Research On The Optimal Microcirculation Route Network With Metro Stations Based On Grid Distributed Travel Demand

With the development of urban economy,the traffic demands in large and mediumsized cities increased rapidly,and the construction of urban public transportation facilities are further accelerated.In recent years,rail transit plays an important role in relieving the pressure of urban passenger transport,but it can not serve every small area in the city,whereas microcirculation buses as a new form of transit can arrive at every part of the city.The flexibility of the micro-circulation bus provides a new method for public transport to solve the “last mile” problem,and also provides more convenient conditions for passengers to transfer between bus and metro,which improves the service level of transit systems.In addition,multivariate data is conducive to research transit network optimization problem with the rise of traffic big data technology,which is of great significance to improve the passenger transport capacity throughout the city.Therefore,this paper investigates an issue for optimizing community microcirculation network with metro stations.In this study,considering a large community area divided into a set of grid square zones,a mixed integer optimization problem is formulated to obtain the optimized microcirculation route network and the service frequency of each route simultaneously,with the objective of minimizing the total transit system cost,including user and supplier costs.A series of realistic constraints,i.e.,the route length,the whole area coverage,the vehicle capacity and the total fleet size,are modeled in this paper.For solving the problem,a solution framework consisting of three main components is proposed: an initial route network generation procedure that ensures all the divided zones are within the coverage of at least one microcirculation route whose length is within a reasonable interval;a network analysis procedure that assigns the demand of each zone to a set of paths on the generated network according to a certain proportion,and determines the service frequency of each route through a fleet size adjusting heuristic;A genetic algorithm(GA)procedure that designs multiple crossover operators and mutation operators to guide the solution population evolving process,and an adaptive weight adjusting heuristic is developed in this process for selecting operators in each iteration according to their previous performance.A simulated example is conducted by applying the solution framework with different calling schemes of GA operators,and related sensitivity analysis and impacts of some important variables on the results are performed.Finally,a real-life case is successfully conducted based on multivariate data.In terms of data acquisition,the demand of multi-source passenger flow in the area is obtained through mobile signaling data and IC card data of public transport and allocated to each grid,and then the optimal route network is obtained by applying the model and algorithms proposed in this paper.The validity of the proposed model and algorithms is proved by comparing with the current route network.