Campus transportation is representative of citywide transportation problems, making sustainable mobility solutions more important considering rising environmental concerns and traffic congestion. This paper investigates the mobility patterns of the population of the University of Tabuk (UT) commuting to the main campus using survey data and explores the opportunities to adopt new sustainable mobility alternatives from the commuters and experts’ points of view. Five transportation mode alternatives are investigated and assessed through a direct survey among a considerable number of UT population. The opinions of 9 experts and decision makers are studied by applying Fuzzy Analytic Hierarchy Process (FAHP) adopting five criteria: Environment, Safety, Economy, Time and Social perception. The experts selected “Safety” as the most important criterion for the selection of a sustainable mode of transport, followed by Economy and Environment. The analysis indicates that more than 86% of UT members commute with private cars. The trip duration is between 10 minutes and 20 minutes for 70% of UT population. For almost all the alternatives, male single students with higher trip duration are the most interested profile in sustainable transportation options. Based on the weights of criteria, FAHP results show the alternative bus from the residence to the university as the best sustainable alternative, followed by the park-and-ride intercampus bus, which was the second-highest alternative in the campus population survey. The findings can provide a basis for developing transportation strategies for UT aimed at alleviating traffic issues and congestion in the surrounding area and enhancing environmental conditions on campus and its vicinity. Keywords: Fuzzy hierarchy decision-making, Population survey, Sustainable transportation, Campus commuters, Mobility pattern.

The impact of ride-hailing regulations on traffic congestion: Evidence from China

ABSTRACT Traffic congestion is significantly affected by the built environment. Existing studies predominantly examine this through correlation analysis, overlooking causal mechanisms. This omission leads to unreliable feature selection in policy models and hinders evidence-based interventions. To address this, this study proposes a three-stage causal framework that rigorously assesses built environment impacts. The first stage identifies statistically significant correlations using multivariable least squares regression. The second stage applies five causal inference models – Granger causality, structural equation model, causal forest, causal impact, and convergent cross mapping – to uncover causality. The third stage assesses how the identified causal factors shape congestion patterns in perpetually congested roadways (PCRs). Applied to New York City (NYC), the United States, the results reveal 19 correlated and 11 causal impacts. Our key findings include: (1) Transit accessibility is the most robust causal factor, while built environment diversity exhibits time-dependent variability; (2) traffic light design demonstrates bidirectional causality with congestion; (3) PCRs exhibit four distinct spatiotemporal patterns, with bridge-related congestion having the most consistent impact. These results yielded policy recommendations for NYC transportation planning: (i) improve the first-and-last-mile connectivity through micro-mobility; (ii) deploy artificial intelligence-driven adaptive traffic signals; (iii) expand the capacity of critical bridge corridors near PCRs.

This study conducts a comprehensive assessment of the public transportation system in the city of Aksaray and proposes improvement strategies to enhance system efficiency and address urban mobility challenges. Increasing population, expanding settlement patterns, and peak-hour demand pressure have led to the inadequacy of the existing transportation infrastructure in meeting passenger needs. Insufficient vehicle capacity on certain routes, outdated infrastructure, traffic congestion, and operational inefficiencies negatively affect the overall performance of the public transport network. The findings highlight the necessity of optimizing route capacity, strengthening traffic management systems, integrating environmentally friendly technologies such as electric buses, and improving passenger comfort and safety. Additionally, the study emphasizes the importance of modernizing the urban transport infrastructure and implementing smart mobility solutions to ensure a sustainable public transportation system. The results provide policy recommendations aimed at increasing operational efficiency, reducing environmental impacts, and enhancing user satisfaction.

Traffic congestion and delayed emergency response remain major challenges in urban transportation due to the limitations of conventional fixed-time traffic signal systems. These systems operate with predefined signal duration and failed to adapt to real-time traffic density or provide automatic priority to emergency vehicles. This paper presents “Green Lane – Smart Signal with Emergency Priority”, an intelligent traffic signal system that dynamically controls signal timing based on real-time vehicle density while enabling priority passage for emergency vehicles. The proposed system integrates camera-based vehicle detection using Python and Open CV with an ESP32 microcontroller for adaptive traffic signal control. Emergency vehicle detection triggers an immediate signal override to create a dedicated green lane, ensuring faster clearance. Experimental results obtain from a prototype implementation show reduced waiting time improve traffic flow efficiency and quicker emergency vehicle movement compared to traditional fixed time systems. The system is low-cost, scalable, and suitable for smart city traffic management applications.

