The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit a...The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.展开更多
This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to addres...This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to address is the extent to which the size of generated path sets can affect the prediction of the static flow in the path-based traffic assignment paradigm. The importance of this question arises from the fact that the need to generate a large quantity of paths may be perceived by analysts as a preventative reason as to the application of path-based stochastic traffic assignment （STA） models for large-scale networks. A simulated path generation algorithm, which allows the number of generated paths to be under modeller＇s control, is applied. Findings show that the size of the generated path sets does not substantially affect the flow prediction outcome in this case study. Further investigations with respect to the relative importance of STA model estimation （or equivalently, parameter calibration） and model specification （or equivalently, error term formulation） are also conducted. A paired combinatorial logit （PCL） assignment model with an origin-destination-specific-parameter, along with a heuristic method of model estimation （calibration）, is proposed. The proposed model cannot only accommodate the correlation between path utilities, but also accounts for the fact that travelling between different origin-destination （O-D） pairs can correspond to different levels of stochasticity and choice randomness. Results suggest that the estimation of the stochastic user equilibrium （SUE） models can affect the outcome of the flow prediction far more meaningfuUy than the complexitv of the choice model （i.e.. model specification）.展开更多
基金the Humanities and Social Science Foundation of the Ministry of Education of China(Grant No.20YJCZH121).
文摘The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.
文摘This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to address is the extent to which the size of generated path sets can affect the prediction of the static flow in the path-based traffic assignment paradigm. The importance of this question arises from the fact that the need to generate a large quantity of paths may be perceived by analysts as a preventative reason as to the application of path-based stochastic traffic assignment （STA） models for large-scale networks. A simulated path generation algorithm, which allows the number of generated paths to be under modeller＇s control, is applied. Findings show that the size of the generated path sets does not substantially affect the flow prediction outcome in this case study. Further investigations with respect to the relative importance of STA model estimation （or equivalently, parameter calibration） and model specification （or equivalently, error term formulation） are also conducted. A paired combinatorial logit （PCL） assignment model with an origin-destination-specific-parameter, along with a heuristic method of model estimation （calibration）, is proposed. The proposed model cannot only accommodate the correlation between path utilities, but also accounts for the fact that travelling between different origin-destination （O-D） pairs can correspond to different levels of stochasticity and choice randomness. Results suggest that the estimation of the stochastic user equilibrium （SUE） models can affect the outcome of the flow prediction far more meaningfuUy than the complexitv of the choice model （i.e.. model specification）.
