GPS data-based prediction method for predicting traffic jam-resulted delay time of bus

Abstract

The invention belongs to the technical field of road traffic detection and discloses a GPS data-based prediction method for predicting the traffic jam-resulted delay time of a bus. The method comprises the steps of pre-processing the historical GPS data of a bus; conducting the statistic analysis on the pre-processed historical GPS data of the bus; classifying the traffic jam state of the bus; discriminating the state of the bus; predicting and correcting the traffic jam-resulted delay time of the bus. According to the technical scheme of the invention, the advantage that a large volume of bus GPS data are available is fully utilized. Meanwhile, in combination with the historical statistical information and real-time GPS data, traffic jam states are classified into six conditions through the decision tree classification method and different prediction calculation methods are adopted for different conditions. Moreover, prediction results are corrected according to the real-time data and the historical traffic jam information. Therefore, more accurate prediction values can be obtained in recurrent and accidental traffic congestion conditions.

Description

technical field [0001] The invention belongs to the technical field of road traffic detection, and in particular relates to a method for predicting delay time of bus traffic jams based on GPS data. Background technique [0002] The bus will be delayed due to the influence of real-time road conditions during driving, but how long the delay is is the focus of many traffic participants such as travelers, drivers, traffic managers and others. Accurately predicting the delay time of buses due to traffic jams can reduce the irritability of passengers and drivers due to congestion, and can also provide reference and support for traffic guidance, traffic control, bus arrival time prediction, scheduling optimization, etc. Help improve urban road traffic management and service level. [0003] During the development of intelligent transportation, a large amount of bus GPS data has been accumulated. These data have the characteristics of large data volume, high real-time performance, w...

AI Technical Summary

Problems solved by technology

There is still a lack of effective research on how to make full use of the advantages and functions of these data to accurately predict the delay time of buses due to congestion.

[0004] The current research mainly focuses on how to reduce the occurrence of traffic jams, and how to achieve traffic guidance and effective management after traffic jams, so as to reduce the impact of traffic jams. However, there is no specific quantitative research and analysis method for the length of delay caused by traffic jams.

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