Journal of Transport Geography最新文献

In the context of diverse urban building density, creating pedestrian-friendly cities is crucial for sustainable development. However, previous studies have revealed potential variations in the influence of building density on walking and in the associations between built environment factors and walking across different building densities. The reasons behind these variations have not been thoroughly investigated. Pedestrian volume on the street is one of the main indicators of walking. Therefore, this study applied the RF + PDP model to explore the nonlinear relationship between building density and pedestrian volume and the interaction effect of building density on the relationship between built environment factors and pedestrian volume. Empirical analysis conducted in Chengdu City revealed the following: (1) Building density influenced pedestrian volume in a nonlinear manner, and the pedestrian volume reached the peak when the building density was at 0.3. (2) There existed interaction effects of building densities and built environment factors on pedestrian volume. (3) The impacts of mesoscale built environment factors (such as distance to transit) and microscale built environment factors (including vegetation index, road index, and sidewalk index) on pedestrian volume were strongly modulated by building density. These findings have important implications for developing targeted planning policies aimed at creating pedestrian-friendly cities.

Encouraging travel to higher-order centers is important for enhancing the quality of life of rural residents and promoting rural development. Although the literature shows that the built environment influences travel behavior, few studies have explored the relationship between the built environment and the frequency of travel to higher-order centers, particularly in rural contexts. Even less attention has been given to the synergetic effects that occur across built environment elements and county heterogeneity. Using data from 659 respondents in three Chinese counties collected between 2019 and 2020, we applied multilevel ordered logistic models to explore the associations between the rural built environment and travel frequency to higher-order centers (including townships, counties, and city centers). The results show that rural residents' travel frequency to higher-order centers decreases with increasing distance from such centers. The relationships between built environment elements and travel frequency to higher-order centers differ according to the level of such centers. Moreover, built environment elements exert synergetic impacts on travel frequency to higher-order centers. The association of the built environment with travel frequency to higher-order centers is moderated by the developmental levels of counties. These findings suggest that interventions in the rural built environment are important for ensuring the enhancement of rural resident travel behavior and access to better services, which, in turn, benefits rural revitalization.

While established studies have explored interventions in the built environment (BE) and transportation sector to mitigate travel carbon emissions (TCE), planners still struggle to determine the most effective units of intervention, identify key variables, and determine their optimal values. This study addresses the gap by employing the extreme gradient boosting (XGBoost) model to create a multi-scale comparative framework. This study revealed that the relationship between the built environment and travel-related carbon emissions varies depending on the zoning and scale of the BE measurement unit. The explanatory power of TCE varies across different geographic units, with the 15-min walk distance buffer of residents being the most effective in explaining TCE. Most variables were nonlinearly associated with TCE, and the precise threshold of the association between BE attributes and TCE was quantified. Based on these findings, we provide precise and nuanced insights into BE interventions to reduce TCE.

Transportation continues to be a significant contributor to CO₂ emissions and may potentially be the final sector to reach its carbon peak in the future. Identifying the drivers of transportation CO₂ emissions (TCE) and understanding their changing patterns is crucial to effectively control TCE. However, previous studies can only obtain fixed parameter values of TCE influencing factors throughout the study period, or although they can obtain the impacts of specific factors on TCE with accompanying changes over the years, they cannot conveniently clarify the changing patterns. Therefore, the key contribution of this study resides in providing a spatially explicit understanding of the heterogeneous primary drivers of TCE across countries, and in uncovering the temporal dynamics of these primary drivers' influences on TCE. The results show that at the country-group level (considering 18 selected countries as a group, collectively representing over 60% of global TCE), the drivers of TCE were GDP, energy intensity, and population in order of contribution. However, for developed countries, GDP and energy intensity contributed less to TCE than for developing countries. In addition, the influence of energy intensity on TCE declined faster than that of GDP, suggesting that decoupling TCE from economic growth should always be the top priority regardless of a country's development level. Policy-wise, for countries where GDP is the primary driver of TCE, measures to reduce transportation activities include industrial upgrading, coordinated planning, and accessibility promotion. For countries where energy intensity is the primary driver of TCE, measures to improve transportation efficiency consist of technology adoption, regulation/pricing, and habit improvement.

Electric mobility is critical to reducing emissions from transport and dependency on Internal Combustion Engine vehicles. This study attempts to model the suitability of the built environment for electric vehicle (EV) adoption in urban areas based on sociodemographics and access to driveways for installing charging infrastructure. A novel approach using geospatial techniques is adopted to detect driveways from multispectral remote sensing information. A region in Dublin, Ireland, has been chosen as the study area. The region is further categorised based on the feasibility of EV adoption using hierarchical cluster analysis. Initial results highlight the disparity in access to low-emission modes to those not dependent on cars. Results from zero-inflated count models at the neighbourhood level reiterate the impact of driveways and sociodemographic factors on EV adoption. The proposed methodology can help evaluate infrastructure availability for widespread EV transition and inform strategic planning. The driveway detection framework may be adapted to other regions while accounting for geographic characteristics.

