Search Results (550)

Under growing urbanization and environmental challenges, sustainable urban mobility has become a critical priority for cities worldwide. Public Transport (PT) systems play a central role in reducing car dependency, lowering emissions, increasing network capacity, and promoting more equitable and efficient access to urban spaces for all users. Hence, the present paper aims to investigate PT preferences in the city of Larissa, Greece. Larissa is a medium-sized city currently serviced only by buses, and is currently focusing on the potential introduction of a new tram system to operate in parallel with existing bus services. To this end, a SP survey was designed and implemented, resulting in 972 observations that were collected for further statistical analysis. Survey results show a slight preference for trams over buses, with 54.63% selecting the tram and 45.37% favoring the buses. Moreover, a context-based segmentation pipeline was established using PCA, DBSCAN and t-SNE algorithms, aiding the visualization of existing clusters for transport choice approaches. Afterwards, a series of mixed logit models was applied, and statistically significant variables influencing mode choice were determined. The study also examines Value of Time (VoT) metrics and finds that respondents assign lower VoTs to trams than to buses, especially in out-of-vehicle segments of the journey, such as waiting and walking, and therefore consider trams as more pleasant and less burdensome. The findings also indicate that passengers place a high value on the quality of infrastructure related to access and waiting times, underlining the need to improve the overall user experience beyond the vehicle itself. In summary, the present research offers valuable insights into how the introduction of a tram system could possibly reshape PT usage patterns when compared with the legacy existing bus services. Full article

Public transportation (PT) is key to breaking the vicious cycle of private vehicles, a critical sustainability challenge in developing countries. The increase in population raises the number of private cars, and this trend continues. PT plays a vital role in reducing car use, traffic congestion, and environmental pollution. Fare is crucial to the system’s ability to encourage passengers to use PT. It affects mobility, the quality of life, and the sustainability of the system. This study aims to examine Istanbul’s optimal fare system using the BWM (Best–Worst Method) for PT fare for the first time. Furthermore, it is the first study to compare fare structures and criteria for Istanbul, Europe’s second-largest city, where transportation affects quality of life. The most frequently used fare structures and criteria in the literature and practice were weighted by experts using BWM surveys for the Istanbul Metro. The results show that distance-based fare (DBF) (43.7%) is the best fare structure, while flat fare (FF) (12.2%) is the weakest. For the criteria weightings, benefit received (24.4%) and social equity (22.7%) are seen as superior. Finally, the impact of the criterion on the fare structure was demonstrated through analysis, and its importance for experts in evaluating PT was highlighted. Full article

Navigation is an essential ability for autonomous systems, and efficient motion planning for mobile robots is a central topic for autonomous vehicle design and service robotics. Most path-planning algorithms produce reference paths with sharp or discontinuous turns, inducing several drawbacks during mission execution, such as unexpected inertial stress and strain on the mechanical structure, passenger discomfort, and unsafe and unpredictable deviation of the real trajectory with respect to the reference planned one. Oppositely, smooth and feasible trajectories are often desired in real-time navigation for nonholonomic mobile robots where the surrounding environment can have a dynamic and complex shape with obstacles. In this paper, we propose a novel technique for the generation of smooth, collision-free, and near time-optimal paths for nonholonomic mobile robots. The proposed method exploits the features of a set of tunable bump functions, with the goal of pursuing smooth reference curves with tunable features (such as curvature, or jerk) yet seeking a reasonable length minimality, thus combining the advantages of the two most adopted techniques, namely Bezier interpolation and Dubins curves. After a thorough description of the analytical methods, the paper is primarily concerned with the design and tuning methods of the path-planning algorithm. Both a graphical method and numerical investigations and examples are performed to fully exploit the algorithm potentialities and to show the efficiency of the proposed strategy. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

Cognitive or visual distraction caused by drivers using mobile phones, operating the central console, or conversing with passengers while driving is a significant contributing factor to road traffic accidents. Aiming to solve the problem that existing driving behavior monitoring systems exhibit insufficient recognition accuracy and low real-time detection performance in complex driving environments, this study proposes a dual-flow driver distraction detection model based on Sobel edge detection (DFSED-Model). The model is designed with a collaborative learning framework: the first flow adopts a lightweight pre-trained backbone network to achieve efficient semantic feature extraction. The second flow utilizes Sobel edge detection to extract the driver’s driving contours and enhances the model’s spatial sensitivity to driving movements and hand movements. Through the feature learning process of the first-flow-guided auxiliary branch, collaborative optimization of knowledge transfer and attention focusing is realized, thereby improving the model’s convergence speed and discriminative performance. The proposed model is evaluated on three widely used public datasets: the State Farm Distracted Driver Detection (SFD) dataset, the 100-Driver dataset, and the American University in Cairo Distracted Driver Dataset (AUCDD-V1). Under the premise of maintaining low computational overhead, the accuracy of the DFSED-Model reaches 99.87%, 99.86%, and 95.71%, respectively, which is significantly superior to that of many mainstream models. The results demonstrate that the proposed method achieves a favorable balance between accuracy, parameter count, and efficiency, and possesses strong practical value and deployment potential. Full article

