Publications

This study examines whether stakeholder-prioritized governance needs and key performance indicators (KPIs) for shared mobility are similar between Europe and the Gulf Cooperation Council (GCC). Understanding these similarities is important for assessing whether governance approaches developed in established mobility markets can inform regulatory oversight in emerging mobility environments. A modified Delphi Poll including surveys followed by Semi-Structured Interviews was conducted with mobility experts from both regions. Survey responses were analyzed using the Analytic Hierarchy Process (AHP), and interview data was examined through reflexive thematic analysis. Findings show substantial alignment in operational governance priorities, particularly regarding safety, service reliability, and accessibility compliance. However, qualitative results indicate that differences in regulatory maturity, institutional capacity, and urban operating environments influence how KPIs are interpreted and implemented in practice. These findings suggest that governance frameworks for shared and mobility may be adaptable across regions at a strategic level, while requiring contextual adjustment in application.

Transit-oriented development (TOD) is regarded as an effective approach for addressing urban sprawl and enhancing urban resilience particularly by supporting the revitalisation of fragile urban areas. From a perceived geographical perspective, this study examines the determinants of travel-related well-being (TRW) and their spatial effects to inform improvements in TOD resilience. Using 1,298 household surveys in Hangzhou, this study applies binary logistic regression, geographically weighted logistic regression, and linear mixed-effects models to examine spatial heterogeneity and temporal dynamics. The results indicate that gender, marital status, housing tenure, vehicle ownership, metro accessibility to workplaces, the quality of the cycling environment, and station accessibility significantly influence TRW, and that these relationships vary across space. The opening of metro stations is associated with improvements in residents’ perceived travel-related resilience. By linking TOD resilience with perceived TRW, this study provides spatially and temporally nuanced evidence to inform resilient TOD planning in socio-spatially fragile urban areas. Gender, marital status, housing status, vehicle possession, and cycling environment quality influence travel-related well-being. Investigates the determinants of neighbourhood satisfaction and TOD’s spatial effects on this. Constructs a theoretical framework to better understand TOD-related resilience and well-being. The opening of TOD has also improved the perceived resilience of travellers.

The emergence of flexible and low-cost solutions for the observation of travel experiences is changing the data collection landscape. Experiments traditionally focusing on the collection of user responses are increasingly enhanced through multimodal data collection methods, which use different data collection instruments and produce big data. This chapter examines data collection using electroencephalography (EEG) with virtual reality for collecting big data on passenger travel experiences, assessing the scope for using these methods to produce high-quality datasets. Through a case study, we analyze the potential of EEG to detect reactions to different stimuli related to trips in autonomous cars and buses and the potential of virtual reality to provide participants with those stimuli. We assess whether participants notice the relevant attributes of virtual scenarios and if the experience of a virtual vehicle differs from the experience of a real one. We also provide details on data collection procedures that tend to be omitted from academic papers but should be more extensively documented, to support further studies, including technical and organizational issues, ethical aspects, and participant recruitment.

This study investigates the integration of charging and Vehicle-to-Grid (V2G) technology within ride-hailing fleet operations, focusing on the implications for profitability and operational efficiency. Through the adoption of agent-based mobility simulation paradigms and the development of a novel two-sided mobility platform simulation framework considering simplified electricity grid dynamics, this research evaluates various charging strategies, including traditional models, smart charging and V2G across different electricity price schedules, fleet sizes and demand configurations. Findings indicate that V2G operations outperform traditional no-charging or only-charging operational strategies, achieving an average increase of 18% in daily fleet profits, while maintaining low trip rejection rates. Nonetheless, V2G operation integration introduces additional deployment and infrastructural complexities, including increased travel distances and more frequent charging and discharging events. These factors can lead to negative externalities (e.g., congestion) and battery wear.

This study presents a novel integration of Graph Neural Networks (GNNs) with Simultaneous Perturbation Stochastic Approximation (SPSA) to enhance the computational efficiency of Simulation-based Offline Origin-Destination (OD) Matrix Calibration. The proposed framework leverages a GNN-based metamodel as a surrogate for computationally intensive transport simulations, utilizing a Node-to-Node Graph Regression (N2N-GR) model trained on 7200 historical simulation datasets. The metamodel effectively approximates traffic flow predictions from OD matrices, thereby significantly reducing the computational overhead associated with traditional SPSA implementations. The findings demonstrate that GNN-based SPSA calibration can achieve substantial efficiency gains while maintaining solution fidelity, presenting a scalable paradigm for advancing traffic demand modeling and transportation system optimization.

