In medium-sized cities, daily travel often follows routine patterns, which may lead to suboptimal route choices. This study examines such trips and evaluates them to assess the influence of travel information. The research is motivated by the growing importance of sustainable urban mobility and the need to address traffic congestion, environmental concerns, and inefficient transportation choices in the city of Volos, Greece. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants from the University of Thessaly, Volos, Greece, were collected. The data were used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed, targeting motorized vehicle users of the examined group. The survey investigated the extent to which shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation. The analysis shows that travelers are more inclined to accept the recommended route after receiving travel information; however, this effect does not translate into choosing a sustainable mode of transport. We also found that women are more likely to change routes than men.

Satellite-derived water indices such as the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) are widely used for surface-water monitoring, yet their direct forecasting as regional-scale time series remains underexplored. This study benchmarks machine-learning (ML) models and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting regionally aggregated NDWI and MNDWI over Thessaly, Greece, across five geomorphological units (entire region, lowlands, highlands, lakes, and rivers) using multi-year Sentinel-2 time series processed in Google Earth Engine. Two experimental configurations are evaluated: (A) models driven only by contemporaneous hydroclimatic predictors (NDVI, precipitation, and thermal variables), and (B) models augmented with lagged index terms to capture temporal persistence. In Experiment A, ANFIS achieves the lowest errors across most subregions, demonstrating strong ability to model nonlinear environmental relationships without temporal memory. In Experiment B, the inclusion of lagged predictors yields substantial accuracy gains for both indices under the benchmarked imputed daily time-series setting, and learning-based models consistently outperform the corresponding persistence and mean baselines. Random Forest provides the most stable NDWI forecasts, particularly in lakes and rivers, while MNDWI performance is more region-dependent, with XGBoost and Polynomial Regression excelling in different contexts. Moving block bootstrap confidence intervals and Diebold–Mariano tests confirm statistically significant improvements within this benchmarked setting. However, observed-only one-step-ahead experiments showed substantially weaker skill, highlighting the dependence of operational forecasting performance on temporal continuity and missing-data treatment. Overall, NDWI exhibits stronger spatial robustness, whereas MNDWI shows greater sensitivity to land-cover heterogeneity and extreme hydrological conditions.

This paper presents a comprehensive study on the accessibility and connectivity of the four university facilities in Volos, focusing on transportation systems including buses, walking, and bicycles. Utilizing Geographic Information Systems (GIS) software, the research assesses the accessibility of Volos' university campuses based on total travel time, serving as the primary metric. Travel times are measured for journeys to and from the campuses, providing insights into overall accessibility. Furthermore, a questionnaire survey was distributed among Volos' academic community, inquiring transportation preferences, travel times, and challenges encountered. Results indicate an average travel time of 20 min to access most campuses via available transportation modes, aligning with survey responses. In addition, participants also shared insights into the obstacles faced by individuals with disabilities when utilizing transportation in Volos and expressed that access to the university campuses of Volos via the offered transportation options is nearly impossible for these individuals. This research sheds light on the transportation dynamics impacting university accessibility in Volos and highlights the disconnection between accessibility standards and the experiences of individuals with disabilities. These findings emphasize the importance of future policy changes and could aid decision makers improve accessibility and connectivity within university settings, ultimately fostering a more inclusive and accommodating environment for students, faculty, and visitors.

We deal with the improvement of public transportation service as for its enormous influence on the lives of residents and government spending. From the policymakers’ point of view the primary goal is to assess the level of public transportation and pinpoint practical ways to improve it in order to increase passenger satisfaction and draw in new customers. Two well-known multi-criteria decision-making techniques, the Fuzzy Analytical Hierarchy Process (FAHP) and the Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), were used through a dynamic questionnaire survey to accomplish this goal. The robust FAHP technique provides a systematic evaluation framework by taking feedback and interrelationships between different criteria levels. However, to obtain a thorough grasp of experts’ opinions about the quality of public transportation services, both approaches had to be used. A real-world situation for the city of Larissa, Thessaly was used in empirical research to show how these ideas can be applied in practice. The usefulness of these decision-making techniques is demonstrated by this study, which also emphasizes how crucial it is to prioritize public transportation system improvements in order to improve citizen lives.

