Journal of Transport Geography最新文献

Human mobility can be considered an effective adaptation strategy in response to environmental risks, given that adverse environmental conditions are often temporary and in-situ adaptation may not always be feasible or desirable. Despite its importance, the concept of short-term mobility as a behavioral response to environmental hazards has been understudied in geographies of health and place and behavioral economics. For a comprehensive understanding of how everyday mobilities are used as a risk adaptation strategy, this study avails of a multiscalar approach's ability to examine the influences of air pollution and extreme cold events on origin-destination movements. The modifiable areal unit problem (MAUP) and modifiable temporal unit problem (MTUP) are analyzed. The results suggest that the scale effects of the MAUP and MTUP are evident from the variations in regression coefficients of the differences in air quality and extreme cold events between origin and destination units. It is also found that the prefecture-level units that include both urbanized city proper and rural countryside, are not an ideal spatial analysis scale. Furthermore, this study reveals that the nexus of air quality differences and short-term movements is significantly impacted by changes in temporal scales. Our results offer fresh evidence on the decision-making process underlying mobility as an adaptation strategy to air pollution and cold events.

The imbalance in Dockless Bike Sharing (DBS) systems is a major concern for planners, causing a significant drop in utilization efficiency. However, limited research quantifies DBS usage efficiency from a supply-demand perspective, also, the understanding of the nonlinear relationship between the built environment and DBS utilization efficiency from the time dimension is lacking, leading to biased assessments and the losses of flexible and effective DBS rebalancing strategies. Therefore, this study quantifies the efficiency of DBS usage from a supply-demand perspective by calculating the average usage interval of DBS facilities within urban subzones, termed duration of stopping usage (DSU), and employs emerging eXplainable Artificial Intelligence (XAI) technology to reveal the time-varying nonlinear impact of the built environment on DSU. The results show that the relative importance of transit accessibility, land use mix entropy and road network density remains stable in the time dimension. The time non-stationarity of the nonlinear relationship between these variables and DSU is primarily manifested in dynamic shifts of thresholds. Notably, the time-varying nature of the relative importance is particularly prominent for variables related to land use facilities. Moreover, the time non-stationarity of the nonlinear relationship is more complex, manifesting not only in threshold shifts but also in changes in correlation. We also propose several spatial transfer methods for DBS facilities, offering fresh insights for crafting flexible and adaptive DBS rebalancing strategies. These findings enhance the interpretability of the inconsistent impact of the built environment on DBS utilization efficiency and provide valuable knowledge for scientific management decisions regarding DBS rebalancing.

Flood disasters threaten urban sustainability, requiring community involvement and resource allocation. However, research on the link between emergency resource accessibility and residents' participation willingness in flood response is lacking. To fill this gap, we surveyed 1351 respondents in Zhengzhou and used the Community Capitals Framework to explore their nonlinear associations. Results show that: 1) Perceived accessibility of emergency resources, especially for rescue forces and medical facilities, typically has a more significant impact on residents' participation willingness in flood response compared to objectively measured accessibility, which includes communication facilities, emergency administration forces, and fire station. 2) Emergency resource accessibility, both perceived and objectively measured, displays nonlinear correlations with residents' participation willingness, with objective measures revealing more pronounced patterns such as (inverted) U-shapes or approximate linearity. 3) Spatial analysis indicates that in less affected flood areas, perceived and objective emergency resource accessibility strongly boost residents' willingness to participate, while in heavily affected areas, perceived accessibility exerts a prominent inhibitory impact. This study contributes to revealing the intricate, nonlinear link between emergency resource accessibility and residents' participation willingness in flood response, underscoring the importance of tailored flood strategies addressing both subjective perceptions and objective resource allocations for bolstering urban resilience.

Understanding how people choose routes in urban environments is essential for effective urban planning. While conventional transportation studies focus on utilitarian decision-making, this research investigates the complex interplay between human-environment interactions and emotional attachments to places, which influence transportation choices. Specifically, we examine the impact of sense of place in pedestrian route choice within a densely populated urban university community. Unlike typical urban settings characterized by clear roads and landmarks, university environments often feature intricate layouts with diffuse pathways, shared spaces, and a lack of clear spatial hierarchies. This complexity challenges conventional spatial knowledge acquisition methods. Individuals navigating such environments tend to rely on socio-sensory wayfinding strategies, developing emotional connections to specific places and routes over time. Consequently, route choices in these contexts may not always be deliberate but rather subconscious and nuanced. Our study focuses on elucidating the impact of the sense of place—a composite of conscious and subconscious perceptions, emotions, and attachments to a location—on daily route decisions. Through structural equation modeling (SEM) analysis, we demonstrate that the sense of place significantly influences route choices within community building complexes, surpassing utilitarian considerations as a primary explanatory factor. These findings underscore the importance of emotional and psychological factors in shaping urban route decisions, offering valuable insights for urban planning and management strategies.

