Teleoperated driving complements automated driving and acts as transitional technology towards full automation. An economic advantage of teleoperated driving in logistics operations lies in managing fleets with fewer teleoperators compared to vehicles with in-vehicle drivers. This alleviates growing truck driver shortage problems in the logistics industry and save costs. However, a trade-off exists between the teleoperator-to-vehicle (TO/V) ratio and the service level of teleoperation. This study designs a simulation framework to explore this trade-off generating multiple performance indicators as proxies for teleoperation service level. By applying the framework, we identify factors influencing the trade-off and optimal TO/V ratios under different scenarios. Our case study on road freight tours in the Netherlands reveals that for any operational settings, a TO/V ratio below one can manage all freight truck tours without delay, while one represents the current situation. The minimum TO/V ratio for zero-delay operations is never above 0.6, implying a minimum of 40% teleoperation labor cost saving. For operations where a small delay is allowed, TO/V ratios as low as 0.4 are shown to be feasible, which indicates potential savings of up to 60%. This confirms great promise for a positive business case for the teleoperated driving as a service.

The growing demand for parcel delivery contributes to traffic congestion, high emissions, and rising costs of freight logistics, particularly in urban areas. To address these issues, new and sustainable last-mile delivery methods must be implemented. However, estimating the impact of different logistics systems is complex, as it depends heavily on consumer adoption of these new delivery methods. This paper presents a simulation model that captures and explores the interconnections between multiple last-mile delivery methods and corresponding consumer preferences. Two key factors affecting consumer preferences are simulated: (1) consumers’ response to the performance and availability of delivery methods, and (2) the sharing of knowledge through word of mouth and familiarisation. System dynamics is applied at the aggregate level to simulate the evolution of consumer preferences for last-mile delivery across multiple methods. At the disaggregate level, an agent-based model simulates the operational performance of these delivery methods, which in turn influences consumer preferences in the system dynamics model. This integrated approach allows for the observation of the evolving interaction between urban logistics supply and demand, providing key performance indicators on consumer preferences and the delivery method operations at consecutive time points. The developed simulation model is applied to a case study in the Rotterdam-The Hague region, a highly urbanised region in The Netherlands. Results show that consumer preferences strongly depend on the carriers’ ability to fulfil the demand. The dynamic interaction between supply and demand creates a reinforcing feedback loop, where the adaptability of carriers is crucial for the long-term success of a delivery method. Additionally, the spatial results reveal that there are zonal differences in the performance of the delivery methods. Further findings indicate that, while total vehicle kilometres and CO₂ emissions will rise due to increasing parcel demand in all scenarios, the average number of van kilometres and CO₂ emissions per parcel will decrease as demand grows.

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.

The Internet of Things (IoT) can bring radical advancements in the domain of waste collection, as it enables the organization of demand-responsive schedules which leads to higher efficiency operations. One major challenge in the deployment of demand-responsive schedules, nevertheless, is the uncertainty they bring in the planning of resources as they follow the daily waste demand. This is undesirable in real-life operations as it makes it difficult to reserve resources and ensure the stability of operational processes. Therefore, waste collection scheduling approaches need to be devised that are not only demand-responsive but also supply-friendly. In this paper, we present a solution approach for the waste collection vehicle routing problem in an IoT context (IoT-WCVRP) that focuses on these requirements. We demonstrate its applicability through a case study of Rotterdam in The Netherlands, where real-life household waste data are used and the observed waste collection operations in the city are compared against the optimized outcomes of the model. The application results show that our IoTWCVRP approach achieves the stated demand and supply trade-off, increases the vehicle utilization rates by 5%, and reduces emissions and travelled kilometres by 6% and 8% respectively.

Integrating renewable energy sources, such as solar and wind, challenges grid stability due to their intermittent nature. Vehicle-to-grid (V2G) technology provides a promising solution by utilizing electric vehicles (EVs) as decentralized energy storage systems, enabling the storage of surplus energy during low demand and its release during peak demand. The effectiveness of V2G depends critically on car usage patterns. Data from the Netherlands Mobility Panel (MPN) of 2022, comprising travel diaries from 2,505 households, was analyzed to explore this. A methodology was developed to create car usage profiles based on parking durations and locations, distinguishing weekday and weekend patterns. The analysis shows that vehicles are predominantly parked at home, with weekday profiles reflecting work-related parking and weekend profiles highlighting increased leisure activity. Households with shared cars showed higher driving activity and shorter parking durations than households with a 1:1 car-to-license ratio or surplus vehicles. Six distinct car usage clusters were identified for weekdays and four for weekends.

