The truck ban policy on freeways in central business districts (CBD) is extensively used in China nowadays to improve traffic safety and reduce traffic congestion. However, this policy will drastically impact freight transportation, especially when the freeway truck volume is high. To mitigate the negative effects and encourage truck drivers to use alternative freeways, this study proposes a freeway tolling problem for different types of trucks to reduce travel costs following the truck ban policy in CBD. It is formulated as a bi-level optimization problem. The upper-level problem optimizes the freeway toll rates for different types of trucks to reduce the total travel cost (TTC) of the network. The lower-level problem is a multiclass traffic assignment model to characterize the equilibrium flow mixed with passenger vehicles and different types of trucks following the toll strategy. The bi-level problem is solved using a line search algorithm developed based on a feasible direction method. Application of the proposed method in Ningbo, China, finds that the proposed solution algorithm can efficiently solve the bi-level problem and converges only after 11 iterations. Compared to the initial state where the truck ban is not implemented, the optimal tolling strategy can effectively reduce the TTC of the network by 8.5%, with an increase of only 1.15% for all trucks. This indicates that the proposed method can effectively nudge truck drivers to use alternative routes with a minor rise in travel costs. Therefore, it can help traffic managers design better strategies to avoid the resistance of truck users following the truck ban policy in CBD.
The integration of cycling and rail transport is a key element of sustainable commuting, yet its success depends on the accessibility of stations, their surrounding environments, and destinations. While quantitative studies have extensively analysed infrastructural and behavioural determinants of bike–train use, fewer have examined how users perceive accessibility in practice. This article explores how accessibility conditions shape the bike–train commuting experience in Flanders, Belgium. Drawing on five focus groups with university staff across four campuses with varying levels of bike–train accessibility, the analysis identifies critical accessibility dimensions at both the home-end and activity-end of trips. Physical accessibility factors, including bicycle parking availability, visibility and proximity to platforms, strongly shape users’ evaluations of station quality. Accessibility is further influenced by the presence of cycle highways, multiple station entrances, and the availability and coverage of shared bicycle systems. Beyond physical elements, perceived accessibility is affected by train frequency and reliability, ease of use, perceived safety, legibility and design of the public realm, and the atmosphere and services provided at stations. The findings demonstrate that accessibility challenges and expectations differ substantially between home-end and activity-end stations: the former is primarily evaluated based on functional access to platforms, while the latter is judged more broadly on service reliability and the comfort of the station environment. While limited to current users, this study offers qualitative evidence on how accessibility conditions shape the usability of bike–train commuting, informing policies aimed at strengthening multimodal integration.
The Mercury simulator, a performance assessment platform, is a stochastic, agent-based model developed over several years during research projects. It features a detailed description of the air transportation system at the European level, including passengers and aircraft, and various important actors such as the Network Manager, airports, etc. This article presents the possibilities offered by the simulator’s current, now open-source version. We describe the core Mercury functionalities and highlight its modularity and the possibility of its usage with other tools. We present a new interface, which supports user-friendly interaction with the simulator, exploring data input/output and parameter settings. We emphasise possible uses as a solution performance assessment tool, which is usable early in the innovation pipeline to better estimate the impact of changes and new systems in the air transportation system. We hope that opening the simulator may encourage other developers to open their models, allowing faster prototyping of new operational concepts early in the innovation pipeline and an in fine support standardisation and higher performance of simulation-based performance assessment tools.
Inefficient public transport is a barrier to increased use and can contribute to time scarcity. Most comparisons of travel time across modes come from accessibility studies that compare trip data and leave several questions unanswered. These include whether the travel time disadvantage of public transport at the trip level translates into a disadvantage at the household level, given the interdependence of mobility choices among household members. Also, whether public transportation is the least time-competitive for social groups that rely on it most, particularly low-income households and those living in low-income neighborhoods. To investigate these questions, the study employs survey data on daily mobility comprised of data from 67,455 individuals aggregated at the household level across 79 German cities in 2017. The data are supplemented by independent 1km-by-1km grid-level data on neighborhood poverty and centrality and are analyzed via multi-level regression models. Results indicate that the travel time disadvantage of public transportation is mitigated at the household level, and there is no indication that disadvantaged households experience longer travel times given equal travel distances when using public transport. However, the travel time disadvantage of low-income households is not fully explained by the use of public transport over cars, car ownership nor neighborhood centrality.
