基于机动车排放的城市信号控制策略研究

发布时间：2018-05-27 20:10

本文选题：信号控制优化 + 交叉口群　；参考：《东南大学》2016年硕士论文

【摘要】：城市道路信号控制会引起到达交叉口进口道的车辆运行工况发生变化,从而使机动车在交叉口的排放大大增加。如何通过有效的信号控制来减少机动车排放已成为城市交通控制研究的一项重要课题。目前直接以交叉口机动车排放为优化目标的信号控制研究还很匮乏,现有的相关研究还仅限于单点信号控制优化。基于此,本文以单个交叉口的机动车排放分析为基础,建立了以排放最小为目标的交叉口群的信号控制优化模型；借助交通仿真与优化模型之间的交互运算,实现了交叉口群机动车排放优化,并制定了相关的信号控制策略。在交叉口排放计算模型方面,本文采用基于交叉口停车状况的排放计算方法。基于比功率分区排放因子对车辆的减速、怠速、加速、匀速四种工况的排放因子进行标定。在排放因子的标定过程中,红灯期间到达的车辆被视为完全停车；绿灯前期,即排队消散期间,到达的车辆被视为不完全停车;绿灯后期,即排队已完全消散后,到达的车辆被视为不停车。结合到达交叉口进口道的个体车辆停车状况与相应的运行工况,本文计算获得交叉口进口道上各个车道的机动车排放量,并对各个车道的车辆排放进行汇集获得交叉口机动车总排放。在交叉口群信号控制优化的过程中,本文保持原信号配时方案中的相位设计和周期时长不变,只对绿灯时长进行优化,并采用遗传算法求解优化模型,通过交通仿真与信号优化控制算法的交互,获得交叉口群的最优配时方案。本文选取昆山市中心城区包含7个相邻交叉口的交叉口群进行实例分析,评估结果证实了优化模型的有效性。最后以延误为信号优化的辅助评价指标,结合排放分析,制定了相关信号控制策略。
[Abstract]:The urban road signal control can cause the vehicle running condition to reach the intersection inlet channel , so that the emission of the motor vehicle at the intersection is greatly increased . How to reduce the emission of motor vehicle through effective signal control has become an important subject of urban traffic control research .
The vehicle emission optimization is realized by means of the interactive operation between the traffic simulation and the optimization model , and the related signal control strategy is established . In the aspect of the intersection emission calculation model , the emission factors based on the cross - port parking condition are adopted . In the calibration process of the emission factors , the vehicles arriving during the red light are considered to be completely stopped ;
In the early stage of the green light , that is , during the period of queuing dissipation , the arriving vehicles are regarded as incomplete parking ; the vehicles arriving at the later stage of the green light are regarded as non - parking . In the process of optimizing the signal control of the intersection , the paper calculates the vehicle emission quantity of each lane on the intersection inlet passage , and obtains the optimal distribution scheme of the intersection group by using the genetic algorithm to solve the optimization model .
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U491.54

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