Shared Bus Travel Route Matching Based On Crowd Sensing

Urban traffic planning has always been a hot issue in the construction of smart cities.Among them,public transportation route planning is the most important part.As a representative product of the sharing economy,shared buses are becoming more and more popular due to their advantages such as comfort and convenience.As a new type of transportation,methods of mining passenger flow pattern of shared buses and improve passenger’s travel experiences are the primary considerations.Based on the population-weight opportunities model,we define the concept of cost-weight opportunities model.We abstract the flow of passengers between each station into small-scale population mobility.Population mobility will be affected by different incentive mechanisms.In order to explore these incentive mechanisms,we employ crowd sensing methods to collect data and further use statistical methods to summarize the characteristics of passenger.After that,we combine the bus operating costs and further construct the travel persona which can describe passengers’ requirements accurately.At the same time,we develop a passengers OD(OriginDestination)pair matching method based on Bayesian estimation.At last,A MCEA algorithm is designed for shared buses route planning.This paper bases on the real mobile crowdsensing data and urban buses transaction data.We conduct extensive experiments to verify the superiority of our method,including simulation,comparative experiments,and performance analysis experiment.The experimental results show that the proposed route planning scheme can fully improve the efficiency of shared buses operation and improve passenger travel experience,which is of great significance for the prosperity and development of shared buses.