实时信息下公交枢纽内乘客乘车选择行为预测

Passenger Boarding Choice Behavior with Real-Time Information in a Transit Hub

提出一种实时公交信息下,公交枢纽内乘客乘车方案选择行为预测方法.该方法以乘客等车时间和站台乘客排队长度为关键因素,挖掘乘客乘车方案决策规则.然后,针对乘客乘车决策的不确定性,引入云模型完成自然语言与定量信息之间的转化从而进行预测.最后,以大连市华南广场公交枢纽的数据对该方法进行了检验,结果显示,该方法具有较好的预测效果,表明在实时公交信息下引入云模型进行乘客乘车方案选择预测是一种可行方法.

A prediction model of passenger boarding choice behavior under bus real-time information in public transit hub is presented.Passenger waiting time and queue length are considered as the key factors,which are used to explore the decision rules of route selection.Aiming at the uncertainty of passenger route selection behavior,thebehavior forecast is completedby the conversion between natural language and quantitative information with the cloud model.Finally,the model is tested based on the data of Huanan Square hub in Dalian city.The results show that the forecasting model has good simulation and prediction effects,and it is feasible to use the cloud model to predict the passenger boarding choice behavior under bus real-time information.