Travel demand analysis-based bus station point deployment method

Abstract

The invention relates to a travel demand analysis-based bus station point deployment method. According to the travel demand analysis-based bus station point deployment method, travel demand characteristics of Beijing are analyzed based on massive taxi data; passenger hot areas are obtained through trajectory data; data such as road traffic congestion conditions, road types, road width and the number of roads are obtained through GIS map data extraction, and the data are adopted as consideration factors for station site deployment; and station site deployment optimization targets are processed, and a greedy algorithm is adopted to solve problems in station site deployment. Bus station sites obtained through adopting the method is the basis of intelligent bus line design; and routes can be arranged between the bus station sites, so that travel cost of passengers and road resource consumption can be effectively reduced.

Description

technical field [0001] The present invention designs the field of intelligent transportation and big data analysis, especially the problem of intelligent bus line design in intelligent traffic, and is applicable to the problem of bus station deployment in intelligent bus line design. Background technique [0002] At present, with the development of China's economy, the demand for urban transportation has become larger and larger, which has gradually increased the per capita car ownership of residents. On the one hand, the increase in car ownership has facilitated people's travel and provided more convenient services to people's lives. On the other hand, it has also led to more traffic problems, such as traffic congestion, traffic accidents, and vehicle exhaust pollution. Wait. In response to this kind of problem, the researchers proposed that it is necessary to study the changing law of urban traffic demand based on the existing residents' travel data, so as to realize the ...

AI Technical Summary

Problems solved by technology

Although this type of method effectively solves the problem of bus station deployment, it is usually aimed at bus stations with fixed routes, and does not consider the dynamic variability of demand like smart bus design, so this kind of method cannot be fully applied to smart bus stations. Deployment of bus stops

[0007] 而目前的智能公交站点部署算法主要有基于时间表的算法(Ying-ShuaiLI,YaoHY,QinL.BusStationOptimizationMethodBasedonthePrincipleofStationCancelingandStationCombining[J].JournalofChongqingJiaotongUniversity,2011;KhondakerB,WirasingheS.BusStopSpacingandLocationforaCorridorwithMultipleBusRoutes[C] / / CALGARY2013-THEMANYFACESOFTRANSPORTATION.2013.) , Algorithm based on supply / demand model (ZhangX. 2533-2538.), Hybrid Algorithm (Ren Hualing, Gao Ziyou. Research on Bi-Level Programming Model and Algorithm for Dynamic Bus Network Design [J]. Systems Engineering Theory and Practice, 2007(5): 82-89; NuzzoloA, CrisalliU, RussoF .ADOUBLYDYNAMICASSIGNMENTMODELFORCONGESTEDURBANTRANSITNETWORKS[C] / / TransportationPlanningMethods.1999), although this type of method solves the problem of demand dynamics, it still lacks consideration of the passenger hotspot effect and urban traffic characteristics, and needs further research

Images