Urban Air Mobility (UAM) emerges as an innovative solution driven by technological advancements to address the challenges posed by urbanization and sustainability issues. As a key component of the future mobility sector, its successful integration depends on the perspectives of multiple stakeholders, particularly local authorities and aviation experts, who shape the UAM ecosystem and share responsibility for its implementation. However, while aviation professionals currently play a central role in shaping UAM, the involvement of local authorities remains limited despite their position as the ultimate decision-makers regarding the feasibility and deployment of UAM applications within their jurisdictions. This research explores the perspectives of local authorities on the development of UAM services, focusing on spatialtemporal planning and design. The findings highlight the diverse dimensions of UAM's impact, including environmental and socio-economic aspects, as well as concerns related to urban planning, regulation, safety and operations. Specific indicators identified include emissions, time savings, privacy and connectivity. By centering the perspectives of local authorities-often overlooked in existing UAM research-this study contributes to the literature by bridging the gap between aviation-centric and policy-driven approaches. Practically, the results offer a structured framework for municipalities to assess UAM's viability and inform urban planning strategies. Socially, the study underscores the importance of governance in fostering sustainable and accepted UAM implementations. These insights can guide policymakers, urban planners, and industry stakeholders in designing UAM solutions that align with local needs, regulatory frameworks, and broader sustainability goals.
Sustainable Urban Mobility Plans (SUMPs) guide strategic urban mobility toward safer, low-impact modes. Designing and maintaining networks that reduce car dependency requires assessment of walkability and pedestrian safety. This study analyzes the road network of Vittoria (Ragusa, Italy) to support the SUMP with a scalable methodology. Geometric-functional attributes surveyed on-site are integrated with spatial accident data and evaluated qualitatively and quantitatively. The workflow includes: (i) study area definition; (ii) network model reconstruction and data collection; (iii) integrated database construction and spatial analysis in QGIS. Indicators cover carriageway width, sidewalk provision and width, crossings, access ramps, gradient, continuity, and exposure to vehicle-pedestrian crashes. Buffer-based hotspot analysis around high-risk nodes supports a walkability evaluation with additional parameters. Results highlight issues poor sidewalk maintenance, discontinuities, missing ramps and signage, that hinder walkability and may reduce safety. These findings can guide targeted interventions and offer a practical tool for integrating walkability analysis into SUMPs.
Walkability and bikeability are recognised as primary factors in promoting active mobility, acting as foundational principles in contemporary urban planning. These factors may become crucial for the improvement of sustainable accessibility to strategic urban areas, such as railway stations. The node-place dual nature of railway stations generally makes these sites and their urban surroundings highly competitive centralities, especially in metropolitan areas, as they express a high 'enabling potential', thus attracting investment for regeneration purposes. Although the literature offers various methodologies for the study and evaluation accessibility, walkability and bikeability, there is still a lack of a consolidated, widely adopted and interdisciplinary approach that allows systematic and comparative analyses in the gravitation area of railway stations. This paper presents the first results of research developed in the framework of the Italian Sustainable Mobility Centre (CNMS), involving academic and business partners, aimed at filling the gap and developing a systematic suite of methods, indices and tools, for supporting sustainability assessment of regeneration projects related to train station areas in metropolitan contexts. The workflow of the methodological approach, based on GIS analysis techniques, audit protocols, machine learning and behavioral choice modelling, is presented together with an experimental application on the case study of Val D'Ala in Rome. The outcomes are expected to both refine scientific and technical knowledge and improve business procedures.
