Search Results (203)

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article

Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion. Full article

Scheduled timetable information has been used extensively in studies concerned with estimating travel times in accessibility research. Fewer studies to date have involved the use of real-time public transport data to help investigate the impacts of travel disruptions or cancellations of service on reported spatial and temporal patterns of accessibility. The aims of this paper are to introduce, describe, and compare the salient features and relative merits of alternative data sources relating to real-time transport data that could be utilized in such applications. By drawing attention to the potential of real-time data originating from such sources, this study makes recommendations for those considering building on the use of scheduled data to incorporate travel time reliability within transport applications. We conclude by highlighting the need for further research that explores the potential of using openly available sources of real-time traffic data in studies that incorporate accessibility analysis. Full article

Road traffic accidents (RTAs) are a leading cause of physical injury worldwide, but they also frequently result in post-traumatic stress disorder (PTSD). This systematic review examines the prevalence, predictors, comorbidity, and treatment of PTSD among RTA survivors. Four electronic databases (PubMed, Scopus, EBSCO, and ProQuest) were searched following PRISMA 2020 guidelines. Articles were included if reporting on the presence of post-traumatic stress disorder as a result of a road traffic accident in adults aged 18 years and older. Including peer-reviewed journal articles and awarded doctoral theses across all publication years, and written in English, Macedonian, Serbian, Bosnian, Croatian, and Bulgarian, identified 259 articles, and using Literature Evaluation and Grading of Evidence (LEGEND) assessment of evidence 96 were included in the final review, involving 50,275 participants. Due to the heterogeneity of findings, quantitative data were synthesized thematically rather than through meta-analytic techniques. Findings are reported from Random Control Trial (RCT) and non-RCT studies. PTSD prevalence following RTAs ranged widely across studies, from 20% (using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 criteria) to over 45% (using International Classification of Diseases, 10th Revision, ICD-10 criteria) within six weeks post-accident (non-RCT). One-year prevalence rates ranged from 17.9% to 29.8%, with persistence of PTSD symptoms found in more than half of those initially diagnosed up to three years post-RTA (non-RCTs). Mild or severe PTSD symptoms were reported by 40% of survivors one month after the event, and comorbid depression and anxiety were also frequently observed (non-RCTs). The review found that nearly half of RTA survivors experience PTSD within six weeks, with recovery occurring over 1 to 3 years (non-RCTs). Even minor traffic accidents lead to significant psychological impacts, with 25% of survivors avoiding vehicle use for up to four months (non-RCT). Evidence-supported treatments identified include Cognitive Behavioural Therapy (CBT) (RCTs and non-RCTs), Virtual Reality (VR) treatment (RCTs and non-RCTs), and Memory Flexibility training (Mem-Flex) (pilot RCT), all of which demonstrated statistically significant reductions in PTSD symptoms across validated scales. There is evidence for policy actions including mandatory and regular psychological screening post RTAs using improved assessment tools, sharing health data to better align early and ongoing treatment with additional funding and access, and support and interventions for the family for RTA comorbidities. The findings underscore the importance of prioritizing research on the psychological impacts of RTAs, particularly in regions with high incident rates, to understand better and address the global burden of post-accident trauma. Full article

Traffic calming has emerged as a key urban strategy to reduce vehicle speeds and mitigate road traffic risks, with increasing recognition of its broader implications for public health, human behaviour, and urban liveability. This systematic literature review examines the multifaceted impacts of traffic-calming measures—from speed limit reductions to physical infrastructure and enforcement-based interventions—by synthesising findings from 28 peer-reviewed studies. Guided by the PRISMA framework, the review compiles research exploring links between traffic calming and outcomes related to public health, behaviour, and urban quality of life. Research consistently indicates that such interventions reduce both the frequency and severity of collisions, improve air and noise quality, and promote active mobility. These effects are shaped by user perceptions: non-motorised users tend to report higher levels of safety and accessibility, whereas motorised users often express frustration or resistance. Beyond safety and environmental improvements, traffic calming has been associated with greater use of public space, stronger social connections, and enhanced environmental aesthetics. The findings also show that key challenges may affect the effectiveness of traffic calming and these include negative attitudes among drivers, mixed outcomes for air quality, and unintended consequences such as traffic displacement or increased noise when interventions are poorly implemented. Overall, the findings suggest that traffic calming can serve as both a public health initiative and a tool for enhancing urban liveability, provided that the measures are designed with contextual sensitivity and supported by inclusive communication strategies. Full article

