In order to improve the transfers inside an Urban Rail Transit (URT) station between different rail transit lines, this research newly develops two Ordinal Logistic Regression (OLR) models to explore effective ways for saving the Perceived Transfer Time (PTT) of URT passengers, taking into account the difficulty of improving the transfer infrastructure. It is validated that the new OLR models are able to rationally explain probabilistically the correlations between PTT and its determinants. Moreover, the modelling analyses in this work have found that PTT will be effectively decreased if the severe transfer walking congestion is released to be acceptable. Furthermore, the congestion on the platform should be completely eliminated for the evident reduction of PTT. In addition, decreasing the actual transfer waiting time of the URT passengers to less than 5 minutes will obviously decrease PTT.
With the development of rail transit network, the transfer between different subway lines has become an inevitable travel activity for many travelers. Considering the importance of passenger's transfer experience, this study selects the metro transfer time perception from the passenger's point of view as the research object. Based on the perceived and actual transfer data of Beijing subway passengers, the paired-samples T test is used to verify the differences in passengers' perception of transfer. Then this study constructs the ordinal logistic regression model to analyze the influencing factors of transfer time perception, and explores ways to reduce it through scenario analysis. The results show that transfer passengers generally overestimate their transfer time and transfer distance. Additionally, the actual transfer waiting time is a key influencing factor because its reduction will significantly cause the decline in transfer time perception, especially when it is reduced to "3 minutes to 5 minutes".
In order to optimize transit network layout and service frequencies from the view point of operators and utilizers, this research constructs a multi-objective model and proposes the solution algorithm. The model is established from the perspective of operators with the goal of minimizing total operating costs for one day, and from the perspective of the utilizers to minimize the total travel time, respectively. Moreover, simulated annealing algorithm and genetic algorithm are combined to solve the proposed multi-objective model. Simulated annealing algorithm is used as the main framework of the solution algorithm to minimize operating costs, while genetic algorithm is used as the subroutine of simulated annealing algorithm to optimize total travel time. The application results of a numerical experiment verified that the proposed optimization model and the solution algorithm are able to optimize the network layout and service frequencies at the same time.
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