As a solution to the high greenhouse gas emissions and declining quality of life caused by private vehicles, the shared mobility hub is introduced. The shared mobility hub is a place where multiple modalities come together, including public transport and shared private mobility. As the shared mobility hub is a relatively new solution, limited research is available on the topic, especially on finding potentially suitable locations for allocating them. In this research, this knowledge gap is addressed by developing and testing a generic methodology to determine suitable locations for a specific type: the regional shared mobility hub. The regional shared mobility hub is located outside a city center being able to act as an intermodal point of transfer. The developed methodology is a combination of two existing methods: the GIS Multi-Criteria Analysis (MCA) and Multi-Actor Multi-Criteria Analysis (MAMCA) available in the literature. The method is able to score and weight different criteria which determine regional shared mobility hub suitability, taking the end-user (traveler), operator, and government perspectives into account in the weighting. Results are presented in multiple heat maps based on scenarios with varying stakeholder weight importance. The methodology developed consists of five criteria that measure location suitability (potential demand at a certain location, hub implementation costs, generalized travel costs from and to the hub, link to surroundings, and societal impact) measured by nine attributes. In this method, the choice is made for the Analytic Hierarchy Process (AHP) to determine the criteria weights. The developed methodology is applied to the region of Rotterdam (The Netherlands) to analyse if the methodology produces useful results for policy implementation. From multiple analyses, it appears that the methodology is suitable for tackling the location suitability determination problem, as it produces intuitive results.

This study focuses on network configurations to accommodate automated vehicles (AVs) on road networks during the transition period to full automation. The literature suggests that dedicated infrastructure for AVs and enhanced infrastructure for mixed traffic (i.e., AVs on the same lanes with conventional vehicles) are the main alternatives so far. We utilize both alternatives and propose a unified mathematical framework for optimizing road networks for AVs by simultaneous deployment of AV-ready subnetworks for mixed traffic, dedicated AV links, and dedicated AV lanes. We model the problem as a bilevel network design problem where the upper level represents road infrastructure adjustment decisions to deploy these concepts and the lower level includes a network equilibrium model representing the flows as a result of the travelers’ response to new network topologies. An efficient heuristic solution method is introduced to solve the formulated problem and find coherent network topologies. Applicability of the model on real road networks is demonstrated using a large-scale case study of the Amsterdam metropolitan region. Our results indicate that for low AV market penetration rates (MPRs), AV-ready subnetworks, which accommodate AVs in mixed traffic, are the most efficient configuration. However, after 30% MPR, dedicated AV lanes prove to be more beneficial. Additionally, road types can dictate the viable deployment plan for certain parts of road networks. These insights can be used to guide planners in developing their strategies regarding road network infrastructure during the transition period to full automation.

With the advent of automated vehicles (AVs), new infrastructure planning concepts such as subnetworks of AV-ready roads have been proposed to improve the performance of transportation networks and to promote the adoption of AVs. However, these subnetworks should evolve over time in response to the growing AV demand, which necessitates a multi-stage modeling approach. In this study, we propose multi-stage deployment of AV-ready subnetworks and formulate it as a time-dependent network design problem, which is a bi-level mixed-integer programming problem. The lower level is a simultaneous travel mode and route choice equilibrium with continuous decision variables, and the upper level is a design problem including infrastructure investment decisions to determine which roads to upgrade and include in AV-ready subnetworks for mixed traffic. We use a case study of a real road network to demonstrate the concept. Since computational efficiency is a key factor for solving such large-scale problems, we develop two efficient and tailored evolutionary heuristics to solve the problem, and compare their performance to a computationally demanding Genetic-algorithm-based solution method. The results indicate that the proposed algorithms can efficiently solve this large-scale problem while satisfying constraints in all scenarios, and outperform Genetic algorithm, particularly in the scenario with larger number of stages. Moreover, in all scenarios, deployment of AV-ready subnetworks leads to improvements in network performance in terms of total travel time and cost. However, the improvements are always accompanied with increased total travel distance. The extent of changes depends on AV market penetration rate, AV-ready subnetwork density and timing of densification.

