A bus stop estimation method based on data fusion

Abstract

The invention discloses a bus stop estimation method based on data fusion, by analyzing the bus route data, the bus GPS data and the bus IC card swiping data are fused to obtain the user's boarding and bus arrival information. The bus GPS data and bus IC card swiping data are fused to obtain the user's boarding and bus arrival information, regression and exploration mechanism is introduced, wherein the regression mechanism considers users' historical visiting stops; exploration mechanism combines the general rule of residents' bus trip distance and the bus attractiveness of bus stops to realize the prediction of drop-off point of users' bus trip. Through the fusion of various traffic data, the method can effectively extract the information of the user's bus stop and bus route.

Description

technical field [0001] The invention belongs to the technical field of traffic, and in particular relates to a method for estimating a bus stop stop based on data fusion. Background technique [0002] With the rapid development of our country's economy and the acceleration of urbanization, various types of vehicles on urban roads are increasing, which has brought great pressure to urban traffic. As a green and energy-saving travel mode, bus travel is strongly advocated by the government, and it is also one of the most important modes of transportation for urban residents. Therefore, understanding residents' bus travel characteristics is of great significance to urban bus route planning, bus dispatching, and land layout. At this stage, almost all urban buses are equipped with bus card readers. Residents can get on the bus and swipe their cards to realize bus travel. However, most of the bus systems in domestic cities do not need to get off and swipe their cards. The bus card...

AI Technical Summary

Problems solved by technology

[0003] 1) Most of the current OD research on residents' travel is mainly to divide the city into regions, focusing on the macroscopic regional OD analysis, but it is difficult to effectively predict the passenger's bus stop at the micro level;

[0004] 2) According to human activity habits, the travel of residents has important regression characteristics, that is, residents tend to commute to and from the most frequently visited places. Existing research mainly builds bus travel chains for residents' bus travel without considering residents' regression An important intrinsic feature of travel

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