Cars remain the most widely used mode of transport today. However, in many urban areas, high car usage leads to negative externalities such as congestion, pollution, and inefficient land use. Optimising parking policies in cities is a promising approach to reduce these externalities, though it often involves trade-offs; for example, reducing parking space can increase the time drivers spend searching for a spot. We present a model to optimise parking capacities in urban areas using a multi-objective framework that simultaneously minimises (1) travel time, (2) distance travelled by car, and (3) the number of parking spaces. We address this problem using a bi-level programming framework as parking capacity decisions (upper level) influence driver route and parking choices (lower level), which in turn affect the objective values. Our main methodological contribution lies in enhancing the upper level optimisation through a novel mutation operator, which helps achieve lower objective values. We apply our model to the city of Delft, the Netherlands, demonstrating that a diverse set of solutions with low objective values can be obtained. Moreover, we show through an example within this case study that our model can help policy-makers assess trade-offs in the conflicting objectives.

Automated driving developments should be considered when making decisions about investments in physical and digital infrastructure. This paper proposes four scenarios for automated driving developments in the Netherlands in 2040 and 2060 taking into account uncertainties regarding future penetration rates, the level of connectivity, the operational design domain, and the expected impacts of automated driving: 1) Late transition, 2) Automated vehicles on main roads, 3) Car-topia, and 4) Share-topia. To derive these scenarios, an extended switchboard method is introduced in which multiple driving forces for automated driving can be varied. The main driving forces were identified based on expert surveys. For each scenario, a modelling approach is used to compute the impact of automated driving on vehicle kilometres driven and congestion. The extended switchboard method offered more flexibility than existing scenario methods. The model-based impact assessment provided more conservative and probably more accurate insights into the expected impacts of automated driving on vehicle kilometres driven and congestion than expert estimates from the literature. The results show that in all scenarios automation leads to an increase in the number of trips, vehicle kilometres driven and congestion. In the scenarios with autonomous vehicles, congestion is expected to increase up to 17%. The higher the penetration rates of connected automated vehicles, the smaller the increase in congestion (1.5%-11%). The results indicate that investments in digital infrastructure are needed to prevent capacity reduction due to autonomous driving. The scenarios “car-topia” and “share-topia” may require additional physical infrastructure on motorways and regional roads, and/or the implementation of demand management strategies.

Existing activity-based and agent-based simulations alone often failed to capture the interaction between individual activity scheduling and detailed urban traffic dynamics. ActivitySim provides a representation of individual activity schedulings but often lacks detailed traffic dynamics, whereas MATSim can capture detailed interactions between travellers and mobility systems but often overlooks several decision-making factors, such as activity scheduling shift, household interactions and land-use influences. To address these limitations, this paper presents an Activity- and Agent-based Co-simulation framework that integrates ActivitySim and MATSim, both of which are open-source software popularly adopted in each research community. ActivitySim generates individual activity schedules and location choices, which serve as synthetic travel demand input for MATSim. MATSim then simulates detailed mobility interactions, with its outputs aggregated into zonal level-of-service matrices and fed back to ActivitySim for iterative scheduling adjustments. The feedback loop bridges the strengths of both models and is applied to the MRDH (Rotterdam-The Hague Metropolitan) region in the Netherlands. The initial MRDH model for the base-year reference scenario demonstrates that the proposed co-simulation framework effectively replicates existing mobility patterns, paving the way for fine-grained intervention evaluations like ride-hailing services in the future.

The advent of Connected and Automated Vehicles (CAVs) has ushered in substantial changes in the transportation sector, particularly impacting the resilience of road networks. CAVs can exchange real-time information about road conditions, allowing them to bypass congestion and optimise their routes, thereby enhancing network resilience through dynamic rerouting. Additionally, these vehicles significantly affect road capacity, further bolstering the overall resilience of the network. As a result, it is essential to assess the impact of CAVs on road network resilience comprehensively. However, to the best of the authors’ knowledge, there is a notable gap in research that thoroughly evaluates the resilience of large-scale road networks, taking into account all dimensions of resilience, such as redundancy, robustness, and recovery speed. This paper aims to fill this gap by assessing the influence of CAVs on the resilience of a large-scale road network in Belgium. Utilising a simulation-based approach, the study quantifies the network's resilience triangle, addressing all facets of network resilience. The findings reveal that the integration of CAVs can markedly improve network resilience under various scenarios, with improvements ranging from 4.4% at a 10% penetration rate to 59.9% at full penetration. These insights are valuable for researchers and policymakers focused on the implementation of autonomous vehicles.