This paper presents a bi‐level model to optimize automated‐vehicle‐friendly subnetworks in urban road networks and an efficient algorithm to solve the model, which is relevant for the transition period with vehicles of different automation levels. We formulate the problem as a network design problem, define solution requirements, present an effective solution method that meets those requirements, and compare its performance with two other solution algorithms. Numerical examples for network of Delft are presented to demonstrate the concept and solution algorithm performances. Results indicate that our proposed solution outperforms competing ones in all criteria considered. Furthermore, our findings show that the optimal configuration of these subnetworks depends on the level of demand; lower penetration rates of automated vehicles call for less dense subnetworks, and thereby less investments. Nonetheless, a large proportion of benefits are already achievable with low‐density subnetworks. Denser subnetworks can deliver higher benefits with higher penetration rates.

This chapter aims to provide an overview of the overall set-up of transport models and their applications, plus a reflection on transport modeling itself. Main characteristics of transport models are discussed with special attention for the four main components: trip generation, trip distribution, modal split, and network assignment. Both aggregate and disaggregate model approaches are considered. Furthermore, a description is given of practical issues when building and using these models in practice, with special attention for quality control. The main focus is on passenger transport but related models for freight transport models and land use and transport interaction are briefly discussed. The chapter concludes with a reflection on the value and limitations of transport modeling and an overview of new modeling developments.

Long-distance person transport contributes significantly to the GHG-emissions of all person transport. The EU aims for reducing the emissions by inducing a significant modal shift to the train, raising its market share to at least 50% on medium distances in 2050. This implies that the current share has to be multiplied by four. The paper examines the impact of significant modal shifts on the GHG emissions of long-distance transport by Europeans. The impact is estimated for three scenarios, a) no modal shift (trend), b) doubling train use, and c) larger shifts that give the targeted 50% market share. The basic assumption is that the probability of a modal shift to the train is higher when the appropriateness of the train compared to the currently used mode is better. Therefore, different modal shifts are assumed for different segments of the long-distance travel market, indicating different standards of the train mode compared to the best performing alternative. In the segment where the train currently is inferior, no shift is assumed. The main result is that the potential for reducing emissions in long-distance travelling is limited. It is unlikely that reductions larger than 20% can be achieved. The main reason is that the segment where the train is inferior includes the majority of the mileage and emissions of long-distance travel. Moreover, it is the fastest growing segment. If a reference is made to only the segments where the train is competitive, the possible reductions are significantly larger.

Long-distance travel contributes significantly to climate change. One of the mitigation options is a shift to more sustainable modes. An efficient policy on modal choice demands knowledge on which market segments are promising for a mode. The paper describes a method for breaking down the travel market into segments that are homogeneous with respect to the appropriateness of a mode for the travellers; the method is applied to all typical long-distance modes. The appropriateness of a mode depends on the demanded standards and the extent the standards of a mode meets the demand. A number of variables that define the appropriateness are identified, and by crossing the most important variables a large number of small, elementary market segments are defined. These are building stones for the mode-specific larger segments with a certain standard of a mode. Based on the explanatory power of a variable for modal choice and the standard of a mode for journeys in the variable category, a standard score of each mode is calculated for each elementary segment. Segments where the score of a mode compared to the score of the best performing alternative is similar (that is: within defined limits) are clustered into one of five segments with a certain standard of the mode. Some results: the proportion of long-distance journeys where a mode has at least a comparable standard is 79% for the car, 60% for the train, and 30% for the airplane. Expressed in mileage, the proportions are 40%, 33%, and 75% respectively.

