Big data-based optimization method of ground public-transportation fixed-line timetable

Abstract

The invention discloses a big data-based optimization method of a ground public-transportation fixed-line timetable. First, all collinear public-transportation lines are extracted for target lines, which need to be studied, according to GPS trajectory data, IC card swiping data of the collinear public-transportation lines and number data of vehicles arriving at each station in unit time are extracted, and vehicle running time data are extracted; then all the extracted data are analyzed and processed; passenger preference coefficients are calculated on the basis of passenger flow data, full-dayworking time periods of the public-transportation lines, maximum passenger capacity of the public-transportation service vehicles, standard full-load rates, first / last-vehicle departure time, and maximum and minimum vehicle departure intervals are determined; and then the processed data are input into a constructed multi-objective optimization model to obtain an optimized public-transportation vehicle departure timetable. The optimization method of the ground public-transportation fixed-line timetable provided by the invention can satisfy passenger flow demand, enhance public-transportation operation efficiency, reduce public-transportation travel delay, improve service reliability, and improve passenger satisfaction degrees.

Description

technical field [0001] An effective bus departure timetable is an important means to balance the contradiction between the service needs of passengers and the operating costs of bus companies. Under the premise of not increasing the bus operation cost, the present invention utilizes massive IC card swiping data and GPS track data to allocate OD passenger flow to the passenger flow of the collinear bus line, optimize the bus departure timetable, and optimize the bus departure interval and available timetable. Correlative matching of changing public transport travel demand, increase occupancy rate, control full load rate, and obtain the optimal operating schedule of the target route, which plays an important role in improving the efficiency and satisfaction of public transport operations, belongs to the field of urban public transport systems. Background technique [0002] Public transportation is an important part of urban infrastructure construction. Timetable optimization ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems of manual customization of domestic bus timetables and difficulties in obtaining bus operation and passenger flow demand data along the route, this invention proposes an optimization method for ground bus fixed line timetables based on big data, which is based on urban bus dispatching Modeling the problem of bus allocation and frequency optimization of the same type of bus in China; at the same time, based on the departure frequency and passenger flow data of multiple bus lines, it is proposed to maximize the passenger flow carrying capacity on the target line and minimize the total running time. Under the target that the full load rate is closest to the standard full load rate, a model of bus departure frequency optimization problem is established, and the original bus schedule is optimized to obtain a bus schedule that adapts to the traffic conditions along the route and the demand for bus passenger flow

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