Method for predicting bus passenger waiting time range based on IC card data

Abstract

The invention discloses a method for predicting bus passenger waiting time range based on IC card data. The method comprises the following steps: step 1, a sample set is created; step2, RVM is modeling and the parameter calibration thereof are performed, steps, the time headways of the Bus A and Bus B on the second target station are predicted; the biggest characteristic of the invention comprises that the method can predict that the next bus which will arrive at some station at some point to some point, the blank of the bus arrival time range prediction is filled, the method has great innovative significance, and provides a more reasonable prediction method than the other patents; the method adopts IC card data to extract a mass of passenger information from a plurality of buses without using the vehicle-mounted GPS, the method is convenient and fast, the data processing cost is reduced, the IC card data can obtain the number of people who get on or off the bus at every station, so that the bus stay time at every station is obtained, and the above number and time can not be provided by the GPS data.

Description

Technical field [0001] The invention relates to the technical field of public transportation information processing, in particular to a method for predicting the waiting time range of bus passengers based on IC card data. Background technique [0002] With the increasing development of transportation, the proportion of public transportation in people's travel is also increasing. Especially in some large cities, more and more people choose bus and subway to travel. On the one hand, the cost of such travel is relatively cheap. On the other hand, the reliability of subway travel time is high. In the process of bus operation, due to factors such as traffic congestion, stop time at the station and changes in the number of people getting on and off the bus, the arrival time of bus vehicles is irregular. Especially during peak hours, the time of bus arrival is not stable, and it is prone to cross-cars, which greatly affects people's travel efficiency, reduces the service level of publi...

AI Technical Summary

Problems solved by technology

[0004] The above patents have the following deficiencies: 1. The limitations of the algorithm itself

The BP neural network is very sensitive to the initial network weight, which is easy to fall into local optimum; the Kalman filter adopts the method of continuous approximation, and the prediction accuracy of the arrival time of the first few stops of the bus is poor, and the calculation amount is large, and the real-time performance is poor; SVM The algorithm is very sensitive to the penalty factor, which has a great impact on the prediction accuracy, and the selection of the kernel function is limited by Mercer's theorem; 2. The data collected by GPS is limited, and it is impossible to obtain the residence time of the bus at the bus stop, and it is also impossible to obtain the bus stop time. The number of people getting on and off the bus at a certain station will affect the prediction accuracy; and GPS communication is easily affected by the external environment (such as bad weather, high buildings), which is extremely unfavorable to the judgment of bus travel time; 3. Only the predicted bus arrival time is given. This prediction method cannot guarantee the reliability of the prediction results, so it is necessary to predict the range of passenger waiting time under the premise of ensuring reliability, so as to improve the "sense of safety" of passengers when waiting

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