Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned problems by contributing to a more sustainable and equitable transport system. This thesis defines mobility hubs as places where shared cars, mopeds, and e-bikes are offered to improve connectivity and ameliorate mobility options. Given a limited budget, cities would like to optimize the locations of mobility hubs to maximize benefits. This problem is solved in this thesis by presenting an optimization model that allows the distribution of mobility hubs and allocation of shared cars, mopeds, and e-bikes to maximize social welfare. The algorithm can provide the optimal locations for the hubs and their respective capacity in terms of vehicles, while accounting for multimodal trips. It focuses on maximizing the utility of the population rather than the operators’ profits. The model is divided into several modules: computational modules that calculate the number of people that would like to use a mobility hub; a mathematical optimization module to optimize the capacity, availability, and relocation of shared vehicles; and finally, a genetic algorithm that performs several iterations to find the optimal distribution of hubs. The model developed is applied in a case study for the city of Amsterdam. Several scenarios are performed to assess how the distribution of hubs varies depending on the budget provided to construct them. The results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer with more vehicles. Areas with higher population density are prioritized when lower budgets are invested in building the hubs. Additionally, the shift towards shared modes and the travel time savings are minimal. The benefits increase considerably when investments lead to complete coverage of the area by the network of mobility hubs. A modal split of 5% for the shared modes is expected when Amsterdam is fully covered by 288 hubs. From an environmental point of view, only 32 % of the shared trips replace trips previously made by car, leading to a limited CO2 emissions reduction of 1.27%. To conclude, the model developed is one of the first models that optimizes the location and capacity of multimodal hubs to maximize social welfare by considering multimodal trips. It has the ability to quantify the benefits of introducing mobility hubs depending on the investments made.

Urban areas are seeing influx of population and therefore are experiencing increasing stress on the systems currently in place. In traffic networks, a larger population means that their are inevitably a short term increase in mobility demands. To accommodate this, governments and and the private sector are proposing new solutions for mobility. One such focus is the introduction of new transport modes. A common classification for these modes is Mobility-as-a-Service (MaaS), which includes modes such as carsharing, shared last-mile transportation like bicycles, and ridesharing. MaaS also overlaps with other new modes such as autonomous and electric vehicles. Modeling these types of modes and their subsequent interactions with each other and traditional modes requires more complex modeling techniques than have been traditionally used in transport modeling.

The purpose of this thesis is to propose a method for using agent-based modeling and simulation (ABMS) to assess traffic network resilience, as ABMS has the dynamic qualities that match well with the time variant nature resilience.. This gap is especially prevalent when considering novel modes of transport, such as ridesharing. Data from the metropolitan region of Rotterdam and The Hague (Metropoolregio Rotterdam Den Haag or MRDH) is used for a case study on the corridor between Rotterdam and The Hague. The goal is to prove feasibility of a method for assessing resilience in traffic networks using ABMS.

A resilience framework for urban mobility is proposed that uses six categories to characterize resilience: (1) reflective, redundant, flexible, resourceful, inclusive, and integrated. Using this framework as a guide, three metrics are proposed for measuring resilience using agent-based simulation: origin-destination (OD) travel time, link travel time, and link volume. These metrics are used to compare scenarios that either include a disturbance in the network or do not, which in this case occurs for a 30 minute period on the A4 roadway between Rotterdam and The Hague. While the OD travel time metric is limited in its usability, the link travel time metric makes apparent the the recovery time to achieve normal operating conditions after a disturbance. In the presented case study, scenarios that included ridesharing had worse recovery time then the car only scenario, as well as a higher maximum travel time across the disturbed link. The link volume metric contextualizes these results, showing that while the overall volume throughout the simulation is lower for the ridesharing cases, the volume during the disturbance across the disrupted portion of the A4 roadway is higher. The higher volume shows why the travel time is higher with the presence of ridesharing in the disturbance scenario. These results are subject to the limitations of the model, though, which include dynamic routing that may not avoid the disturbed portion of the network when the disturbance occurs.