Mobility as a Service (MaaS) and new mobility concepts mutually inspire each other, provide alternatives for the private car-oriented transport system as we know it, and will offer more mobility choices in a single journey than ever. This multitude of mobility choices however poses challenges in modeling the travelers’ mode choices in travel demand prediction models. To address these challenges, this paper develops a multimodal tour-based mode choice model as part of an activity-based demand model. By explicitly modeling access and egress modes, this choice model creates multimodal mode chain sets on a tour level based on restrictions with respect to personal vehicle ownership, MaaS subscription ownership and vehicle states, and subsequently makes mode choices for every traveler. For the creation of these mode chain sets, we introduce the concept of mode categorization. Seven mode categories are proposed, which include both private and shared mobility concepts. This categorization makes sure that modes are mutually sufficiently different in nature, so that reasonably unbiased mode chain choices can be made. Furthermore, the reduction to seven categories enables the study of large scenarios, while the introduced categories still represent new and already existing modes well. The potential of the model is illustrated by simulating travel demand in the Metropolitan region Rotterdam-The Hague. The results show that our model is capable of making plausible mode choices in the presence of MaaS and new mobility concepts, and can be used to assess the impact of mobility hubs where access and egress mode choice is important.

Understanding preferences and behaviours in road freight transport is valuable for planning and analysis. This paper proposes a data-driven vehicle routing and scheduling approach for use as a descriptive tool to study road freight transport activities. The model developed seeks to capture planners’ or drivers’ preferences in order to reproduce observed road freight activities. The model is based on a parametrized time-dependent vehicle routing problem whose parameters can be estimated from a set of observed planned tours. We propose a Bayesian optimization technique for parameter estimation of the model. Empirical results show that the model can fit real-world data accurately and synthesize tour flows close to reality.

Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Shared mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned challenges by contributing to a more sustainable transport system. These are places where shared cars, mopeds, and e-bikes are offered to improve connectivity in urban areas. In this paper, we investigate the impact of efficiently allocating multimodal shared mobility hubs on modal split, service level, and environmental factors while assuring economic feasibility. Given a limited budget, cities would like to optimize the hubs’ locations to maximize the population's benefits. For that purpose, we introduce a multi-stage design algorithm model that distributes the hubs and allocates fleets of shared cars, mopeds, and e-bikes to maximize travel utility for all the population traveling using traditional and/or shared modes while accounting for multimodal trips. The model is divided into several modules: computational modules that calculate the demand for the hubs; an optimization module to optimize the hubs’ capacities, availability, and relocation of shared vehicles; and finally, a genetic algorithm to find the optimal hub distribution. Our proposed model is one of the first that optimizes the location and capacity of multimodal hubs by considering multimodal trips in a large network. Additionally, it allows to assess mobility, spatial, and environmental impact of shared modes. The model is applied to the case of Amsterdam, the capital of The Netherlands, with around 800,000 inhabitants. After running several scenarios with different budgets allocated to build the hubs, results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer hubs with higher capacity. That is because the travel time savings increase considerably when investments lead to complete coverage of the area by the hubs network. A modal split of 5% for the shared modes is expected when Amsterdam is covered by 288 hubs. From an environmental point of view, only 32% of the shared trips replace trips previously made by ICE and electric cars, leading to a limited CO2 emissions reduction of 1.27%. Hence, introducing shared modes and mobility hubs without push measures for the use of private cars appears to offer limited benefits to decrease the negative impacts of private car usage.

Connected and Autonomous Vehicles (CAVs) may exhibit different driving and route choice behaviours compared to Human-Driven Vehicles (HDVs), which can result in a mixed traffic flow with multiple classes of route choice behaviour. Therefore, it is necessary to solve the Multiclass Traffic Assignment Problem (TAP) for mixed traffic flow. However, most existing studies have relied on analytical solutions. Furthermore, simulation-based methods have not fully considered all of CAVs’ potential capabilities. This study presents an open-source solution framework for the multiclass simulation-based TAP in mixed traffic of CAVs and HDVs. The proposed model assumes that CAVs follow system optimal with rerouting capabilities, while HDVs follow user equilibrium. It also considers the impact of CAVs on road capacity at both micro and meso scales. The proposed model is demonstrated through three case studies. This study provides a valuable tool that can consider several assumptions for better understanding the impact of CAVs on mixed traffic flow.

