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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., Transport and Planning, Transport and Planning

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., Transport and Planning

Els passatges, entesos com una tipologia constructiva important i convencional del teixit urbà estan a punt de desaparèixer a les actuals ciutats globalitzades. Investigacions prèvies assumeixen teòricament que els passatges faciliten el flux continu als espais públics, afavoreixen l’accessibilitat i milloren la vitalitat del carrer. Tot i això, no hi ha evidència suficient que demostri el seu potencial millorant la connectivitat dels espais. Aquest estudi té com a objectiu investigar com els passatges afecten físicament i visualment la connectivitat dels carrers i, per tant, com milloren la integració espacial a les xarxes d’aquests. L’estudi de camp es va dur a terme a petita escala a l’àrea d’un mercat comercial on hi ha molts passatges interconnectats. La metodologia utilitzada combina una anàlisi gràfica visual, axial i segmentària de la sintaxi espacial; amb una observació sistemàtica del volum de vianants i les opcions de ruta. Les nostres troballes suggereixen que els passatges augmenten tant la connectivitat física i visual com la integració espacial en proporcionar moltes rutes diverses, curtes i alternatives per al moviment dins de la xarxa de carrers. Tot i això, les troballes també van mostrar que l’ús efectiu dels passatges està predominantment associat a alguns dels seus atributs espacials. En aquest sentit, sempre que els passatges tinguin corredors curts i directes que proporcionen connexions en diferents direccions, contribuiran a l’accessibilitat dels carrers i a la vitalitat de les ciutats., Passages, an important and conventional building typology of the urban fabric, are about to disappear in today's globalized cities. Previous research theoretically assumes that passages allow continuous flow of public spaces, support accessibility, and enhance street vitality. However, there is not enough evidence to provide their potential for improving connectivity. This study aims to investigate how passages affect street connectivity physically and visually and thus how they improve spatial integration in street networks. The field study was conducted in a small-scale commercial market area, in which there are many interconnected passages. The methodology consists of axial, segment and visibility graph analyzes of space syntax, and a systematic observation of pedestrian volume and route choices. Our findings suggest that passages increase physical and visual connectivity and spatial integration by providing many diverse, short, and alternative routes for movement within the street network. However, the findings also showed that the effective use of the passages is predominantly associated with their spatial attributes. As long as passages have short and direct corridors, providing connections in different directions, they can contribute to the accessibility of streets and the vitality of cities., Los pasajes, entendidos como una tipología constructiva importante y convencional del tejido urbano están a punto de desaparecer en las actuales ciudades globalizadas. Investigaciones previas asumen teóricamente que los pasajes facilitan el flujo continuo en los espacios públicos, favorecen la accesibilidad y mejoran la vitalidad de la calle. Sin embargo, no hay evidencia suficiente que demuestre su potencial mejorando la conectividad de los espacios. Este estudio tiene como objetivo investigar cómo los pasajes afectan física y visualmente a la conectividad de las calles y, por lo tanto, cómo mejoran la integración espacial en las redes de las mismas. El estudio de campo se llevó a cabo a pequeña escala en el área de un mercado comercial en el que hay muchos pasajes interconectados. La metodología utilizada combina un análisis gráfico visual, axial y segmentario de la sintaxis espacial; con una observación sistemática del volumen de peatones y las opciones de ruta. Nuestros hallazgos sugieren que los pasajes aumentan tanto la conectividad física y visual como la integración espacial al proporcionar muchas rutas diversas, cortas y alternativas para el movimiento dentro de la red de calles. No obstante, los hallazgos también mostraron que el uso efectivo de los pasajes está predominantemente asociado a algunos de sus atributos espaciales. En este sentido, siempre que los pasajes cuenten con corredores cortos y directos que proporcionan conexiones en diferentes direcciones, contribuirán a la accesibilidad de las calles y a la vitalidad de las ciudades., Peer Reviewed

