Search Results (225)

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility. Full article

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies. Full article

This study investigates the factors influencing urban residents’ interest in and visits to forests and explores strategies to promote forest space utilization. A survey was conducted among 5000 residents of Tokyo’s 23 wards, one of the world’s most densely populated urban areas, using an online questionnaire. The collected data were analyzed using least absolute shrinkage, selection operator (LASSO) logistic regression, and piecewise structural equation modeling (pSEM). The analysis revealed that nature experiences in current travel destinations, particularly scenic walks, had a significant positive effect on both forest interest (standardized path coefficient = 0.19) and forest visits (0.30). These experiences were also significantly influenced by childhood nature experiences and frequent local walks. Conversely, factors negatively affecting forest visits included the lack of private vehicle ownership (−0.13) and increasing age (−0.21). While previous studies suggest that older individuals tend to visit natural areas more frequently, our findings indicate the opposite trend. One possible explanation is the low car ownership rate among Tokyo residents, which may limit accessibility to forests. These findings provide valuable insights for policy design, particularly regarding strategies to enhance forest accessibility and engagement among urban populations. Full article

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning. Full article

With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value. Full article

Microtransit services that provide pooled on-demand transportation with dynamic routing have been used in low-density areas since the 1970s, but improvements to routing technology have led to a resurgence of interest in the past decade. Questions remain about the effectiveness of microtransit to serve riders in low-density, car-dependent suburban areas. Better understanding of the factors underlying microtransit ridership can improve usage of these services and shift travelers to more sustainable modes in suburban areas. We compile a database of suburban microtransit programs from 32 public transit agencies in the U.S. to study internal factors (e.g., operating hours, service area) and external factors (e.g., population density, vehicle ownership) impacting ridership using a random effects model. We find that internal agency factors have a greater effect on microtransit ridership than external factors. The most impactful factor is operating a point deviation service, where vehicles have scheduled stops at one or more checkpoints within the service area (e.g., transit center or shopping center), rather than zone-based services, where vehicles pick up and drop off passengers at any time within a service area. There is high potential to convert some zone-based services to point deviation services; 52% of zone-based service areas contain a transit center that could be used as a checkpoint. For the remaining zone-based service areas, maximizing ridership may not be feasible, and using ridership as an evaluation metric can be misleading. Instead, metrics that capture the accessibility, safety, or customer satisfaction impacts of microtransit may be more appropriate for these services. Full article

Since private cars and vans accounted for more than 25% of global oil consumption and about 10% of energy-related CO₂ emissions in 2022, increasing the share of electric vehicle (EV) ownership is considered an important solution for reducing CO₂ emissions. At the same time, reducing emissions entails certain economic losses for those countries whose exports are largely covered by the oil trade. The explosive growth of the EV segment over the past 15 years has given rise to overly optimistic forecasts for global EV penetration by 2050. One of the major obstacles to such a development scenario is the limited availability of resources, especially critical materials. This paper proposes a mathematical model to predict the global EV fleet based on the limited availability of critical materials such as lithium, one of the key elements for battery production. The proposed model has three distinctive features. First, it shows that the classical logistic function, due to the specificity of its structure, cannot correctly describe market saturation in the case of using resources with limited serves. Second, even the use of a special multiplier that describes the market saturation process taking into account the depletion (finiteness) of the used resource does not obtain satisfactory economic results because of the “high speed” depletion of this resource. Third, the analytical solution of the final model indicates the point in time at which changes in saturation rate occur. The latter situation allows us to determine the tracking of market saturation, which is more similar to the process that is actually occurring. We believe that this model can also be validated to estimate the production of wind turbines that use rare earth elements such as neodymium and dysprosium (for the production of powerful and permanent magnets for wind turbines). These results also suggest the need for oil-exporting countries to technologically diversify their economies to minimize losses in the transition to a low-carbon economy. Full article