Public transportation, essential in urban and suburban settings for its environmental and operational efficiencies, is becoming increasingly sophisticated with Automated Vehicle Monitoring (AVM) technologies. These systems capture and process a wide array of data in real-time, facilitating unprecedented service monitoring and management. This research uses vast GPS (Global Positioning System) datasets from bus routes to analyze spatial anomalies in bus trajectories. Using modern AVM systems, the study applies Machine Learning techniques, specifically Random Forest and Extreme Gradient Boosting (XGBoost), to classify trips (defined as individual journeys from a specific origin to a specific destination at a given time) based on detected anomalies such as route deviations and unexpected interruptions. This classification enables targeted corrective actions, enhancing service reliability. Public transport operators in Italy and Europe receive remuneration based on various factors, including kilometers covered, and face penalties for not meeting service quality conditions. The Machine Learning tool developed to detect and classify spatial anomalies offers significant advantages in cost management, compliance with service obligations, and operational efficiency. The study outlines a methodical approach involving feature engineering on GPS points, aggregation into trip datasets, preliminary categorization of anomalies, and detailed multi-class classification using advanced algorithms. Focusing on a practical application, the study evaluates GPS data from Autoguidovie, a local operator in northern Italy. The highest-performing model, Extreme Gradient Boosting, demonstrated a classification accuracy of 88.72%. In the case study, the model identified 9,511 trips affected by spatial anomalies out of 27,832 trips analyzed, generating nearly 4 million GPS points. This result enabled the appropriate management of anomalies through proper analysis and corrective actions.
With the rise of mass transit systems in India, numerous mode choices are available for commuters to cover the first and last mile of their trip. Factors such as age, gender, travel cost, and travel time, among others, influence these choices. Despite the extensive studies of various variables, there has been limited research into soft factors such as user attitudes and safety perceptions. The aim of this research is to investigate the impact of safety perception on mode choice for first and last-mile connectivity on the Lucknow Metro Red Line, using correlation and multinomial logit regression. The findings indicate that factors such as vehicle conditions, safety measures, driver characteristics, supporting infrastructure, and nearby traffic conditions influence commuters' safety perceptions and therefore should be explored further to foster a more sustainable mass transit feeder system.
In the conditions of high uncertainty, the question of determining the transport terminal location as an important link of the logistics system through which the movement of cargo flows becomes especially actual. This aspect significantly affects the logistics costs and the final price of the product for consumers. The conducted research shows the role of transport terminals in the organization of multimodal cargo transportation in the conditions of infrastructure reconstruction. & Acy; model for choosing a strategy for the multimodal transport terminal location was developed. It allows to optimize cargo flows. A model for estimating the expected effect of using the public-private partnership mechanism for the creation of a multimodal transport terminal was developed. It allows to choose the most effective financing mechanism and consider the stakeholders' interests. This approach can be used in the development of the infrastructure reconstruction strategy in the post-war period.
Public transport and non-motorized modes of transport are complements to each other. Large investments are made In India for the construction and maintenances of road and railway infrastructure, but inadequate pedestrian facilities discourage people to choose walking and public transport as their mode of transport. Hence, it is crucial to assess the level of accessibility and pedestrian routes near bus stations. This study examines Pedestrian Level of Service (PLOS) around the bus stops and access time to reach each bus stop in Kazhakoottam, one of the fastest-growing areas of the Thiruvananthapuram district, Kerala. The manual assessment of PLOS around bus stations in a road network is very tedious. To overcome this limitation a PLOS tool is developed in GIS environment. This PLOS tool considers the main factors influencing walkability, such as walking speed, pedestrian count, space for pedestrians, and crossing time. An algorithm for access time measurement, which calculates the amount of time needed to reach each matrix cell, beginning from a transit station is developed in GIS environment. The developed PLOS tool is very effective and gives above 90% accurate results.
The research study explores impact of high-speed rail on railway ballast performance, addressing issues like fouling, cyclic rail loads, and geotechnical factors. (Dingqing Li, James Hyslip, Ted, 2019) Spectral Analysis of Surface Waves (SASW) technology is an nondestructive technology employed to ascertain appropriate ballast health by measuring shear wave velocity (SWV). The research examines SWV variations in differently aged tracks before, during, and after high-speed train travel. The study recorded shear wave velocities before, during & after passage of the train at two locations, depicting the semi-elastic nature of the ballast bed. Shear wave velocities increase with fouling amount, with the optimum fouling point (OFP) and critical fouling point (CFP) being 13%, 13.75%, 15.5%, and 19%, respectively based on the samples collected. The strength tests, which included abrasion and impact strengths, were also performed at the two sites shown. Additionally, in-situ sample collection and analysis of the ballast gradation were carried out, revealing poor gradation of the ballast. Strength tests and ballast gradation analysis were conducted, revealing poor gradation of the ballast.
