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Beschreibung:
Mass rapid transit systems (MRT) have become the most important public transport mode in many major cities globally, given its advantages in larger capacity, faster velocity and higher reliability compared with other modes. Hence, various projects directed to improve service quality of MRT systems by optimizing operation strategies have been conducted worldwide. Among numerous methods to adjust operation parameters for MRT services, timetable optimization has emerged as the simplest and most effective cure. On one hand, a demand responsive timetable has the potential to improve service quality by reducing both waiting time and crowdedness on trains. On the other hand, for transit operators, a demand responsive timetable will improve operation stability and hence reliability. In this study, we propose an optimization model to solve timetable scheduling considering fine grained dynamic demand data. The objective is to minimize the total waiting time for all the passengers. Historical demand data extracted from smart card system is used as inputs. Then, a predefined demand responsive timetable will be generated with the optimization model. Since operational constraints such as the number of available trains and minimal headway are considered as boundary conditions, we ensure that the proposed schedule actually has the potential to be implemented in actual operation. A case study on a representative MRT service is conducted and presented in this paper. In the absence of effective/operated timetable, simulations on the optimal timetable obtained from this proposed model and simplified timetable as extracted from Google Maps are performed based on detailed travel demand extracted from smart card transactions. The result demonstrates the applicability of the proposed model in designing a demand sensitive timetable and its potential to minimize waiting times. However, simplified assumptions concerning potential operational constraints when switching directions or manpower scheduling, restrain the immediate application ...