Railway operations are vulnerable to unexpected disruptions that should be handled in an efficient and passenger-friendly way. This paper focuses on a real-time train timetable rescheduling problem in a railway system with a seat-reserved mechanism during disruptions. An integer linear programming model with a compensation mechanism is established to provide a better overall solution for dynamic and multistage rescheduling problems. The main part of the model considers the strategies of retiming, adding train stops and reordering to reduce the impact of passengers, while the compensation mechanism part mainly deals with the uncertainty of disruption durations. The related constraints mainly include section track capacity, station track capacity,passenger transfer rules and some robust measures under the uncertainty of disruption durations. A multistage decision optimization framework is established, and a rolling horizon algorithm is embedded in it to approximate the desired optimal solution. A series of numerical experiments based on a real case of China’s high-speed railway subnetwork are carried out to verify the effectiveness and efficiency of the proposed optimization approach.

Train delay is an inevitable phenomenon in urban rail transit system, but it usually has ripple effects by propagating to other trains and lines. In particular, last train delay has serious effect on transfer passengers’ regular trips. Transfer passengers from last trains concretely fall into two types in this paper: passengers transferring from the last train of feeder line to the last train of connecting line (LtoL) or from the last train of feeder line to un-last train of connecting line (LtoU). Then the last train network delay management model is presented with two objectives: maximizing connecting passengers and minimizing Average Transfer Waiting Time (ATWT) of LtoL transfer passengers. To solve large-scale practical problems rapidly, an efficient genetic algorithm is designed based on this model. Finally, the Beijing subway network is taken as a case study to verify the effectiveness of this model. Under various last train delay scenarios, the results show that the bi-objective model can increase the number of connections between last trains and connecting passengers, meanwhile, there is a big decline in the ATWT of LtoL transfer passengers.

The transport network structure plays a crucial role in transport dynamics. To better understand the property of the bus network in big city and reasonably configure the bus lines and transfers, this paper seeks to take the bus network of Beijing as an example and mainly use space L and space P to analyze the network topology properties. The approach is applied to all the bus lines in Beijing which includes 722 lines and 5421 bus station. In the first phase of the approach, space L is used. The results show that the bus network of Beijing is a scale-free network and the degree of more than 99 percent of nodes is lower than 10. The results also show that the network is an assortative network with 46 communities. In a second phase, space P is used to analyze the property of transfer. The results show that the average transfer time of Beijing bus network which is 1.88 and 99.8 percent of arbitrary two pair nodes is reachable within 4 transfers.