标题： 交通信号对日常交通模式的评估 显示英文标题

标题： Evaluation of Traffic Signals for Daily Traffic Pattern

摘要： 转向交通量数据对于交通信号设计、交叉口几何规划、交通流和拥堵分析至关重要。 本研究提出了三种称为动态、静态和混合配置的方法用于基于TMC的交通信号。 开发了一种基于视觉的跟踪系统，使用交通摄像头估算拉斯维加斯六个交叉口的TMC。 具有兼容格式的交叉口设计、路线（例如，车辆行驶方向）和信号配置文件被综合并导入到仿真城市移动性中进行信号评估，使用真实数据。 基于估计等待时间的初步实验结果表明，90秒和120秒的周期时间对所有交叉口效果最好。 此外，四个交叉口在动态信号定时配置下表现更好，另外两个性能较低的交叉口具有较低的总车辆数与交叉口腿总车道数的比率。 由于日常交通流量通常表现出双峰模式，我们提出了一种混合信号方法，在动态和静态方法之间切换，以适应高峰和非高峰交通状况，从而改善交通流管理。 因此，内置的交通生成模块为4小时（包括高峰时段）创建车辆路线，信号设计模块根据静态、动态和混合方法生成信号调度周期。 每个区域（即西、北、东、南）的车辆数量分布被不同加权，以生成多样的交通模式。 对6个交叉口进行4小时模拟时间的扩展实验结果表明，基于区域的交通模式分布影响信号设计选择。 尽管静态方法在均匀分布的区域交通情况下表现良好，但混合方法在西-东和北-南区域交叉口的高权重交通情况下表现良好。 显示英文摘要

摘要： The turning movement count data is crucial for traffic signal design, intersection geometry planning, traffic flow, and congestion analysis. This work proposes three methods called dynamic, static, and hybrid configuration for TMC-based traffic signals. A vision-based tracking system is developed to estimate the TMC of six intersections in Las Vegas using traffic cameras. The intersection design, route (e.g. vehicle movement directions), and signal configuration files with compatible formats are synthesized and imported into Simulation of Urban MObility for signal evaluation with realistic data. The initial experimental results based on estimated waiting times indicate that the cycle time of 90 and 120 seconds works best for all intersections. In addition, four intersections show better performance for dynamic signal timing configuration, and the other two with lower performance have a lower ratio of total vehicle count to total lanes of the intersection leg. Since daily traffic flow often exhibits a bimodal pattern, we propose a hybrid signal method that switches between dynamic and static methods, adapting to peak and off-peak traffic conditions for improved flow management. So, a built-in traffic generator module creates vehicle routes for 4 hours, including peak hours, and a signal design module produces signal schedule cycles according to static, dynamic, and hybrid methods. Vehicle count distributions are weighted differently for each zone (i.e., West, North, East, South) to generate diverse traffic patterns. The extended experimental results for 6 intersections with 4 hours of simulation time imply that zone-based traffic pattern distributions affect signal design selection. Although the static method works great for evenly zone-based traffic distribution, the hybrid method works well for highly weighted traffic at intersection pairs of the West-East and North-South zones.