Urban transport is a key factor in the economic, cultural and productive development of any society. However, today's road networks are burdened with a much larger volume of traffic as they were designed to serve earlier societies. In order to meet the new travel needs, it is necessary to implement new methods and systems that not only respond to the new challenges and needs but also provide travelers with sustainable travel solutions. Mobility as a Service (MaaS) aims in this direction by ensuring the cooperation of different mobility providers by integrating all public transport modes with sharing and pooling services (e.g. carsharing, carpooling) and various means of micro-mobility (e.g. e-scooter, e-bike) as well as subscriptions and travel pack-ages to meet and serve every need of citizens. The investigation of the acceptance of Mobility as a Service by the residents of Patras through a questionnaire survey showed that most of them, although they understand the benefits of implementing this platform, are quite reluctant to adopt it. This paper is about investigating the acceptance of Mobility as a Service by the residents of Patras through a questionnaire survey. A great number of the people who participated in this questionnaire prefer to make their transfer by private car, by their own time and comfort. Therefore, for this implementation of such a needed mobility platform to make sense, it is necessary both to keep the residents in Patras informed about the innovative technological developments and to upgrade the infrastructure. Mainly to change the public transport in Patras in order to make it more accessible and efficient for its inhabitants.

This paper aims to explore trends in the application of big data and Machine Learning (ML) in Water Resources Management (WRM) by categorizing research studies into distinct scientific subfields. A comprehensive analysis was performed on articles published between 2018 and 2024. Leveraging a dataset of 6,430 collected papers, 173 articles were evaluated using bibliometric techniques to track the development of academic interest and recognize pivotal studies. Our suggested unsupervised classification model established categories and organized relevant articles according to their specific scientific focus, using keywords extracted from titles, abstracts, and author-defined keywords, with stop-words excluded. The model achieved a validation accuracy of 90.16

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

The European Union’s policy aims for the wide-scale deployment of automated mobility by 2030, i.e., within the next programming period (2028–2034), with the deployment of autonomous road vehicles (AVs) in cities playing a key role. Researchers suggest that AV deployment will have complex impacts on urban development, which are difficult to quantify due to scarce real-life data. The present research aims to evaluate different policy pathways of AV deployment for sustainable urban development in the next EU programming period. A multicriteria analysis is conducted, combining AHP and VIKOR, with the participation of experts across Europe. Initially, the potential impacts on sustainable urban development are weighted as evaluation criteria. Then, different pathways are evaluated against these criteria, i.e., AV deployment as collective and/or private transport in specific areas and periods or in the whole Functional Urban Area (FUA) on a 24 h basis. An interesting finding is that the effect on the city’s spatial development, not thoroughly examined by literature, is highly ranked by experts. Regarding policy pathways, autonomous collective transport with 24 h service of the FUA emerged as the optimum alternative. The proposed methodology provides a tool for planners, researchers, and policy makers and a framework for an open debate with society.

This paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies were evaluated from a dataset of 3532 collected papers, and bibliometric techniques were applied to capture the evolution of research interest over the years and identify the most influential studies. The proposed unsupervised classification model defined categories and classified the relevant articles based on their particular scientific interest using representative keywords from the title, abstract, and keywords (referred to as top words). The model’s performance was verified with an accuracy of 91% using Naïve Bayesian and Convolutional Neural Networks approach. The analysis identified eight research topics, with urban transport planning and smart city applications being the dominant categories. This paper contributes to the literature by proposing a methodology for literature analysis, identifying emerging scientific areas, and highlighting potential directions for future research.

The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.

