Norio Tomii

An optimal delay management algorithm from passengers' viewpoints considering the whole railway network

Abstract We propose an algorithm for optimal delay management. Delay management means a decision whether or not to keep a connection between trains and is made by dispatchers when trains are delayed. We set dissatisfaction of all passengers in the whole railway network as a criterion and develop an algorithm which seeks for a delay management plan which minimizes passengers’ dissatisfaction. The algorithm is a combination of simulation and optimization. The simulation part consists of a train traffic simulator and a passenger flow simulator which work in parallel. The train traffic simulator forecasts future train diagrams considering the dynamic interaction between trains and passengers. The passenger flow simulator traces behaviour of all the passengers one by one and calculates how many passengers get on/off at each station. This information is given to the train traffic simulator and necessary dwell times are calculated. Passengers’ dissatisfaction is also estimated from the results of the passenger flow simulation. In the optimization part, we use the tabu search algorithm. We will show the details of our algorithm together with numerical results using real world data.

A MIP-based timetable rescheduling formulation and algorithm minimizing further inconvenience to passengers

Abstract This paper presents a timetable rescheduling algorithm based on Mixed Integer Programming (MIP) formulation when train traffic is disrupted. We minimize further inconvenience to passengers instead of consecutive delays caused by the disruption, since loss of time and satisfaction of the passengers are considered implicitly and insufficiently in the latter optimization. We presume that inconvenience of traveling by train consists of the traveling time on board, the waiting time at platforms and the number of transfers. Hence, the objective function is calculated on the positive difference between the inconvenience which each passenger suffers on his/her route in a rescheduled timetable and that in a planned timetable. The inconvenience-minimized rescheduling is often achieved at the cost of further train delays. Some trains dwell longer at a station to wait for extra passengers to come or to keep a connection, for instance. In the MIP model, train operation, each passenger’s behavior and the amount inconvenience are simultaneously expressed by a system of integer linear inequalities. As countermeasures against the disruption, changes of train types and rolling stock operation schedules at termini as well as changes of departing order of trains and assignment of a track to trains in stations are performed. We also consider capacities of a line between adjacent stations as well as those of a track in stations. We have conducted numerical experiments using actual data and have obtained better rescheduled timetables in terms of customer satisfaction within practical time in proper solution space.

An Algorithm for Train Rescheduling using Rescheduling Pattern Description Language R

This paper proposes an algorithm for automatic train rescheduling with a train rescheduling pattern language processing system. Intended for restoration from heavy train traffic disruption, our proposed algorithm has inherent abilities to make effective train rescheduling plans. While the previous algorithm tries to make a train rescheduling plan in small steps, the proposed one surveys the train timetable at first and applies “train rescheduling patterns” to prepare rescheduling plans. Applying actual train schedule data, the paper confirmed that the algorithm works satisfactorily. For severe train traffic disruption caused by an accident, in particular, requiring the suspension of train operations for more than an hour, the algorithm is helpful for preparation of adequate rescheduling plans for practical application.

Taking Effective Delay Reduction Measures and Using Delay Elements as Indices for Tokyo’s Metropolitan Railways

In the Tokyo metropolitan area of Japan, several minutes of delays often happen during morning rush hour. This is because a massive number of passengers use trains for commuting. Then trains and platforms are congested. Above all, once a train delays due to increasing dwell time, the subsequent train has to wait outside a station or slow down if the platform is occupied by the preceding train. This process is repeated over and over again and delays increase more and more. So the authors had to identify how often or how large delays occur and take delay reduction measures, because it is pointless to blindly carry out delay reduction measures. The measures should be carried out effectively; otherwise investigations and running costs become high. In this paper, the authors introduce delay reduction measures and indices, which indicate the frequency of delays and the scale of delays.

Punctuality analysis by the microscopic simulation and visualization of web-based train information system data

In order to increase the robustness of a railway system, the detailed analysis for a combination of tracks, signalling systems, timetables and operation of trains is indispensable. The authors introduce an approach to foresee and analyse punctuality for railway lines where trains are densely operated. The authors approach consists of three phases, namely, the analysis of the current situation of train operation (observation phase), the forecasting of the future situation of train operation (forecast phase) and the analysis of train operation after some changes are made (checking phase). In the observation phase and in the checking phase, the authors visualize train traffic record data using the Chromatic diagram. In the forecast phase, the authors use a microscopic simulator that the authors have developed. In this paper, the authors show their experience of applying this approach when a part of a track was relocated in the Odakyu electric railway company.

Identification of causes of delays in urban railways

This paper presents an algorithm that can be used to identify urban railway line delays. The algorithm looks for the delay cause by tracking a critical path on a PERT network that is created from train traffic record data. Small delays that occur often have large impacts on many other trains in the line, so it is useful to be able to identify the location of the delay, and take action to mitigate it.