The Model Of Predicting Bus Arriving Time Based On The Finite State Automata

To select the public of the means of transportation for travelers, the bus arrival timecan be concluded as the trip of the concerns of the public traffic informations, this paperbased on the analysis on the influence factors of bus arrival time analysis, and designarrival station time prediction model, in order to improve the public transport vehiclearrival station time prediction accuracy and also play a role in making reliability ofwork to the city public traffic system service development. abstractFirstly the paper analyzes the impact of affecting factors of the public transportvehicles arrival time influence factors from the bus stop time, delay time and trafficcondition three aspects, and their quantitative methods.Second, by the division of the bus vehicles operation states, the paper determinedthe running state of public transport vehicle transfer function and status transfercondition of identification methods, and then establishes the public transport vehicle inthe running states of finite state automata model.Public transport vehicles will be predicted for three time-segments of HarbinNO.63buses car GPS terminal bus arrival vehicles for the pretreated historical datas ofthe running time. Kalman filter, BP artificial neural network and ARIMA time serieswere used to forecast travel time of the peak, flat peak and low time NO.63buses fromJiangong District to Railway Station of Harbin. According to three model predictionerrors, to make sure most suitable mode. Based on the finite state automata get the busstation time prediction model.Finally, using the Harbin NO.63buses car GPS terminal bus station vehicles forthe historical data, contrast the average percentage of absolute error (MAPE) betweenbased on finite state automata prediction model and Kalman, BP model seperately, andthe results show that our model compared Kalman model and the BP model MAPE ofvalue increased by47.08%and31.63%respectively.