The fast development of urbanization has led to imbalances in cities, causing congestion, pollution, and urban sprawl. In response to the growing concern over the distribution of demand and supply, a more coordinated urban structure is addressed in comprehensive planning processes. In this study, we attempt to identify urban structure using a Network-Activity-Human model under the Transit-Oriented Development (TOD) concept, since TOD is usually regarded as an urban spatial planning tool. In order to explore the strengths and weaknesses of the urban structure, we define the TOD index and unbalance degree and then classify the urban areas accordingly. We take the city of Beijing as a case study and identify nine urban types. The results show a hierarchical urban structure: the city center covers most of the hotspots which display higher imbalances, the surroundings of the city center are less developed, and the city edges show higher potentials in both exploitation and transportation development. Moreover, we discuss the extent to which the spatial scale influences the unbalance degree and apply a sensitivity analysis based on the goals of different stakeholders. This methodology could be utilized at any study scale and in any situation, and the results could offer suggestions for more accurate urban planning, strengthening the relationship between TOD and spatial organization.

This study explores a network configuration concept for vehicle automation levels 3–4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. We assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. Accordingly, we introduce an approach for road selection and present relevant operational concepts. To evaluate the impacts of this configuration and model different vehicles’ route choice behavior in mixed traffic, a static multi-class stochastic user equilibrium traffic assignment with a path-size logit route choice model and a Monte Carlo labeling route-set generation is adapted. Two user-classes are distinguished: vehicles with automation levels 0–2 and vehicles with automation levels 3–4 having a different passenger car unit to account for lower driving headways, lower value of time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, no deterioration in travel conditions is observed for the rest of the vehicles in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters.

Insight into factors influencing the choices people make in case of an evacuation from a natural disaster can help governments and emergency management personnel to manage people in case of such a situation. One of the aspects that influences the choices that people make in such a situation is herding. Since herding has not been quantified, this paper focuses on quantifying the effect of herding on the decision to evacuate by using an experimental setup that is based on the serious game Everscape. Around 400 people participated in 13 experiments with this setup. Choice models were estimated with the data from these experiments by including observable characteristics of herding as an attribute into the utility function. It is concluded that an important step is made in quantifying herding. It is shown that the more people someone sees leaving, the more inclined this person is to leave. Seeing people leave has more impact than seeing people stay. When people have no information from official sources, they tend to use other people as a source of information. In case of a disaster, this might result in following people who make the situation even more dangerous (for themselves and possibly for others as well). The information provided by official sources is therefore essential in managing people in the best possible way in case of a natural disaster.

This study explores a network configuration concept for vehicle automation levels 3-4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. The authors assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. A static Multi-Class Stochastic User Equilibrium traffic assignment based on the Path-Size logit and a Monte Carlo-Labeling combination approach for route-set generation is adapted to model the behavioral differences of vehicles in mixed traffic. Two user-classes are distinguished: vehicles with automation levels 0-2 and vehicles with automation levels 3-4 having a different Passenger Car Unit to account for lower driving headways, lower Value of Time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, the rest of the vehicles do not suffer deterioration in their travel conditions in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters. Finally, a steady decline in total travel cost is observed with increase in market penetration rate of higher automation levels.

Modern cars are increasingly being equipped with automated driving functions. For governments it is important to gain insight in the mobility impacts of automated vehicles. This is important as the introduction of automated vehicles affects current investment decisions about infrastructure projects and other policy measures like road pricing. Quantitative literature with respect to the impact of automated vehicles focuses mostly on capacity implications. Literature about large scale mobility impacts is mainly qualitative. This paper introduces a System Dynamics model (SD-model) to quantitatively explore the impacts of early forms of automated vehicles (level 1, 2 and 3) on mobility. The model is explorative and can be used to evaluate different scenarios in a short time. This model is applied in a case study for the Netherlands to assess the impact of automated vehicles on mode choice, time of day choice and travel times on characteristic relations in the Netherlands. In contrast to other studies the SD-model is able to simulate the effects of AVs over time, can simulate mixed automated vehicle types and has a constant feedback between the assignment and the demand side of the model. A scenario for autonomous driving and a scenario for cooperative driving is considered. The simulations show that car traffic will increase and the level of congestion does not necessarily decrease and might even increase on some relations, especially in the autonomous scenario. Furthermore, in the cooperative scenario the increase in number of trips by car is larger, the average speeds are higher and there is less congestion compared to the autonomous scenario.

