Abstract
Traffic jams in large cities, in addition to having a very high economic cost, cause an increase in emissions generated by vehicles over the same route being driven under normal conditions. In recent years, there has been a rapid evolution in the technologies applied to the field of autonomous vehicles. There are currently commercial solutions for assisted driving and semi-autonomous driving systems, with very favorable forecasts for reaching a completely autonomous vehicle scenario in the coming decades. This new environment generates opportunities and challenges to reduce congestion in scenarios with autonomous or semi-autonomous vehicles. This paper focuses on the automatic optimization of the passage of vehicles through intersections. The intersections are one of the most conflict-generating elements in a traffic network. This type of conflicts arises because the intersections must manage multiple traffic flows with different priorities and preferences, often leading to traffic jams. The problem has been addressed by proposing three mechanisms to model any type of intersection, to calculate the roads with fewer points of conflict between their inputs and outputs, and to optimize the arrival rate of vehicles using a Genetic Algorithm to achieve the maximum performance of the intersection. To validate this solution, a cellular automata simulator has been developed, which can be adapted to both autonomous and conventional vehicle scenarios and can provide realistic results when certain conditions are met. The results obtained have been compared with other traditional solutions (priority and traffic lights) using microscopic traffic simulations, and with those obtained in other studies showing the advantages of the proposed system. The proposed systems achieve a throughput improvement between 9.21% and 36.98% compared with the traditional solutions.
Highlights
Congestion is one of the big problems to solve in large cities
While in the previous section the optimized calculation of these paths has been performed in an isolated manner, we focus on obtaining the optimal input traffic flows to guarantee the maximum traffic rate in the intersection, without rising conflicts between vehicles
We have defined three simulation scenarios in which the intersection is managed using fixed signaling based on priorities, traffic lights, and the crossing patterns obtained in section IV-B
Summary
Congestion is one of the big problems to solve in large cities. The INRIX Global Traffic Scorecard [1] assesses the impact of congestion 24 hours a day in 1,360 cities in 38 countries over 5 continents and calculates its cost as $461 billion in 2017, only considering data from the US, UK, and Germany. One of the study areas explored in autonomous vehicles scenarios is the definition of new techniques applied to reduce the conflict points in a road network. This work is focused on optimizing the passage of vehicles through a generic intersection automatically We do this by means of an automatic optimization system that achieves the maximum performance of an intersection in a CAV scenario. First we will study intersection optimization alternatives such as: infrastructure designs that favor the passage of vehicles for a given way, management systems based on variable signaling, and optimizers that reduce the delays caused by intersections using bio-inspired algorithms (Section II). The advantages of the work will be highlighted and future lines of work will be indicated (Section V)
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