The Sustainable Development Goals (SDGs) have generated a framework of goals, targets and indicators which aim at – among others – ending poverty, improving health, reducing inequality, and spurring economic growth. In this scoping study we examine the focus, methods and approaches used to extract interlinkages between the SDGs and their targets. To that end, we perform a systematic literature review and discuss the outcomes of an expert solicitation workshop with SDG experts to generate a framework of analysis. We evidence that the interlinkages domain is still exploratory, largely neglecting the local level and being disconnected to policy making. Methods used predominantly account for statistical and conceptual associations and correlations between SDGs, targets and indicators, and not for causal ones; hardly replicated, reproduced and verified. We therefore propose a framework of analysis, which takes into account those gaps to generate a process of integrating interlinkages into the policy and decision making process.

Shared-e-scooters are being introduced in cities worldwide, with their introduction often being distant from the actual service characteristics understanding, potential benefits, and threats realization. This research explores scooter use by examining approximately nine million scooter trips from five North American cities (Austin; TX, Calgary; AB, Chicago; IL, Louisville; KY, Minneapolis; MN). By investigating the spatiotemporal hourly and daily use, we found that demand patterns tend to be similar in the different cities. Trip characteristics (speed, duration, and distance) are almost empirically consistent across the five cities; however, there is evidence that trip characteristics change over time in the same city. We also examined the impact of exogenous factors on scooter demand, and found that weather (temperature, wind speed, precipitation, and snow), day of the week, infrastructure (bike lanes, sidewalks, and shared bike stations), sociodemographics (gender, age, and income), land use, and accessibility to transit significantly impact demand. Findings highlight the need for evidence-based examination of shared-e-scooters and regulatory processes to guide policy decisions by the different stakeholders.

abstract
Multimodality in Public Transport has been proven to be one of the main drivers of sustainability and economic feasibility for the last few decades. Consequently, user satisfaction for transfers is the key to adequately serving demand. This research studies on commuters’ perception of comfort at interchanges, focusing on the connection between metro systems and other modes. Satisfaction analysis and modelling is conducted using weighted regression, factor analysis and ordered logit models for nine transfers at major interchanges in two Indian cities (New Delhi and Kolkata) and one German city (Munich); aiming at revealing the differences in user satisfaction in developing and developed economy, and for different Public Transport quality and interchanges. The results indicate that factors of transfer quality, accessibility and physical hindrances are significant in Indian case and the human factor, and transfer quality are significant in the case of Munich, Germany. Additionally, it is found that perceived comfort differs on commuters’ experiences with transfer distance and time.

Whilst there is research on how Covid-19 impacted travel demand and transport business, little attention has been paid on how Covid-19 has affected authorities' transport planning priorities and the actions taken to protect the public while travelling. This paper attempts to shed light on: a) how the transport planning priorities changed during the Covid-19 lockdowns in 2020/2021, and b) how the planning phases of the Sustainable Urban Mobility Plan (SUMP) framework can be strengthened to support a more resilient emergency planning environment. To address these questions, an online questionnaire was designed followed up by personal interviews from selected European cities. Data collection took place in November and December 2020, when most European countries were in lockdowns. Thirteen public authorities participated in the online questionnaire, while nine out of them were further interviewed. A mixed methods approach is used to analyse the quantitative and qualitative data and bring the results together to assess how SUMP priorities have changed. The results showed that the priority planning objectives were different in the period during the 2020/2021 lockdowns compared to the period before that. Public transport system planning was a priority in both periods, while planning for shared mobility and Mobility as a Service was further prioritised in the 2020/21 lockdowns. The main reasons for prioritising specific planning objectives were to secure public health, minimise environmental impact, support economic recovery and address social equity. The changes in the priority of planning objectives were also diverse between smaller and larger urban areas. Most of the actions adopted to accommodate the prioritised planning objectives were already defined before Covid-19, indicating that the lockdowns have acted as an accelerator of specific existing planning objectives.