The driver reaction time (DRT) of road drivers not only significantly affects driving stability and performance but also causes traffic congestion and accidents, so its detection is important for traffic safety. In this paper, the effect of driver characteristics and habits (i.e., education, job status, gender, age, experience, health status, and driver habits) on DRT is examined through tests performed in a driving simulator on a randomly selected sample group (49 males and 21 females). Before the tests, 14 different sub-sample groups were created by identifying the characteristics and habits of the drivers based on their own statements through a survey conducted with the sample group. From the DRTs determined for each subsample group, the average DRT and deviation of male and female drivers were calculated separately. The results show that there are significant variations in DRT according to driver characteristics and habits. The DRT of female drivers is approximately 22% higher than that of male drivers. The DRT increases as the driver's age increases. The effect of the driver experience on the DRT is unclear, but the DRT decreases with daily vehicle usage time when considered as an indicator of experience. It was also determined that the DRT becomes shorter as drivers’ educational attainment increases. The DRT is varied by the job status of the drivers; while the unemployed have the highest DRT, public sector employees and students have a significantly lower DRT. In addition, health conditions such as chronic illness, the use of glasses, and fatigue cause a notable increase in DRT. Lifestyle factors such as smoking (negative) and sports activities (positive) affect DRT. While habits such as using mobile phones and CD players/TV while driving negatively affect DRT, seat belt use positively affects DRT.

ABSTRACT Bengaluru, India's Silicon Valley, faces severe mobility challenges due to rapid urbanization, population growth, and increasing reliance on private vehicles, resulting in traffic congestion, long commute times, and deteriorating air quality that undermine the quality of life. Transit-Oriented Development (TOD) offers a sustainable approach by integrating land use and transportation planning to reduce car dependency, promote compact development, and enhance accessibility. This Study examines the potential of TOD along the North–South Hebbal-Silk Board corridor, a critical axis with high transit demand and persistent congestion. The research uses secondary data from government reports, transportation studies, and site analyses to evaluate existing infrastructure, identify bottlenecks, and benchmark successful TOD strategies from global cities. Findings indicate that TOD implementation in Bengaluru can reduce travel times, increase public transit ridership, and improve non-motorized transport infrastructure while addressing environmental concerns through lower emissions and reduced sprawl. However, challenges remain in ensuring affordability, inclusivity, and effective inter-agency coordination. The Study concludes that TOD can serve as a transformative strategy for Bengaluru if supported by institutional integration, equity-oriented planning, and systematic monitoring. It further recommends scaling its application across other urban corridors in India to promote sustainable and inclusive urban growth.

This paper establishes a dynamic network loading model that considers the impact of lane changing on traffic flow from a dynamic system optimal perspective. To account for lane-changing effects, a link-based double queue model is utilized and extended into the lane-changing double queue model by incorporating the concept of lane-changing intensity. Moreover, a dynamic system optimum problem using the proposed model is demonstrated to examine the influence of lane-changing behaviour. To address the nonlinear component resulting from lane-changing intensity, a solving approach combining a heuristic genetic algorithm and the linear programming solver is proposed. The effectiveness of the proposed model and solution procedure is evaluated using both a simple network with two intersections and a mid-sized network in numerical experiments. The results provide empirical evidence of the model's ability to capture both lane-changing effects and possible spillbacks phenomenon. Moreover, the model reveals the relationship between lane-changing manoeuvres and traffic congestion.