As urbanisation increases, in many places, the transport system is suffering from problems that may affect large parts of the urban population, such as traffic congestion or increased air pollution. In both cases, a better distribution of traffic flows could contribute to establishing a more sustainable transport system, and to improve the situation from a societal point of view. In this paper, we use cartographic symbolisation for communicating favourability of route options for achieving a societal benefit. Since map symbols can evoke different emotional responses in the viewer, we investigate to which extent map symbols evoke positive and negative emotions and whether these influence route choice decision making. We created different cartographic visualisations and designed a user study that investigates the effectiveness and suitability of these different visualisation variants for influencing route choice based on two scenarios: traffic and air quality. Fourteen route maps were prepared using different map symbols to symbolise societally favourable and non-favourable route options. The results of this study show that map symbols can be used effectively for influencing route choice towards choosing the favourable route for the two tested scenarios. While visualisations that modify only lines were more effective in the traffic scenario, area symbol modifications were more effective for the air quality scenario. The symbolisation evoked a wide range of emotions in participants. While non-favourable routes mainly evoke negative emotions (particularly fear), favourable routes mainly evoked positive emotions (particularly contentment) or no emotions. The results further demonstrate that for some of the visualisation variants, emotions felt in response to the map visualisations contributed significantly to changing the route choice decisions in favour of the societally favourable route option. The findings of this research demonstrate the relationship between route ch

One of the aspects that policymakers should consider when promoting cycling is the route choice behaviour of current cyclists. This study develops a behaviourally realistic route choice model for different types of everyday cyclists and cycling trips. The analysis is based on a large-scale crowdsourced dataset of GPS trajectories including 134,169 trips from 6,523 cyclists. The model is estimated as a joint path-size logit model and accounts for a wide range of bicycle network attributes, such as bicycle infrastructure type, land use, surface type or cycle superhighways. The findings of the model reveal, for example, that infrequent cyclists feel less safe on large roads, but this effect can be accommodated with protected bicycle tracks. Interaction with other motorised and non-motorised transport modes is found to be a deterring factor for cyclists and they prefer scenic water and green areas over high-rise urban environments, especially on long trips. The model performs very well on a hold-out sample, also when considering the similarity between the observed and predicted route, not only their binary consistency. Finally, we formulate several policy measures relevant to promote cycling. Building long, continuous stretches of dedicated, protected bicycle infrastructure outside of the high-rise urban environments has the greatest potential to make cycling attractive.

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost., Transport and Planning, Transport and Planning

Based on the status quo-dependent route choice model in Xu et al. (2017), this study encapsulates the route choice model into traffic assignment modeling and establishes a Status quo-dependent User Equilibrium (SDUE) model. The proposed SDUE model adopts the adaptive value of time (VOT) to incorporate three kinds of route choice behavior: cognitive limitations and capability constraints, satisficing behavior and inertial behavior, and variation in VOTs, which is further compared with existing UE models. The connection between the SDUE model and existing UE models is analyzed. It is theoretically proved that equilibrium solutions from previous UE models can all be included in the SDUE solution set by letting VOTs vary among users and across scenarios. Moreover, a reduced SDUE model is formulated to help calculate possible SDUE flow patterns mathematically. Numerical examples are given to illustrate that the proposed SDUE model could include the non-convex solution set and various route choice behavior by adjusting the adaptive VOT-related parameters, which is beyond the existing UE models. The non-uniqueness of the SDUE solutions and model applications are discussed.

This dissertation forms part of the North/South-line Impact Study, a four-year interdisciplinary research project on the effects of the opening of a new metro line in Amsterdam connecting the north with the south of the city. Our main research question focuses on mobility and accessibility: "What are the effects of the opening of the North/South metro line for the entire transportation system in the Greater Amsterdam Metropolitan region?". In particular we examine how the new metro line affects: (1) changes in multi-modal travel behavior, (2) changes in the number of trips and travel distance per transportation mode, and (3) travel times and congestion for car and bicycle. We examine each of these three dimensions in turn, in the three parts of this thesis.