Shared Autonomous Vehicles (SAVs) are expected to be used for regular and pre-planned trips. Such trips are suitable for reservation-based services, wherein the customer needs to book for a trip in advance. Systems enabling reservation of trips can allow for better planning of routes and schedules, and if optimally designed, enable higher efficiency. The primary objective of this research is to model the effects of such a system, by formulating and solving the combined Dynamic User Equilibrium and Shared autonomous vehicle Chain Formation (DUESCF) problem. The problem is formulated as a bilevel model based on game theory, involving road users and SAV service operator. Given a situation where conventional private and shared autonomous vehicles co-exist, road users select paths and departure times to maximize a perceived utility (commonly treated as minimizing a disutility) by forming a DUE (fixed point problem), and the SAV service operator tries to maximize the performance by forming appropriate SAV chains (combinatorial problem). The final objective of this bilevel model is a traffic assignment that includes SAV chain formation, such that both road users and SAV service operator obtain optimal solutions by reaching a Nash equilibrium, where no player is better off by unilaterally changing their decisions. A solution approach, based on Iterative Optimization and Assignment (IOA) method, is proposed with path flow and SAV performance changes as convergence criteria. Furthermore, the solution approach is tested for its robustness, and a scenario analysis is carried out to evaluate the impacts of reservation-based SAV services. The results show that a ridesharing SAV system is better compared to a carsharing and a mixed system consisting of both, in terms of total system travel time, congestion levels, total vehicle kilometres travelled and vehicle requirements.

Pooled-ride services have a significant potential for reducing traffic externalities and enhancing transportation systems in the urban environment. These services and their users’ characteristics still need further inspection and exploration. We investigated factors encouraging the shift from the currently used modes to pooled-ride-services, the choice between different pooled services vehicles types, and the frequency of use of pooled-rides, using data collected via a large-scale online survey conducted in Mexico City, Mexico (CDMX) for a start-up that organizes pooled rides, Jetty. We modeled the pooled-ride-service adoption process as a function of the users’ sociodemographics, latent travel attitudes, accessibility to public transportation, trip characteristics, reasons to use the service, and users’ activities during the trips. We estimated hybrid choice models and binary logit models, which show that users’ sociodemographic and travel attitudes are the main factors impacting the shift from different modes to pooled rides. Service-related characteristics such as multi-tasking, trip fare, and avoiding parking problems also impact the shift decision. On the other hand, the frequency of service use is mainly impacted by trip characteristics such as total trip distance, and the headway at the user’s home location nearest Metro stations. Income, employment status, number of cars in the household, and gender were the only sociodemographic factors impacting the service use frequency directly and indirectly.

Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies. Particularly useful for travel analysis is when Social Media users allow (voluntarily or not) tracing their movement using geotagged information of their communication with these online platforms. In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns, study differences and commonalities among these cities, and explore the nature of user location recurrence. The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models. Identification of repeated rates of location (recurrence) can be used to define activity spaces. Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here. The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.

Abstract Railway network operations form complex systems. Any disruption adversely impacts the operations, causing long delays. Many studies investigate the effect of a railway incident; however, a holistic quantification is lacking. This study aims to present an estimation framework for flexible traffic management systems, which can help reduce network delays and enable dispatchers to make better-informed decisions. An incident's impact on the network is estimated by creating a sequence of models, which predict two key variables. Firstly, the incident duration is predicted, which is used to predict the second variable: total delay caused by the incident. Various influencing attributes are examined, such as weather, network and railway-related attributes. Their relationship with the response variables is studied in order to understand the incident's impact. Using incident data from the Danish Railways, machine learning models are estimated. The results show that neural networks outperform other competing models for total delay modelling, resulting in improved prediction by the estimation framework, thus giving higher accuracy than the stand-alone models in the study.

Studies in several cities indicate that ridesourcing (ride-hailing) may increase traffic and congestion, given the substitution of more sustainable modes and the addition of empty kilometers. On the other hand, there is little evidence if smartphone apps that target shared rides have any influence on reducing traffic levels. We study the effects of a shared-mobility service offered by a start-up in Mexico City, Jetty, which is used by travelers to book a shared ride in a car, van or bus. A large-scale user survey was conducted to study trip characteristics, reasons for using the platform and the general travel choices of Jetty users. We calculate travel distance per trip leg, for the current choices and for the modes that riders would have chosen if the platform was not available. We find that the effect of the platform on vehicle kilometers traveled (VKT) depends on the rate of empty kilometers introduced by the fleet of vehicles, the substitution of public versus private transport modes, the occupancy rate of Jetty vehicles and assumptions on the occupancy rate of substituted modes. Following a sensitivity analysis approach for variables with unavailable data, we estimate that shared rides in cars increase VKT (in the range of 7 to 10 km/passenger), shared vans are able to decrease VKT (around −0.2 to −1.1 km/passenger), whereas buses are estimated to increase VKT (0.4 to 1.1 km/passenger), in our preferred scenarios. These results stem from the tradeoff between the effects of the occupancy rates per vehicle (larger vehicles are shared by more people) and the attractiveness of the service for car users (shared vans attract more car drivers than buses booked through Jetty). Our findings point to the relevance of shared rides in bigger vehicles such as vans as competitors to low occupancy car services for the future of mobility in cities, and to the improvement of public transportation services through the inclusion of quality attributes as provided by new shared-mobility services.

