Bert van Wee, professor in Transport Policy at Delft University of Technology, the Netherlands, faculty Technology, Policy and Management, is retiring. Given his large contributions to EJTIR as Editor-in-Chief, editorial board member, author and reviewer, this Editorial Note is dedicated to his work for EJTIR. Over 25 years, van Wee published 18 papers and 1 book review in EJTIR covering a wide range of topics from road pricing to urban rail transport, vehicle automation and port throughput. What his studies have in common is that they explore how transport policies affect land-use and travel behaviour, as well as the economic and wider societal impacts of those policies. Bert van Wee’s generalist view on the transport system is rare, but, given the rising complexity of the system, increasingly needed to indeed be able to address the future challenges.@en

We investigate the correspondence between network-based public transport network (PTN) supply indicators and passenger demand at the node level, by systematically assessing correlations between node centrality measures and passenger boarding counts across different graph representations of PTNs. At the stop-level, undirected L- and P-space representations with three different edge weightings: unweighted, service-frequency-weighted, and in-vehicle-time-weighted are analysed. In each case, we calculate degree, closeness, betweenness and eigenvector centralities and examine the relation shapes. At the route level, we examine degree and eigenvector centrality for unweighted and weighted C-space representations. We introduce a modified C-space representation with self-loops, with service frequencies as self-loop weights, and propose eigenvector centrality as a route-level supply indicator. Stop- and route-level properties are integrated using the B-space representation. This methodology was applied to a case study for a bus PTN in Ljubljana, Slovenia. Results show strong correspondence between passenger demand and degree and eigenvector centrality scores in the frequency-weighted P-space (correlation ≈0.7−0.8). Notably, the relationship between eigenvector centrality and passenger counts in the new C-space representation with self-loops exhibits logarithmic behaviour. Furthermore, the results suggest a minimum eigenvector centrality threshold (≈10⁻³) for a route to start facilitating passenger use. The route-level results from the B-space analysis show exponential convergence of passenger counts to route eigenvector centrality. Results of the stop-level analysis are in line with previous research and deepen the understanding of centrality measures as supply indicators. Most significantly, the route-level analysis is novel, and the results open promising venues for further research.

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To facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks.

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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.

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Shared on-demand mobility services, also known as microtransit, have become a major mobility provider around the world, yet this has predominantly taken place within urban areas. In areas with lower population density and poor quality public transport, such services could substantially improve accessibility. In early 2023, a regional microtransit pilot was carried out in the Ljubljana Urban Region in Slovenia. To assess the preferences towards such a service, a stated preference experiment is carried out among pilot participants, comparing car, public transport and microtransit for their daily commute. The obtained data is modelled using a Panel mixed logit model, with random parameters modelled as normally or log-normally distributed. Additionally, we also model for potential nesting effects among the alternatives. The results show participants perceive microtransit as a viable alternative, with public transport commuters finding it particularly attractive, whereas car commuters see it on par with the car. Parking price and a guaranteed parking spot tended to be key factors for decision-making. Simulating different policies, we conclude that combining subsidising microtransit and higher parking prices is the most effective strategy for achieving a modal shift primarily from car to microtransit while not affecting public transport as much.@en

To understand why ridesourcing markets may be prone to evolve towards potentially socially undesirable equilibrium states, we conceptualize the network effects present in ridesourcing provision. In addition, we propose an agent-based model that allows simulating the effect of market conditions and platform strategies on system performance, accounting for such network effects. This day-to-day model captures sequential decentralized processes characterizing both sides of the two-sided ridesourcing market, i.e. information diffusion, platform registration, platform participation, and learning based on experience. We apply the model on a case representing Amsterdam, the Netherlands. Our simulation results suggest that a profit-maximizing ridesourcing platform may trade-off market transaction volume for higher earnings on successful transactions, a strategy that is harmful to the interests of travellers and drivers, and possibly of (very) limited benefit to the platform. Moreover, we find that ridesourcing operations may be viable even when potential supply and demand in an area are limited.@en

Limited available market share data seems to suggest that ridesourcing platforms benefit from, even thrive on, socio-economic inequality. We suspect that this is associated with high levels of socio-economic inequality allowing for cheap labour as well as increasing the share of travellers with a considerably above-average willingness to pay for travel time savings and comfort. We test the relation between inequality and system performance by means of an agent-based simulation model representing within-day and day-to-day supply-demand interaction in the ridesourcing market. The model captures travellers’ mode choice with a heterogeneous perception of relevant time components, as well as job seekers’ participation choice with heterogeneous reservation wage. Our experiments cover scenarios for the entire spectrum ranging from perfect equality to extreme inequality. For several of such scenarios, we explore alternative platform pricing strategies. Our analysis shows a strong, positive relationship between socio-economic inequality and ridesourcing market share. This is the outcome of the combination of cheap labour and time-sensitive ridesourcing users, reinforced by network effects inherent to ridesourcing markets. We find that driver earnings are minimal in urban areas with large socio-economic inequality. In such contexts, drivers are likely to face a high platform commission, and yet, fierce competition for passengers.

