Automation of Collection of Primary Data for Development of a Passenger Origin-Destination Trip Correspondence Matrix Based on Computer Vision and Neural Network Technologies

The origin-destination trip matrix is a fundamental characteristic of a transport network, and development of a reliable correspondence matrix is the most important task in organising passenger traffic. It is the basis on which the public transport route network of a city (region) is built and optimised.

Currently, collection of initial information for construction of a travel correspondence matrix is carried out through field surveys comprising questionnaire surveys of the population; accounting for movement of passengers according to the coupons issued to them; checkers, tellers manually counting passengers in vehicle compartments; simple surveys of passengers. Besides, mathematical modelling is used based on statistical data on the number of residents in various districts of the city, employees in enterprises and students in educational institutions, as well as on available data on the characteristics of passenger traffic along certain routes. All these surveys are very expensive and are carried out once over few years; they give a large error, which is why decisions made on the basis of these data are far from being optimal.

There are a lot of solutions in the software and hardware market that provide automated collection of data on passenger flows. They are based on the use of infrared sensors or of video recording. However, none of these systems provide information about the points of entry and exit of each passenger. The objective of this study was to develop methods for automating the collection of reliable information about passenger trips, that will be the base for building up-to-date and reliable passenger trip correspondence matrices. This task can be solved by constant monitoring of passengers’ trips with fixing places of entry and exit of each passenger.

The study describes the possibility of creating software based on computer vision and artificial intelligence which will provide automation of collection of primary information about travel of each passenger from the place of boarding into the vehicle to exit from it, that is, automation of data generation to build a passenger trip correspondence matrix. 

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