Background: Over the past decade, Shanghai primary and middle schools have installed and updated direct-drinking water facilities in compliance with local policies, but few studies have assessed the schools providing direct-drinking water access. Methods: A cross-sectional study was conducted with 167 public primary, middle, and high schools across Pudong New Area, Shanghai during Autumn 2024. The type, location, and working condition of all direct-drinking water facilities throughout each school were documented by trained research staff using a direct observation protocol. Information on school direct-drinking water quality was obtained from the routine monitoring program. Data were analyzed for comprehensive assessment of direct-drinking water facilities in the schools. Results: On average, each school had one faucet of direct-water facility per 41 students; 70% of the schools met the requirement for minimum direct-drinking water access, and >90% placed facilities in high-traffic areas. In addition, 83% of the schools selected water facilities with nanofiltration and a hot water system, and most only provided hot water (above 50 degrees Celsius). For school direct-drinking water quality, the concentrations of hardness, chemical oxygen demand (COD), and total dissolved solids (TDS), as well as pH values, were improved significantly, but the total bacteria count was prone to not meeting the requirement for standards in middle and high schools, which could be caused by insufficiency of chlorination in pumping stations or neglecting to clean facilities promptly. Conclusions: Wide usage of school direct-drinking water facilities could help most public schools to meet local policies for minimum student drinking water access in Shanghai, but microbial contamination was the potential threat. Water temperature is the key factor affecting students’ drinking water, providing an optional water temperature for students’ preferences and concerns. National sanitary standards of direct-drinking water quality and relevant additional regulations should be established and implemented in China. Full article

Urban Air Mobility (UAM) has high potential as an ecofriendly transportation mode that can alleviate traffic congestion on the ground and reduce travel times by utilizing three-dimensional airspace. However, efficient vertiport operational plans are needed for UAM to become an accessible transportation mode for the public. In this study, the numerical analysis program MATLAB (R2023a) and the traffic simulation software VISSIM (PTV VISSIM 2024) were used to model vertiport operations and analyze the on-ground traffic environment, including vertiport capacity and UAM aircraft delays. Additionally, on-time performance was considered by applying uncertainties to the intervals between consecutive generations and the turnaround time to simulate situations where UAM aircraft cannot adhere to their scheduled arrival and departure times. Operational scenarios were developed by varying the interval time between UAM aircraft generated in the simulation (3–10 min) in two cases: (1) without considering the on-time performance and (2) considering the on-time performance. This study aimed to maximize vertiport capacity and minimize UAM aircraft delay times. In addition, the reduction of delay times and improvement of turnaround efficiency directly contribute to sustainable urban airspace management by lowering ground energy use and environmental impact. In Case 1, the vertiport was most efficient at an interval time of 7 min. In Case 2, capacity was maximized at an interval time of 6–7 min while delay times were minimized at an interval time of 8–10 min. The simulation results provide valuable insights for developing not only efficient but also environmentally responsible vertiport operational plans, contributing to the successful and sustainable implementation and scalability of UAM systems. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

Smart cities are urban areas that use contemporary technology to improve citizens’ overall quality of life. These modern digital civil hubs aim to manage environmental conditions, traffic flow, and infrastructure through interconnected and data-driven decision-making systems. Today, many applications employ intelligent sensors for real-time data acquisition, leveraging visualization to derive actionable insights. However, despite the proliferation of such platforms, challenges like high data volume, noise, and incompleteness continue to hinder practical visual analysis. As missing data is a frequent issue in visualizing those urban sensing systems, our approach prioritizes their correction as a fundamental step. We deploy a hybrid imputation strategy combining SARIMAX, k-nearest neighbors, and random forest regression to address this. Building on this foundation, we propose an interactive web-based pipeline that processes, analyzes, and presents the sensor data provided by Basel’s “Smarte Strasse”. Our platform receives and projects environmental measurements, i.e., NO₂, O₃, PM_2.5, and traffic noise, as well as mobility indicators such as vehicle speed and type, parking occupancy, and electric vehicle charging behavior. By resolving gaps in the data, we provide a solid foundation for high-fidelity and quality visual analytics. Built on the Flask web framework, the platform incorporates performance optimizations through Flask-Caching. Concerning the user’s dashboard, it supports interactive exploration via dynamic charts and spatial maps. This way, we demonstrate how future internet technologies permit the accessibility of complex urban sensor data for research, planning, and public engagement. Lastly, our open-source web-based application keeps reproducible, privacy-aware urban analytics. Full article

The primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout critical locations on the premises, allowing security personnel to observe all areas for specific behaviors that may signal an emergency or a situation requiring intervention. A significant challenge arises from the fact that individuals cannot maintain focus on multiple screens simultaneously, which can result in the oversight of crucial incidents. In this regard, artificial intelligence (AI) video analytics has become increasingly prominent, driven by numerous practical applications that include object identification, detection of unusual behavior patterns, facial recognition, and traffic management. Recent advancements in this technology have led to enhanced functionality, remarkable accuracy, and reduced costs for consumers. There is a noticeable trend towards upgrading security frameworks by incorporating AI into pre-existing video surveillance systems, thus leading to modern video surveillance that leverages video analytics, enabling the detection and reporting of anomalies within mere seconds, thereby transforming it into a proactive security solution. In this context, the AiWatch system introduces digital twin (DT) technology in a modern video surveillance architecture to facilitate advanced analytics through the aggregation of data from various sources. By exploiting AI and DT to analyze the different sources, it is possible to derive deeper insights applicable at higher decision levels. This approach allows for the evaluation of the effects and outcomes of actions by examining different scenarios, hence yielding more robust decisions. Full article

Technological innovation toward electrification and digitalization is revolutionizing aviation, paving the way for new aeronautical paradigms and novel modes to transport goods and people in urban and regional environments. Advanced Air Mobility (AAM) leverages vertical and digital mobility, driven by safe, quiet, sustainable, and cost-effective electric vertical takeoff and landing (VTOL) aircraft. A key enabler of this transformation is the development of vertiports—dedicated infrastructure designed for VTOL operations. Vertiports are pivotal in integrating AAM into multimodal transport networks, ensuring seamless connectivity with existing urban and regional transportation systems. Their design, placement, and operational framework are central to the success of AAM, influencing urban accessibility, safety, and public acceptance. These facilities should accommodate passenger and cargo operations, incorporating charging stations, takeoff and landing areas, and optimized traffic management systems. Public and private sectors are investing in vertiports, shaping the regulatory and technological landscape for widespread adoption. As cities prepare for the future of aerial mobility, vertiports will be the cornerstone of sustainable, efficient, and scalable air transportation. Full article

Bus lanes with intermittent priority (BLIP) are designed to optimize road resource allocation. The advent of connected and automated vehicles (CAVs) promotes the implementation of BLIP. However, it is crucial to find an effective method to intermittently grant right-of-way to CAVs. In this paper, we introduce a BLIP method with spatio-temporal clear distance (BLIP-ST) and a CAV control method in an intelligent transportation environment. When CAVs access BLIP-ST, the constraints of the moving gap between buses are considered. When CAVs leave BLIP-ST, coordination with the nearest CAV platoon on the adjacent lane is considered to cope with situations where CAVs cannot find the appropriate space. Then, the proposed method was simulated by an open boundary cellular automaton model. The results showed that with the same inflow, a CAV-sharing bus lane could significantly improve road traffic efficiency, and it is the most significant when the CAV penetration rate is medium, with the average road speed increasing from 6.67 km/h to 30.53 km/h. Meanwhile, when the CAV penetration rate is medium, BLIP-ST operates with the best efficiency at different strategies. This was due to the fact that when the penetration rate is too high, BLIP-ST is excessively occupied, which affects public transportation priority. When the penetration rate is too low, BLIP-ST cannot be fully utilized. In addition, regardless of the penetration rate of CAV, CAV platoon collaborative lane changing is better than single CAV collaborative lane changing in terms of improving road traffic efficiency and can increase the average road speed by 8–19%. Full article