This study addresses optimizing the fruit and vegetable logistics chain at the Port of Valparaíso, Chile, a key hub for exports in the Southern Hemisphere. Through an integrated approach, it combines logistics platforms (in Limache and Quillota), blockchain technology, and a Capacitated Facility Location Problem (CFLP) mathematical model. These tools help mitigate traffic congestion, high logistics costs, and limited traceability, achieving up to a 25% savings in operating expenses, a 30% reduction in CO₂ emissions, and a 50% decrease in waiting times. Integrating blockchain ensures immutable records, improving supply chain trust and the quality of exported products. This proposal, aligned with Industry 5.0 principles, promotes economic resilience and sustainability, positioning the Port of Valparaíso as an international benchmark for logistics innovation. The framework can be replicated in other ports, contributing to more efficient and sustainable supply chains and reducing urban congestion. Finally, the paper discusses social and political risks associated with infrastructure development, compares MILP with other optimization methods (heuristics or metaheuristics), and expands on the model’s potential application to different ports, including dry ports or those with limited capacity.

ABSTRACT Urban traffic congestion generates complex ripple effects (i.e. the progressive spread of congestion from a localized point to a broader network), yet existing models lack the capacity to quantitatively capture these spatial dynamics due to the oversimplified representations of traffic behavior, failing to model the continuity and heterogeneity of congestion propagation. This study proposes a network-constrained field-based framework that integrates field theory with a path flow model for fine-grained, heterogeneity-aware quantification of congestion’s ripple effects. First, we develop a traffic field model that captures the continuous influence of congestion across all related road segments. Second, path flow is incorporated to quantify driving subjectivity and propagation heterogeneity. Third, a flow-assisted discretization method is introduced to enable the joint modeling of human mobility patterns and spatiotemporal traffic dynamics, making the model computationally tractable and adaptable to real-world data. Our model achieves 0.88–0.89 overlap accuracy compared to the benchmark model, demonstrating its strong performance in identifying affected road segments while reflecting an 11–12% difference attributed to heterogeneity consideration. Additionally, our model captures a more comprehensive range of congestion effects, identifying 3.64–4.74 more high-order affected segments that the benchmark model may overlook. Results reveal that the stability of congestion characteristics at the macro-level overshadows the variations caused by daily life-driven crowd movement. Turn-level analysis further uncovers directional asymmetries and distinct patterns for vehicles entering and exiting congested segments. This framework provides a practicable tool for quantifying congestion impacts, supporting precise analysis of propagation dynamics and data-driven traffic management strategies.

The development of AI technology has brought about advancement and innovation in transportation, leading to various changes in the traffic environment. Among these changes, there are autonomous vehicles, a hallmark of the Fourth Industrial Revolution, expected to reduce traffic accidents and related casualties, alleviate traffic congestion, and enhance mobility support for transportation-vulnerable groups. The realization and expansion of autonomous vehicles have become an imminent reality rather than a promising blueprint. However, for the realization and expansion of autonomous driving to become truly effective, a well-established legal framework governing traffic is essential - just as it was in the past and continues to be today. Law and regulation have always been necessary for any society, and examining the establishment and amendment of laws over time allows us to trace the evolution of legal systems and understand their changing dynamics. In particular, when designing legislation to address new paradigms and environmental changes, it is crucial to consider the past, present, and future trajectories of the law. Korea’s current traffic laws can be traced back to the Automobile Control Regulations enacted during the Japanese colonial period. Traffic legislation is intimately connected to everyday life and must reflect similarities in living environments and cultural contexts. The Automobile Control Regulations encompassed the elements of the Road Traffic Act, the Automobile Management Act, and the Transport Business Act being currently executed. These contemporary laws can be regarded as having evolved from the original framework of the colonial-era regulations. While the priorities of the laws and contexts of different eras may vary, structural and conceptual similarities between them persist inevitably. The essence of law lies in regulating daily life to ensure that society works safely and smoothly. This principle is especially pertinent to road traffic legislation. From this perspective, it provides valuable insight into the historical trajectory and direction of Korea’s traffic legislation to examine the Automobile Control Regulations of the Japanese colonial period. In the era of autonomous vehicles, ensuring traffic safety must remain the foundation of all legal considerations. Although the Japanese colonial period represents a painful chapter in Korean history, we should move forward from just dwelling on its suffering. Instead, we should identify and meaningfully reinterpret the useful elements from that period to reconstruct them in a way that contributes positively to the future. This approach allows us to honor history while fostering constructive development in the legal framework of autonomous driving.

