基于浮动车数据的快速交通拥堵监控

浮动车技术是近年来智能交通系统中所采用的、获取道路交通信息的先进技术手段之一,可作为大规模实时交通监控的数据源.由于浮动车数据规模庞大,从大量移动对象中有效处理流数据是其中一大难点.采用相似轨迹聚类的思想,结合与拥堵特征相关的交通参数,提出了拥堵同伴发现算法.该算法能从浮动车轨迹流数据中筛选出可能发生拥堵的浮动车数据,从而对拥堵区域变化趋势进行概化预测,由预测结果决定负载处理方式.此外,设计基于预测的多优先级调度算法用以实现整个监控流程.提出的方法可有效降低处理浮动车数据的代价,实现快速交通拥堵监控.通过在城市路网中大规模出租车轨迹数据上的实测,验证了这种算法的有效性和优势.

Rapid Traffic Congestion Monitoring Based on Floating Car Data

Floating car technology is the essential source to acquire the road traffic information in intelligent transportation systems. It can be used as the data source for large-scale real-time traffic monitoring. It's a challenge of handling stream data effectively in a large number of moving objects because of the huge scale of (floating car data, FCD). In this paper, a congestion companion discovery algorithm is proposed by adopting the idea of similar trajectory clustering and utilizing traffic parameters with congestion characteristics. The candidate congestion FCD can be filtered out from the floating car trajectory stream for approximately predicting the trend of congestion areas. While the load shedding decision-making is determined by the prediction, an algorithm of multi-priority scheduling based on prediction is designed to achieve the whole monitoring process. Our method can effectively reduce the processing cost of FCD, and rapidly monitor traffic congestion. Both efficiency and effectiveness of our method are evaluated by a very large volume of real taxi trajectories in an urban road network.