使用机器学习技术将社会优先级纳入快车道交叉路口的交通监控

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY Transport and Telecommunication Journal Pub Date : 2023-02-01 DOI:10.2478/ttj-2023-0001
O. Barzilai, Havana Rika, Nadav Voloch, Maor Meir Hajaj, O. L. Steiner, N. Ahituv
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引用次数: 0

摘要

仅考虑交通量的交通灯监测不一定是十字路口绿/红分配时间的最佳方法。一个“聪明”的分配还应该考虑到车辆乘客的必需品和这些乘客应该服务的人的需求。本文研究的是一个“智能”交叉路口,其中几辆汽车从不同的方向接近交叉路口,并且在每个方向上都设置了红灯和绿灯的时间间隔序列。本文提出的新方法不仅基于交通拥堵参数,还基于乘客的社会和经济特征(例如,残疾人、医生、某个组织因紧急情况而急需的员工)。本文提出了基于社会和经济标准的灵活进入许可的快速通道来增强智能路口。机器学习(特别是强化学习(RL))被添加到路口的算法中,目的是优化路口的社会效用。在本研究中,路口的效用定义为在给定一定红绿时间分配的情况下,总社会经济潜在效益。这被定义为奖励函数的度量,其中包含对穿过路口或前进位置的车辆的积极因素和对保持在其位置的车辆的消极因素。此外,具有高优先级的车辆的权重值也是等式的一部分。智能路口的简化版本已被使用,作为将RL纳入快速车道(FL)的“智能”路口的模型。具体来说,Q-Learning算法用于最大化奖励函数。仿真结果表明,通过FL对高优先级车辆进行优先排序受到奖励分量的权重和因素的影响。进一步的研究应该将具有FL的“智能”交叉口提升到一个更复杂、更现实的交叉口,使用不同数量的车辆通过交叉口。
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Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane
Abstract Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve. This paper deals with a “smart” junction, where several cars approach the intersection from different directions and a traffic light is set to comply to a sequence of time intervals of red and green lights in each direction. The novel approach presented here is based not only on traffic congestion parameters, but also on the social and economic characteristics of the passengers (e.g. a handicapped person, a medical doctor, an employee who is extremely required in a certain organization due to an emergency situation). This paper proposes to enhance the smart junction with a fast lane, which has a flexible entry permit based on social and economic criteria. Machine learning (specifically, Reinforcement Learning (RL)) is added to the junction’s algorithm with the aim of optimizing the social utility of the junction. For the purposes of this study, the utility of the junction is defined by the total social and economic potential benefits given a certain red/green time allocation is set. This is defined as the measure of the reward function which contains positive factors for vehicles which crossed the junction or advanced their position and a negative factor for vehicles which remains in their positions. In addition, a weight value for the vehicles with high priority is also part of the equation. A simplified version of the smart junction has been used, serving as a model for incorporating RL into the “smart’ junction with Fast Lane (FL). Specifically, the Q-Learning algorithm is used to maximize the reward function. Simulation results show that prioritizing high priority vehicles via FL is influenced by the weights and factors given to the reward components. Farther research should enhance the “Smart” junction with FL to a more complex and realistic one using a varying amount of vehicles crossing the junction.
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来源期刊
Transport and Telecommunication Journal
Transport and Telecommunication Journal TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.00
自引率
0.00%
发文量
21
审稿时长
35 weeks
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