Search Results (427)

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

The increasing demand for active and light mobility (including bicycles, e-bikes and e-scooters) has become a key driver of sustainable urban transport, calling for a renewed approach to urban planning. A central challenge is redesigning infrastructure around users’ needs, inspired by the “15-min city” concept developed by Carlos Moreno. However, the existing literature on user preferences in this domain remains fragmented, both methodologically and thematically, and often lacks integration of user behaviour analysis. This paper presents a structured review of recent international studies on factors influencing route and infrastructure choices in active and light mobility. The findings are organized into an analytical framework based on five macro-criteria: external and infrastructural factors, transport mode, user typology, experimental methodology and infrastructure attributes. The synthesis tables aim to summarize the findings to guide planners, researchers and decision-makers towards more inclusive, adaptable and effective mobility systems, through the development of user-oriented planning tools, attractiveness indexes and strategies for cycling and micromobility networks. Moreover, the review contributes to an ongoing national research initiative and lays the groundwork for developing decision-making tools, attractiveness indexes and route recommendation systems. Full article

The transition to environmentally friendly mobility inevitably requires users to use sustainable modes of transport. Rapid urbanization, along with the growing demand for efficient, inclusive, and ecological transport systems, has highlighted the urgent need for research and analysis into the acceptability and experiences of transitioning to sustainable modes of transport. This article proposes a six-step procedure to support the selection of vehicles for car-sharing fleets in cities. The analysis utilizes the Analytic Hierarchy Process method, which allows for the comparison and evaluation of five vehicle variants with different powertrains, taking into account various evaluation criteria: ecological and economic. To refine the research, criterion weights were determined based on original surveys among six car-sharing operators and eighty-seven experts in the field of decarbonization of urban transport. The results indicated that plug-in hybrid vehicles are the most advantageous option for car-sharing fleets, providing a balance between emissions, cost-effectiveness and operational flexibility. The solution obtained is in line with expectations, confirming that the proposed analytical approach is a reliable decision support tool that reduces the risk of making the wrong decision regarding the choice of powertrains. Full article

Despite the critical role of parking supply in urban transportation, the nonlinear relationship between parking facilities and commute mode choice remains poorly understood. This study systematically examines the nonlinear influences of the built environment, with a focus on parking facilities, on commuting mode choice using 2019 survey data from Xi’an. A Gradient Boosting Decision Tree (GBDT) model combined with Accumulated Local Effects (ALE) analysis was applied to capture complex relationships. The parking-related variables encompass factors such as parking fees, distance to the nearest parking lot, number of parking spaces, and parking density. Key findings indicate that car ownership, gender, land use mix-work, and distance to CBD-work, distance to CBD-home, and number of parking spaces-home at home are significant predictors. Notably, the number of parking spaces proved more influential than parking density. A positive correlation was observed between parking supply at workplaces and car usage, with a sharp increase in the probability of car ownership when supply exceeds 2800 spaces/km². Similarly, a threshold of 7500 spaces/km² around residences significantly promotes car dependence. The results underscore the importance of incorporating nonlinear parking supply effects into travel demand forecasting and provide insights for developing targeted parking management policies. Full article

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility. Full article

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

Urbanisation and the increasing concentration of populations in cities present significant challenges for achieving sustainable mobility and advancing the energy transition. Private vehicles, particularly those powered by internal combustion engines, remain the primary contributors to urban air pollution and greenhouse gas emissions. This situation has prompted the European Union to accelerate transport decarbonisation through comprehensive policy frameworks, notably the “Fit for 55” package, which aims to reduce net greenhouse gas emissions by 55% by 2030. These measures underscore the urgency of shifting towards low-emission transport modes. In this context, electric vehicles (EVs) play a key role in supporting Sustainable Development Goal 7 by promoting cleaner and more efficient transport solutions, and Sustainable Development Goal 11, aimed at creating more sustainable and liveable cities. Despite growing policy attention, the adoption of EVs remains constrained by users’ concerns regarding purchase costs, driving range, and the availability of charging infrastructure, as shown by the findings of this study. In this context, this study explores the determinants of EV adoption in Italy by employing a combined methodological approach that integrates a stated preference (SP) survey with discrete choice modelling. The analysis aims to quantify the influence of economic, technical, and infrastructural factors on users’ willingness to switch to EVs, providing insights for policymakers and industry stakeholders to design effective strategies for accelerating the transition toward the sustainable mobility. Full article

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article

The Electric Vehicle (EV) industry is developing rapidly, and EVs are becoming an increasingly important choice for the future of transportation. Therefore, accurately forecasting the electricity demand for EVs is crucial. This paper presents a hybrid deep learning model for EV charging load prediction based on Multivariate Variational Mode Decomposition (MVMD), Improved Artemisinin Optimization algorithm (IAO), and Deep Representation Learning Extreme Learning Machines (DrELMs). Firstly, MVMD decomposes the original data into several modal components. Secondly, IAO optimizes the hyperparameters of the DrELM model. Finally, the trained IAO-DrELM model predicts multiple modal components following MVMD decomposition to obtain the final predictions. Experimental results show that the proposed model outperforms eight other models, achieving the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) error values and the highest Coefficient of Determination (R²) value. Full article