Route choice modeling under travel time uncertainty is essential for analyzing travelers’ mobility patterns. This paper investigates the impact of travel time uncertainty on route choice behavior in user equilibrium models based on a generalized mean-variance metric (GMV). This model can capture the influence of risk attitudes and schedule unpunctuality on route choice using a generalization of expected travel time, variance, and expected early or late arrival penalties, of which travelers are assumed to minimize the GMV of trips considering a certain on-time arrival probability. This paper establishes the properties of GMV and formulates the GMV-based static user equilibria as a variational inequality (VI) problem, for which the existence and uniqueness of the solutions are also analyzed. An effective traffic assignment algorithm without path enumeration is developed to solve the proposed user equilibrium problem. Numerical examples are conducted to demonstrate the properties of the proposed model. KEYWORDS: Travel time uncertainty, route choice, generalize mean-variance, user equilibrium

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour., Transport and Planning, Transport and Planning

Accurate evaluation of the service quality of public transport is imperative for public transport operators, providers of competing mobility services and policy makers. However, there is no consensus on how public transport should be evaluated. We fill this research gap by presenting a structural approach to evaluate three common manifestations of public transport (route sets, timetables and line plans), considering the two predominant route choice models (shortest path routing and logit routing). The measures for service quality that we derive are consistent with the underlying routing models, are easy to interpret, and can be computed efficiently, providing a ready-to-use framework for evaluating public transport. As a byproduct, our analysis reveals multiple managerial insights.

We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network., We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network.

Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers’ route choice preferences and do not adequately accommodate drivers’ heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers’ preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers’ common aspects of route choice preferences and yet adapts to each driver’s own preference. In addition, any evolvement of individual drivers’ preferences can be simultaneously integrated to update the common preference for further individual drivers’ preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers’ navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport and Planning

This paper develops a new route choice modelling framework that deals with both route correlations and unrealistic routes in a consistent and robust way. To do this, we explore the integration of a correlation-based Path Size Logit model with the Bounded Choice Model (BCM) (Watling et al. 2018. “Stochastic User Equilibrium with a Bounded Choice Model.” Transportation Research Part B: Methodological 114: 254–280). We find, however, that the natural integration of these models leads to behavioural inconsistencies and/or undesirable mathematical properties. Solving these challenges, we derive a mathematically well-defined Bounded Path Size (BPS) model form that utilises a consistent criterion for assigning zero choice probabilities to unrealistic routes while eliminating their path size contributions. A closed-form and fixed-point BPS model are consequently formulated. Subsequently, we consider parameter estimation in a simulation study and on a real-life large-scale network using GPS data, where computational feasibility is demonstrated. Estimation results show the potential of the BPS models to give improved fit relative to non-bounded versions (and BCM).

Bakalářská práce se zabývá analýzou dopadu na veřejnou dopravu z hlediska změn používání, chování a volby trasy cestujících vlivem pandemie COVID-19. Za pomoci elektronického dotazníkového šetření byl proveden výzkum změn chování cestujících ve veřejné dopravě v České republice. Výstupem byly následně zjištěny výrazné změny chování cestujících v rozhodování a volbě trasy., The work deals with the analysis of the impact on public transport in terms of changes in the use, behavior, and choice of passenger routes due to the COVID-19 pandemic. With the help of an electronic questionnaire survey, research was conducted on changes in the behavior of passengers in public transport in the Czech Republic. The output subsequently revealed significant changes in passenger behavior, decision-making and route selection., Dopravní fakulta Jana Pernera, Problematiku řešenou ve své bakalářské práci dokázal student odpovídajícím způsobem obhájit. Prokázal schopnost samostatné práce. Na doplňkové otázky reagoval pohotově, se znalostí věci a bez závažných nepřesností je zodpověděl., Dokončená práce s úspěšnou obhajobou

Shared mobility refers to transportation services that are shared among users. In recent years, applications of shared mobility have become more and more prevalent in daily lives. A few examples of these applications include ride-sharing, food delivery, and urban logistics. One of the most fundamental challenges is the Route Planning in Shared Mobility (RPSM) problem. Formally speaking, given a set of workers and requests, the RPSM problem aims to find a suitable route (i.e., a sequence of request’s origin and destination) for each worker to optimize some platform-defined objective. This thesis studies two mainstream RPSM problems, namely monetary-aware RPSM and temporal-aware RPSM. In the first problem, we aim to maximize the number of served requests or the total revenue of the platform, which are commonly-seen objectives in ridesharing platforms like Uber and Didi Chuxing. To address monetary-aware RPSM, our idea is to iteratively search the most profitable route among the unassigned requests for each worker, which is much simpler than the existing methods. Unexpectedly, we prove this simple idea has an approximation ratio better than 0.5, which is nearly optimal. In the second problem, we aim to minimize the makespan (i.e., maximum travel time) of workers and total latency (i.e., total waiting time) of requests, which are popular objectives in food delivery and urban logistics. To solve temporal-aware RPSM, we propose a algorithmic framework that simultaneously approximates both objectives. Leveraging the data structure of geometric embeddings, hierarchically separated tree (HST), we show this framework achieves an approximation ratios of O(log n) for either objective, where n is the number of requests. To further improve the efficiency, we study the construction and update algorithms of HSTs and propose faster and better solutions to this spatial index, which can benefit many other applications like clustering and facility location planning. Finally, extensive

