To update a public transportation origin-destination (OD) matrix, the link choice probabilities by which a user transits along the transit network are usually calculated beforehand. In this work, we reformulate the pr...
详细信息
To update a public transportation origin-destination (OD) matrix, the link choice probabilities by which a user transits along the transit network are usually calculated beforehand. In this work, we reformulate the problem of updating OD matrices and simultaneously update the link proportions as an integer linear programming model based on partial knowledge of the transit segment flow along the network. We propose measuring the difference between the reference and the estimated OD matrices with linear demand deficits and excesses and simultaneously having slight deviations from the link probabilities to adjust to the observed flows in the network. In this manner, our integer linear programming model is more efficient in solving problems and is more accurate than quadratic or bilevel programming models. To validate our approach, we build an instance generator based on graphs that exhibit a property known as a "small-world phenomenon" and mimic real transit networks. We experimentally show the efficiency of our model by comparing it with an Augmented Lagrangian approach solved by a dual ascent and multipliers method. In addition, we compare our methodology with other instances in the literature.
作者：
Lin, YuqianYang, MinSoutheast Univ
Jiangsu Key Lab Urban ITS Jiangsu Prov Collaborat Innovat Ctr Modern Urban Sch Transportat Nanjing Jiangsu Peoples R China
Passengers' travel experience on previous days will potentially influence their transit route choices on the next day. The day-to-day information is an important reference for decision-making of the transit assign...
详细信息
ISBN:
(数字)9780784482292
ISBN:
(纸本)9780784482292
Passengers' travel experience on previous days will potentially influence their transit route choices on the next day. The day-to-day information is an important reference for decision-making of the transitassignment. This paper investigates the frequency-based transit assignment problem in a learning-based, day-to-day dynamics environment taking the learning and prediction behavior into consideration. The passengers' route cost updating, as well as the flow updating, are explicitly considered and incorporated in the day-to-day learning process of the transitassignment model. With the proposed cost updating and flow updating process, the frequency-based optimization model of the day-to-day transit assignment problem is formulated. Afterwards, the model is solved with the method of successive averages. Finally, the numerical experiment validates the effectiveness of the proposed model and algorithm. It is revealed that the passengers' flow evolution process will tend to be stable due to the day-to-day learning mechanism.
暂无评论