Robust public transport networks are important, since disruptions decrease the public transport accessibility of areas. Despite this importance, the full passenger impacts of public transport network vulnerability have not yet been considered in science and practice. We have developed a methodology to identify the most vulnerable links in the total, multi-level public transport network and to quantify the societal costs of link vulnerability for these identified links. Contrary to traditional single-level network approaches, we consider the integrated, total multi-level PT network in the identification and quantification of link vulnerability, including PT services on other network levels which remain available once a disturbance occurs. We also incorporate both exposure to large, non-recurrent disturbances and the impacts of these disturbances explicitly when identifying and quantifying link vulnerability. This results in complete and realistic insights into the negative accessibility impacts of disturbances. Our methodology is applied to a case study in the Netherlands, using a dataset containing 2.5 years of disturbance information. Our results show that especially crowded links of the light rail/metro network are vulnerable, due to the combination of relatively high disruption exposure and relatively high passenger flows. The proposed methodology allows quantification of robustness benefits of measures, in addition to the costs of these measures. Showing the value of robustness, our work can support and rationalize the decision-making process of public transport operators and authorities regarding the implementation of robustness measures.

Automated Driving (AD) is expected to deliver various benefits beyond those possible with manual driving for transport systems and the environment, yet there are many uncertainties with respect to the development path of AD to full automation. (SAE International, 2016) defines five levels of vehicle automation summarized in Figure 1. Automated driving system (ADS) can take over more driving tasks at higher automation levels until finally at level 5, ADS can handle the full range of driving complexity and it is feasible in all driving modes. However, the transition period to full automation might be long and full of uncertainties. Two incremental paths toward full automation have been observed so far. (CPBR, 2015) describes them as “something everywhere” and “everything somewhere”. Most traditional car manufacturers are embracing “something everywhere” path, i.e., gradually improving ADS in existing vehicles and shifting more driving tasks from the driver to ADS over time. Then the user is responsible for using the ADS wisely. This is also consistent with SAE automation levels. The other alternative, which was recently adapted by Google, involves aiming at full automation within a limited domain (e.g., only certain road types) and expanding this domain to more road types and more complex driving situations. This means the absolute ADS autonomy can only be realized in specific conditions. Then the challenge is to define those conditions specifically. For both paths, infrastructure is a defining factor. It can either facilitate or prevent higher automation capabilities. During the transition period to full automation, safe operation of levels 3-4 at their full automation capacity will highly depend on the type of infrastructure they encounter. For road authorities it is important to know how ready the road infrastructure is for safe automated driving. However, the academic literature and the field reports do not offer sufficient information to answer this question. (Farah et al., 2018) point out numerous knowledge gaps regarding infrastructure for AD. Therefore, we embarked on providing some insight into the matter via an expert workshop.

Conventional travel time reliability assessment has evolved from road segments to route level. However, a connection between origin and destination usually consists of multiple routes, thereby providing the option to choose. Having alternatives can compensate for deterioration of a single route, therefore this study assesses the reliability and quality of the aggregate of the route set of an OD-pair. This paper proposes two aggregation methods for analyzing the reliability of travel times on the origin-destination level: 1) an adapted Logsum method and 2) a route choice model. The first method analyses reliability from a network perspective and the second method is based on the reliability as perceived by a traveler choosing his route from the available alternatives. A case study using detailed data on actual travel times illustrates both methods and shows the impact of having variable departure times and the impact of information strategies on travel time reliability.