Activity-based travel demand models provide a high level of detail when modeling complex travel behavior. Since stochastic simulation is used, however, this high level may induce large random fluctuations in the output, necessitating many model reruns to produce reliable output. This may become prohibitive in terms of computation time when comparing travel behavior between multiple scenarios, in which case each scenario requires its own simulation. To alleviate this issue, we study the use of common random numbers, which is a technique that reuses the same random numbers for choices made by travelers between scenarios. This ensures that any observed difference in output across scenarios cannot be attributed to mutual differences in drawn random numbers, eliminating an important source of random fluctuation. We demonstrate by a numerical study that common random numbers can greatly reduce the number of runs needed, and thus also the required computation time, to obtain reliable output.

Nowadays, many cities are intending to reduce the use of private vehicles. Governments are incorporating new mobility services and are adapting their parking policies to promote a more sustainable mobility, as both strategies are believed to have the potential to reduce private vehicle use. To understand the effects of these strategies, one needs to be able to model complex travel behaviour up to a very high level of detail. Owing to their flexibility, robustness and ability to model travel activity behaviour on an individual level, activity based travel demand models (ABM) offer a highly suitable methodology for this purpose. In this paper, we employ this methodology to perform a case study in a metropolitan region in the Netherlands which surrounds and includes the cities of Rotterdam and The Hague. This region is of vital economic importance and has a very developed and dense road network. The population of this region is growing, which motivates the ambition to improve its accessibility and move towards sustainable mobility. Therefore, the findings of this study are important to similar regions seeking to do this as well. After setting up a suitable, calibrated ABM able to perform a comprehensive study on the effects of new mobility services and parking policy adaptations in the above-mentioned region, we design seven scenarios to give quantitative answers to policy-related questions on how altering features can reduce the extent to which private vehicles are used for travelling. These features include the availability of mobility hubs (hubs on neighbourhood level where sustainable travel modes are linked), the availability of car/bike sharing services, the availability of ‘Mobility as a Service’ (MaaS) subscriptions, the amount of parking capacity in the region and the parking costs. We also study what the impact would be of an improved public transport service with lowered public transport travel times to and from the city centers, and the impact of an improved cycling network infrastructure with significantly lowered travel times for bike and e-bike travellers. Based on the case study, we find that the introduction of mobility hubs alone has limited impact. However, combining this with making sharing services available to the public through MaaS subscriptions, there is a potential to reduce the number of car trips significantly, while the number of trips undertaken by a more sustainable (shared) e-bike increases as well as the number of so-called multi-modal mode trips (trips undertaken by a combination of various modes). Furthermore, improving the public transport service and micromobility network further increases the potential of mobility hubs in terms of making mobility more sustainable. The case study also shows that limiting parking capacity and increasing parking costs in the city centers is especially helpful for the reduction of vehicle use, leading to an improved car flow.

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional deep neural network that predicts traffic volume, speed, and vehicle loss hours in the system with high accuracy. The model allows us to quantify the benefits of different degrees of adjustment of truck departure times towards the off-peak hours. In our case, travel time reductions over the network are possible up to 10%. Freight demand management can build on the model to design departure time advisory schemes or incentive schemes for peak avoidance by freight traffic. These measures may improve the reliability of road freight operations as well as overall traffic conditions on the network.

This paper introduces an advisory-based time slot management system (TSMS) to control truck arrivals at seaport terminals with the aim to reduce congestion at terminal gates. A modeling framework is proposed, developed, and applied to assess the impact of a truck arrival shift for a case study in the Port of Rotterdam. This system is designed to apply control policies on truck inflow while taking the behavioral aspect of truck operating companies (TOCs) into account. Discrete choice modeling is used to infer the time-of-day preferences of TOCs for container pick-ups from the exchange of information between port and hinterland stakeholders. These preferences are used to shift truck arrivals to the off-peak period which consequently reduces the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TSMS, a simulation platform that resembles terminal operations has been developed using discrete-event simulation. For the allocation of trucks to a certain time of day, a choice-based stochastic assignment heuristic is designed to approximate the optimum configuration of the truck arrival shift policy experiment. The optimum truck arrival shift design shows that significant gain can be obtained even at a low shift rate.

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher. To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively long period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.