This dissertation forms part of the North/South-line Impact Study, a four-year interdisciplinary research project on the effects of the opening of a new metro line in Amsterdam connecting the north with the south of the city. Our main research question focuses on mobility and accessibility: "What are the effects of the opening of the North/South metro line for the entire transportation system in the Greater Amsterdam Metropolitan region?". In particular we examine how the new metro line affects: (1) changes in multi-modal travel behavior, (2) changes in the number of trips and travel distance per transportation mode, and (3) travel times and congestion for car and bicycle. We examine each of these three dimensions in turn, in the three parts of this thesis.

Accurate evaluation of the service quality of public transport is imperative for public transport operators, providers of competing mobility services and policy makers. However, there is no consensus on how public transport should be evaluated. We fill this research gap by presenting a structural approach to evaluate three common manifestations of public transport (route sets, timetables and line plans), considering the two predominant route choice models (shortest path routing and logit routing). The measures for service quality that we derive are consistent with the underlying routing models, are easy to interpret, and can be computed efficiently, providing a ready-to-use framework for evaluating public transport. As a byproduct, our analysis reveals multiple managerial insights.

Route choice modeling under travel time uncertainty is essential for analyzing travelers’ mobility patterns. This paper investigates the impact of travel time uncertainty on route choice behavior in user equilibrium models based on a generalized mean-variance metric (GMV). This model can capture the influence of risk attitudes and schedule unpunctuality on route choice using a generalization of expected travel time, variance, and expected early or late arrival penalties, of which travelers are assumed to minimize the GMV of trips considering a certain on-time arrival probability. This paper establishes the properties of GMV and formulates the GMV-based static user equilibria as a variational inequality (VI) problem, for which the existence and uniqueness of the solutions are also analyzed. An effective traffic assignment algorithm without path enumeration is developed to solve the proposed user equilibrium problem. Numerical examples are conducted to demonstrate the properties of the proposed model. KEYWORDS: Travel time uncertainty, route choice, generalize mean-variance, user equilibrium

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour., Transport and Planning, Transport and Planning

We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network., We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network.

We propose a route choice model in which traveler behavior is represented as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives, and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization, and computation is straightforward even for large networks. We estimate and validate the model using a large dataset comprising 1,337,096 GPS traces of trips in the Greater Copenhagen road network.

There has been a steady increase in demand for goods over the past half-century and therefore there is a continuous need for well-organized freight transportation systems. Optimal use of roadways as the primary and fundamental sector of the freight transportation system is essential. Efficient movement of freight is vital to the rivaling economies of cities and metropolitan areas, and truck highway corridors comprise an essential ingredient of the regional freight transportation system, along with rail and intermodal facilities, river-port barge terminals, and air cargo facilities. To achieve efficient, reliable and robust freight movement, the Fixing America’s Surface Transportation (FAST) Act requires the Federal Highway Administration (FHWA) to establish a National Highway Freight Network (NHFN) to strategically direct Federal resources and policies toward improved performance of the NHFN. The main objectives of FAST are to make the Federal surface transportation more streamlined, performance-based, and multimodal, and to address challenges facing the U.S. transportation system, including improving safety, maintaining infrastructure condition, reducing traffic congestion, improving the efficiency of the system and freight movement, protecting the environment, and reducing delays in project delivery.

