Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations

Taking into consideration the overlapped influences of multiple rail transit stations upon land use characteristics, this study newly develops a multi-objective land use allocation optimization model to decide the land use type and intensity of every undeveloped land block of an urban area. The new model is solved by successively utilizing the non-dominated sorting genetic algorithm and the technique for order performance by similarity to ideal solution to obtain the least biased Pareto-optimal land development scheme. The study area is an urban region around two metro stations in Beijing of China. The influencing scopes of these two stations are overlapped in part, and many of the land blocks in the study area are not yet developed. It is shown that the newly developed land use allocation optimization model is able to rationally achieve multi-objectives in coordination to the most extents for the sustainable urban development in view of the integrated effect of multiple rail transit stations

Bayesian network modeling analyzes of perceived urban rail transfer time

This study proposes a Bayesian network (BN)-based approach to research the relationships between metro transfer perception time (MTPT) in different seasons and its influencing factors, and explores the strategies on reducing the MTPT for the improvement of the transfer experiences of passengers. Taking the city of China, Beijing, as the study area, the data related to the MTPT are collected in different seasons. Based on study data, BN modeling results indicate that factors affecting the MTPT in four seasons are not the same. The results of scenario analysis of BN demonstrate that the improvement of the transfer environment is effective for passengers in spring and autumn, while the passengers in summer pay more attention to the time and the space comfort of the walking stage of transfer. In addition, passengers in winter are concerned about the time and the space comfort of both walking and waiting stages of transfer

Solving urban electric transit network problem by integrating Pareto artificial fish swarm algorithm and genetic algorithm

This study presents a multi-objective optimization model for the urban electric transit network problem with the aim of simultaneously designing the layout of bus routes, the frequency and the location and size of charging stations by making a tradeoff between two inconsistent objectives from the perspectives of passengers and operators. A Pareto artificial fish swarm algorithm (PAFSA) embedded with the genetic algorithm (GA) is developed to solve the proposed model. The PAFSA is designed to iteratively search for the proper network configuration satisfying two conflicting objectives. During which, the demand assignment with real-time transit information is performed to update the frequency of each newly designed route. The GA embedded into the PAFSA iteratively decides the locations of charging stations and the number of chargers to be installed in each charging station. A case study of the transit network in an urban region of a city in China is implemented, revealing that the proposed approach is able to rationally design a relatively large-scaled transit network with searching for the best fits between two inconsistent objectives