Modern transportation systems are severely hampered by urban traffic congestion, which causes delays and fuel consumption. Proactive control techniques and intelligent traffic management depend on accurate congestion prediction. In order to predict congestion across urban edge networks, current study presents a deep learning-based framework that combines an attention mechanism with a bidirectional Long Short-Term Memory (LSTM) network with custom learnable attention layer, flowed by focal loss for addressing the data imbalance. A numerous dataset was generated by using SUMO, therefore over 2 million sequence was generated, including 12 spatiotemporal features that were extracted from the dataset. A large scale map was used and the prediction was based on edge level. The model can efficiently learn temporal dependencies and spatial patterns thanks to our preprocessing pipeline, which consists of temporal windowing, edge ID encoding, and cyclical time transformations. Trained with a 30-step sliding window, the model achieved low error metrics (MAE: 0.0744, RMSE: 0.2728), an F1-score of 0.90, and a classification accuracy of 92.56%. Our architecture performs better at detecting congestion events than recent state-of-the-art models. Thus the potential for scalable implementation in urban traffic forecasting systems of deep spatiotemporal learning models trained on realistic but synthetic simulation data.

This article examines whether, and under what conditions, there is room for new mosques in Belgian cities by analyzing how media controversies around mosque projects are assembled. We study a corpus of press articles (2014–2024) using a two-step approach: First, keyword mapping identifies dominant discursive patterns across six themes (mobility, legality, size and visibility, social cohesion and integration, security and extremism, financing). Second, argument coding links lexical signals to public modes of judgment through actor–network theory (ANT) and controversy registers. Applied to five case studies across Flanders, Wallonia, and the Brussels-Capital Region, this framework offers comparative depth. The results show that identity and security controversies frequently outweigh strict urban planning controversies; neutral planning criteria (e.g., traffic congestion, permit compliance) are often recoded as symbolic markers of alterity. Regional contrasts provide nuance to this pattern: in Flanders, politicization through security/identity is salient; in Wallonia, debates emphasize size, form, and spatial integration; in Brussels-Capital, technico-legal compliance intertwines with aesthetic visibility. Media operate as boundary objects that hierarchize registers and amplify controversies. We conclude that mosques are treated less as ordinary urban infrastructure than as contested symbols of belonging and visibility. Moving toward negotiated pluralism requires institutional mechanisms that ensure transparency, equal treatment, local anchoring, and symbolic requalification.

The article presents a method for assessing the traffic demand on city streets and roads based on open data using a gravity model. The relevance of the research topic is due to the limited availability of reliable and comprehensive data on actual traffic congestion in Russian cities. The aim of the study is to develop an approach that can serve as an al-ternative or supplement to assessing and forecasting road congestion in conditions of limited data. The initial data used are the parameters of urban neighborhoods – population, density and diversity of services, as well as land use types – which allow calculating indicators of demand and attractiveness for travel between neighborhoods. Based on this data, a correspondence matrix is formed, reflecting the relative volume of traffic flows between nodes of the street and road network, presented in the form of a weighted directed graph. The method allows assessing the demand and potential congestion of city roads without using closed or expensive road traffic data, which is especially relevant when designing new sections of the street and road network and planning urban infrastructure. An experimental test was carried out on the example of Vasilyevsky Island in St. Petersburg using open data from OpenStreetMap. The proposed approach can be useful for improving the efficiency of urban planning and transport management.

The increasing prevalence of electric vehicles (EVs) in urban logistics presents challenges such as route planning, energy constraints, and demand management. EVs’ limited range, charging requirements, and sensitivity to traffic conditions necessitate advanced optimization strategies. Fleet management systems are thus evolving into intelligent, modular platforms that not only plan delivery tasks but also interact with real-time data and respond to dynamic disruptions. Among these, traffic congestion remains a critical factor that can severely affect route reliability and lead to time window violations. In this study, a modular fleet management system architecture is proposed, capable of real-time monitoring, dynamic rerouting, and traffic-aware decision-making. The system introduces a standardized data structure called the Routing Markup Language (RML), which formalizes the communication between components and supports various route outputs including simulation and vehicle-level execution. Adaptive Large Neighborhood Search (ALNS) is applied for route planning using real-world order data from a water distribution company operating in the Büyükdere district of Eskişehir. The system also features a dynamic reassignment mechanism that responds to vehicle failure scenarios, ensuring continued operation with minimal disruption. Traffic scenarios are evaluated through the Simulation of Urban Mobility (SUMO) environment to assess route robustness under varying conditions. The proposed approach integrates routing optimization, dynamic disruption handling, and simulation-supported fleet monitoring into a cohesive system, offering a responsive and data-driven solution for sustainable urban logistics.

