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Abstract The optimisation of multimodal transportation is constantly evolving, striving to provide commuters with seamless mobility and sustainable networks. Multimodal transportation problems often, however, present optimisation challenges because of their high dimensionality, compounded by network size, modelling criteria, and modes. Among these challenges is computational complexity, which can be reduced with the use of metaheuristic solution approaches that strive to find an acceptable solution within a reasonable timeframe. In addition, as machine learning finds integration within real-world applications, the demand for parallel computing and robust computational infrastructure is on the rise. Given these rapid shifts, this paper is motivated to present a comprehensive systematic literature review on the optimisation of multimodal transportation, focusing on the urban mobility of passengers, using metaheuristics. After conducting a systematic bibliographic search, a thorough classification of studies based on their problem scope, mathematical formulation, methodology, temporal- and network settings is conducted. Overall, findings provide insights into tackling the challenges of multimodal urban transport optimisation for future investigation, addressing concerns over scalability and efficiency for real-time deployment.
Chau et al. (Fri,) studied this question.