Research On Demand Response Bus Route Optimization In Typical Travel Scenarios In Suburban Areas

With the continuous development of residential land around the city,a large number of residents gather in the suburbs.The traditional public transport service is difficult to overcome the peak travel passenger flow pressure under the "separation of occupation and residence" mode,and it is difficult to benefit the travel of vulnerable groups.Demand responsive public transport can not only meet the diverse needs of passengers for travel time and comfort,but also can allocate transportation capacity on demand for enterprises to save operating costs.This paper focuses on the suburban area travel corridor,considering the characteristics of different audiences of demand responsive public transport,simplifies diversified travel into two typical scenarios:commuter travel oriented and community travel oriented,and explores the optimization of public transport routes in the two scenarios.The main research contents are as follows:(1)From the perspective of five system elements,this paper expounds the service characteristics of demand responsive public transport,and summarizes the service advantages of this mode.This paper analyzes the characteristics of residents' travel in the actual suburban areas of China,and explores the travel needs of customers in this area.Extract the key factors that affect the bus route optimization,focus on the design of the improved gs-dbscan clustering algorithm to determine the location of the dynamic bus stop in the service area,overcome the problem of slow execution speed and large memory occupation,and provide a theoretical basis for further research on the bus route optimization.(2)Taking into account the interests of both passengers and operation units to build an integrated optimization model.Firstly,a model of the lowest operating cost with energy consumption as the main measurement index is established,which is constrained by vehicle operation rules,road conditions and time constraints.According to the emphasis of two kinds of leading groups on travel time and comfort,in the former scenario,using the time value calculation method,the paper establishes the lowest recourse cost sub model of passenger travel time and violation of time window constraints.In the latter scenario,AHP fuzzy evaluation method is used to establish the lowest recourse cost sub model of various comfort factors on passenger satisfaction.(3)The immune genetic algorithm is designed to solve the optimization model,and the coding rules,immune operation,genetic operation,population renewal strategyand population selection strategy are designed.The case study shows that the algorithm has strong global and local search ability,avoids the premature convergence of traditional genetic algorithm,and its self adaptability and memory function ensure fast execution speed.(4)Taking Beijing No.322 bus operation line as the simulation experimental line,this paper analyzes and summarizes the travel behavior characteristics of three typical groups of people on the line by using the historical travel data,and uses gs-dbscan clustering algorithm to determine the location of the combined station in the region,and uses immune genetic algorithm to solve the route optimization scheme in two scenarios,which verifies the effectiveness and feasibility of the optimization model.(5)Based on the vehicle unit and roadside unit,the passenger flow data analysis module and road network data module are designed,including the data monitoring and analysis of vehicle speed,congestion inside and outside the vehicle and adverse weather Analyze and realize real-time operation control of vehicle.