For car traffic it was found that a more crowded area leads to a lower speed and a lower arrival rate. The relation between crowdedness and speed (or arrival rate) can be expressed in a network fundamental diagram, or macroscopic fundamental diagram (MFD). Similar concepts have been shown for pedestrian and train traffic. In this paper, we extend the concept to three spatial dimensions. While simulations have explored some concepts, we present for the first time empirical results of the relation between the crowdedness in the air and the performance of the “network.” We base our results on several months of data of airplanes around Amsterdam Schiphol Airport. Similar to car traffic, we observe a reduction in speeds as the number of airplanes in the area increases. However, even at the highest observed densities, we do not see a reduction in flows. This is because of active and intensive management (based on departure/landing possibilities), comparable to perimeter control in traffic, as well as a minimum airplane speed. This paper introduces an interesting concept of applying a MFD to three-dimensional (3D) spaces. We also show to what extent the performance reduction is caused by speed reduction and to what extent it is caused by less efficient routes. The MFD concept can eventually be used to also manage 3D airspaces for applications with less strict microscopic air traffic management than the current management around airports.@en

Although numerous studies used Virtual Reality (VR) to study pedestrian behavior, there is an ongoing debate about the validity of using VR for studying pedestrian behavior. This study aims to contribute toward the validation of immersive VR systems for pedestrian wayfinding behavior studies by conducting a direct comparison of a field experiment and a matched virtual experiment. Both experiments feature three identical wayfinding assignments across multiple floors in a building. To evaluate the ecological validity of VR, three metrics of three different levels of wayfinding behavior are adopted, namely travel time (level: wayfinding performance), wayfinding strategy (level: decision-making), and angular speed of the head (level: observational behavior). Our findings show that VR can be used to study pedestrian wayfinding strategy in buildings with a single floor. However, there are significant differences in pedestrian wayfinding strategy between the field experiment and the VR experiment. Additionally, we found significant differences in the angular speed of the head between the two experiments. It suggests that researchers should take caution when using VR as a research tool to study the wayfinding strategy and the observational behavior of pedestrians in multi-story buildings.@en

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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Efficient crowd management is essential for optimizing the performance of pedestrian infrastructures, either in terms of crowd flow or pedestrian levels of safety and comfort. This study investigates the impact of one type of crowd management measure, namely lighting, on pedestrian behavior. Using Virtual Reality experiments, the impact of lighting, both the brightness level and the light color, on pedestrian route choice is studied. A virtual maze was designed, featuring 10 T-intersections, where the light conditions are varied at each T-intersection to study its impact on pedestrian route choice. Our study shows that pedestrian route choice is strongly influenced by the light color in a virtual environment. Pedestrians prefer to follow paths with green-colored lights and avoid paths with red-colored lights, irrespective of the light color on the other path. Moreover, pedestrians slightly prefer to use the path with a higher brightness level. Lastly, the results indicate that pedestrians do have a slight right-handed tendency on average, however, this effect cancels out almost completely when other guidance information is present in the scenario. Altogether, the findings suggest that lighting can impact pedestrian route choice behavior.
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Physical distancing has been an important asset in limiting the SARS-CoV-2 virus spread during the COVID-19 pandemic. This study aims to assess compliance with physical distancing and to evaluate the combination of observed and self-reported data used. This research shows that it is difficult to operationalize new rules, that context affects compliance, that there needs to be a need for compliance, and that rules require upkeep. From a methodological point of view, this study found that the combined methods provide a comprehensive picture of compliance behaviour, that it is challenging but essential to mitigate response fatigue in long-term monitoring studies, and that it would be interesting in future research to learn how actual behaviour is influenced by personal narratives.

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To promote urban sustainability, many cities are adopting bicycle-friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map-matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map-matching methods face challenges in accurately selecting the best-mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map-matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi-objective map-matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second-largest city in the Netherlands, is selected as the case study city, and real-world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high-traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

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Drivers initiate a discretionary lane change when they perceive an anticipated improvement in their own driving condition from moving to another lane. However, such a lane change can slow down other vehicles on the target lane, and even worse initiate a disturbance. In this work, we argue that the blocking effect triggered by individual lane changes results from the heterogeneity in the desired speeds of vehicles, and thus using desired speed information of vehicles when regulating lane-changing decisions can improve traffic efficiency. In doing so, our work also exemplifies the usefulness of incorporating user preferences into control decisions. The proposed lane guidance system uses an optimization-based approach to update the target range of desired speeds on each lane in real time, and accordingly recommends individual lane changes. The control system coordinates the lane-changing decisions at the link level, for which the road stretch is subdivided into multiple sections that are controlled independently. We evaluate the performance of the lane guidance system in micro-simulation, for different network demands and desired speed distributions. The results highlight that the proposed approach utilizing the desired speed preferences of drivers results in positive efficiency gains for most traffic compositions in free flow. Moreover, the highest gains are expected in medium to high demand, and when the traffic composition includes a higher proportion of vehicles desiring higher speeds. The gains also increase when the desired speeds of vehicles that want to drive fast and those that want to drive slower are more separated.

