Bus bunching predication method based on IC card data

Abstract

The invention discloses a bus bunching prediction method based on IC card data, and belongs to the technical field of public transport information processing. The prediction method comprises the steps of bus IC card data collection, data processing, practical bus bunching state detection, data training and learning and bus bunching prediction, wherein bus bunching prediction uses a least square support vector machine (LS-SVM) algorithm. IC card data of buses is combined, a lot of passenger information is extracted from multiple buses, a vehicle-mounted GPS system is not needed, operation is convenient and rapid, and the data processing cost is reduced; the LS-SVM algorithm can be used to realize bus bunching prediction more rapidly and more effectively, passengers can better understand operation state of buses and regulate travel time reasonably, and a public transport operation department can adjust the departure interval of buses timely and improve the service level of public transport; and data processing is simple, the cost is low, and the prediction precision is higher.

Description

technical field [0001] The invention relates to the technical field of public transport information processing, in particular to a bus train prediction method based on IC card data. Background technique [0002] In the actual bus operation process, due to factors such as traffic congestion, station stop time, and changes in the number of people getting on and off the bus, the arrival of buses is irregular. Especially during peak hours, passengers often wait at the bus station for ten minutes or more without seeing a bus coming, but once they arrive, they find that it is not just one car, but several cars arrive at the same time, and the bus arrives at the same time. Passenger loads tend to be uneven. Reduced bus service levels, causing safety hazards. [0003] In fact, the delay of a bus at a certain station may lead to an increase in the time it takes to reach the next station, and at the same time cause an increase in the number of passengers and waiting time at the next...

AI Technical Summary

Problems solved by technology

However, the current bus arrival forecast is combined with the vehicle GPS system, and only for one vehicle, the distance from the station and the estimated arrival time are given.

Although this can give bus passengers a certain reference, in fact, during peak hours, road congestion is serious, leading to the phenomenon of bus stringing, causing subsequent vehicles to enter the station before the previous one, making the predicted bus arrival time different from the passenger's actual time. The waiting time does not match, and the vehicle-mounted GPS system requires a large storage space, and the positioning accuracy is low. We need to find a better way to solve the above problems

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