We propose a methodology for using the records from Wi-Fi packet sensors to model route choice and time spent at different locations within a tourist area. In contrast to other route choice problems, in tourist areas it is often the case that “the route is the goal, ” so we expect route choice behavior to be less destination oriented. Data were collected from Higashiyama Ward, Kyoto, Japan, which is highly frequented by tourists. We utilized Wi-Fi packet sensors, which avoid the need for mobile applications but provide only spot data. Based on the data, we first discuss the extraction of tourist trip chains and the construction of a simplified time-space network with “stay” and “move” links. The network and link attributes are based on the location of the sensors as well as map data. Recursive Logit (RL) models are then employed to formulate tourists’ route choice behavior. The existence of different kinds of “points of interests” (POIs) is shown to explain route choice as well as the time spent on roads with shops, restaurants and sightseeing spots.

With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. While earlier research has examined route choice preferences in relation to travel time and travel cost (or toll), there is little guidance on the influence of information provision mechanisms. By accommodating for information provision attributes, the proposed research contributes to our understanding of the design of an active traffic management (ATM) system by quantitatively estimating the inherent trade-offs across the various attributes affecting route choice. Specifically, the research designs and elicits data using a web-based stated preference (SP) survey to understand road users’ preferencesin the Greater Orlando Region, USA. In the empirical analysis, the data compiled is utilized to develop random utility maximization and random regret minimization based panel mixed multinomial logit models. Route choice behavior is modeled using a comprehensive set of exogenous variables including trip characteristics, roadway characteristics and traffic information characteristics. The model results are utilized to conduct a comprehensive trade-off analysis across various attributes for the two model frameworks. In this research effort, we also customize the trade-off computation for regret minimization model for accommodating variable interactions. The trade-offs results provide useful insights on travel information provision (when and how).

Virtual Reality (VR) is a valuable tool for studying pedestrian behaviour in complex and realistic scenarios. However, it has remained unknown how different VR technology would influence pedestrian behaviour. This paper presents VR experiments that were conducted with 70 participants using a desktop VR or a HMD VR to perform four different wayfinding tasks in a multi-story building. Quantitative analysis of pedestrian behaviour data and user experience data were performed in order to investigate the impact of the technological differences between the two VR techniques. It was found that participants had better wayfinding task performance in the desktop group. However, the route and exit choice and user experience were overall similar between the two groups. The findings suggest that one could adopt more ‘simple’ VR technologies for studies featuring ‘simple’ wayfinding tasks., Accepted Author Manuscript, Transport and Planning

Given the aim of increasing public transport patronage, it is important to understand how passengers perceive different trip characteristics. Most of the existing studies about public transport demand and route choice assigned a higher value of time to transfers than in-vehicle time and used a general transfer penalty to capture an average increase in the travel disutility because of the amount of transfers. However, it is likely that there are nuances to the transfer behaviour depending on specific transfer conditions that existing models do not capture and hence it is difficult to evaluate measures aimed at improving transfers to make public transport more attractive. This study presents a route choice model for the large-scale multimodal public transport network in the Greater Copenhagen Region where a variety of transfer attributes were explicitly considered within a unified model framework. The model was estimated on an extensive revealed dataset of 4,810 observed routes that made it possible to evaluate the rates of substitution of transfer related attributes. The results revealed that travellers do consider attributes for transfers such as ease of wayfinding, presence of shops and escalators at stations when choosing routes in the public transport network and this influences the attractiveness of the respective routes with a quite large range of the transfer penalty from 5.4 min compared to bus in-vehicle time for the best possible transfer to 12.1 min for the worst. Furthermore, the study revealed some differences in the preferences for transfer attributes across passengers. This suggest a quite large potential for improving transfers and hence public transport patronage focusing on the attributes of the transfers.