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The potential lack of realism in stated-preference surveys is particularly acute in contexts where disaggregate real-world data is challenging to obtain. Mobility-on-Demand (MOD) services present one such context. The MOD context is unique due to factors such as service reliability (difference in stated vs. actual travel and waiting time) and current mode inertia which affect the choice of MOD services and are difficult to infer from revealed preference data. Further, travel mode choices are repetitive and constitute a relatively easy choice situation. Consequently, individuals may utilize simple non-compensatory strategies. In this study, we design a survey to mimic real-world choice sets using a joint revealed and stated- (RP-SP) preference survey approach. We construct the complete journey of individuals taking into account departure time, access and egress mode, current primary mode and origin–destination pair. A Choquet Integral (CI)-based choice model with endogeneity correction is employed, thereby allowing to approximate non-compensatory behaviour. Results confirm the presence of non-compensatory behaviour across all mode users (car, public transport and bike). Reliability and inertia effects are most pronounced for car users including the potential for a combined departure time-mode shift towards MOD. Owing to non-compensatory behaviour and inertia, higher travel costs cannot be fully compensated by shorter waiting and travel times and a differential pricing strategy may be required to increase MOD market share. Failure to account for common unobserved factors between the RP and SP choices results in inflated attribute importance.

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The COVID-19 pandemic poses an unprecedented challenge for public transport systems. The capacity of transport systems has been significantly reduced because of the social distancing measures. Therefore, new avenues to increase the resilience of public urban mobility need to be explored. In this work, we investigate the integration of bike sharing and public transport systems to compensate for limited public transport capacity under the disruptive impacts of the COVID-19 pandemic. As a first step, we develop a data analysis model to integrate the demand of the two underlying systems. Next, we build an optimization model for the design and operation of hybrid mixed-fleet bike sharing systems. We analyze the case of the subway and public bike sharing systems in Milan to assess this approach. We find that the bike sharing system (in its current state) can only compensate for a minor share of the public transport capacity, as the needs in fleet and station capacity are very high. However, the resilience of public urban mobility further increases when new design concepts for the bike sharing system are considered. An extension to a hybrid free-floating bike and docked e-bike system doubled the covered demand of the system. An extension of the station capacity of about 37% yields an additional increase of the covered demand by 6.5%–7.5%. On the other hand, such a hybrid mixed-fleet bike sharing system requires many stations and a relatively large fleet to provide the required mobility capacity, even at low demand requirements.@en

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.

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We investigate the evolution of residential segregation patterns in the Netherlands, with a focus on the population with a non-western migration background. Unlike previous research relying on predefined spatial structures, this study employs a regionalization approach to track the evolution of social enclaves in 82 municipalities from 2015 to 2020. Enclaves have become more mixed in municipalities with historically homogeneous social enclaves whereas in the other municipalities, they have become more homogeneous. In addition, we find a positive association between the increase in the share of population with a non-western migration background at the municipality level and the spatial growth of social enclaves. These insights contribute to a deeper understanding of residential segregation in the Netherlands, offering a valuable foundation for informed policymaking.

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An excessive use of private cars for transportation has multiple negative effects on our society. It is therefore paramount to identify the underlying factors driving car usage among different segments of travellers. In this study, we aim to identify and characterise traveller segments in terms of their car-related attitudes and how different sociodemographic attributes, travel behaviour characteristics (such as using cars as the primary mode of transportation), and the accessibility of their place of residence vary amongst different population segments. Through Confirmatory Factor Analysis and Latent Profile Analysis we identify five different classes, namely car-detractors, −hesitants, −positives, −friends, and −lovers. More favourable car attitudes are associated with higher car ownership and access but, interestingly, there are no significant differences in terms of travel frequency and trip length distribution. This suggests that attitudinal differences relate mostly to modal preference rather than to the underlying travel demand patterns and the associated activities. We also see that car-less individuals are more likely to be car detractors, suggesting that the experiencing barriers for owning a car may contribute to developing more negative attitudes towards cars. Our results shed light on the different influencing factors and characteristics of individuals that consider, or not, a car-centred mobility.@en

Public transport agencies need to leverage on emerging technologies to remain competitive in a mobility landscape that is increasingly subject to disruptive mobility services ranging from ride-hailing to shared micro-mobility. Customized bus (CB) is an innovative transit system that provides advanced, personalized, and flexible demand-responsive transit service by using digital travel platforms. One of the challenging tasks in planning and operating a CB system is to efficiently and practically schedule a set of CB vehicles while meeting passengers’ personalized travel demand. Previous studies assume that CB passengers’ preferred pickup or delivery time is within a pre-defined hard time window, which is fixed and cannot change. However, some recent studies show that introducing soft flexible time windows can further reduce operational costs. Considering soft flexible time windows, this study first proposes a nearest neighbour-based passenger-to-vehicle assignment algorithm to assign CB passengers to vehicle trips and generate the required vehicle service trips. Then, a novel bi-objective integer programming model is proposed to optimize CB operation cost (measured by fleet size) and level of service (measured by passenger departure time deviation penalty cost). Model reformulations are conducted to make the bi-objective model solvable by using commercial optimization solvers, together with a deficit function-based graphical vehicle scheduling technique. A novel two-stage human–machine collaborative optimization methodology, which makes use of both machine intelligence and human intelligence to collaboratively solve the problem, is developed to generate more practical Pareto-optimal CB scheduling results. Computation results of a real-world CB system demonstrate the effectiveness and advantages of the proposed optimization model and solution methodology.@en

