A method for estimating the od of bus passengers based on intelligent bus system data

Abstract

The invention discloses a bus passenger OD estimation method based on intelligent public transport system data, by integrating multiple data sources of the intelligent public transport system, analyzing the travel time and space characteristics of public transport passengers to calculate the passenger boarding, getting off, and transfer stations, and obtaining known The inter-station OD matrix of passengers at the bus station is calculated, and the matrix is ​​expanded according to the distribution of the number of passengers at the same boarding station, and the inter-station OD matrix of all IC card samples is obtained. The present invention considers the fact that passenger card swiping may occur after the bus leaves the station in the case of domestic bus congestion, clearly distinguishes the transfer behavior, and uses multi-day travel data to mine passenger travel patterns, improving the alighting time of a single trip in a day The site calculates the success rate and obtains a more reasonable and reliable OD matrix. The present invention is easy to implement, has strong practicability, can handle a large amount of data, can quickly obtain the time-space data of boarding, disembarking, and transfer activities in the entire network of urban public transport, and can better serve the planning and operation management of public transport in large cities, and has comparative advantages Good socioeconomic benefits.

Description

technical field [0001] The invention belongs to urban intelligent public transport data mining, in particular to the field of passenger OD information acquisition based on intelligent public transport system data. Background technique [0002] With the continuous development of the urban social economy, the urban population and land use scale continue to increase, the level of motorization continues to increase, and the number of cars has increased significantly, while the growth of urban road capacity has been relatively slow. pollution and other issues. It has become the consensus of all walks of life to give priority to the development of public transportation to alleviate the current traffic problems and meet the challenges of future transportation. Public transportation has the characteristics of low road occupancy rate, large transportation volume, greenness, efficiency, and fairness, and has inherent advantages in reducing urban traffic congestion and improving the e...

AI Technical Summary

Problems solved by technology

[0004] First, it is strictly considered that the boarding time of passengers is between the time of entering and leaving the bus station, and it does not take into account the fact that in the domestic bus system, due to the congestion in the bus, there may be cases where passengers board from the back door and the bus has already left the station when swiping the card Due to the situation, some boarding stations are estimated incorrectly

[0005] The second is to ignore the travel behavior of passengers at special stations such as the first and last stations of the line, and only assume the two-point model of commuter passengers "leaving early and returning late", which has the shortest spatial distance from the two transfer behaviors of passengers before and after, and the judgment of transfer travel is too simple

[0006] The third is that only the passenger’s travel data of the day is used in the process of calculating the alighting station, and the continuous travel data of passengers for multiple days is underutilized

[0007] Fourth, the amount of data processed by the proposed algorithm for getting on and off stops is relatively small, and only a small number of bus lines are selected as examples. However, in reality, passengers’ boarding and boarding and transfer behaviors often involve the scope of the entire bus network, so the practicability remains to be seen. promote

Images