Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow

Abstract

In order to evaluate train timetables from point of view of the passengers, it is indispensable to estimate the disutility of passengers. This can be done by accurately estimating the movements of passengers and trains. In particular, when there is a large number of passengers, the interactions between the passengers and trains must be considered. To this end, we have developed a microsimulation system to simulate both train operation and passengers' train choice behavior. The system can simulate the train choice behaviors of more than one million passengers as well as their positions in trains. It is possible to estimate the delays caused by crowding in trains as well. The system is based on models of different attitudes of the passengers with respect to the train choice behavior, which includes the choice of the earliest train, transfer avoidance, and avoiding crowding; a passenger's train choice behavior reflects his/her preferences. We applied this system to an actual railway line in a metropolitan area and evaluated two train schedules by calculating the generalized cost, which reflects each passenger's disutility based on his/her experience. Numerical experiments confirmed that the proposed method is very useful for evaluating timetables from the point of view of the passengers.

Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

“…This is supported by the lack of freedom of movement for passengers within the coach, especially in crowded conditions. However, it is worth noting that, although in the literature there are few studies on the microscopic behaviour of passengers on the platform, we adopted the proposal of [34] for determining the initial position of passengers when a train arrives. Another equivalent approach would be based on distributing passengers uniformly on the platform.…”

“…The former includes the position of access/ egress facilities on the platform [34] and train stop type (e.g. short stop or large stations [35]).…”

“…Dwell times are computed as a function of travel demand flows evaluated by simulating explicitly user behaviour on the platform when a train arrives. Moreover, the proposed methodology also takes account of station layout, assuming, as proposed by [34], that passengers during the boarding process choose the door on the basis of their exit position (for example, stairs or elevator) in the alighting stop so as to minimise the walking distance at their own destination station. Obviously, the choice of the preferred door cannot be always satisfied, especially in high crowding conditions.…”

“…Indeed, in these cases, passengers tend to modify the selected door (choosing adjacent doors and/or adjacent coaches) as a function of the crowding degree in order to board as soon as possible. Hence, in these contexts, the assumption on the starting position of passengers on the platform (according to the approach proposed by [34] or other equivalent approaches) represents only the initialisation of the loading algorithm.…”

Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

“…A predictive train rescheduling model incorporating the model predictive control (MPC) mechanism and the non-analytical prediction model are proposed to be taken into consideration synergistic safe and efficient operations in high speed trains [12]. A microsimulation system about train timetable evaluation from the viewpoint of passengers to simulate both train operation and passengers' train choice behavior is developed [13].Further research is necessary based on some of the above research results, and three crucial factors about multi-objective automatic train operation (ATO) target velocity trajectory optimization should be taken seriously. First, there are many uncertainties and complex relations in actual automatic train operation (ATO), and it is difficult to obtain ideal optimization results only by using traditional optimization algorithm.…”