The asymmetric demands of metro lines in megacities can cause high passenger wait times and substantial underutilization of vehicle capacity. The problem is difficult to address because of passenger flow uncertainties and random delays. We propose a modular transit system (MTS) that allows a metro fleet to be dynamically dissembled and assembled in identical modules (or carriages) on metro terminals. A formal formulation of this issue is provided with a nonlinear programming (NLP) model that considers train power, greenhouse gas emissions, wind resistance, and operational economics. Then, a linearization of the NLP further facilitates its fast solution. By utilizing numerical experiments based on Shenzhen Metro data, we illustrate the mathematical model’s viability and confirm the model's usefulness in terms of the economic, low-carbon, and ecological consequences. Then, the robustness of the proposed model and the sensitivity analysis with various parameter values are reported.
In contrast to private transport, public transport (such as buses, trams, light trains, and metros) is a mode of transportation for passengers employing group travel systems accessible to the general public (Wu et al., 2021). Most public transportation services follow predetermined routes with predetermined points of boarding and alighting. With set routes and predetermined timetables, they often charge a fixed cost for each trip (Guo et al., 2017). However, the asymmetric distribution of passenger demand across different periods is considered a tough and persistent transit operational problem in megacities, which causes either large passenger wait time costs or considerable vehicle capacity waste (Shi et al., 2020). Therefore, passenger flow uncertainty and random delays vie to make these problems harder to address (Wang et al., 2020).
The conflict between spatially and temporally shifting passenger demand and fixed capacity to deliver transportation is a challenge that has persisted for a long time in metro transportation in megacities. These tough and persistent metro operational problems cause massive passenger wait time costs or considerable vehicle capacity waste (Pei et al., 2021). As shown in Fig. 1, taking the Shenzhen Metro system as an example, the passenger arrival demand rates on a typical day of operation for a metro transport system exhibit substantial temporal changes. Some MT operators suggest providing peak- and off-peak-based schedules to fit the asymmetric distribution of passenger demand across different periods. However, although this technique enhances MT system service quality to some level, there are still unresolved issues (Niu and Zhou, 2013). During peak hours, the passenger arrival rate is so high that passengers may be required to wait for many trains before boarding. During off-peak hours, the number of passengers on the subway is sometimes small. This means that there is a low load percentage and wasted energy for fixed-capacity carriages. (Chen et al., 2019). This research proposes an effective operational strategy to solve the identified problems that concurrently optimize dispatch headways and carriage capacities. The study is based on metro terminal data and can be easily extended to other urban public transit systems.
NEXT Future Transport is attempting to revolutionize the method of transporting people and objects (Aleksandar et al., 2012; Ali-Eldin and Elmroth, 2021; Casado et al., 2020; Qu et al., 2022; K. Zhu et al., 2022 ´ ). This innovative smart transportation system is built on swarms of modular electric vehicles according to the modular vehicle concept advanced by the NEXT Future transport company (Next Future Transport, 2022). As seen in Fig. 2, each module may compose or decompose from other modules. Because of this adaptability, in-route transfers are possible, which improves the system capacity rate, reduces costs and traffic, and promotes passenger ubiquity while simultaneously improving passenger comfort.
This paper focused on the challenging asymmetric distribution of the passenger demand problem of metro systems and proposed a modular transit technology-based nonlinear programming model to weight the value of train power energy, greenhouse gas emissions, wind resistance, and operational economics to optimize the total system cost. In the model, the MT system allows the metro fleet to be disassembled and assembled in identical carriages dynamically at metro terminals, bringing new perspectives to the problem. Since nonlinear programming fails to provide an exact solution, we rigorously formulated the problem based on a series of linearization operations.
We illustrate the practicability of the mathematical model by study cases. We gather real world data from the Shenzhen Metro and validate the efficiency of the proposed model in terms of its economical, lowcarbon, and environmental effects. The result shows that the total cost of the MT system can be 26.08 % lower than that of the existing system. The resiliency of the proposed model under a variety of parameter configurations is examined to test an additional parameter value trend.
The metro system is a perfect public transport system for modular transit. The carriages are easier to operate, composing and decomposing under the organization of the modular operation concept. However, this organization process will also encounter many challenges. Since the length of metro platform is fixed, the passenger travel guidance on the platform must be adjustable with the change of the metro train carriages, which will be very interesting research in future modular transportation system modeling, which will bring more new opportunities and challenges to the future research work. Moreover, this work can be extended in additional directions in the future. First, transportation electrification can provide more choices in public transit systems, which can bring additional challenges, e.g., vehicle charging and parking (Eliasson, 2021; Kopplin et al., 2021; Yagcitekin and Uzunoglu, 2016), battery degradation and replacement (Pelletier et al., 2017; Yang et al., 2018; Zhang et al., 2021), and charging facility options and locations (Agrawal et al., 2016; Erdelic et al., 2019; Jang, 2018). Second, connected and autonomous technologies in transportation will create a complete industrial revolution (Li et al., 2022; Peng et al., 2021; J. Zhu et al., 2022), which can be a good opportunity for modular vehicles to be used based on a connected and autonomous environment, leading to more joint optimization problems.