The implementation of travel time reliability (TTR) in route choice behaviour is still not very common in transport models, especially not in a public transport context. The reasons probably are that it is difficult to measure and that there is no agreement how it best can be represented in utility functions. Typically, it is represented by a standard deviation, however, particularly in public transport choices it is more likely that travellers think about the consequences of unreliability in travel times in terms of buffer times. This paper contributes to the literature by comparing five different model specifications of TTR in public transport route choices that are either based on standard deviations or on buffer time indicators. The models are estimated from choices observed in a stated choice experiment. To address heterogeneity, a latent class model is estimated. The results suggest that the reliability buffer time indicator outperforms the standard deviation indicator. Furthermore, the reliability buffer time parameter is only statistically significant in two of the four classes. The other two classes are particularly sensitive to making transfers and to low frequencies of public transport services, suggesting different strategies to deal with TTR.

Bij mobiliteitsbeprijzing zijn meerdere actoren, zoals verschillende overheden en de spoorwegen, betrokken. In dit paper wordt een model gepresenteerd op basis van speltheorie dat de voorkeuren en interactie van deze actoren analyseert. Zo een model kan tijdens het planningsproces gebruikt worden om conflicterende belangen te identificeren en er worden daarnaast verschillende oplossingen voorgedragen. We gebruiken de aanpak van Transferable Utility games (TU-spellen), waarbij de resultaten van de best mogelijke strategie voor iedere mogelijke coalitie als bouwstenen gelden. Niet eerder werden deze bouwstenen voor een soortgelijke setting gedefinieerd. Vervolgens worden drie bekende oplossingsconcepten voor TU-spellen uit de literatuur toegepast, namelijk de kern, de Shapley-waarde en de compromis-waarde. Om de relevantie van een dergelijke aanpak aan te tonen wordt een casestudie van de Randstad gebruikt. Hierbij wordt naar een kilometerheffing, een cordontolheffing en de tarieven van de trein gekeken; deze worden elk door een andere actor bestuurd. Het onderliggende transportmodel van deze casestudie maakt gebruik van recente methodologische ontwikkelingen, en heeft daardoor een goede balans tussen efficiëntie en realisme. Tevens zijn de reistijdwinsten en emissievermindering in kaart gebracht. De resultaten laten zien dat in de oplossing bij samenwerking tussen de actoren, er geen cordontolheffing hoeft te worden ingevoerd, omdat de kilometerheffing – die wordt ingezet om de sociale welvaart te verbeteren – een betere maatregel is om het achterliggende doel van de cordontolheffing, de economische positie van de stad, te bereiken.

A majority of the trips are made by just one transport mode that is mostly the car, walking, or –in some countries– the bicycle. For a small proportion of the trips, generally a few percent, two or more modes are used. The latter include particularly trips where a collective mode (e.g. bus, train, airplane) is the main mode and the trip distance is rather long. In travel segments with high shares of collective public transport and rather long trip distances, the proportion of multimodal trips can exceed 50%. Knowledge on volume and characteristics of multimodal trips is important, among others, for policymakers that aim at promoting public transport, for those who have to plan and design facilities at train stations and other nodes where people make interchanges, and for those who want to simulate multimodal travelling in transport models.
Information on the use of transport modes, including multimodal use, is generally asked for in the national and regional travel surveys. Because of the relative complexity of multimodal trips, reporting on such trips is more troublesome and may be less accurate than reporting on unimodal trips. As a consequence, multimodal trips demand for more effort to achieve accurate information. Consecutively, the quality of the registration of multimodal trips may depend relatively strong on the set-up of the survey.
The paper explores the impact of travel survey design on the quality of the registration of multimodal trips using survey data from the Netherlands. This country has a long tradition in surveying mobility behaviour; since the start in 1978 every year a large survey has been conducted. In the long period after 1978, the survey design has a few times been strongly adapted. The adaptations caused several trend breaks in registered travel behaviour, including huge changes in the performance of multimodal travelling. The paper analyses the relation between the survey design and the quality of the description of multimodal trips and gives some recommendations about how an accurate registration of multimodal trips can be achieved. It also shows which aspects of multimodality are rather robust with respect to survey design changes, and which aspects are highly volatile. The most prominent example of the latter is the registration of interchanges between vehicles of the same mode (e.g. the train); an accurate registration of such interchanges makes high demands on the set-up of the survey.