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems. Full article

Public transport is vital for social and economic life, but many people with disabilities still face exclusion due to both physical and psychosocial barriers. This study examined how psychosocial barriers influence public transport travel behavior among people with mobility, vision, and hearing disabilities in the City of Tshwane, South Africa. A quantitative survey was conducted using a structured questionnaire among 214 respondents. The results showed that fear of crime, lack of personal safety, anxiety when travelling alone or to unfamiliar places, and negative treatment by drivers and co-passengers are major deterrents to public transport use. Psychosocial barriers were significantly associated with travel behavior and a strong preference for private cars as well as ride-hailing services. Group comparisons revealed that individuals with vision disabilities experience significantly higher levels of transport-related fear compared to other groups. People with mobility and vision disabilities are more affected by negative attitudes from co-passengers compared to people with hearing disabilities. Psychosocial barriers are associated with low trip frequencies for non-essential activities, indicating suppressed travel. The study concludes that achieving inclusive urban mobility requires addressing psychosocial barriers alongside physical accessibility to ensure safe, dignified, and independent travel for people with disabilities. Full article

The recent expansion of mass public transit in Bangkok has increased demand for public motorcycle taxis as a first- and last-mile solution for sustainable urban mobility. This study presents the results of a real-world demonstration project that transitioned 50 conventional public motorcycle taxis into electric motorcycles supported by a battery-swapping system. The project evaluated vehicle performance, operational patterns, electricity consumption, and greenhouse gas (GHG) emissions under actual traffic conditions. Electric motorcycles deployed in taxi services must accommodate additional passenger load, provide sufficient acceleration for dense urban traffic, and sustain high daily travel distances. The findings show that participating riders accumulated a total driving distance of 759,354 km during the project period, demonstrating the technical and operational feasibility of electrification in high utilization fleets. Based on measured electricity consumption and Thailand’s grid emission factor, the transition resulted in an estimated reduction of approximately 1708.4 metric tons of CO₂ equivalent emissions, excluding additional benefits associated with modal shifts to mass public transit. The results further indicate that battery-swapping infrastructure is a critical operational enabler, as daily travel distances frequently exceed the single-charge range of typical electric motorcycles. Scenario projections aligned with Thailand’s 30-by-30 electric vehicle policy target suggest that large-scale electrification of motorcycle fleets could contribute substantially to national mitigation efforts, supporting the country’s accelerated goal of net-zero emissions target by 2050. Full article

Ridesplitting is a promising strategy to enhance vehicle efficiency in urban mobility services during mega-events. However, designing dispatching algorithms that effectively balance high service rates with acceptable passenger delays under high-demand, reservation-based scenarios remains a significant challenge. To address this issue, this study proposes a novel two-stage dispatch framework: Offline Grouping and Online Matching (OGOM). In the offline stage, the request grouping problem is formulated as a weighted hypergraph maximum matching (WHMM) problem. A sequence inference (SI) method is introduced to accelerate the construction of candidate ridesplitting trips, and the WHMM problem is solved optimally using the Gurobi solver. In the online stage, the dispatch process is completed within an event-based simulation environment built with MATSim. The framework is validated through a comprehensive case study of the Hangzhou Asian Games. The results demonstrate that the proposed OGOM framework achieves a mean service rate of 92.12%, representing an 8.74% improvement over a rolling horizon batching benchmark. Concurrently, the average passenger delay is maintained between 2 and 4 min across all simulation runs. Furthermore, the framework reduces the average request completion distance by over 30% compared to a non-ridesplitting baseline. The proposed SI method also shows a 49.35% reduction in computation time for hypergraph construction compared to conventional methods. These findings confirm that the OGOM framework provides an effective and scalable operational strategy for mega-event ridesplitting services, simultaneously improving service quality through optimized supply–demand matching and controlled passenger delays. Full article

While pricing policy has emerged as a critical demand-side lever for decarbonizing mobility, its bidirectional effects on modal shift remain unexplored. Dynamic pricing in high-speed rail (HSR) creates a double-edged environmental outcome: advance discounts attract passengers from aviation, yet last-minute premiums may reverse these gains. Using 2.4 million price observations from Madrid–Barcelona (2019), we introduce a carbon leakage framework that quantifies this phenomenon within a multi-source validated framework. Our analysis reveals a structural tension: while early-bird pricing attracts 274,431 annual passengers from aviation—saving 23,650 tonnes CO₂/year—last-minute scarcity premiums systematically drive passengers back to air travel. Multi-source calibrated elasticity ($\epsilon =-0.95$, validated through triangulation across CNMC corridor data, meta-analytic evidence, and recent empirical studies within the range $[-1.91,-0.75]$) shows that 22.3% of last-minute tickets exceed the EUR 120 aviation threshold, creating 1511 tonnes CO₂ leakage annually (6.4% offset of gross savings). Critically, this leakage ratio is shown to be structurally independent of elasticity specification, being determined by the price distribution shape rather than demand parameters. Scenario analysis suggests that under static assumptions, price caps at EUR 110–120 would eliminate leakage while preserving an estimated 94% of operator revenue, though general equilibrium effects remain unmodeled. These findings identify illustrative scenario thresholds for carbon-aware revenue management, demonstrating that demand-side decarbonization requires not only attracting passengers to sustainable modes but also preventing their reversal to high-carbon alternatives. Full article

