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Mass Rapid Transit (MRT) systems, i.e. subway systems, all over the world, are experiencing an increase in ridership. This also means that in case of an unplanned MRT service disruption, the number of affected passengers is larger, requiring a fast and comprehensive response. In this paper, we thus study the disruption management of MRT systems. We develop an optimization model to identify the optimal bridging plan in response to an MRT disruption, so that the negative effects of a disruption could be minimized. Our approach supports deployment of multiple types of bridging buses, reflecting the diversity of vehicle types in a typical public transportation provider’s vehicle fleet. The optimization objective of our approach is to decrease the travel delay of passengers and increase the number of passengers who can be served. We demonstrate the effectiveness of our approach on a hypothetical case study in the central business district of Singapore. Moreover, we validate our analytical results with microscopic simulation, showing that our simplified analytical optimization approach can be used for disruption response planning. Some deviations indicate, however, that a combined simulation and optimization approach yields better results to obtain an effective bridging plan.

Crowding on Mass Rapid Transit (MRT) Systems has been the subject of intense scrutiny from a multitude of academic fields. Crowding prediction can assist system managers in finding effective ways to ease traffic pressure. Forecasting crowding levels is a challenging task due to a series of complex factors. Deep learning models are a common approach often applied to traffic prediction, but due to the imbalance within available data, these models that are based on historical data cannot forecast crowding in Rapid Transit Systems effectively. To solve this problem, this study proposes a model named the weighted resample bidirectional recurrent neural network (WRBRNN). First, the training data were split into different sub‐datasets according to certain predetermined labels. During this training time, every mini‐batch size sequence was weighed and resampled from different sub‐datasets. In this study, the authors carefully arranged the traffic data attributes into several time series, allowing the bidirectional time series information and the model to make reliable predictions. This work performed a case study of the Bay Area Rapid Transit, US system with a year's worth of historical data from 2017. Their results reveal that the model WRBRNN performed well when predicting crowding in an MRT system. [ABSTRACT FROM AUTHOR]

This paper presents key findings of a study conducted under Asian Regional Research Programme in Energy, Environment and Climate on least cost options to meet the projected transport service demand in eight selected Asian cities. It also presents the barriers to the adoption of the selected cleaner transport options. Furthermore, it discusses some policies and measures to promote the cleaner transport options. The study finds that high initial cost and lack of infrastructure (especially for compressed natural gas operated vehicles and mass rapid transit system) are the major barriers to the selected options. [ABSTRACT FROM AUTHOR]