基于马尔科夫链的轻轨乘客轨迹预测新算法

利用重庆轻轨的乘客刷卡数据，分析了乘客出行特征，并提出了一种基于马尔科夫链的乘客轨迹预测算法。该算法首先利用贝叶斯分类器对乘客下次出行轨迹进行分类；然后，根据乘客最近一次出行轨迹与其常住地的关系，预测其下次出行轨迹。在真实轻轨交通数据集上的实验结果表明，该算法对乘客出行轨迹的预测效果优于LTMT、RNN和2-MC；同时，该算法基于大数据处理框架Spark进行编码，减少了运行时间。

Novel Algorithm of Light Rail Passenger Trajectory Prediction Based on Markov Chain

By utilizing the smart card data from Chongqing light rail system, the travel characteristics of light rail passengers are analyzed and a trajectory prediction algorithm based on Markov chain is proposed. In the algorithm, the next travel trajectory of a passenger is classified by Bayesian classification and then predicted according to the relationship between the passenger's last travel trajectory and her/his residence. Experimental results based on real datasets show that the algorithm outperforms LTMT, RNN and 2-MC on predicting passenger's next travel trajectory. Meanwhile, the algorithm is coded on Spark, a big data processing framework, which reduces its runtime.