This paper proposes a decision-making framework for a multiple-period planning of electric vehicle (EV) charging station development. In this proposed framework, transportation planners seek to implement a phased provision of electric charging stations as well as repurposing gas stations at selected locations. The developed framework is presented as a bi-level optimization problem that determines the optimal electric charging network design while capturing the practical constraints and travelers’ decisions. The upper level minimizes overall vehicle CO emissions by selecting optimal charging stations and their capacities, while the lower-level models travelers’ choices of vehicle class (EV or conventional) and travel routes. A genetic algorithm is developed to solve this problem. The results of the numerical experiments describe the sensitive nature of EV market penetration rates in the urban traffic stream and overall vehicle CO emissions to EV charging station availability and capacity. The findings can assist transportation agencies in designing effective EV charging infrastructure by identifying optimal locations and capacities, as well as in creating policies to encourage EV use over time. This study supports broader efforts to reduce air pollution and promote sustainable transportation by promoting EV adoption in the long term.

New mobility concepts such as Mobility as a Service (MaaS) are emerging as potential solutions to move people more sustainably in an increasingly urbanized world. Planning for this multi-modal mobility requires a whole system approach (STEEP - social, technical, economic, environmental, and political) to evaluate alternative future scenarios and address varied stakeholder concerns. A strategic planning tool was selected that can model alternative scenarios for how urban mobility systems may evolve over time. A sustainable mobility scorecard was defined, comprised of individual metrics generated from the tool's output. The Analytical Hierarchy Process (AHP) was selected and applied to generate stakeholder weightings from an online survey of U.S. transportation planning professionals. Those weightings were applied to the scorecard to demonstrate their influence on alternative planning outcomes. Results include the scorecard metrics assessed with the greatest relative importance to sustainability; increases in no car ownership, increases in the transit/walk/bike mode share especially in lower income populations, maintaining the average peak traffic speed (actual/posted), and reducing cars per capita. The resulting weighted scorecard, part of a strategic assessment methodology for mobility sustainability (SAMMS), is then used to evaluate four future planning scenarios with contrasting trends (socio-demographics, travel behavior, employment, land use, transport supply) for the greatest overall sustainable mobility outcome.

With the rapid growth of instant delivery services in China, the number of couriers is rising due to low entry barriers such as minimal educational requirements, flexible hours, and competitive salaries. However, the industry faces challenges like excessive workloads and high accident rates, which could reduce couriers' job satisfaction. While the literature on couriers' job satisfaction is extensive, the application of holistic needs-based theories remains unexplored, particularly through advanced quantitative methods. This study operationalizes Maslow's Hierarchy of Needs Theory (MHNT) as a multi-dimensional construct and incorporates it into a Structural Equation Modeling (SEM) framework to examine its hierarchical impact on job satisfaction. Additionally, it explores the impact of physical health, occupational discrimination, and new technologies on couriers' job satisfaction. To test the framework and derive a nuanced understanding of factors influencing courier job satisfaction, data from 490 couriers in Shanghai, China, and nearby areas was collected. To account for differences in employment types, the survey data was split into full-time and part-time courier groups, with a multigroup analysis conducted using a structural equation model. The results show differing factors influencing job satisfaction. Part-time couriers are significantly affected by compensation and working environment, while full-time couriers are, besides compensation and working environment, also influenced by career development. These findings enhance the understanding of work conditions and motivators for couriers across different employment types within the instant delivery sector, offering key insights to enhance courier job satisfaction and promote sustainable development of this business.

Amidst the pressing need for sustainable transportation, Shared Automated Electric Vehicles (SAEVs) emerge as an increasingly explored solution with the potential to revolutionize mobility. Yet, understanding the environmental impacts of operating this mobility solution at different scales remains sparse. This study addresses this by integrating Agent-Based Modelling (ABM) and Life Cycle Assessment (LCA) to assess the environmental impacts of SAEVs at municipal, subregional and regional scales. ABM simulates travellers’ behaviour and SAEVs deployment strategies, yielding dynamic patterns along a typical day, while LCA provides a structured framework for assessing the life cycle environmental impacts. This process involves creating an ABM that reflects a representative mobility scenario, and a modified ABM scenario where private car and bus trips are replaced with SAEV services. The analysis extends the different scales, providing both short-term and long-term perspectives on LCA impacts. Findings revealed significant reductions in global warming potential (up to 91%), but challenges include increased operational intensity, human toxicity (up to 240%), and mineral resource scarcity (up to 229%). Vehicle kilometres travelled, and fleet replacement needs are key factors influencing long-term environmental impacts. Larger-scale implementation yields greater environmental benefits compared to smaller-scale deployment.

