Multi-mode public transport time table optimizing method for last transfer demand

Abstract

The invention discloses a multi-mode public transport time table optimizing method for last transfer demand. The method comprises the following steps: screening a key transfer relation and an associated site according to passenger flow characteristics; acquiring running information of rail transit and ground public transportation based on a planned time table and historical running data; acquiring a passenger transfer walking time and a number of bus station parking berths through investigation; proposing a time table optimization target and an adjusting strategy, and establishing a night-time time table two-stage optimization model; establishing a time table optimization model for a last train transfer problem of the rail transit at a first stage, and acquiring an optimum adjusting scheme by utilizing a branch and bound method; and at a second stage, based on the rail transit time table generated at the first stage, considering the constraint of the bus station berths number, establishing a bus time table optimization model for the ground public transportation and the rail transit last train transfer problem, and acquiring an optimum adjusting scheme by utilizing a branch and bound method. The method can be used for systematically decreasing a transfer failure rate in a multi-mode public transport network.

Description

[0001] Field [0002] The invention relates to a method for optimizing a multi-mode public transport timetable facing the transfer demand of the last shift, and belongs to the field of urban public transport operation management. Background technique [0003] Considering operating cost constraints and resource utilization efficiency, domestic cities do not provide 24-hour rail transit services. The last train becomes the last chance for passengers to use the rail transit service to complete their journey on that day. Due to the existence of the transfer relationship, the timetable of the last train on a single line not only affects the travel of passengers on this line, but also extends the impact to the entire rail transit network and the ground bus lines that have a transfer relationship with it through the station to a greater extent. For transfer passengers, only when the time of arrival at the transfer station is at least earlier than the time when the last train of the ...

AI Technical Summary

Problems solved by technology

The substantial increase in travel costs will seriously affect passenger satisfaction with public transport services, especially for some low-income groups who still have to work at night

[0004] The existing research on the last train transfer problem is still in its infancy and only involves the rail transit system. Most of them focus on reducing the transfer failure rate and the transfer failure rate by coordinating the planned departure time, planned running time, and planned stop time of the last train on each line. Taking the waiting time into account, the impact of the last train schedule adjustment on the departure plan of the non-last train in the late night period is not considered; and heuristic algorithms such as genetic algorithms are often used to solve the model, which cannot guarantee the global optimality of the solution

The existing results cannot be directly applied to solve the problem of the last shift between the ground bus and the rail transit. The late-night timetable of the ground bus needs to consider the coordination with the last shift timetable of the rail transit, and it is also necessary to extend the operating time as much as possible so that it can be used on the rail transit. Provide basic public transportation services for travelers as an alternative after the traffic stops operating; and due to the constraints on the number of bus stops, it is necessary to avoid the phenomenon that multiple bus lines arrive at the station at the same time but queue up to enter the station when the timetable is coordinated; therefore, for different methods It is necessary to carry out special and detailed research

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