Inefficient cargo handling at regional ports leads to extended dwelling times, increased logistics costs, and local traffic congestion. This study aims to model and analyze the loading and unloading process at Sinjai Port, Indonesia, to identify bottlenecks and propose efficiency improvements. Using queuing theory, we applied a multi-server, multiphase model to operational data collected from 320 freight vehicles. Results indicate the port handles five main cargo types: staple goods (41%), stone (24%), sand (22%), textiles (8%), and general cargo (5%). The queuing analysis revealed that the average time a vehicle spends in the system (Ws) is highest for staple goods at 0.7 minutes, identifying it as the critical bottleneck. General cargo and stone had the fastest service times at 0.4 minutes. We conclude that targeted measures, such as streamlining administration for staple goods, enhancing specific equipment, and integrating information systems, are necessary to improve efficiency at Sinjai Port. This study demonstrates the utility of queuing models for diagnosing specific operational inefficiencies in mixed-cargo regional ports.
Traffic signals are the most widely used form of control for urban intersections, serving not only to regulate the right-of-way and protect conflictingvehicle movements,but also to allocate greentime, phases, and cycle lengths that influence overall traffic performance. Variations in signal timing directly affect vehicle delay, queue formation, and efficiency at intersections. In this study, the performance of the selected intersection was first examined under the existing signal timing plan. Four alternative signal timing scenarios were then developed and modelled using microscopic simulation to identify potential improvements. Intersection performance was evaluated based on five key operational indicators: queue length, average delay, number of stops, carbon monoxide (CO) emissions, and fuel consumption. The outcomes of each scenario were subsequently analysed using the CRITIC method to provide an objective ranking. Results revealed that all proposed scenarios improved the level of service (LOS) compared with the base case, with Scenario 2 emerging as the most effective alternative.
This study assesses the systematic risk of 45 publicly listed U.S. transportation companies using the CAPM framework, grouping them into five categories. We apply the ARDL technique, which provides dual risk insights-short-term and long-term. This method reduces endogeneity concerns, as each variable is modeled through a single-equation approach. The transportation sector plays a crucial role in the U.S. economy, accounting for up to 10% of GDP, with the logistics market approaching $2 trillion in 2019. Our analysis shows that long-run (LR) investments are less sensitive to market fluctuations and tend to be more profitable, whereas short-run (SR) investments are more exposed to market volatility but can be offset by long-run recoveries. The study provides useful guidelines for equity investments in the transportation sector, underscoring its potential for substantial returns despite its cyclical nature. Measuring systematic risk is thus essential for making informed investment decisions in this field.
This study explores the perceptions of logistics sector employees regarding Turkey's strategic role in international transportation under the Belt and Road Initiative. The sample includes 402 employees working at border gates, ports, railway terminals, and logistics centers, selected via convenience sampling. Data were analyzed using SPSS 27.0. Results show that Turkey's position between Asia and Europe is considered a major logistical advantage. While road and rail infrastructures are rated positively, port infrastructure is seen as needing improvement. Logistics service quality, digitalization, and customs operations received favorable evaluations. However, border congestion, bureaucratic transit procedures, and regional political instability are perceived as key challenges. Despite these issues, a cautiously optimistic view of future opportunities prevails. Significant differences in perceptions were observed by gender, age, experience, sector, region, and job unit. These findings highlight the importance of designing transportation policies based on sectoral perceptions and expectations.
There is a growing trend towards autonomous vehicles (AV). Such vehicles have demonstrated the potential to reduce the traffic crashes related to human errors. In recent years, the number of AV crashes has increased. Most research related to these crashes indicates that AVs are prone to being rear-ended. However, there has been little analysis of how these incidents occur or what factors contribute to them. A lack of detailed datasets on AVs further complicates the characterization of these crash dynamics, especially in a mixed-traffic environment. This study bridges these gaps and tackles the problem of rear-end collision in AVs by using the NHTSA crash data, which includes 2184 crashes between ADAS and ADS vehicles. To give a comprehensive overview, three distinct models were utilized, focusing on ADS and ADAScontrolled vehicles separately. The analysis shows that Pre-Crash Movements of both vehicles, Speed Gap Ratio and Roadway Description are the key predictors of rear-end collisions in both systems. ADS vehicles showed greater sensitivity to roadway surface, missing road markings and larger vehicles, while ADAS systems were more influenced by the presence of other entities and traffic incidents.
