A fast method for estimating railway passenger flow

Abstract

To improve the situation for crowded commuters in Japan, it is important to plan a train schedule that considers passenger behavior, such as their choice of trains and the transfer stations used to reach their destinations. However, it is difficult to directly measure such detailed behavior using the present infrastructures, with which we can only get OD (Origin-Destination) data from the automatic ticket gates. The obtained OD data only consists of the number of passengers for each origin-destination and the time each passenger passes through the gates. In this article, to contribute to the planning phase of a new train schedule, the authors propose a method for estimating railway passenger flow using OD data. This paper firstly points out that the problems of estimating passenger flow can be boiled down to a shortest path problem of graph theory by assuming a certain passenger behavior model. By representing train operations in a graph structure, we can assume that a passenger will use the minimum cost path to his/her destination. This paper secondly proposes a method for conducting fast searches of the graph structure. The method uses the fact that railways operate on a time schedule. This method can estimate passenger flow fast enough so as to apply it to a practical train schedule planning support system. Lastly, the authors show the results of applying the passenger flow estimation system to a railway in an urban area in Japan.

The simulation of passengers’ time-space characteristics using ticket sales records with insufficient data

“…In contrast to the macroscopic models proposed by Myojo [4,5] and Nagasaki et al [6], Barry et al [1] proposed a microscopic methodology to estimate OD tables from the AFC records of MetroCard in New York City. The results were used for other purpose such as traffic assignment in transportation planning model.…”

“…Nagasaki et al [6] also proposed a similar approach to estimate passenger flow but formulated passengers' route choices as a shortest path problem. Nagasaki's model considered not only journey time and transfer barrier factors but also congestion factors.…”

Interactive timetabling system with function of estimating train congestion on timetable planned

“…First, each node has data about both the next node on the shortest path and the shortest required time in order to reach every station. Second, according to the arrival or the departure time of each node, the shortest path search is conducted in a reverse direction (a direction going back in time) in the same way as Nagasaki et al [3] proposed. Third, both the shortest required time and an arc which should be traced on the shortest path from the node are recorded or updated as the data of the shortest path, according to the sum of the time data of another node linked by the arc and the cost of the arc.…”

“…For example, we built a prototype 'Multi-Agent Train Simulator with Intelligent Information Exchange' which has a function by which passengers' flow and delay time of each train are estimated simultaneously by using the data and timetable data (Kunimatsu et al [2]). Also, Nagasaki et al [3] proposed a fast-estimating algorithm of passengers' flow under a given timetable by preliminary search in a reverse direction (a direction going back in time).…”