A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles.

交通拥堵的常见原因是相交的车辆路线集中。在现有的交通系统中，要在每个车辆之间独立地确定路线的情况下，很难减少相交的路线。联网自动驾驶汽车的发展为解决交叉路线问题提供了机会，因为车辆的路线可以在全球范围内进行协调。我们对交通管理系统进行原型设计，以优化联网自动驾驶汽车的交通。该系统基于流交通数据向车辆分配路线。我们为系统开发了两种路由分配算法。该算法可通过减少相交路线来帮助缓解交通拥堵。在模拟的交通管理系统中，进行了广泛的实验，以将所提出的算法与两种最新的路线分配算法与合成和真实道路网络进行比较。实验结果表明，所提出的算法在车辆行驶时间方面优于竞争对手。