In developing countries, the most significant issue is road traffic accidents (RTAs). In fact, the road accident has an important influence on the health and social policy. Al Sulaymaniyah city, in the northern part of Iraq, was considered as a case study. The rapid urbanization and motorization increase these problems in the city. The current study takes into account the gender disparity, seasonal patterns, and the severity of the traffic accidents. Thus, this study aims to analyze and forecast the gender disparity in road traffic crash rates. The Chi-square test and SARIMA model were employed on the collected data. The outcomes show that male drivers contribute at a higher rate of fatalities and injuries than female drivers. Furthermore, the seasonal patterns indicate that the fluctuations are affected by weather conditions and post-pandemic mobility changes. A valuable insight was provided for policymakers and urban planners via the critical patterns. This insight is crucial for local authorities to develop strategies that improve road safety and reduce the negative impact of traffic accidents on health and social life.
Active mobility contributes to making urban areas more liveable and attractive, as well as offering numerous public health and environmental benefits. Cycling and walking routes play a significant role in the development of a sustainable and intermodal urban mobility system and are well suited for integration with digital mobility services such as MaaS. In this context, enhancing bicycle and pedestrian accessibility to urban destinations and services can serve as a catalyst for the broader revitalization and redevelopment of entire urban areas. From this perspective, the study aims to (i) identify suitable areas for improving cycling infrastructure and pedestrian access, and (ii) rank these areas based on their potential to host related services effectively. These two goals are oriented to support local policy makers in identifying the parts of a district that can be primarily and effectively destinated to host biking stations and that can promote the regeneration of the urban context. To this end, a methodology combining multi-criteria overlay analysis and the TOPSIS method within a GIS environment is developed and applied to the Fuorigrotta neighborhood in Naples, with a focus on the 11-18-year-old population group. The methodology, on the basis of Ad Hoc defined criteria, allows the definition and the classification of potential areas where cycling can be improved through cycling support services and which can be the driving force for the restoration of the urban context in which they are located. The study provides a support tool for decision-makers to improve accessibility to intermodal services and, at the same time, contributes to the triggering of broader redevelopment processes, with particular attention to peripheral areas adjacent to transport infrastructures, where the quality of public spaces is often inadequate. The study is part of the project 'National Centre for Sustainable Mobility'- Spoke 8, funded by the PNRR, aimed at implementing a new model of mobility, as a service, accessible and inclusive, for a more sustainable urban mobility system.
This study investigated the effects of cognitive distractions on driving behavior using a car driving simulator. A sample of 46 participants was divided into two groups: a distracted experimental group and a non-distracted control group. Distractions included listening to music on highways, texting on suburban roads, and following navigation systems in urban areas. Speed and lane position were recorded every 0.25 seconds, and ANOVA tested for significant differences. Results showed that listening to music increased average speed, texting reduced speed and caused greater lane deviation, and using a navigation system led to speed variations due to periodic shifts in attention. These findings highlight that even common, legal distractions can compromise driving safety. Understanding these impacts is essential for improving road design. By optimizing infrastructure to account for distracted driving, civil engineers can reduce risks and traffic accidents, and advance the UN Agenda 2030 goals for sustainable and safer transportation systems.
People with disabilities face many challenges related to transportation accessibility. One approach to help address this issue is to make ridesharing services accessible and affordable for individuals with disabilities. This paper aimed to compare drivers' intentions to share rides with persons with disabilities and persons without disabilities. Based on previous studies on ridesharing, we identified key topics that were the research subject. Data was collected in Serbia, and 2,068 valid respondents were collected. In general, we found that drivers have a slightly higher intention to share a ride with persons without disabilities. The impact of constructs on drivers' intention to share a ride was examined using multiple linear regression models. Five constructs showed a statistically significant impact in both the model that examined drivers' intention to share a ride with persons with disabilities and the model that analyzed persons without disabilities. Trust, Altruism, Perceived risk, Subjective norms, and Perceived usefulness impacted both models. On the other hand, Reciprocity influenced drivers' intentions to share rides with individuals without disabilities, while Attitudes and Perceived ease of use had an impact on drivers' intentions related to individuals with disabilities. The results of this paper will help decision-makers to more precisely define measures and activities for the inclusion of persons with disabilities in ridesharing. Primarily, ridesharing should be implemented for categories of persons with disabilities that do not require additional engagement from the driver (e.g., persons with hearing impairment).