Escalators are an integral part of multi-level public buildings. The researchers have studied the flow characteristics on the escalators, but the guidelines to evaluate pedestrian flow conditions at escalators are missing. This paper proposes the Level of Service (LOS) criteria to assess the flow conditions on and around the escalators. A study was conducted at Delhi Metro stations in India. Arrival flow data and passengers’ flow at escalators are recorded, and arrival density, passenger walking speed towards an escalator, and flow data are extracted. Considering that the theoretical capacity on escalators is never reached, the reference capacity as proposed in the literature is used to estimate flow (v) to (reference) capacity (Cf) ratio. Analysis indicated that the percentage of pedestrians walking on escalators varied between 3.78% and 8.45%; reference capacity varied between 81 and 178 ped/m/min; and the walking speed of pedestrians towards the escalator varied between 1.92 m/s and 0.10 m/s. This indicated the effect of density and flow condition. The maximum density in the approach area is estimated as 3.2 ped/m2. The LOS of the selected escalators based on different criteria indicated that the selected escalators generally operated at either LOS-C or LOS-D. Criteria for planning or implementation of a new facility are also recommended. This would help the professionals and planners in taking an informed decision regarding infrastructural improvements in public buildings.
This paper analyses multimodal mobility platforms which enable the matching of supply and demand for transportation services, including new forms such as ride-hailing, ride-pooling, or vehicle sharing. Typically, such platforms appear as mobile applications (apps), sometimes evolving into “super apps” that even include non-transport services. This paper aims at answering the following research questions: (1) Which digital mobility platforms dominate the global market and how can they be characterized?; (2) What economic impacts can be expected for mobility companies from a possible EU regulation to facilitate platform-based bookings and multimodal travel (e.g., the EU Multimodal Digital Mobility Services (MDMS) initiative)?; and (3) Which overarching recommendations can be derived for policymakers concerning such a possible regulation? To investigate these questions, we first identify and characterize the most important mobility platforms based on data from an analytics firm for the mobile app market. The characteristics considered include platform usage, the number of integrated mobility services, and the countries in which they are operated. We then analyse statements from stakeholders collected during a public consultation on the EU MDMS initiative to identify significant SWOTs (strengths, weaknesses, opportunities, threats) for individual stakeholder groups in connection with such a possible future EU regulation. Based on this, we derive regulatory recommendations for policymakers.
This paper investigates the causal impact of the initial stage of the COVID-19 pandemic (i.e., 2020) on airlines’ crisis response strategies from the supply perspective in the EU-US market. An analytical framework examining three strategies, namely market exit, capacity retrenchment, and route hierarchy persevering, is established by utilizing the difference-in-differences (DID) method, which jointly considers dynamics in pandemic severity, border control policies, and route structure. The results demonstrate that the COVID-19 pandemic led to the disappearance of diversity in the business models of the EU-US market in the short term. Analysis of individual airlines’ capacity retrenchment strategies reveals heterogeneity in the impact of COVID-19. The route hierarchy persevering analysis exhibits three distinct strategies of airlines, i.e., connectivity-oriented, antitrust immunity joint venture-oriented, and market presence-oriented.
Third party use of charging infrastructure for electric buses on major passenger transport hubs offers potential to make better use of existing electricity grid capacity, especially given the current lack of grid capacity. This requires flexibility in the charging operation, which is hampered due to lacking ownership of this opportunity to share charging infrastructure. This research aims to develop understanding of the perception of drivers and barriers for stakeholder organizations (SOs) to initiate collaboration on flexible use of this infrastructure. A survey under 141 respondents was applied to collect these perceptions on the drivers and barriers, the most logical initiator and motives behind these choices. Results indicate that presence of sufficient benefits, fitting organizational culture and entrepreneurial freedom are main drivers for initiation of collaboration. No specific barriers stood out. Furthermore, public parties – specifically the transport authority – are indicated as most logical initiator. The paper shows that challenge for initiating collaboration lies in the unclarity around the mandate to take initiative. Although the presence of mandate is not seen as main driver for one’s own organization, it is seen as reason why other organizations should be the initiator. The lacking overall mandate for collaboration causes inertia, and thereby is the reason why the fire is not lit yet. Fortunately, this challenge of ownerless opportunities in complex SO-environments can be overcome, but it does require a new way of looking at how infrastructure is applied and managed.
