A traffic forecasting method for urban shared travel

Abstract

The invention discloses a flow prediction method for shared travel in cities, the steps of which are: obtaining the topology structure of the urban traffic network, obtaining travel modes and related data in the urban traffic network, and obtaining the OD traffic demand in the urban traffic network; constructing The urban multi-modal random P2P ride-sharing user balances the transportation network, and establishes the traffic demand constraints and ride-sharing matching constraints of the network; comprehensively considers the travel time perceived by travelers, the loss of in-vehicle comfort, and the online operation mechanism of the ride-hailing vehicle. Calculate the generalized travel cost of travelers; based on the logit travel selection behavior and the principle of random P2P ride-sharing user equilibrium, the network traffic is allocated to obtain the path traffic flow of the urban multi-modal complex network. The invention can effectively predict the traffic flow of the modern urban traffic network, thereby providing a reliable basis for the relevant departments to formulate management measures and industry standards.

Description

technical field [0001] The invention belongs to the field of urban traffic engineering, and particularly relates to a flow prediction method for shared travel in cities. Background technique [0002] With the increasing number of private cars year after year, traffic congestion has become one of the most important problems faced by city managers. However, the practical experience of alleviating traffic congestion in various countries shows that simply building more roads cannot solve the problem of traffic congestion, and may even encourage more people to drive on the road. Ridesharing (also known as ridesharing) gathers travelers whose travel trajectories partially or completely overlap into one vehicle, and reduces the number of motor vehicles on the road and eases traffic by improving the space utilization of the vehicle. The problem of congestion has already been practiced in major cities in my country. These changes require urban traffic flow forecasting methods to not...

AI Technical Summary

Problems solved by technology

However, the practical experience of various countries in relieving traffic congestion shows that blindly building more roads cannot solve the problem of traffic congestion, and may even encourage more people to drive on the road invisibly

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