Background: Ride-sharing services have emerged as an alternative mode of urban transportation in Kathmandu Valley, aiming to address issues such as traffic congestion and limited public transport options. However, user satisfaction with these services remains uncertain due to varying service quality and operational challenges. Purpose: The purpose of the study is to assess the user's satisfaction with the ride-sharing service in Kathmandu Valley. Specifically, the study assesses the current status of ride-sharing services in the Kathmandu Valley to examine the impact of service quality on user satisfaction with ride-sharing services in the Kathmandu Valley, to identify the various challenges faced by ride-sharing services, and to propose potential solutions to address these challenges. Design/methodology/approach: This study adopted an explanatory research design. Expectation confirmation theory (ECT) is used for the study because SERVQUAL is based on the expectancy-disconfirmation paradigm, which states that service quality is defined as the degree to which consumers' pre-consumption expectations of quality are confirmed or contradicted by their actual perception of the service experience. The primary data for this study were collected from 417 respondents, using a non-probability convenience sampling method. Structured questions were administered through the KOBO toolbox to gather the necessary information. The collected data were then analyzed using descriptive and inferential statistics in MS Excel and SmartPLS 4.0. Findings: Tangibility, reliability, responsiveness, and empathy directly influence users' satisfaction in using ride-sharing services in the Kathmandu Valley; however, assurance had no direct influence on users' satisfaction. In the context of using ride-sharing services, Nepali customers are not yet accustomed to using ride-sharing services compared to developed countries. Besides, the major challenges faced by ride-sharing service users include long wait times, safety concerns, unavailability of rides, unfriendly or unprofessional driver behavior, pricing issues, vehicle cleanliness, payment options, and difficulty using the app. The major solutions to the challenges are short wait times, enhanced safety measures, sufficient ride availability, friendly or professional driver behavior, better pricing models, improved vehicle maintenance, more driver training programs, easy-to-use apps, and more payment options. Conclusion: This study concludes that tangibility, reliability, responsiveness, and empathy have a positive and significant relationship with users' satisfaction, whereas assurance is insignificantly correlated with user satisfaction. Keywords: Ride-sharing Service, Users' Satisfaction, SERVQUAL Dimension, Service Quality, Kathmandu Valley

In Intelligent Heterogeneous Vehicular Ad Hoc Networks (IH-VANETs), long urban roads with a high density of vehicles and a maximum number of road signals increase unpredictable delays in terms of long travel times and heavy traffic congestion. These unpredictable delays are exacerbated by the rapid increase in vehicle density and irregular traffic flow on roads with high traffic signals. To address this gap, an optimized fitness-based enhanced ant colony optimization (OF-EACO) for IH-VANETs is proposed. OF-EACO aims to find optimal, uncongested short roads with low vehicle density and fewer traffic signals, thereby providing shorter travel times for vehicles without traffic congestion and unpredictable delays. To achieve this goal, the novel road fitness function of the proposed OF-EACO assigns a high road fitness score to roads according to short length, low vehicle density, and low signal count to support quick travel of vehicles between two ends without delay and traffic congestion. OF-EACO's roulette wheel takes the road fitness scores of available roads as input and outputs the optimal road. The optimal road is rich in all aspects and is intended to reduce travel time through short and un crowded roads. A network simulator is used to simulate the proposed OF-EACO, existing vehicular multi-hop routing algorithm with intelligent transportation system (VMR-ITS), and improved distance-based ant colony optimization routing (IDBACOR).Simulation results of the proposed OF-EACO indicated that, due to the use of optimal roads, it was able to achieve significant improvements in terms of vehicle travel cost, road establishment time, convergence speed, road traffic congestion overhead, routing overhead, Computational overhead, Computational Complexity, Actual Wall Time Analysis, and Energy Consumption compared to VMR-ITS and IDBACOR.

