中文摘要：

网络动态交通流的统计分析技术是目前移动计算及智能运输系统领域的一个重要研究方向．然而，现有的交通流统计分析方法（如基于固定传感器的方法、高空交通流监视方法、浮动车法等）存在着信息量少、数据处理复杂、精确度及效率低下、通信代价高昂等缺陷．为了有效地提高交通流统计分析的效率与精度，提出了一种基于网络受限移动对象数据库的交通流统计分析方法（networkconstrained moving objects database based traffic flow statistical analysis，NMOD—TFSA）．通过对移动对象所提交的位置更新信息进行联机统计，NMOD—TFSA能够实时地获取交通网络各部分的动态交通参数．由于在数据采集时考虑了道路网络的拓扑结构，NMOD—TFSA有效地降低了通信及计算的代价；此外，NMOD—TFSA所采集的数据能够反映移动对象完整的时空轨迹，因此为数据分析提供了更为丰富的信息，提高了数据处理的精度．实验结果表明，与目前通行的浮动车法相比，NMOD—TFSA有效地降低了通信及计算代价，提高了交通流统计分析的精度与灵活性．

英文摘要：

With the advancement and integration of mobile computing technology and intelligent transportation systems （ITS）, automatic traffic flow analysis has become an important research issue. However, current traffic flow statistical analysis methods, such as stationary sensor/camera based methods （monitoring from traffic sensors or optical devices）, air/space borne methods （monitoring from airplanes or satellites）, and floating-car based methods （monitoring from floating/probe cars）, have a lot of limitations in terms of data sampling costs, data processing efficiency, and information analysis accuracy. To solve these problems, a new traffic flow analysis framework, network-constrained moving objects database based traffic flow statistical analysis （NMOD-TFSA） model is proposed, in this paper. Through an online statistical analysis mechanism based on the spatial-temporal trajectory data submitted by moving objects, NMOD-TFSA can get the real-time traffic parameters of the transportation network. By taking the topology of the traffic network into consideration, NMOD-TFSA can reduce the communication and computation costs in data sampling. Besides, in data analysis, NMOD-TFSA can provide better accuracy since the analysis is based on the precise spatial-temporal trajectories of moving objects. The experimental results show that compared with the floating-vehicle method, which is widely used in current traffic flow analysis systems, NMOD-TFSA provides improved performances in terms of communication and computation costs, flexibility, and accuracy.