As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, we study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, we delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. We find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver's revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.

This paper presents the first results of a Delphi survey aimed at eliciting experts' opinions on possible scenarios of integration between passenger and freight transport enabled by a Mobility as a Service (MaaS) platform. Combining freight shipments with passenger trips is a promising addition to the MaaS business model that could help to reduce the number of freight vehicles and contribute to a more efficient use of passenger transport services and modes. The research objective of this paper is therefore to explore the feasibility of an extended version of MaaS including freight, called “MaaS for Passenger and Freight” (MaaS4PaF). Passenger and freight transport experts were asked to express their opinions with respect to the potential market penetration, business ecosystem, and implementation of this concept. Results allow to elaborate on opportunities and barriers, especially related to uncertain business models and the multitude of actors involved, and propose a research agenda to further investigate the feasibility and potential of MaaS for passenger and freight transport integration.

The adherence to a timetable with precise departure and arrival times becomes increasingly challenging in real-world scenarios due to the daily fluctuations in rail traffic, leading to uncertainties that complicate effective real-time traffic management. In this paper, we introduce and optimise timetable flexibility to enhance operational robustness and reduce conflicts resulting from minor train path deviations. We propose a Train Rescheduling with Flexibility (TRF) model, relying on a Mixed Integer Linear Programming (MILP) formulation. The primary objective is to minimise timetable deviation, while maximising timetable flexibility. The punctuality threshold is utilised to optimise time allowances within the real-time traffic plan, considering passenger connections and preventing early departures. A real-life case study that focuses on part of the Dutch railway characterised by complex track layouts and heterogeneous rail traffic is used to validate our model. Furthermore, we investigate the impact of predictive delays on flexibility, along with conducting sensitivity analyses on key parameters such as flexibility weight and punctuality threshold. The results of our optimisation model demonstrate its effectiveness in exploiting timetable flexibility to deal with disturbances.

The rapid growth of the online food delivery industry has led to a significant increase in the number of delivery riders navigating urban streets, predominantly using bikes and e-bikes. This growth has been accompanied by a concerning rise in crashes involving these riders, posing a critical challenge for city authorities and policymakers. Promoting safer riding behavior, such as choosing safer routes while delivering food, can potentially reduce crash risks. With this motivation, this paper aims to evaluate the effectiveness of strategies that encourage riders to choose safer routes and estimate the value riders place on reducing the risk of road crashes. The paper presents a stated preference experiment conducted with food delivery riders in Amsterdam and Copenhagen to assess two targeted strategies: ’safety information’ and ’monetary incentives’, designed to encourage riders toward selecting safer routes. The results from the route choice model show that presenting information about safety against crashes on different routes and offering monetary incentives can effectively motivate riders to choose safer routes, even if these are longer. The trade-offs riders make between safer and shorter routes were quantified by calculating the Value of Risk Reduction (VRR) and Willingness to Accept (WTA) indicators, which offer valuable insights into riders’ safety preferences. These indicators highlight how much riders value risk reduction and the compensation required to choose safer routes. Furthermore, the findings reveal that factors related to riders’ working arrangements and socio-demographic profiles significantly influence their route choice decisions. The paper concludes with a discussion about the practical challenges associated with implementing the strategies to enhance rider safety and proposing potential solutions that can be useful for food delivery platforms and policymakers.

Offering shared mobility options at transit stops can potentially increase the service area of a stop and consequently, possible detours in transit lines can be eliminated to decrease in-vehicle travel times for through-passengers and reduce operational costs. However, current research mostly focusses on shared mobility options and expected behaviour only, whilst not looking at this integrated transit network design problem. Additionally, most focus of current studies is on the integration of shared mobility in urban areas and/or around train stations, leaving a gap on suburban areas and transit lines with lower demand. In order to answer our research question “what the effects are of increased route directness for low-demand transit lines in conjunction with offering shared mobility at transit stops”, we developed a mesoscopic model extension for the aggregated four-step transport model to model changes in travel behaviour as a result of straightened transit lines and the simultaneous integration of shared modes. Discrete choice models are used to accurately model first and last mile preferences of people, based on the access and egress distance, demographics and available (shared) modes. Finally, the probability of passengers cancelling their complete trip as a result of increased first and last mile distances is also explored. This model framework was applied to nine case studies in the Netherlands. The synthesis of the case studies resulted in key factors contributing to a promising redesign of the transit network. The main factor is that through-passengers should significantly outnumber local passengers, by at least 75%-25%. Additionally, the increase in access and egress times should not be significantly larger than in-vehicle time savings of through-passengers. Moreover, it is found that the mode share of micromobility in the first and last mile is approximately 15% across the different cases, whereby the highest usage can be seen for people under the age of 25 and for distances greater than 1 km. Finally, it is concluded that the additional costs of shared mobility are on average only 10% of the savings in operational costs.

