Decision support tools for hybrid urban mobility systems

Abstract

Urban mobility systems are under growing pressure due to urbanization, increasing traffic congestion, widespread car ownership, and the associated environmental consequences. While public transport has long been promoted as a sustainable solution to these problems, conventional fixed-route systems often struggle to provide efficient and flexible mobility solutions, particularly in low-density areas and for access and egress connections. In response to these challenges, hybrid mobility systems have emerged as a promising solution. These systems combine multiple transportation modes, including public transport, on-demand transportation, shared mobility, and active modes, to provide more flexible and personalized mobility solutions. By integrating complementary transport modes, hybrid mobility systems have the potential to improve accessibility, reduce car dependency, and enhance the efficiency and sustainability of mobility systems. However, the design and operation of these systems introduce complex planning problems, requiring advanced tools to support strategic, tactical, and operational decisions. This PhD dissertation focuses on the development of optimization-based planning tools to support decisions regarding the design and operations of hybrid mobility systems.

The first part of this dissertation addresses the integration of on-demand transportation services with public transport and walking. On-demand transportation has gained increasing attention as a flexible mobility mode capable of complementing fixed-route public transport, particularly in areas where traditional services are inefficient or insufficient. However, existing research on the integration of on-demand mobility systems and public transport has mainly focused on minimizing operational costs from the perspective of the service provider, while service quality, from the perspective of the passenger, has received limited attention. Since user acceptance is crucial for the successful implementation of hybrid mobility, this dissertation develops an optimization model that simultaneously considers operational efficiency and service quality. The proposed model optimizes vehicle routing and scheduling decisions of the on-demand vehicles, all while accounting for synchronization with public transport services and minimizing the traveled distance and passenger travel times. Due to the complexity of the resulting optimization problem, a heuristic solution algorithm based on the Large Neighborhood Search metaheuristic framework is developed. In addition, extensive numerical experiments are performed to evaluate the performance of the algorithm and to derive managerial insights regarding the trade-off between operational efficiency and service quality.

The second part of this dissertation investigates the integration of Bike-Sharing Systems with public transport. Bike-sharing is recognized as an important component of multimodal mobility systems, particularly for improving access and egress connectivity. In practice, bike-sharing stations are often located near public transport stops to facilitate seamless transfers and expand the accessibility of public transport networks. However, existing literature has predominantly studied Bike-Sharing planning problems in isolation, without considering the interaction with existing public transport infrastructure. To address this research gap, this dissertation develops a mathematical optimization model for Bike-Sharing network design that explicitly accounts for the public transport network. The model determines optimal bike-sharing station locations and capacities while considering multimodal travel patterns, investment constraints, and different system components, such as relocation activities. The proposed model provides a decision-support framework for urban planners and Bike-Sharing operators seeking to design integrated and efficient mobility systems.

This dissertation contributes to the growing field of planning problems for hybrid mobility systems by providing optimization models and solution approaches that bridge the gap between theoretical research and practical decision-making in mobility systems. Through comprehensive numerical analyses, this research provides insights into the design and operation of integrated mobility systems, and it demonstrates the importance of considering interactions between transportation modes rather than optimizing individual services independently. The proposed decision-support tools can assist policymakers, mobility operators, and urban planners in designing sustainable and user-oriented hybrid mobility systems that balance operational efficiency, economic feasibility, and passenger satisfaction.