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This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Validated using two distinct real-world datasets reveals the superiority of S3VM-based classifiers over traditional SVM classifiers in driving style classification. Simulation results show that an increase in driving aggressiveness is correlated with higher safety issues and greater environmental impacts. Further elucidation of these impacts from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics indicates that less aggressive drivers can lead to the formation of vehicular platoons, thereby encouraging more aggressive drivers to adopt a milder driving style. Importantly, the formation of these platoons is influenced by both the proportion and spatial distribution of less aggressive vehicles. The proposed approach promises advantages in reducing the negative impacts of driving heterogeneity, thus benefiting Intelligent Transportation Systems (ITS) by improving traffic safety and sustainability.

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Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers’ preference to use automation and their behaviour has not yet been thoroughly investigated. In this study, a driving simulator experiment is conducted, where participants drive a CAV in the presence of a DL with different utilization policies. Drivers have the possibility to choose between driving in an automated mode or in a manual mode. In automated mode they could adjust the driving speed and time headway and initiate automated lane changes. Two utilization policies were examined: mandatory versus optional use of DLs when driving in an automated mode. The impact of the presence and utilization policy of the DL on drivers’ preference to use automation and their behaviour in car-following and lane changing are investigated. The study found that while the presence of a DL does not increase drivers’ preference for automation use, it encourages drivers to utilize the DL more when the utilization policy is mandatory (i.e., drivers can only use automation mode when driving on this lane). Furthermore, drivers are more conservative in automated mode and when driving in mixed traffic. However, they perform closer car-following and merge into smaller gaps when driving on DLs which on one hand can increase the capacity of the DLs, but on the other hand can increase the risk of collisions. These results are useful for road operators, and in setting-up a more realistically traffic simulation studies.

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Building fires can be considered a risk to the health and safety of occupants. Environmental factors in building fires might affect the speed of an evacuation. Therefore, in this study participants (N = 153) were tested in an experimental design for the effects of (1) a fire alarm, (2) darkness and (3) the use of emergency exit signs on building evacuation time. In addition, the effects of age and gender on evacuation time were investigated. The main results indicate that the combination of a fire alarm, darkness and not illuminated emergency exit signs had a significant negative influence on evacuation time, namely an increase in evacuation time of 26.6% respectively 28.1%. Another important finding is that age had a significant negative effect on evacuation time. The increase in evacuation time was at least 30.4% for participants aged 56 years or older compared to participants aged 18–25 years. For gender no significant effect was found. Building and safety managers can use these results by including longer evacuation time considerations – based on darkness and older age – in their evacuation plans. Future research should focus further on investigating the effects of personal and psychological characteristics on evacuation behaviour and evacuation time.

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Understanding user's perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference survey in August 2021, yielding 936 valid responses. The respondents were faced with static pre-trip information on the expected waiting time, followed by the actually experienced waiting time for their selected alternative. We analyse this data by means of an instance-based learning (IBL) approach to evaluate how individuals respond to service performance variation and how this impacts their future decisions. Different novel specifications of memory fading, captured by the IBL approach, are tested to uncover which describes the user behaviour best. Additionally, existing and new specification of inertia (habit) are tested. Our model outcomes reveal that the perception of unexpected waiting time is within the expected range of 2–3 times the value-of-time. Travellers seem to place a higher reward on an early departure compared to a penalty for a late departure of equal magnitude. A cancelled service, after having made a booking, results in significant disutility for the passenger and a strong motivation to shift to a different provider. Considering memory decay, our results show that the most recent experience is by far the most relevant for the next decision, with memories fading quickly in importance. The role of inertia seems to gain importance with each additional consecutive choice for the same option, but then resetting back to zero following a shift in behaviour.