The Sustainable Development Goals (SDGs) set forth by the United Nations aim to eradicate poverty, protect the environment, and promote global prosperity by 2030. Within this framework, Goal 11 targets explicitly sustainable cities and communities, emphasizing the need for accessible, safe, and sustainable transportation systems for all individuals, including those with disabilities. However, despite these aspirations, individuals with disabilities often face unique challenges and barriers in accessing transportation services. This study delves into the complexities of transportation accessibility for people with disabilities, aiming to understand their perceptions and expectations of service quality regarding reliability, tangibles, cleanliness, safety, comfort, personnel, and stops. In a comprehensive survey involving 302 individuals with disabilities, data were collected considering strata such as visual impairment, hearing impairment, chronic illness, and physical disability. In the study where cluster analysis was applied to examine the common and unique assessments of individuals with disabilities, both demographic characteristics and transportation habits were evaluated to determine the most effective inputs. The optimal results were obtained using disability level, car ownership, access to stops, and frequency of service use, while the inclusion of other sociodemographic variables (such as age and income) negatively affected the quality of the clustering process. By analyzing service quality independently for each cluster, the study unveils potential variations in how people with disabilities perceive and evaluate transportation services. The findings shed light on the distinct evaluation approaches employed by people with disabilities based on their characteristics, highlighting the need for tailored transportation planning and policy-making solutions. For example, in the overall assessment of individuals with disabilities, vehicle ergonomics was not highlighted as an area for improvement, but it emerged as the aspect with the least satisfaction among individuals with higher levels of disability. By addressing these nuances, policymakers and stakeholders can better understand and meet the diverse needs of people with disabilities, contributing to the creation of more inclusive and accessible transportation systems in line with the SDGs. Full article

With the continuous increase in car ownership, alleviating traffic congestion and reducing carbon emissions have become key challenges in urban traffic management. This study constructs a multi-agent model to evaluate the impact of various traffic optimization interventions on citizens’ travel behavior and traffic carbon emission levels. Different from previous mathematical models, this model integrates computer technology and geographic information systems, abstracting travelers as agents with self-control capabilities who can make independent decisions based on their own circumstances, thus reflecting individual differences in travel behavior. Using the real geographical and social environment of the high-density travel area in Xiaodian District, Taiyuan City as a case study, this research explores the overall improvement in the urban transportation system through the implementation of multiple traffic optimization interventions, such as a parking reservation system, the promotion of the park-and-ride mode, and the optimization of public transportation services. Studies have demonstrated that, compared to reducing bus fares, travelers exhibit a greater sensitivity to waiting times. Reducing bus departure intervals can increase the proportion of park-and-ride trips to 25.79%, surpassing the 19.19% increase observed with fare adjustments. A moderate increase in the proportion of reserved parking spaces can elevate the public transport load to 49.85%. The synergistic effect of a combined strategy can further boost the public transport share to 50.62%, while increasing the park-and-ride trip proportion to 33.6%, thereby highlighting the comprehensive benefits of implementing multiple strategies in tandem. When the parking reservation system is effectively implemented, carbon dioxide emissions can be reduced from over 800 kg to below 200 kg, and the proportion of vehicle cruising can decrease from over 20% to under 15%. These results underscore the critical role of the parking reservation strategy in optimizing traffic flow and advancing environmental sustainability. Full article

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations. Full article

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development. Full article

This study explores the transportation behaviors of university students residing in dormitories in Kütahya, Türkiye, emphasizing their preferred modes for educational trips. Utilizing a Multinomial Logit model, the research analyzes the influence of socio-demographic factors, trip characteristics, and environmental perceptions on mode choice. The results indicate that public transport and walking are the predominant modes, with significant negative associations being observed between car ownership and the likelihood of choosing these sustainable options. Key findings reveal that, as trip distances increase, students are more likely to use public transport, while higher income levels decrease reliance on both public transport and walking. Male students demonstrate a higher preference for these modes compared to female students. Environmental perceptions, including feelings of safety and satisfaction with infrastructure, play a critical role in shaping transportation choices, highlighting the need for improved lighting, walkability, and public transport quality. These insights have important implications for transportation policy, suggesting that reducing private vehicle reliance and enhancing public transport services can significantly promote sustainable travel behaviors. Overall, the study underscores the importance of comprehensive transportation policies that not only enhance infrastructure and service quality but also consider environmental perceptions and safety to promote sustainable travel behaviors among university students. Full article