Numerous urban mobility projects attempt to utilize near zero-CO2 emission practices in order to reduce greenhouse effect. Most initiatives attempt to mitigate the effects of climate change through controlling the resources consumed by human activity. In this study, the assessment of the degree by which these initiatives affect urban mobility and resulting impacts utilizes a fuzzy semi-quantitative methodology with the inclusion of all stakeholders into an innovative Decision Support System (DSS). This holistic approach effectively assesses the interaction of linguistic evaluations and fuzzy set theory to produce a Fuzzy Cognitive Map (FCM) that overcomes the ambiguity caused by a lack of quantitative evidence. The study strands include scenarios that emphasize the impact to sustainable urban mobility due to urbanization and the rise of urban actors (social, technical, transport, and economic) in influencing environmentally friendly decision-making. We concentrate in the use case of Larissa, Greece (city of the CIVITAS initiative) that started implementing the sustainable urban development plan in 2015. The suggested decision-making tool employs analytics and optimization algorithms to direct responsible authorities and decision-makers towards sustainable urban mobility of Larissa and eventually decarbonization of the urban and suburban area.

It is acknowledged by numerous researchers that island residents experience inequality with respect to their accessibility in comparison to the rest of Greek residents, as they are dependent on the frequency, quality of service, and capacity that ferry operators provide. Consequently, to the fact that the ferry business is a free market (due to: (i) no barriers to entry; and (ii) minimum state interventionism in routings), current ferry services operate as "selfish routings" and do not consider the transport system as a whole. This may lead to social welfare loss. This work proposes a hybrid methodology using two MCDA methods (AHP and PROMETHEE) to evaluate whether total ferry fleet assignment to routes is optimal in a holistic manner, from a transport system perspective. Eight "key" routes are considered. For all routes, 6 criteria are introduced, on the basis of which different ferries are evaluated. These criteria are passenger capacity (number), lane meters (number), cabin berths (number), speed (knots), maneuvering ability of ship (index), and comfort (index). Weights of criteria for each route are calculated (8 times, one for each route) through AHP approach, by expert's input from different categories of stakeholders. Results indicate that the current ferry system can become more efficient by permutation (i.e. exchanging) of currently operating ferries between different routes. The proposed fleet assignment: (i) increases the quality of service for several routes; (ii) increases the accessibility of islands (as it contributes towards better capacity management and leads to less skipped port calls due to bad weather); and (iii) increases efficiency due to greater load factors.

In recent years, cities have been at the epicenter of the transition to sustainable mobility, with many of them exploring ways to integrate bike-sharing systems to public transportation. In the context of the development of bicycle-sharing systems at an international level, the goal of this paper is to investigate the acceptance level of these systems by Greek and Cypriot users. Towards this direction, a structured literature review was initially conducted on the legislative and regulatory framework in Greece and Cyprus, as well as on existing sustainable mobility projects. Then, a survey was conducted in Volos and Nicosia to examine the current situation in these cities. From the data collected, descriptive and inferential statistics were performed, and the results demonstrated that the participants have positive beliefs regarding the use of shared mobility systems. The intention of using a bicycle-sharing system is determined by various factors, such as age, gender, income, etc.

The city logistics take a considerable part of the urban transportation. Inevitably, the last mile deliveries are partly responsible for degrading the environment. Towards more environmentally friendly city logistics the use of different means of transport has been tested; one among them is the drones. The aim of this paper is to provide a better understanding of the use of drones for the last mile deliveries. More specifically, to give insights into how the parcel distribution could be deployed within an urban environment with the use of drones. Data were collected from a transport operator and two scenarios were synthesized reflecting the current situation and the future one. An analysis of the stakeholders involved was performed and for the analysis of the scenarios, two integer mathematical programming models were formulated and implemented with the case study. Thereafter, the results of the two scenarios were assessed with relevant indicators that were extracted from the literature. The results showed that the use of drones for urban distribution reduced the carbon dioxide emissions, the average delivery time per package and the distribution costs. The results of the literature review and the stakeholder analysis indicate that the lack of a legal framework is the most important obstacle towards the use of drones for parcels' distribution in urban environments.

