Model (SICM) is developed to identify complex constraints in multi-vehicle collision scenarios. Thirdly, a geometric Translation-based Intersection Coordination Algorithm (TICA) is proposed to calculate the ideal location of time blocks in CDGs and then obtain the near-optimal design speed in the form of combinatorial optimization. Twelve groups of test scenarios with different traffic volumes were designed and tested on a MATLAB-based simulation platform. Simulation results showed that the proposed method can resolve all the collisions and instruct the vehicles to pass signal-free intersections collaboratively without stopping in low to medium level of congestion.
Highlights
With the rapid increase in population and transportation demand, intersections have become major bottlenecks in urban road networks as well as one of the most dangerous types of urban traffic infrastructures
In order to keep the balance between the growth in travel demand and the supply of transportation capacity, policy-makers and researchers have adopted various approaches to optimize road infrastructures and enhance road capacity in the past decades, including left-turn waiting areas [3], variable approach lanes [4], dynamic lane assignments [5], and integrated waiting areas [6]
Congestion has gotten worse over the last several years. The reason why these approaches failed to significantly alleviate congestion is that they only optimized the structure of road macroscopically but ignored the impact of microscopic driving behaviors and traffic orders
Summary
With the rapid increase in population and transportation demand, intersections have become major bottlenecks in urban road networks as well as one of the most dangerous types of urban traffic infrastructures. According to an estimate from the Texas A&M Transportation Institute (TTI), due to traffic congestion, the population in the United States will consume 10 billion more hours commuting and purchase an extra 3.6 billion gallons of fuel at a total congestion cost of $237 billion by 2025 [1]. Congestion has gotten worse over the last several years. The reason why these approaches failed to significantly alleviate congestion is that they only optimized the structure of road macroscopically but ignored the impact of microscopic driving behaviors and traffic orders
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