Taiwan High-speed Rail System Research Articles

Design of Multi-agent Emergency Dispatching Model to Strengthen Resiliency of Taiwan High-speed Rail System

Although HSR (high-speed rail) systems can provide good service quality, such the systems have not been designed to handle disaster-related events very well, i.e., the trains dispatching work during a disaster. In other words, if there is no experienced train dispatchers in the control center of a HSR system, extended delays and possible catastrophic system failures might occur, thus exacerbating the impact caused by the disaster. Recently, a novel approach called the multi-agent system (MAS) is being increasingly used in transportation applications. Within a multi-agent assisted environment, a stakeholder such as train driver and depot manager can consultant with the corresponding MAS agent about the best trains dispatching strategy. The proposed ontology-based, multi-agent model was tested by using Taiwan HSR real dispatching cases. The results show the potential of assisting train dispatchers in handling disaster scenarios in a more efficient and effective way.

Demand adaptation towards new transport modes: the case of high-speed rail in Taiwan

This study aims to explain the factors affecting ridership changes on the relatively new Taiwan high-speed rail system (THSR). The analysis is based on monthly ridership data from January 2007 to December 2013. We also discuss the impact of THSR on competing modes such as air demand. Econometric time-series models are used for ridership estimation. First, a seasonal autoregressive integrated moving average model was applied; showing that the ridership thrives and that the trend prediction fairly well performed if applied to data after 2012. Second, to specify the impact of explanatory variables, a first-order moving average model was fitted. Results show that ridership, population and fuel price have a positive effect, while unemployment and car ownership tend to reduce the THSR ridership. We include as a separate factor ‘months since operation start’, showing that this factor is significant and discuss its relation to demand adaptation. Implications for general equilibrium modelling for new transport systems are discussed. Moreover, ridership data from two specific stations are used to test the importance of predominant trip purposes for demand estimation.

Optimization-based Train Timetables Generation for Taiwan High-speed Rail System Considering Circulation and Disturbances

As more and more governments are planning or constructing their high-speed rail (HSR) systems, the central control mechanism of such systems, i.e., train timetables, should be investigated more in order to cope with various disturbances due to disasters. Several optimization-based approaches have been successfully utilized for generating stable and reliable train timetables; however, few researchers have considered train circulation issues, especially for HSR systems, even though it could be such a way to reschedule the timetable against disturbances. This research proposed a scheduling optimization model that has the capability to accommodate not only basic requirements but train circulation as well for Taiwan HSR system. The sensitivity analysis was applied in order to identify how disturbances propagate in the original timetable and which actions to be taken in order to mitigate the impact instead of cancelling trains. With proper enhancement, the proposed model could be a good simulation tool to help predict the effect of disruptions on the timetable without doing real experiments.

Hierarchical analytic network process and its application in environmental impact evaluation

Dependence (influence) relationship among criteria in complex decision-making problems is difficult to identify through supermatrices in the analytic network process (ANP) because of the trouble in preserving consistency. To alleviate the complexity in such situations, the first objective of this paper is to propose a hierarchical ANP that synthesises hierarchical structures and networks, where hierarchies are used to represent outer dependency and networks serve to delineate inner dependence. In the ANP applications, expertise is widely applied to assess influence weights; however, in some cases the criteria assessed are all related to measurable attributes, and it is difficult to estimate their dependence due to the lack of relevant knowledge. Therefore, the second objective of this paper is to introduce a statistical method to obtain influence weights based on measurable data rather than subjective judgement. The third objective of this paper is to present a new technique for deriving global weights, which can largely reduce the computational complexity of powering supermatrices. Finally, a real-world case of Taiwan high-speed rail system is used to demonstrate the use of these approaches.

High-speed rail in Taiwan: New experience and issues for future development

This study aims to identify some possible issues and challenges for Taiwan's High Speed Rail (HSR) system, which was constructed and is operated under a Build–Operate–Transfer (BOT) model. The operational experiences in the initial stage for equivalent systems in Japan, France, Germany, and elsewhere are introduced herein. This study first presents Taiwan's HSR system development and conducts an ex post cost–benefit analysis of this transportation system. Second, unsatisfied ridership is examined to look for possible solutions to increase it. Third, the paper examines the impact of HSR on the intercity transportation market. Finally, the integration between HSR and various existing transportation modes is discussed. Several policy suggestions are included, which are useful for the decision makers of transportation systems’ entrepreneurs, the central government, and the local authorities to derive a comprehensive post-HSR planning strategy for a more integrated transportation system.

A Qualitative Decision Support for Environmental Impact Assessment Using Fuzzy Logic

In environmental impact assessment (EIA), experts produce conclusions not only according to scientific data but also political factors and social values. Subjective judgment plays a significant role in EIA. Traditional multi-criteria decision-making methods cannot provide best decision support because they are incapable of modeling qualitative human thinking process. On the other hand, fuzzy logic is widely recognized as a tool with the ability to compute using words to model qualitative human thinking processes in the analysis of complex systems and decisions. This study attempts to utilize fuzzy logic as a decision-support approach for EIA. The evaluation knowledge represented by "if-then" fuzzy rules is employed to measure the significance of environmental impacts. In this paper, 10 indicators and 28 subindicators are considered in the assessment framework using the Taiwan High-Speed Rail System as the case study.

Optimizing Distance-Based Fares and Headway of an Intercity Transportation System with Elastic Demand and Trip Length Differentiation

An efficient fare structure combined with good service quality can stimulate demand and increase revenue. This paper presents an optimization approach for an intercity transportation system where demand is sensitive to fare and wait time and the fare sensitivity further varies with trip length. The approach jointly optimizes service headway and distance-based fare structure to maximize total profit, subject to service capacity and fleet size constraints. The optimal fares are also differentiated on the basis of trip length. An efficient solution method is developed and applied to solve the profit maximization problem. The model is applied to a case study of the Taiwan High-Speed Rail System. Numerical results, including optimal solutions and sensitivity analyses, are presented. Insights that may lead to managerial decisions are discussed.

Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

It is essential to understand the characteristics of strong motion for reducing the negative impacts in a high-risk area. In this work, a combination of seismic parameters including epicentral distance, focal depth, and magnitude from historical records at 30 checking stations were used in back-propagation neural network model, to estimate peak ground acceleration at ten train stations along the high-speed rail system in Taiwan. The estimation was verified with available microtremor measurement at a specified station, and the calculated horizontal acceleration was checked with the existing building code requirements. A potential hazardous station was identified from the neural network estimation, which exhibited a significantly higher acceleration than that of the design value. The obtained results might be useful for revising the currently applied building code at this region to further fit in the actual earthquake response.