Real-time travel time estimation is essential for intelligent transportation systems (ITS), yet operational traffic data streams are often incomplete due to sensor failures, communication delays, and limited coverage. This paper investigates the effectiveness of interpolation techniques for reconstructing temporally continuous travel-time profiles from real-time speed and density observations. Two approaches—linear interpolation and spline interpolation—are implemented and evaluated across varying traffic regimes (normal flow, dense traffic, and extreme congestion). Model performance is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) against reference travel-time measurements. The results show that interpolation-based methods consistently outperform a conventional baseline relying on average observed speeds, improving estimation accuracy by up to approximately 15%. Linear interpolation yields competitive performance under stable conditions, while spline interpolation achieves lower MAE and RMSE under congestion, indicating stronger robustness to nonlinear traffic dynamics. Additionally, interpolation improves service availability and estimated time of arrival (ETA) reliability with minimal computational overhead, supporting practical deployment in resource-constrained environments. These findings suggest that interpolation provides a lightweight and effective enhancement for real-time travel time estimation and can serve as a reliable preprocessing layer for advanced predictive models in future work.

As urban traffic congestion becomes increasingly severe, supply chain transportation faces the dual challenges of declining efficiency and mounting environmental burdens. To address this, this study constructs a Green Vehicle Routing Problem (GVRP) framework that integrates real-time traffic information. First, K-means clustering technology is employed to classify urban road networks by congestion levels, establishing a three-tier classification system of low, medium, and high congestion zones. Subsequently, polynomial regression methods are utilized to establish a quantitative relationship model between vehicle speed and carbon emission intensity. Based on this theoretical foundation, a multi-objective optimization framework is designed that comprehensively considers environmental costs and traffic impedance factors, with performance comparison tests conducted between genetic algorithms and classical shortest path algorithms. Experimental results demonstrate that genetic algorithms perform excellently when handling high-congestion road segments, significantly reducing carbon footprint while shortening transportation cycles. This research provides scientific basis and operational paradigms for enterprises to construct sustainable supply chain networks based on dynamic traffic information.

One of the key challenges in the development of modern cities is maintaining the functional stability of the transport system under conditions of increasing travel demand. The long-standing dominance of the car-oriented model of urban planning has led to a significant growth in the number of private vehicles, which, in turn, results in the overloading of the street and road network, the formation of traffic congestion, and a reduction in travel speed. Consequently, travel times increase, road safety deteriorates, and both noise pollution and the concentration of harmful emissions in the atmosphere rise. In addition, a substantial portion of urban space is allocated to car-oriented infrastructure, limiting opportunities for the development of public, recreational, and pedestrian areas, and thereby reducing the overall quality of the urban environment. In light of the challenges posed by car-centered urban development, there is a need to reconsider urban transport policy in favor of sustainability-oriented models. One of the key directions of this transformation is strengthening the role of public transport, which, provided high service quality, can ensure population mobility and accessibility without the need for private car use. The development of competitive public transport contributes to reducing traffic loads on the network, mitigating negative environmental impacts, and promoting the rational use of urban space - all of which are essential prerequisites for establishing a balanced urban transport system. A justified transition toward such a system requires the application of a multi-criteria approach to the formation of the urban transport system, which accounts for the interests of all stakeholders. These include private vehicle users (costs associated with car ownership and operation), transport operators (operational expenditures), and public transport users (travel time and comfort characteristics), as well as society as a whole through the environmental impacts of transport activities. Considering these aspects makes it possible to determine rational parameters for the operation of the urban transport system and to ensure its sustainable and balanced development. The study proposes a multi-criteria approach to the formation of a sustainable urban transport system with a focus on the priority development of route-based public transport. The developed approach comprehensively considers economic, social, and environmental efficiency criteria of transport subsystems, harmonizing the interests of private car users, public transport operators, and passengers while minimizing the negative impact on the environment.