Virtual Reality (VR) is a valuable tool for studying pedestrian behaviour in complex and realistic scenarios. However, it has remained unknown how different VR technology would influence pedestrian behaviour. This paper presents VR experiments that were conducted with 70 participants using a desktop VR or a HMD VR to perform four different wayfinding tasks in a multi-story building. Quantitative analysis of pedestrian behaviour data and user experience data were performed in order to investigate the impact of the technological differences between the two VR techniques. It was found that participants had better wayfinding task performance in the desktop group. However, the route and exit choice and user experience were overall similar between the two groups. The findings suggest that one could adopt more ‘simple’ VR technologies for studies featuring ‘simple’ wayfinding tasks., Accepted Author Manuscript, Transport and Planning

Routes with a high share of greenery are attractive for cyclist and pedestrians. We analyze how strongly such green routes differ from the respective fast routes using the openrouteservice. Greenness of streets was estimated based on OpenStreetMap data in combination with Sentinel-II imagery, 3d laser scan data and administrative information on trees on public ground. We assess the effect both at the level of the individual route and at the urban level for two German cities: Dresden and Heidelberg. For individual routes, we study how strongly green routes differ from the respective fast routes. In addition, we identify parts of the road network which represent important green corridors as well as unattractive parts which can or cannot be avoided at the cost of reasonable detours. In both cities, our results show the importance of urban green spaces for the provision of attractive green routes and provide new insights for urban planning by identifying unvegetated bottlenecks in the street network for which no green alternatives exist at this point.

We propose a model in which a utility maximizing traveler assigns flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. This model can be estimated from route choice data, where the full set of route alternatives is included and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization and is straightforward even for large networks., We propose a model in which a utility maximizing traveler assigns flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. This model can be estimated from route choice data, where the full set of route alternatives is included and no choice set generation is required. Nevertheless, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization and is straightforward even for large networks.

Navigation apps have become more and more popular, as they give information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, people tend to learn and adapt to different situations. What if the experience gained from such a learning task is shared via an app? In this paper, we analyse the effects that adaptive driver agents cause on the overall network, when those agents share their aggregated experience about route choice in a reinforcement learning setup. In particular, in this investigation, Q-learning is used and drivers share what they have learnt about the system, not just information about their current travel times. Using a classical commuting scenario, we show that experience sharing can improve convergence times that underlie a typical learning task. Further, we analyse individual learning dynamics to get an impression how aggregate and individual dynamics are related to each other. Based on that interesting pattern of individual learning dynamics can be observed that would otherwise be hidden in an only aggregate analysis., Funding Agencies:National Council for Scientific and Technological Development (CNPq) 307215/2017-2CAPES 001

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. Keywords Traffic flow Disturbance Time-varying OD Route choice Network disruption Dynamic travel time

This paper describes a method to estimate the parameters of a Recursive link-based Logit model (RL) using measurements of a set of spatially fixed proximity sensors, with limited hit rates, which can uniquely identify people, such as Wi-Fi-, RFID- or Bluetooth-sensors. The observed ‘route’ of an individual, where we focus on pedestrians in an urban or event context, is modelled as the sequence of sensors that have identified the individual during his or her trip. Obviously, these ‘routes’ contain large gaps, which makes traditional estimation techniques not applicable. Although we do not exactly know what happens within these gaps, we do have some specific insight about the individuals behavior between two identifications; we know with a certain probability which is related to the hit rate of the sensors, that the individual did not cross another sensor location between the two identifications. This paper therefore describes a method to estimate the parameters of an RL model that specifically exploits this knowledge. The framework also allows us to formulate a probabilistic link utilization estimation method, which can be used to estimate link flows in a network based on the sensor observations. The effectiveness of the methodology is demonstrated in simulation using an artificial network, after which the methodology is tested on a real data set, collected at a Dutch music event., Transport and Planning, Transport and Planning

This research examines the effects of topological (e.g., street configuration) and geographic (e.g., land use, building height) features of the built environment (BE) on children independent mobility (CIM) to discretionary (e.g., park, shop) and nondiscretionary (e.g., school) destinations. Using travel data collected through travel diary and route mapping, and BE data of comparable routes collected through virtual BE audit, a wide range of promoters and barriers of CIM were identified. The findings highlight that, to design children-friendly environment and maximise CIM, geographic features need to be considered in tandem with topological features of the BE, stratified by destination type.