Public transport systems in rapidly urbanizing Gulf cities confront the simultaneous challenge of decreasing emissions while guaranteeing equal access for riders, many of whom rely on transit for economic reasons. Sustainable smart city development necessitates bus services that are both efficient and sensitive to rider needs in adverse weather conditions. This study develops and evaluates a wellbeing-focused planning framework for Dubai’s bus network, filling gaps in prior research that primarily focuses on temperate, choice-based transport environments. The study uses Generalized Structured Component Analysis (GSCA) to analyze how Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA) impact overall journey wellbeing, based on a cross-sectional survey of 491 riders collected from July–August 2024. Data were collected during peak summer conditions, and the analysis followed a structured workflow that operationalized the proposed constructs into measurable indicators and estimated both the measurement and structural components of the GSCA model to find planning relevant wellbeing drivers. The model shows a strong fit (FIT = 0.684; GFI = 0.991; SRMR = 0.056), with SEA (β = 0.504) having the greatest influence on wellbeing, followed by SOA (β = 0.344) and PEPC (β = 0.070). Affordability and information quality are key SEA metrics, highlighting the necessity of economic access and multilingual, real-time communication. Overall, the findings indicate that wellbeing is most strongly shaped by accessibility-oriented service experience attributes particularly affordability and information quality followed by operational assurance, while comfort-related conditions remain significant under high heat exposure during waiting and transfers. On the other hand, the research indicates that operational reliability helps mitigate environmental discomfort in hyper-arid areas. The report suggests focusing on equal prices, digital information accessibility, dependable operations, and climate-adaptive infrastructure to promote sustainable mobility and long-term public transport use. Full article

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments. Full article

The transport sector is among the largest final energy consumers and greenhouse gas (GHG) emitters in the European Union. Consequently, reducing energy-related externalities has become a central objective in the EU’s sustainability and decarbonisation policies. This study quantifies the disutility costs associated with energy consumption and emissions across major passenger transport modes—cars, buses, and trains—using a harmonised dataset encompassing 28 EU countries. To do so, a comprehensive disutility cost framework is established, integrating time losses, monetary costs, infrastructure requirements, noise, local air pollutants, and GHG emissions, and combining correlation, regression, and clustering analyses. The results indicate that car transport incurs the highest transport disutility costs, primarily due to congestion-related energy inefficiencies and GHG emissions. In contrast, rail transport demonstrates the lowest cost, energy- and emission-related disutilities across most EU countries. Bus transport represents an intermediate solution, providing lower emission intensity compared to cars but exhibiting higher energy-related disutilities than rail systems. The findings highlight that a modal shift toward rail- and bus-based transport systems can substantially reduce transport-related energy demand, emissions, and income expenses with transport cost at the EU level. While transport innovations and digitalisation may improve system efficiency, their benefits are unevenly distributed, suggesting that energy-focused transport policies should be complemented by measures to ensure inclusive access to low-emission mobility solutions. Full article

Modern urban transport systems face the critical challenge of fully integrating regional and international hubs into local mobility strategies. This article addresses the role of airports in shaping sustainable urban mobility, with a specific focus on their inclusion in Sustainable Urban Mobility Plans (SUMPs). Despite airports being major generators of passenger and freight traffic, they are often treated as isolated “transport islands” in spatial planning. The primary objective of this research is to develop and validate an original method for assessing the integration and transport accessibility of airports using the AirportSustainIndex. The methodology is based on a mathematical Weighted Sum Model (WSM), integrating twelve technical, economic, and environmental criteria, including travel times and costs for public vs. private transport, frequency of rail and bus connections, availability of electric vehicle infrastructure, and tariff integration. The analysis is supported by Geographic Information Systems (GIS) tools and OpenStreetMap data, allowing for a precise reflection of real-world network accessibility. The study covers two significant aviation hubs in Poland: Katowice Airport in Pyrzowice and Poznań-Ławica Airport. The results reveal a paradox: Katowice Airport, despite its significant distance from the agglomeration center (approx. 36 km), achieved a markedly higher sustainability index (0.554) than Poznań-Ławica Airport (0.301), which is located close to the city center (approx. 7 km). Key factors determining this outcome include the high frequency of metropolitan bus lines (“M” lines), the implementation of new rail infrastructure, and a coherent parking policy for low-emission vehicles. The article demonstrates that physical distance from the center is not the primary barrier to building sustainable mobility, provided that high intermodality and integration within the SUMP framework are ensured. The presented research tool is universal and can be applied by policymakers and urban planners to optimize airport-city connectivity, a necessary condition for achieving EU climate goals in the transport sector. Full article