This contribution presents GreenGo, a smartphone application designed to promote sustainable mobility habits through user engagement and a data-driven approach. In alignment with the European Green Deal's objectives for smart and sustainable mobility, GreenGo collects mobility data while incentivizing environmentally friendly travel behaviours in according with principles of behavioural economics and user profiling to implement targeted nudging techniques. The paper outlines the ongoing transition from traditional mobility services to modern, personalized, and data-informed approaches. Particular attention is paid to the challenges of mobility data sharing, including concerns related to user trust, transparency, and privacy, showing how well-designed incentive mechanisms can be applied to foster active participation. Unlike other similar initiatives, GreenGo is not only designed to reward sustainable travel behaviour, but also to acknowledge and value users' willingness to share mobility data-recognizing such data as a crucial resource for optimizing transport services and benefiting the broader community. This dual objective adds value in terms of user engagement as individuals feel empowered to contribute to the common good. To enable effective personalization, GreenGo adopts a continuous profiling methodology that integrates dynamic sensor-based data processing with occasional questions delivered by in app cards. This dual-layered approach captures both observable behaviours and the underlying motivations behind travel choices, offering a more holistic understanding of user preferences and addressing the limitations of purely behaviour-based analyses. GreenGo aims to provide local authorities with actionable insights to support the transition toward more sustainable mobility patterns through personalized engagement and reward strategies.
Citizen involvement in urban transport planning is crucial for developing policies that respond to the real needs of the community, promoting inclusivity, equity, and social responsibility. The BEC2SCHOOL project aims at providing communities and policy-makers with methods and tools to enhance public participation in transport decision-making processes and foster the co-design of sustainable mobility solutions, taking advantage of smart technologies and citizen sensing. Pilot schools are selected and invited to install low-cost traffic sensors. Workshops and training sessions on sustainable mobility and transport externalities are organized. The workshops include students' participation in a co-design process for the requalification of school street areas, using a multicriteria approach. This study presents the first results of the participatory process implemented in one workshop held at a school in the city of Catania, Italy. This approach aims at supporting the evaluation and selection of the solutions to improve sustainable mobility near schools, considering the data collected by sensors, and actively involving the students by directly inferring their opinions. In particular, the workshop integrated methodological innovations, such as the use of low-cost traffic sensors and the application of a multi-criteria approach in the co-design process, which allowed students to actively and effectively be engaged. Preliminary results highlighted students' priorities in social and environmental areas, with a strong emphasis on reducing pollution and improving road safety. Additionally, the effectiveness of involving schools directly in the participatory design process was confirmed, showing how a data-driven approach can enhance the impact of the proposed solutions. Results confirm the effectiveness of a participatory data-driven approach in analysing problems and co-creating solutions together with stakeholders, paving the way for future initiatives that actively engage school communities in the creation of more sustainable and liveable cities.
This paper explores what happens when everyday travel routines are disrupted, analysing longitudinal evidence on how commuting behaviour adapts and recovers after a mobility shock. Treating the Covid-19 pandemic as a natural experiment of large-scale behavioural disruption, the analysis of three travel-to-work modes across survey waves reveals a sharp yet temporary reconfiguration of commuting patterns: public transport use declined sharply, remote work expanded markedly, and private transport proved most stable mode. Transition matrices indicate that most behavioural shifts were short-lived, reflecting a return to prepandemic habits once restrictions were lifted. Interpreted through the lens of the habit discontinuity and mobility biographies frameworks, the findings suggest that while the pandemic temporarily disrupted mobility routines, it did not generate structural change. Policy implications highlight the need to sustain positive behavioural shifts such as remote work and active commuting and to restore confidence in public transport as part of the post-pandemic mobility transition.