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging., Transport and Planning

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. Keywords Traffic flow Disturbance Time-varying OD Route choice Network disruption Dynamic travel time

This paper describes a method to estimate the parameters of a Recursive link-based Logit model (RL) using measurements of a set of spatially fixed proximity sensors, with limited hit rates, which can uniquely identify people, such as Wi-Fi-, RFID- or Bluetooth-sensors. The observed ‘route’ of an individual, where we focus on pedestrians in an urban or event context, is modelled as the sequence of sensors that have identified the individual during his or her trip. Obviously, these ‘routes’ contain large gaps, which makes traditional estimation techniques not applicable. Although we do not exactly know what happens within these gaps, we do have some specific insight about the individuals behavior between two identifications; we know with a certain probability which is related to the hit rate of the sensors, that the individual did not cross another sensor location between the two identifications. This paper therefore describes a method to estimate the parameters of an RL model that specifically exploits this knowledge. The framework also allows us to formulate a probabilistic link utilization estimation method, which can be used to estimate link flows in a network based on the sensor observations. The effectiveness of the methodology is demonstrated in simulation using an artificial network, after which the methodology is tested on a real data set, collected at a Dutch music event., Transport and Planning, Transport and Planning

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging., Transport and Planning

Much of our knowledge about migratory behaviour comes from single-population tracking studies. Some such studies had a considerable impact on migration research at large. However, it is often unclear how representative such studies are for other populations, even of the same species. We compared migration corridors and schedules of GPS-tracked adult Western Marsh Harriers (Circus aeruginosus) from two nearby breeding areas within the West African–Eurasian flyway: the Low Countries (LC, N = 6) and southern Sweden (SW, N = 4). Assuming the migration patterns revealed by decade-old satellite-tracking of SW harriers are representative for the species, we expected LC harriers to make a narrow loop migration involving a western spring detour out of Africa, but according to an earlier schedule tuned to the earlier onset of spring in the Low Countries. In autumn, LC harriers migrated significantly further west than SW harriers all the way to their common non-breeding range. In spring, both groups detoured westward across the Sahara, as expected, and diverged towards their breeding areas after reaching mainland Europe. LC harriers migrated slightly earlier than SW harriers in autumn. However, LC harriers unexpectedly left their non-breeding sites up to a month before SW harriers, after which they made long stop-overs in northwest Africa. Late-departing SW harriers forewent these stop-overs, and thus caught up with LC harriers in northwest Africa, so that both groups reached their breeding areas simultaneously. While we anticipated strong overlap between LC and SW migration corridors in spring, we failed to anticipate the earlier and more variable spring departures of LC harriers. Early spring departures did not result in earlier arrivals by LC harriers. Instead, we suspect they departed early to escape faster deteriorating foraging conditions at their non-breeding sites. Such environmental modulation of migratory behaviour may complicate generalization of migration patterns

Path Size Logit route choice models attempt to capture the correlation between routes by including correction terms within the route utility functions. This provides a convenient closed-form solution for implementation in traffic network models. The path size terms measure distinctiveness of routes; a route is penalised based on the number of other routes sharing its links, and the costs of those shared links. Typically, real road networks have many very long routes that should be considered unrealistic. Such unrealistic routes are problematic for the Path Size Logit (PSL) model because they negatively impact the choice probabilities of realistic routes when links are shared. The Generalised Path Size Logit (GPSL) model attempts to address this problem by weighting the contributions of routes to path size terms according to the ratio of route travel costs. However, the GPSL model is not internally consistent in how it defines routes as being unrealistic: the path size terms consider only travel cost, whereas the route choice probability relation considers disutility including the correction term. To solve these challenges, this paper formulates a new internally consistent Adaptive Path Size Logit (APSL) model wherein routes contribute to path size terms according to the ratio of route choice probabilities, ensuring that routes defined as unrealistic by the path size terms, are exactly those with very low choice probabilities. The APSL route choice probability relation is an implicit function, naturally expressed as a fixed-point problem. A proof is provided for the guaranteed existence of solutions, as well as conditions for the uniqueness of solutions. A Maximum Likelihood Estimation procedure is given for estimating the APSL model with tracked route observation data, and this procedure is investigated in a simulation study where it is shown it is generally possible to reproduce assumed true parameters. APSL is then estimated using real tracked route GPS d