Shared automated electric vehicles (SAEVs) have the potential to transform regional transportation, particularly in low-density areas where accessibility and resource optimization are challenging. However, their integrated economic impact on operators, users, environment, and society have been little explored. This paper presents a cost-benefit analysis methodology, incorporating a flow-based integer programming model, to assess the viability of SAEV services in a regional interurban context. The case study is based on mobility data from the Aveiro and Coimbra regions (Portugal). We evaluate the replacement of all motorized intermunicipal trips with various SAEV configurations, including automated cars (with and without pooling), automated minibuses, and a mixed fleet (cars and minibuses). Results indicate that SAEV providers can achieve profitability with fares ranging from €0.08 to €0.36 per kilometer. Even at these rates, SAEV services generate economic benefits for users, particularly pooled car-based services, as private car expenses dominate current mobility costs. Additionally, all SAEV configurations contribute to cost reductions related to air pollution, noise, global warming potential, and road accidents, with pooled services offering the greatest savings. A series of SAEV transition scenarios using a fleet of pooled cars also demonstrated benefits for all stakeholders, albeit lower than those from fully replacing motorized trips. A second sensitivity analysis confirms that reducing vehicle acquisition costs is key to lowering fares and increasing user savings. This paper represents one of the first evaluations of large-scale SAEV services for intermunicipal trips with significant distances between urban centers, contributing insights into smart and sustainable transportation solutions for such contexts.

Modular autonomous vehicle (MAV), as a novel mode of public transportation, is anticipated to reshape the next generation of transportation systems, with the potential to adapt to diverse travel demand patterns in urban areas. In this study, we consider a MAV hub-and-spoke public transportation system (MAV-HSPTS). Each MAV can operate independently for first-mile/last-mile transport of passengers. Multiple MAVs can assemble into a modular bus, operating synchronously on mainline corridors with predetermined routes, stations, and timetables. We formulate a MAV routing, scheduling and repositioning model in a rolling horizon framework to capture the operation of the system. A heuristic algorithm that assigns passenger requests to MAVs is developed to reduce the computation time. The model and solution algorithm are evaluated on a bus transit corridor in Shanghai, China. Results demonstrate that the MAV service can reduce passenger travel time by over 20 % compared to conventional bus service, and over 90 % of the passengers could benefit from the convenience of in-bus transfers. MAV reposition proves to be an effective method to reduce the operational costs, especially in scenarios with imbalanced demand distribution.

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found that users of different modes not only have varied general preferences for different shared modes but also have different sensitivity for attributes such as travel time and cost. Public transport users are more likely to switch to eHUBS modes than car users. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester. Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers.

People want to rely on optimization algorithms for complex decisions but verifying the optimality of the solutions can then become a valid concern, particularly for critical decisions taken by non-experts in optimization. One example is the shortest-path problem on a network, occurring in many contexts from transportation to logistics to telecommunications. While the standard shortest-path problem is both solvable in polynomial time and certifiable by duality, introducing side constraints makes solving and certifying the solutions much harder. We propose a proof system for constrained shortest-path problems, which gives a set of logical rules to derive new facts about feasible solutions. The key trait of the proposed proof system is that it specifically includes high-level graph concepts within its reasoning steps (such as connectivity or path structure), in contrast to using linear combinations of model constraints. Using our proof system, we can provide a step-by-step, human-auditable explanation showing that the path given by an external solver cannot be improved. Additionally, to maximize the advantages of this setup, we propose a proof search procedure that specifically aims to find small proofs of this form using a procedure similar to A* search. We evaluate our proof system on constrained shortest path instances generated from real-world road networks and experimentally show that we may indeed derive more interpretable proofs compared to an integer programming approach, in some cases leading to much smaller proofs.

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.