The rapid urban growth and proliferation of private vehicles in Pakistan have intensified challenges, such as traffic congestion, longer travel times, environmental harm, road safety risks, and adverse public health outcomes. Despite global emphasis on sustainable modes of transportation, these options remain underutilized and receive limited policy attention in Pakistan. This study investigates the barriers hindering the adoption of active and public transport in Islamabad and Rawalpindi and evaluates the role of technological factors in influencing commuters’ willingness to use public transit. Data were collected through a structured questionnaire survey and analyzed using descriptive statistics, exploratory and confirmatory factor analyses, and structural equation modeling. The findings reveal varying commuter preferences across different modes and demonstrate a higher willingness to use active modes of travel when favorable conditions are available. The dominant barriers to active travel include long travel distances and durations, insufficient infrastructure, social stigma, and a lack of cycle storage facilities. For public transport, the major obstacles identified are overcrowding during peak hours, poor accessibility, excessive travel times, and a lack of comfort and convenience. The study also highlights the potential technological interventions, such as real-time travel planning apps, secure parking space provision, and smart ticketing systems, to improve the attractiveness and usability of public transport. Overall, the study provides valuable insights for policymakers seeking to develop evidence-based strategies that encourage the use of sustainable transport options. By addressing both infrastructural and perceptual barriers, such interventions can foster a transition towards more sustainable urban mobility systems in Pakistan. Full article

The demand for reliable Environmental Product Declarations (EPDs) of asphalt mixtures is growing, particularly as they are increasingly used in public road construction tenders across Europe. However, the reliability and comparability of EPDs remain limited due to two main challenges: (i) significant variability in dataset selection for key materials such as bitumen and aggregates, and (ii) uncertainty regarding the influence of operational factors, including aggregate moisture, mixing temperature, and transportation. The objective of this research is to assess the influence of dataset selection and operational parameters on the environmental performance of an asphalt mixture, focusing on improving the reliability of EPDs. Within this research, a Life Cycle Assessment (LCA) was conducted using a cradle-to-gate approach (A1–A3), including modules C1–C4 and D, in compliance with EN 15804:2019+A2:2020. Primary data were collected from an asphalt plant in Lithuania, while secondary data were obtained from the Ecoinvent database. The sensitivity analyses were performed to investigate the variation of data set choices and key operational factors that influence the environmental impact. The assessment was carried out using the Simapro 9.6 software and the EF 3.1 impact assessment method. The results indicate significant sensitivity to dataset selection, particularly for bitumen and dolomite production, leading to environmental impact variations of up to 41.8% and 35.3%, respectively. Among operational factors, reducing aggregate moisture from 5% to 3% by sheltering stockpiles helps achieve the highest environmental impact reduction (3.2% under the Aggregate Single Score), while lowering mixing temperatures to 130 °C resulted in a 1.6% decrease. Transportation mode selection contributed to emission variations between 1.8% and 6.7%, with long-distance aggregate transport increasing emissions by up to 14.6%. The research findings underscore the critical need for harmonizing dataset selection and optimizing operational processes to improve asphalt sustainability. Standardizing datasets is essential for ensuring fair and transparent EPD generation for asphalt mixtures, particularly when used in road construction tenders, as seen in several European countries. Future research should explore the integration of reclaimed asphalt (RA) and assess its potential environmental benefits. Full article

As an emerging technology for sustainable, safe, energy-efficient, and smooth traffic flow, autonomous public transport (APT) has been widely studied in recent years. However, the influence of conventional public transport (CPT) on behavioural intentions toward APT is largely overlooked. While APT is in its nascent phase, users’ choices may be shaped by their perceptions and attitudes toward CPT. Therefore, identifying these perceptions and examining their effect on behavioural intention is crucial. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT) is integrated with the satisfaction-loyalty model to analyze the key factors influencing behavioural intentions toward APT. To obtain more precise findings, this study examined public transport by type, including rubber-tired systems, urban rail, and bus rapid transit, rather than as a single mode, unlike many previous studies. A survey (n = 1271) was employed to validate the theoretical model among CPT users in Istanbul. The results indicate that loyalty to CPT significantly influences behavioural intention toward APT. Moreover, users of different CPT types have distinct priorities influencing their intention to use APT. While users of rubber-tired systems prioritize effort expectancy, social influence and facilitating conditions, users of urban rail systems consider social influence, trust and loyalty to CPT to be decisive factors. Furthermore, users of bus rapid transit systems consider performance expectancy, effort expectancy, trust, and loyalty to CPT as key factors influencing their behavioural intention. The findings are expected to enrich theoretical research on behavioural intention toward APT and guide future integration and transition between CPT and APT. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development. Full article