Long-distance / International travel has seen little attention in the past, largely due to the irregular and sporadic nature of such trips. And yet, a single long-distance trip can amount to a distance equivalent to a year’s worth of commute trips, resulting in a similar, if not worse, environmental footprint. Understanding travellers’ behaviour is therefore just as relevant for such trips, as it is for everyday commute trips. As international travel is slowly picking up from the COVID-19 pandemic, it has been marred by an abundance of national and regional pandemic-related safety measures. While their primary goal is to protect the local population from infection, these safety may also make travellers feel safer while travelling. This perceived safety can differ from the true efficacy of the measures. In this research, we investigate people’s perception of eight COVID-19-related safety measures related to long-distance trips and how subjective perception of safety impacts their mode choice among car, train and aircraft. We employ a Hierarchical Information Integration (HII) approach to capture subjective perceptions and then model the obtained data by means of a Latent Class Choice Model, resulting in four distinct segments. To extrapolate the segments onto the rating experiment of HII, we apply a weighted least squares (WLS) regression, to obtain segment-specific safety perception. Two segments show a relatively high value-of-time (72€/h and 50€/h), tend to be more mode-agnostic and prefer determining the level of risk by themselves (relying primarily on infection and vaccination rate). The remaining two segments have a lower value-of-time (38€/h and 15€/h) and have strong mode affinity, for the train and car respectively. Future research could look into a way that segments the sample based on both the mode choice and rating experiment, providing additional insights into the heterogeneity of individuals in their perceptions.@en

Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study we derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose we estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. We find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, our model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Our study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.

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The COVID-19 pandemic has impacted people's everyday lives, as avoiding being in crowded places became the number one societal rule. Crowdedness has therefore increasingly affected decisions such as a place visit via a specific path, the selection of a public transport stop, itinerary, etc., thereby making related information increasingly relevant. The objective of this study is to examine the route and travel choices of pedestrians and public transport users, with the provisioning of travel information related to crowdedness levels. To that end, a choice experiment was designed to elicit travelers' preferences. Discrete choice models were estimated based on data collected from 465 individuals in Greece. Results showed that crowd avoidance plays a significant role in shaping mobility decisions for both pedestrians and public transport users. Factors such as place of residence, age, the importance of COVID-19 measures and arrival time are found to affect the likelihood of switch routes in response to information about high levels of crowdedness.

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The economic development of cities is closely connected to their transport infrastructure and systems. Implementing effective transport policies is important to enhance the efficiency of the exiting transportation system. An important question is the degree to which these policy effects align with the initial objectives of these policies over time. Monitoring and understanding the longitudinal effects of transport policies is critical for effective decision-making.@en

Reducing the impact of disruptions is essential to provide reliable and attractive public transport. In this work, we introduce a topological approach for evaluating recoverability, i.e., the ability of public transport networks to return to their original performance level after disruptions, which we model as topological perturbations. We assess recoverability properties in 42 graph representations of metro networks and relate these to various topological indicators. Graphs include infrastructure and service characteristics, accounting for in-vehicle travel time, waiting time, and transfers. Results show a high correlation between recoverability and topological indicators, suggesting that more efficient networks (in terms of the average number of hops and the travel time between nodes) and denser networks can better withstand disruptions. In comparison, larger networks that feature more redundancy can rebound faster to normal performance levels. The proposed methodology offers valuable insights for planners when designing new networks or enhancing the recoverability of existing ones.

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Recent advances in the development of modular transport vehicles allow deploying multi-purpose vehicles, which enable alternate transport of different demand types. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity flow. We solve a series of use case scenarios using an exact optimization algorithm and an adaptive large neighborhood search algorithm. We compare the performance of a multi-purpose vehicle fleet to a mixed fleet of single-purpose vehicles. Depending on cost parameters, our findings suggest that in certain scenarios, the total costs can be reduced by an average of 13% when multi-purpose vehicles are deployed, while at the same time reducing total vehicle trip duration and total distance traveled by on average 33% and 16%, respectively. The required fleet size can be reduced by 35% on average when operating multi-purpose vehicles. The results can be used by practitioners and policymakers to determine if the combined service of passenger and freight demand flows with multi-purpose vehicles in a given system will yield benefits compared to existing transport operations.

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