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出行时间是评估交通系统运行效率与实施信息诱导的重要依据。在现有研究成果的基础之上,利用视频检测器采集的瞬时车速数据和信号配时数据作为模型的输入数据,应用离散时间马尔科夫链的方法,建立主干道出行时间的估计模型。该模型利用瞬时车速与平均车速的转化关系,估计平均车速,进而估计路段的出行时间;并以平均行程速度的阈值界定路段的拥堵与畅通情况,将各路段是否拥堵定义为主干道系统的状态,构造一个无记忆性的马尔科夫随机过程。最后,将模型应用于江苏省淮安市某主干道上,在交通高峰段与非高峰段,对模型的准确性与有效性进行验证。结果表明,模型的结果具有较高的估计精度。
Travel time is an important basis to evaluate the operation efficiency of transportation system and to guide the implementation of information. Based on the existing research results, the instantaneous speed data and signal timing data collected by the video detector are used as the input data of the model, and the estimation model of the travel time of the main road is established by using the discrete time Markov chain method. The model estimates the average vehicle speed by estimating the average travel speed based on the relationship between instantaneous vehicle speed and average vehicle speed, and then estimates the travel time of the road sections. The congestion and unblocking of road sections is defined by the average travel speed threshold, and the congestion of each road section is defined as the status of main road system , Construct a memoryless Markov random process. Finally, the model is applied to a trunk road in Huai’an, Jiangsu Province, and the accuracy and validity of the model are verified in the peak and off-peak sections of traffic. The results show that the model has high estimation accuracy.