The emergence of social media resulted in a high number of people using them. Their flexibility makes them more popular compared to conventional methods of information sharing. Transport-related information can be shared cost-efficiently and timely on platforms such as Facebook, Instagram, and Twitter. The shared content's form can be a text, a photo, or a video enabling more accurate transport-related information. The main objective of this study is to investigate to what extent social media have an impact on the mobility choices of people, filling the gap of previous studies that paid little attention to the influence of social media on transport-related purposes. A systematic literature review was performed in SCOPUS database to identify any record that is related to social media and urban mobility. Only records with an important and relevant contribution to the topic were kept. In addition, a thorough review of transport-related social media accounts and content was performed to investigate their influence. Social media metrics such as reactions, comments and shares of posts are measured to determine the real influence of the accounts and their content. The analysis ends up with an appropriate scheme and form of a transport-related account and content that would be more influential in today's social media landscape.

Daily trips in medium-sized cities are based more on habitual patterns, resulting sometimes in less optimal route choices. In this study, an attempt is made to investigate trips in a medium-sized city and evaluate them to explore the impact of travel information. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants of the University of Thessaly, Volos, Greece was collected. The data was used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed targeted at motorized vehicle users of the examined group. The survey investigated the extent that shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation.

As the world becomes more urbanized, there is a strong need for urban public transport to provide sustainable alternative solutions against private-vehicle usage. However, the opportunities for seamless journeys through public transport are still limited and the need for properly designed and operated transport interchanges is vital. The present paper investigates the perceptions and the users' level of satisfaction when using the New Railway Station of Thessaloniki in Greece and the Riga International Coach Terminal in Latvia, in terms of services provision and station's operation. In total, 36 indicators were tested, grouped in eight quality factors, namely travel information, wayfinding information, time and movement, access, comfort and convenience, station attractiveness, safety and security and emergency situation handling. Attitudinal surveys were implemented to determine key performance factors that affect travelers' satisfaction when using the two terminals. Data were collected through on-line questionnaires and were elaborated through descriptive and inferential statistics, including Mann-Whitney two-sample U-testing to assess differences between the samples in variables measured on a 5-point Likert scale, Spearman bivariate correlations to measure the strength of association between the quality indicators and multiple regression analyses to examine the effect of selected attributes on the general satisfaction level of travelers. Results showed that both interchanges perform better in physical quality attributes, like access, travel and wayfinding information provision, but they do not satisfy users' aesthetics expectations in the internal and external area of the interchanges and the surrounding area and they do not cover adequately their feeling of security and safety in the transfer or waiting areas. These results highlighted the users' preferences and concerns which contribute into a satisfactory overall design of the interchanges. In a nutshell, transport interchange design should satisfy both providing a hub for seamless mobility, but also integrating the station as a part of the public realm.

Increasingly fierce competition in energy industry for alternative fuels has raised demand for fuel storage stations to be one of the pivots towards sustainable urban freight transportation. For zero-emission hydrogen-powered vehicles, these demands focus mostly on storage capacity and refueling-station location selection to maximize freight forwarding efficiency in major urban regions. This research proposes a Decision Support System (DSS) for hydrogen storage station location selection based on Intuitionistic Hesitant Fuzzy (IHF) methodologies. An ensemble of four methods were analyzed and applied for the use case of Larissa city urban region, central Greece. Fifteen evaluation criteria were imposed by experts from the transportation department and the local competent authorities, spanning to hydrogen infrastructure, socioeconomics and fleet and road network. Scenarios such as energy efficiency, pollution prevention and public acceptance in conjunction to the risk and safety considerations play a critical role in the process. All qualitative indicators were represented by triangular intuitionistic fuzzy numbers. The ensemble produced the same ranking indicating the Steiner point locations of the imposed communication tree on the road network as the most preferable scenario.

Crowdsourced deliveries or crowdshipping is identified in recent literature as an emerging urban freight transport solution, aiming at reducing delivery costs, congestion, and environmental impacts. By leveraging the pervasive use of mobile technology, crowdshipping is an emerging solution of the sharing economy in the transport domain, as parcels are delivered by commuters rather than corporations. The objective of this research is to evaluate the impacts of crowdshipping through alternative scenarios that consider various levels of demand and adoption by public transport users who act as crowdshippers, based on a case study example in the city of Volos, Greece. This is achieved through the establishment of a tailored evaluation framework and a city-scale urban freight traffic microsimulation model. Results show that crowdshipping has the potential to mitigate last-mile delivery impacts and effectively contribute to improving the system’s performance.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.