The COVID-19 pandemic has significantly influenced travel choices and the effective functioning of public transport. However, research into the pandemic's effects on public transport, specifically considering the combined impact of both risk perception and prevention tactics, remains limited. This study aims to examine the effect of COVID-19 on passengers' reliance on public transport, considering their risk perception and the strategies implemented for pandemic prevention. Data for this research were gathered through a questionnaire survey conducted in Chengdu, China, in March 2022, during a major outbreak of the pandemic in the city. Employing the Theory of Planned Behavior, the study establishes a structural equation model to analyze the questionnaire data and unveil the COVID-19's impact on passengers' reliance on public transport. The analysis shows that people's perception of infection risk has a significant impact on travel preference, and they pay attention to the convenience of public transport as well as safety. Based on the analysis, relevant suggestions are proposed from the perspectives of passengers, operators, and the government to improve the safety and efficiency of public transport.

As car ownership and urbanization continue to rise worldwide, traffic crashes have become growing concerns globally. Measuring crash risk provides insight into understanding crash patterns, which can eventually support proactive transport planning and improve road safety. However, traditional spatial analysis methods for crash risk assessment, such as the hotspot detection method, are mainly focused on identifying areas with higher crash frequency. These methods are subject to critical issues in risk analysis due to ignoring crash impacts and background traffic volume information. Aside from the two issues, current crash risk assessment methods, especially those aiming for cluster detection, are subject to the modified temporal unit problem, referring to the temporal effects (i.e., aggregation, segmentation, and boundary) in cluster detection. To alleviate these issues, this paper applies an emerging hot spot detection method, called the prospective space-time scan statistic (STSS) method, for assessing the crash risk at a refined network scale and over multiple years in a case study of Hartford, Connecticut. By identifying the spatial and temporal clusters of the crash risk, the study can provide evidence for tailoring road safety management strategies in neighborhoods characterized by high crash risk.

Current methods of cost-benefit analysis (CBA) for transport investments rely on travel-time savings for potential users. This approach presents a consistent and significant historical trend of forecast inaccuracy, and thus has been questioned and criticized. Access, or the ease of reaching valued destinations, can be used as an alternative. Access features a strong correlation with land value which can be measured through hedonic analysis, and subsequently, access gains offered by a transport initiative can be monetised via property uplift. We test this hypothesis and evaluate Sydney's South West Metro Link (SWML).

We first develop linear and semi-log ordinary least square hedonic pricing models for house sales in the Sydney region. The models are set up with structural and neighbourhood attributes in addition to access measures, and result in a statistically significant fit.

Next, we model changes to job access induced by the SWML. Benefits are then quantified by land value uplift and are estimated at $1.87 Billion in 2031 and between $1.53–$3.08 Billion in 2061, which reflect base and transit-oriented development (TOD) scenarios. The project is thus feasible should certain land use and economic conditions be met, including TOD to occur about station localities by 2061 and if project costs are minimal. Although limitations are noted, access-based CBA exhibits significant promise as an alternative approach to appraisal and has direct application in value capture strategy.

Shared micromobility in the U.S. has rebound after the decline caused by the COVID-19 pandemic, with a substantial increase in the adoption of shared e-bikes nationwide. However, research on hybrid e-bike sharing, which combines station-based and dockless systems, is limited. This study addresses this gap by comparing spatial determinants of hybrid e-bike and dockless e-scooter sharing link flows in 32,965 street segments in Portland, Oregon during 2022, using gradient boosting decision tree (GBDT) models. Distance to the city center emerges as the most important determinant for both modes, with closer proximity to the city center associated with higher link flows. Factors such as the presence and types of bike facilities, the availability of streetlights and street trees, and job density also significantly influence e-bike and e-scooter link flows. A notable difference between the two modes is that e-scooter trips are more sensitive to distance to the city center than e-bike trips. Furthermore, bike facilities have a greater impact on e-bike link flows, whereas job density is more influential in determining e-scooter link flows. These findings offer strategies for policymakers and urban planners to promote and manage shared micromobility and optimize the built environment. These strategies include enforcing higher device availability requirements in underprivileged neighborhoods, transitioning e-scooter sharing systems into a hybrid model, expanding the off-street bike trial network and bikeway network, and augmenting the coverage of streetlights and street trees along the bikeway network.

Improving public transport accessibility (PTA) has been considered as an effective measure for promoting sustainable urban development. Based on the grid-level data in Nanjing, China, this paper explores the spatially heterogeneous effects of PTA on road traffic CO₂ emissions using a geographically weighted random forest (GWRF) model. A simulation-based counterfactual analysis framework is further proposed to predict the intervention effects of improving PTA. Two kinds of practical interventions, adding facilities and increasing service frequency, are considered in our counterfactual prediction. The effects of improving PTA across different areas are predicted and compared. Our results indicate that the GWRF model with a properly tuned bandwidth outperforms the conventional random forest model. The results of counterfactual analysis show that improving PTA could achieve greater environmental benefits in suburb areas. With the process of urbanization in Nanjing, the population and economy has grown rapidly in suburb areas. Therefore, it is reasonable to improve public transport services in these areas. Based on our findings, PTA has potential to make a significant contribution to sustainable development of urban transportation. Moreover, our findings with respect to heterogeneous effects likely improve the efficiency of local transport policies that target such areas, helping achieve greater environmental benefits.