This study provides insights into the rarely observed long-distance travel patterns of individuals. Collected smartphone tracking data from June 2022 to May 2023, focused on the Munich metropolitan region, allows us to investigate travel behavior and the occurrence of long-distance travel throughout the year. After comprehensive data preparation, the recorded modal share and the share of observed long-distance trips are compared with the findings of a German travel survey to investigate the benefits of the dataset. Long-distance trips are further analyzed in terms of their occurrence rate and modal share throughout the year. Furthermore, the influence of various sociodemographic characteristics and car ownership on long-distance travel is explored. The primary usage of privately owned cars is also analyzed. Our findings reveal differences in the occurrence of trips, with increased frequency observed during summer, weekends, school holidays, and public holidays. Additionally, the research underscores the impact of sociodemographic factors, particularly household income and age, on elevated levels of long-distance travel activity. Our research indicates that a significant share of car owners in the urban area use the car primarily for long-distance trips.
The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of x-ray scans. This paper presents and experimentally tests a proposed implementation of gamification within highly automated en-route air traffic control. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were tasked to supervise the behaviour of a fully automated conflict detection and resolution system, while manually routing fictional flights safely and efficiently through the sector, avoiding conflicts with both real and fictional flights. Automation anomalies were simulated, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased self-reported concentration levels and reduced boredom. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Thus, while the implementation of such a tool might provide benefits in terms of skill retention and engagement, further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency, and skill erosion in order to understand and mitigate its negative effects.
Cities with high levels of cycling frequently encounter challenges associated with high demand for bicycle parking. One approach to tackle this is the installation of bicycle parking stations that provide weather and theft protection. Due to their high cost and limited capacity, a pricing strategy appears to be useful for managing the occupancy of these facilities. However, there is a shortage of quantitative studies that analyze improvements in bicycle parking and specifically measure the impact of parking fees. Against this background, this paper examines the effect of parking fees on the utility of planned bicycle parking stations at RWTH Aachen University in Germany. The study uses a mixed logit model that is based on a stated preference experiment on bicycle parking (n = 2,960). Based on logsum analysis, the results indicate that parking fees can contribute to bicycle parking demand being spatially more evenly distributed, thereby reducing congestion of parking stations while at the same time generating substantial revenues. In the case study, bicycle parking stations can enhance their benefit-cost ratio by implementing parking fees, provided that facilities with low occupancy are excluded from the parking fee. Therefore, the introduction of a modest fee can be beneficial for single bicycle parking facilities that otherwise face substantial crowding, without compromising, but actually increasing their benefit-cost ratio.
Route guidance in traffic management aims to improve traffic network performance aligned with a system optimum. However, service providers commonly offer user optimal travel advice that can negatively impact centralized route guidance. This paper quantifies and demonstrates the impact of different policy strategies for a centralized route guidance systems where road authorities and service providers work together in a coordinated approach. Cooperation through an intermediary is considered with various policy strategies that consider different approaches and levels of cooperation between road authorities and service providers, which are evaluated using traffic modelling. A use case for the ring network of Milan shows that cooperation between the two parties has the potential to get the best out of the measure by utilizing a system optimum approach, while still allowing service providers to offer individual travel advice. The results of the modelled case study clearly show that the two approaches of far-reaching cooperation and increased compliance have a greater positive effect on traffic network performance in terms of reduced delays, reduced congestion and total time spent. In addition, the future presence of Connected Automated Vehicles (CAV) is also considered in which these vehicle demonstrate full compliance. This shows that with increasing percentage of CAVs that route guidance can have a substantial positive effect compared to low compliance or a smaller penetration rate of automated vehicles.
Since 2023, the on-demand ridepooling service MOIA operates, as part of Hamburg's public transport, an integrated wheelchair-accessible service and offers subsidized fares for individuals with severe disabilities. This study analyzes one year of demand patterns in Hamburg following these enhancements, utilizing data analyses to assess their impact on service usage and accessibility. Findings reveal insights into distinct spatio-temporal user behavior, offering valuable guidance for the development of large-scale urban mobility solutions.
In this study, the effect of freight and passenger transport in different modes on economic growth is determined for 28 selected countries. The Westerlund cointegration test is used to reveal the long-term relationship between freight and passenger transportation and growth. According to the cointegration analysis, all transportation modes (road, rail, and air) are cointegrated with growth. Additionally, machine learning models were used to predict growth based on each transportation mode for each country for the upcoming four years and to determine the importance of the input parameters. According to the importance of the parameter analysis, for the entire panel, rail transport is the most effective transport mode for economic growth. On a country-by-country basis, the findings differ. Rail transport is the strongest transport mode for growth in high-income countries. However, although it is not the dominant mode, the relative impact of air passenger transport is strong. In upper middle-income countries, there generally is not a dominant mode of transport, but in general, freight transport is important to economic growth. In passenger transportation, air passenger transport is the most prominent mode in these countries. In lower middle-income countries, rail freight is the strongest transport mode for economic growth.
