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In many densely populated mega-cities around the world, metro systems are becoming overly saturated by the ever-increasing travel demand, which results in overcrowded stations, excessive delay, and unsafe anxieties among riders. The concept of extra-long trains (i.e., trains longer than station platforms) was recently proposed in the literature as a promising way to increase a metro line's capacity without additional infrastructure construction. This paper develops a general modeling framework to optimize train block configuration and operation protocols of a metro line so that extra-long trains can be used under varying demand distribution and infrastructure setting. The design problem is formulated as the integration of two coupled vehicle routing problems which simultaneously optimizes the train block configuration plan, door opening strategy, stop skipping strategy, and train dispatch schedule. This paper proposes two customized solution methods, including an adapted savings heuristic and a destroy-and-repair algorithm. A series of hypothetical examples are tested to demonstrate how the proposed solution approaches outperform an existing commercial solver even for small to moderate problem instances. Moreover, two real-world case studies, with very different demand patterns from two continents, are presented to test the effectiveness of using extra-long trains. Our results show that a metro line with extra-long train operations is capable of serving up to 20%–30% more passenger demand as compared to that with only regular trains, while at the same time producing a similar or lower passenger average travel time. • Design of extra-long trains (i.e., longer than station platforms) in a general setting. • Mixed-integer program to optimize train block configuration and operation protocols. • Customized solution methods (adapted savings and destroy-and-repair). • Extensive experiments with hypothetical and real-world data (from Beijing and Santiago). • Results show significantly increase in metro line capacity without infrastructure construction. [ABSTRACT FROM AUTHOR]

Abstract: A high-quality Origin–Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis. However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement. This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods. Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection. In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile. The proposed method is applied to two 1-week datasets obtained for different time periods. From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions. The results are available at any desired time–space disaggregation. After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices. [Copyright &y& Elsevier]