Connected and autonomous vehicles are expected to gradually enter the transport modal mix sooner or later. Academic discussions of their expected impacts are well underway, often with conflicting or wide-ranging results. In this chapter, the existing literature is reviewed and summarized, with a focus on the traffic flow efficiency impacts. The expected impacts of autonomous vehicles on the roadway capacity are found not to be unanimous, partly because this seems to depend on the specific modeling conditions. Therefore, common factors that determine whether capacity is expected to increase or decrease are discovered from existing studies, along with determining the factors that influence other traffic flow efficiency-related impacts, such as traffic stability and congestion. An analysis is also performed on the vehicular requirements for autonomous vehicles to serve the mobility demand, and change in vehicle kilometers traveled (VKT) that may happen. Based on the reviewed studies, 19 factors that influence traffic flow efficiency implications of autonomous vehicles are identified and grouped into four categories, namely (i) vehicle characteristics, (ii) travel behavior, (iii) network characteristics, and (iv) policies. The chapter ends with a discussion on policy recommendations in tune with the factors identified. The discussion shows that the policy makers should enact laws to ensure connectivity between AVs to experience significant benefits, integrate CAVs with public transport to avoid mode shifts, incentivize ridesharing to reduce network load, develop suitable parking management policies to avoid empty relocations and introduce congestion pricing to curb induced demand.

The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.

Technological advances have recently led to the development of urban air mobility (UAM), an alternative transportation mode with several concepts including vehicles operated by on-demand fully-automated vertical take-off and landing aircraft (VTOL) for intra-city passenger transportation. However, despite a growing interest in UAM, understanding users’ perceptions to it remains limited. This research aims to identify and quantify the factors affecting the adoption and use of UAM, based on relevant tools from the literature, such as recurring factors in studies on aerial vehicle concepts, ground autonomous vehicles, but also acceptance models, such as the Technology Acceptance Model by Davis et al. (1989). A stated-preference survey was developed to assess the perception of users in terms of adoption time horizon, including options such as the first six years of the service’s implementation, “unsure”, and “never”. The obtained results were evaluated using exploratory factor analyses, and the specification and estimation of suitable discrete choice models, multinomial logit models (MNLs) and ordered logit models (OLMs), with adoption time horizon as dependent variable. Findings revealed the importance of safety and trust, affinity to automation, data concerns, social attitude, and socio-demographics for adoption. Factors, such as the value of time savings, the perception of automation costs, and service reliability, were also found to be highly influential. There was also an indication that skeptical respondents, i.e. answering “unsure”, had a behavior similar to late and non-adopters, i.e. adoption time horizon higher than six years or answering “never”. The summarized results were represented in an extended Technology Acceptance Model for urban air mobility, and provided insights for policymakers and industrial stakeholders.

Of major importance for the evaluation of the demand for emerging transportation systems is the data used. Given the evolution of data availability the last few years, the understanding of the data properties and their characteristics is of major importance. An overview of the data sources commonly used or lately emerging is presented, followed by a classification, based on the component of the transportation system that they represent, aiming at exploring the different benefits and shortcomings of them. A meta-analysis is also performed for the exploration of data quality aspects concerning transport-related datasets.

In the era of constant redefinition of urban transportation patterns, alternative vehicle-sharing transportation systems, such as carsharing and bikesharing, gain increasing attention. In order to effectively deploy, maintain and develop these modes achieving maximum usage, companies and authorities need to understand the underlying factors that affect the demand. In this chapter, we review some of the most recent research papers that investigate the impact of demographic, environmental, economic and travel patterns that affect the adoption of vehicle-sharing systems. The successful adoption of vehicle-sharing systems relies on the effective deployment of stations. The most important exogenous factors that affect the vehicle-sharing systems adoption, as we identified in the literature, are population density, age, household size, education level, proximity to public transportation stations, land use, distance to Central Business District, distance to services, number of enterprises near the stations and vehicle availability. Punctuality of pickup at the origin and arrival at the destination are the most important factors for the acceptance of shared autonomous vehicle.

Identification of factors influencing ridership is necessary for policy-making, as well as, when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the model’s estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also on an international level in three cities in Europe and North America. The results suggest that the model’s performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g., nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments, and transport-related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

Social media has dominated the online lives of individuals and has created a research stream with potential applications in many scientific fields, including transportation. This article attempts to map the landscape of social media, focusing on transportation, by identifying the main streams of research in the pertinent literature and analyzing the taxonomy of the dominant social media in terms of functionalities fulfilled and data availability. Additionally, the authors present a strengths, weaknesses, opportunities, and threats (SWOT) analysis and examine the applicability of data collected from social media sources in a case study.