This paper revisits the concept of air/high-speed rail (HSR) integration in the specific case of congested airports, in which airport slots for (super) short-haul flights are freed by replacing them with high-speed trains. Freed slots are then likely allocated to longer flights, which leads to an increase in GHG emissions induced by flights from/to the airport into question. Such an unexpected effect is investigated through the case of Frankfurt Airport, where the HSR infrastructure was designed to connect smoothly with the airport. The ex post investigation isolates the time window during which airport capacity is stable. It confirms the increase in aviation climate impact. This illustrates that air/HSR integration is not always a relevant solution to curb the impact of aviation on climate change.
In the face of escalating climate change, airports worldwide are finding themselves at the mercy of extreme weather events. This research paper presents a comprehensive system that models key indicators, aiding airport management during such challenging weather conditions. The system adopts an integrated approach, combining various machine learning models to provide a detailed projection of an airport's future state, drawing from past occurrences. The heart of the system is a model that focuses on the airport's peak service rate. This model meticulously correlates weather conditions and runway configurations with the 99th percentile of observed throughput from the training dataset. As such, the peak service rate model provides an estimate of the airport's capacity, which is essential for effective planning and resource allocation. Moreover, the system includes a predictive model that assesses the likelihood of air traffic flow management regulations based on weather data and calendar information. The robustness of this model against noise and uncertainty in the training dataset is fortified by the application of confident learning techniques and the inclusion of monotonic constraints. The system further enhances its capabilities by forecasting the potential entry rate of regulations, expressed in hourly arrivals, providing valuable insights that can guide proactive decision-making. By seamlessly integrating these three models, the system serves as an effective tool for airport operators and airlines. It enables operational optimisation and the development of strategic plans to mitigate the effects of increasing weather-related disruptions.
Walking is globally promoted as an urban transport mode that reduces greenhouse gas emissions and increases physical activity. While it is well-known that accessibility at neighbourhood and urban levels impact walking levels, studies on pedestrian travel behaviour are typically based on area-based measures. Network-based measures of street configuration, connectivity, urban density, and land-use diversity have scarcely been applied. This paper aims to find associations of network-based connectivity and accessibility measures with walking frequencies. We applied a series of multivariable ordinal regressions adjusted for distance to various destinations, building-type diversity, socio-demographic variables, and car ownership. We operationalise connectivity as reach, i.e., the number of streets reached through the network, and density and diversity as weighted reach, i.e., the floor area, population, or retail accessible within walkable distances through the network. Street network configurations are described by topological centrality (integration). Results show significant positive associations of reach, attraction reach, and integration with walking frequency, with the best model fits achieved by accessible total floor area and reach within relatively high network radii (1000 – 2000 m and 6 axial steps), indicating that connectivity and access to a variety of attraction and functions, often beyond administrative borders, encourage walking.
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.
The purpose of this paper is to minimize the total difference between the requested and assigned departure time of aircraft to enhance the efficiency of using limited airport capacity. The mathematical model was formed by employing mixed integer linear programming. The parameters, decision variables, and constraints were defined to cover the problem. The baseline and alternative scenarios were compared to present the improved results. Sensitivity analysis was performed to test the ability of the model with different parameters. Also, in order to test the mathematical model, distinct from both the baseline and alternative scenarios, CHQ airport was based by using its number of parking positions and taxi-in/out durations in the sensitivity analysis. The proposed model reduced the total difference by 20.78% for 30 aircraft for 5 parking positions in the alternative scenario. The results showed that it may well serve to improve the imbalance regarding operational conditions.
This work analyses how the different U-space service providers (USSPs) managing a shared airspace volume will impact on the other’s performance. The paper demonstrates how the various strategic planning USSPs capabilities, ranging from procedures and policies, trajectory representation, as well as the deconflicting strategies, impact on the use of this common resource in terms of effective airspace capacity. The paper brings to practice the concept of Reasonable Time to React by proposing a planning timeline with common milestones for the implementation of the required authorization procedures prior to the flight. The paper analyses how these milestones impact on the effective airspace capacity. Also, the First-Come First-Served planning policy is compared with respect to a more efficient batch planning policy, where flight plan batches are processed to mitigate the potential conflicts existing at the strategic phase. Furthermore, the paper discusses how the capabilities supported by the USSP to represent the trajectory and its associated uncertainty will be also key to optimize the use the airspace. Based on a CORUS-XUAM VLD scenario, a simulation analysis will assess how the USSPs’ capabilities to manage flight planning activities impact on the effective occupation of airspace, jeopardizing in some cases not just the own performance but also that of other airspace users.