This study contributes to the field of cycling route choice by adopting the unprecedented combination of the Metropolis-Hastings (MH) path-sampling algorithm and the Expanded Path Size Logit (EPSL) model. The MH sampling approach is used to generate 15 alternative route choice sets for cyclists. The EPSL multivariate route choice framework is utilized to account for the correlation between sampled and non-sampled alternatives (joint MH-EPSL model). The data used in this paper is drawn from GPS data collected by the City of Toronto using a custom-built smartphone application in 2014–2015. The study focuses on non-work-related cycling trips (shopping, leisure, social and others) in downtown Toronto on weekdays. The estimated results indicate that the presence of bicycle lanes and road medians attractions and number of trees along the path have a positive impact on cyclist route choice. In general, cyclists prefer to take shorter routes on lower speed roads with less public transit stops especially during the evening rush hour, and less willing to take one-way streets, local roads, and steep road segments. These findings are useful to policy makers as well as transportation and urban designers when developing a cycling network aiming to attract more cyclists. Finally, our results indicate that the MH-EPSL model performance is an appropriate framework to study cyclists’ route choice decisions.

Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that passenger distribution on routes can be modeled with a discrete choice model, this has not been considered in timetabling yet. We investigate how a passenger distribution can be integrated into an optimization framework for timetabling and present two mixed-integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices, the other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS).

On important truck-dominated motorways, a large share of traffic consists of trucks. Our hypothesis is that these trucks do not always make optimal routing decisions which cause inefficiencies in the traffic system. Therefore, route choice of truck drivers is of interest to both transport planners and traffic management authorities. The objectives of this paper are two-fold. First, this paper models on-Trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. To the best of our knowledge, this is the first time that Bluetooth data have been used for the estimation of route choice models of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The predictive performance of the model is tested by conducting out-of-sample validation. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38% of truck drivers do not take the shortest time path, 48% of truck drivers do not choose the system-optimal path. These inefficiencies imply that traffic management solutions have the potential to improve the performance of truck-dominated motorways., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport and Planning

Identifying the set of alternatives from which travellers choose their routes is a crucial step in estimation and application of route choice models. These models are necessary for the prediction of network flows that are vital for the planning of public transport networks. However, choice set identification is typically difficult because while selected routes are observed, those considered are not. Approaches proposed in literature are not completely satisfactory, either lacking transferability across networks (observation-driven methods) or requiring strong assumptions regarding traveller behaviour (uncalibrated choice set generation methodologies (CSGM)). Therefore, this study proposes a constrained enumeration CSGM that applies the non-compensatory decision model, elimination-by-Aspects, for choice set formation. Subjective assumptions of traveller preferences are avoided by calibrating the decision model using observed route choice behaviour from smart card data, which is becoming increasingly available in public transport systems around the world. The calibration procedure also returns two key insights regarding choice set formation behaviour: (i) the ranking of different attributes by their importance, and (ii) the acceptable detours for each attribute. To demonstrate the methodology and investigate choice set formation behaviour, the tram and bus networks of The Hague, Netherlands are used as a case study., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport and Planning

Existing knowledge on the impact of built environment (BE) on route choice behaviour is doubtful due to an unresolved tension between two schools of thought. One represented BE geographically and found that most people tend to choose the shortest route. The other represented BE topologically and showed that least directional change is a key determinant of route choice. How do pedestrians make a trade-off between these two factors in route choice? This question is answered using walking route data of 178 pedestrians in Brisbane, Australia. Their reported routes were examined against the corresponding shortest path and least directional change routes using the percent overlap method. The effects of 25 additional BE factors were also estimated in a conditional logit model. The results reveal that: (a) Together distance and direction are likely to explain 53% of route choice decisions; (b) Individually, distance and direction are likely to explain 34% and 46% of route choice decisions respectively; (c) 28% of the chosen routes satisfied both distance and direction criteria. These findings confirm the claims of both schools, but also point that the least directional change route is a preferred option, and that pedestrians tend to minimise both criteria if they can.

This study investigates how students choose their walking routes to a university. Their observed walking routes were compared against the shortest path routes and against the least directional change routes. The urban design features (e.g. streets, benches, fountains, buildings) of these routes were also examined through physical and virtual (e.g. Google Earth image) surveys. The study reveals that students' route choice decision is highly influenced by street configuration – less directional changes are preferred over the shortest path distance – highlighting the need to design urban streets straighter to promote walking.