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 we 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, we introduce delay reduction measures and indices, which indicate the frequency of delays and the scale of delays.
In urban railway lines, it is usually observed that once a small delay happens, it widely spreads out to many other trains. There is a compelling need to identify small delays that very often happen and influence widely and to take an effective countermeasure to decrease those delays by adjusting timetables for example. In this paper, we introduce an algorithm to identify delays that very often happen and influence widely from daily train traffic record data. The key idea of the algorithm is to seek for the causes of delays by tracing back a critical path on a PERT network constructed from train traffic record data. We show the details of the algorithm together with our experience of its application to real world data.
In order to improve safety, platform doors have been installed in some train and subway stations in the Tokyo metropolitan area of Japan. The number of accidents (such as passengers falling from platforms, or coming into contact with moving trains) has decreased. On the other hand, scheduled dwell times have had to be increased to allow for the extra time needed for platform doors to open and close. As such, travel times are longer after the installation of platform doors than before. However, an overall improvement in the stability of train operations has been observed during rush hour. This paper presents an analysis of such phenomenon and valuates the train operation stability.
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