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COVID-19 significantly influenced travel behaviours and public attitudes towards public transport. Various studies have illustrated complicated factors related to long-term travel behaviour, indicating difficulty in understanding and predicting post-pandemic long-term travel behaviour via traditional methods. In these complex circumstances, it is valuable to take advantage of social media data to obtain real-time public opinions to understand dynamic travel behaviour changes from the passenger perspective. The present study provides a means - leveraging Twitter data and state-of-art Natural Language Processing (NLP) technologies - to interpret the underlying associations among public attitude, COVID-19 trends and public travel behaviour. Concretely, New York City has been selected due to its dependence on public transit for daily commuting. More than 500K tweets have been collected from January 2019 to June 2022. Automated text mining, topic modelling, and sentiment analysis have been implemented in these contexts to identify dynamic public reactions. A consistently negative attitude to public transit is detected and five main topics, including derivative topics from COVID-19, are discovered within the COVID-19 duration. Policy makers and transit managers can use these topics to take onboard the public's concerns. The paper thus exemplifies how social media data and NLP technologies can support policy-making progress and can benefit other tasks in the transportation domain.

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This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters. The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function. Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice.

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Our cities are growing at an unprecedented pace. The flexible use of metropolitan infrastructures is the key to maintaining, if not increasing, the current quality of life. The combined use of geo-fence technology and connected vehicles can be the tool to achieve this flexibility. In this paper, we take a first step in the evaluation of the benefits that dynamic geo-fencing could bring. In a simulation-based environment, we employ a computer vision approach to dynamically identify congested areas in a given transportation network. We then compare the performance of perimeter control based on dynamic geo-fencing vs conventional perimeter strategies, based on a fixed, pre-determined area-a scenario mimicking traffic management approaches currently deployed in large metropolitan areas worldwide. Simulation results highlight a reduction of more than 20% of the Total Time Spent in a regular Manhattan grid network, encouraging further efforts to validate the efficiency of dynamic geo-fencing in addressing externalities (congestion, pollution, noise, etc.) in more realistic scenarios.

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Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.@en

Crime has major influences in urban life, from migration and mobility patterns, to housing prices and neighborhood liveability. However, urban crime studies still largely rely on static data reported by the various institutions and organizations dedicated to urban safety. In this paper, we demonstrate how the use of digital technologies enables the fine-grained analysis of specific crimes over time and space. This paper leverages the rise of ubiquitous sensing to investigate the issue of bike theft in Amsterdam—a city with a dominant cycling culture, where reportedly more than 80,000 bikes are stolen every year. We use active location tracking to unveil where stolen bikes travel to and what their temporal patterns are. This is the first study using tracking technologies to focus on two critical aspects of contemporary cities: active mobility and urban crime.@en

As digitalisation is making its way into public transport (PT) services, policy approaches to ensure that such services remain inclusive are at best fragmented, at worst inexistant. This study pieces together existing initiatives and lessons learnt in the transport sector itself, and takes inspiration from other fields with a more mature understanding of digitalisation. We interviewed twenty-two experts working either in the PT sector or in other sectors such as healthcare and public administration to present an overview of possible measures to foster inclusion in PT in the digital era. We used both triangulation and a two-step respondent validation process to improve results’ trustworthiness. We conclude that there is no one-size-fits-all, but a series of complementary strategies to address digital inequality. A focus on an inclusive design from the start, courses, showing the added value of digital tools, specialist products and non-digital alternatives are building blocks to foster a more inclusive PT system in the era of digitalisation. The role of the public transport staff ought not to be underestimated in digital transformations. Importantly, securing the issue of unequal access to public transport due to digitalisation at a decision-making level is essential. Nevertheless, there is only so much that the transport sector can do. Tackling more systemic issues that often underlie digital barriers like poverty and low literacy is crucially relevant. While the present study was conducted in the Netherlands, the presented measures can be applied in other countries by stakeholders working on inclusive digital transformations in (public) transport services.

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On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations.

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When making trips in urban environments, cyclists lose time as they stop and idle at signalized intersections. The main objective of this study was to show how augmenting the situational awareness of traffic signal controllers, using observations from moving sensor platforms, can enable prioritization of cyclists and reduce lost time within the control cycle in an effective way. We investigated the potential of using observations from connected autonomous vehicles (CAVs) as a source of new information, using a revised vehicle-actuated controller. This controller exploits CAV-generated observations of cyclists to optimize the control for cyclists. The results from a simulation study indicated that with a low CAV penetration rate, prioritizing cyclists by tracking reduced cyclist delays and stops, even with a small field of view. As the delay of car directions were not taken into account in this study, the average car delay increased considerably with an increasing number of cyclists. Future work is needed to optimize the control that balances the delays and stops of cyclists and cars.

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Bicycle networks are made up of different types of infrastructure for cars, bikes and mixed use, which has resulted in various definitions being used to describe them. However, it’s crucial to bring these definitions together to understand the structural differences among them and the impact of choices and investments in bike infrastructure. This study examines different definitions of bicycle networks in 47 cities, analysing scaling effects and various network metrics for four different definitions. Understanding structural characteristics of different bicycle networks definitions contributes to the body of knowledge necessary for design interventions by policymakers.@en