Method for optimizing public bicycle station layout by applying GPS data of mobile phone

Abstract

The invention discloses a method for optimizing public bike station layout by applying mobile phone GPS data. The method comprises the following steps of (1) collecting GPS track data of passengers through an operator; (2) dividing the collected trajectory data into staying and moving trajectories, and dividing the moving trajectories into five trajectories including cars, bicycles, walking and the like through a random forest model; (3) extracting ODs of all bicycle travels, and establishing a probability sum model of all OD tracks by applying a geometric probability model; (4) setting the number of stations and the radius of a probability region according to the actual situation, and iterating the optimal central point positions of all probability regions by using PP-PSO; and (5) findingout the most suitable station position in the probability region. According to the method, bicycle requirements are separated, bicycle travel tracks are extracted, defects that in a previous method,a traffic mode is not detected, and an OD matrix is lacked are overcome, the defect that actual construction conditions are uncertain is overcome, and the optimality of public bicycle station settingis greatly improved.

Description

technical field [0001] The invention relates to a site layout design method for urban public bicycles, in particular to the optimization of existing public bicycle sites, and belongs to the field of traffic planning. Background technique [0002] As a representative of smart and sustainable urban development, the public bicycle system is a hot spot in transportation, public health, and urban planning. Public bicycles not only bring convenience to short-distance travel, but also expand the influence of public transport stations, especially subways, improve the travel structure for medium and long distances, and save road resources. However, due to the unreasonable setting of bicycle stations, the public bicycle has been greatly affected to play its due role. In the past practical layout optimization, demand forecasting was the first difficulty. It was necessary to estimate potential traffic demand according to the location (such as shopping malls, hospitals, subway stations,...

AI Technical Summary

Problems solved by technology

But this method has many defects. First, this method does not detect the actual transportation mode of passengers (such as trains, subways, private cars, buses, bicycles, walking, etc.); second, this method lacks the OD matrix

The second difficulty is the uncertainty of construction conditions in specific areas

The conditions under which the bike stations were built are less certain as it is not possible to gather specific information about the bike stations

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