Travel surveys provide valuable inputs to a variety of models. While they are well-established, and reliable, they are also associated with large expenses (both in terms of time and actual cost). With the emergence of social media and alternative data sources, researchers and practitioners often wonder if such data sources can supplement (or obviate the need for) conventional data collection efforts. In this research, we empirically examine of the use of different social media data for such applications. Data from three popular Social Media is examined and compared with data from conventional travel-diary surveys for the city of Thessaloniki, Greece. Both spatial and temporal aspects of the activities’ representation are considered. The findings of this research contribute towards the identification of methodologies that can support the inference of meaningful travel information from Social Media, which could in turn improve ITS applications.

Network criticality indicators, such as the Unified Network Performance Meas-ure, provide powerful tools for interested entities who aim to assess those parts of the network, the closure of which would mostly affect its overall perfor-mance. The progress in complex networks analysis on the premises of graph theory allowed for advances on the identification of network characteristics and alternative sets of indicators for the evaluation of network performance. The aim of the present paper is to lay out the contribution of network analytics (in the form of complexity and criticality indicators) in disaster management, with the road network of the Peloponnese region, Greece acting as a case study. Findings show that adopting interdisciplinary advances can provide use-ful insights to entities, responsible for the mitigation, preparedness, response, and reconstruction phases of disaster management and support them in the complex decision making process.

Social media can be valuable in transportation research, as data collected contain information for mobility patterns and behavioural characteristics that could not be previously obtained. This paper introduces a methodology for the collection and analysis of data from Social Media, for transportation-related uses, first in a generic methodological framework and then particularly for Twitter. The data collection methodology is applied using Twitter and some initial descriptive statistics are presented concerning the characteristics of users and their posts. Results from spatial analysis is presented, conducted using data from the geotagged service of Twitter. The results of the analysis present trends on the use of Twitter for leisure activities and a correlation between high income areas and number of tweets posted. Finally, the potential of using data from Social Media is discussed with the issues that arise.

Leisure activities travel demand plays an important role in transportation, as it accounts for a significant amount of trips performed. Due to data limitations the examination of this trip purpose has received moderate attention so far. The evolution of pervasive systems in conjunction to the increased use of Social Media provide a large amount of data that can be used for the development of leisure activities travel demand models. This study provides an initial investigation of the data collected from Social Media and the potential of using it for leisure activities demand modelling. A data analysis framework is provided that distinguish user classes of residents and tourists and investigates the temporal and spatial patterns as well as the probability of deriving activity locations.

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers' behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers' parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers' parking behaviour can be incorporated into traffic assignment models and used to support parking systems.

Leisure activities travel demand plays an important role in transportation, as it accounts for a significant amount of trips performed. Due to data limitations the examination of this trip purpose has received moderate attention so far. The evolution of pervasive systems in conjunction to the increased use of Social Media provide a large amount of data that can be used for the development of leisure activities travel demand models. This study provides an initial investigation of the data collected from Social Media and the potential of using it for leisure activities demand modelling. A data analysis framework is provided that distinguish user classes of residents and tourists and investigates the temporal and spatial patterns as well as the probability of deriving activity locations.

This paper presents the application of complex network analysis and graph theory measures for transportation networks. Concerning complex network analysis, the networks used were examined on their structure and their properties. The analysis focus on small world networks and the scale-free property. It was found that small world properties can be an appropriate modelling structure for transportation networks also in Greece while the scale-free property is not widely met. The analysis was performed on the highway network of Europe, the Thessaloniki urban network and the Thessaloniki Public Transport network. For the two latter, graph measures were applied and analysed.

This paper presents the validation of the evacuation 3D simulator known as Everscape against the conventional Stated Preference data collection methods. Everscape was developed under the cooperation of TU Delft and the Tokyo institute for the inference of the behaviour in case of a disaster. It seems that people behave much alike the human interactions observed in such situations and that unobserved features where able to be captured. However, as it is a rather new method its validity is still in question. This paper presents the comparison of the two data collection methods (Simulator and Stated Preference). The comparison is based on two statistically identical samples for which the decisions taken are investigated. The research concludes that at least for the statistically identical samples examined the results are statistically the same. Conclusions are drawn for the benefits of using such a tool against the conventional Stated Preference data collection methods.