This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.

Commuting and working, as important parts of workers' daily life, significantly influence their subjective well-being. However, the complicated interplay among short-term commute or work activity satisfaction, medium-term domain satisfaction and long-term life satisfaction remains unclear. Utilizing the survey data on transportation and well-being of Beijing's residents in 2022, this study explores two assumptions. The first, based on bottom-up theory, posits that a typical commute or work activity may influence domain satisfaction and life satisfaction. The second, drawn from top-down theory, suggests a reverse causality. The findings reveal that life satisfaction can influence domain satisfaction and short-term commute or work activity satisfaction directly. However, the effect from short-term satisfaction to life satisfaction is predominantly indirect, mediating by daily travel and job satisfaction. Compared with travel satisfaction, the relationship between work activity and overall job satisfaction is primarily unidirectional, from the short-term to the medium-term. Moreover, the impacts of commute to work activity satisfaction and of daily travel to job satisfaction are strong and significant, substantiating the spill-over effect theory. This exploration into individuals' commute and work experience offers policy implications for the government and provides insights into enhancing people's quality of life.

Food accessibility has been a subject of growing interest due to its impact on public health outcomes. This paper describes a spatial analysis method to identify gaps in geographic food access and correlate them with a variety of demographic and socioeconomic factors. The proposed food accessibility metric is the square footage of supermarkets that can be reached within 10 min travel time by walking, biking, driving, and 30 min travel time by walk/transit. The spatial analysis is conducted for the centroids of each census tract within a study area, and the approach is illustrated with an application for the state of Massachusetts. Correlations between demographic and socioeconomic explanatory variables and food accessibility are explored using the Gradient Boosted machine learning model. More specifically, the explanatory variables are percent minority population, percent of population in poverty, vehicle ownership, and population density. The spatial analysis shows a strong correlation between food accessibility and population density. The machine learning model is then used to identify gaps in food accessibility for each transportation mode while controlling for community characteristics. The residuals of the model reveal which communities have the lowest food accessibility relative to other similar communities within the state. This research provides a quantitative method to identify communities that have reduced access to food relative to state-wide trends. Lastly, it provides insights for where policy interventions would be valuable for improving food access in addition to recommendations on increasing food accessibility.

As the sharing economy expands in China, the emergence of ride-hailing services has diminished the market share of the taxi industry. As a regulated and publicly convenient service with a dedicated customer base, traditional taxi industry needs to improve its own competitiveness and maintain its market share. However, the specific circumstances under which taxis can gain a competitive edge over ride-hailing services are not well-understood. Aiming to uncover the competitive potential of taxis in the ride-source market, this study proposes a methodology to explore the Competition and Cooperation Relationship (CCR) between taxis and ride-hailing services on a multidimensional spatio-temporal scale. By taking Beijing, China as a case study, we first compare different impacts of Points of Interest (POI) on the traffic volume of taxis/ride-hailing services through Geographically and Temporally Weighted Regression (GTWR) models, and explore the corresponding times and locations where taxis/ride-hailing services are more likely to attract passengers. Based on the findings that there are strong correlations between traffic volume and spatio-temporal conditions, we establish the Competition-Cooperation Index (CCI) as a quantitative measure to characterize the CCR and then analyze the spatio-temporal distribution of CCI to identify the times and locations where taxis hold advantages in cooperative or competitive relationship relative to ride-hailing services. Furthermore, we investigate the underlying reasons for these patterns, discovering that CCI has a close connection with land use. The results of our analysis show that taxis exhibit competitive advantages over ride-hailing services under some specific circumstances and can further enhance their competitiveness by proposed targeted measures. The findings of this study provide valuable insights for both industries in formulating growth strategies and for governmental agencies in setting policies.

The increase in the share of motorization in short-distance trips is a significant contributor to the decline in the share of active travel (AT) and will further pose a challenge to sustainable transport. While many studies have explored the relationship between the built environment (BE) and AT, few have focused on short trips. Additionally, most studies have ignored the important role of the streetscape. To address these gaps, this study utilizes street view big data to quantify street view elements and applies extreme gradient boosting decision trees (XGBoost) to 2020 household travel data in Wuhan. The results indicate that streetscape attributes are more important than land use in predicting short-distance AT, with streetscape being more than 40% relative importance in both models. The contribution of almost all streetscape elements cannot be ignored. Among them, the percentage of driveways showed the highest predictive power. Among land use attributes, population density has the highest relative importance. In addition, most of the independent variables are nonlinearly correlated with the explanatory variables, and this study quantified these association thresholds. These results suggest that optimizing the street built environment has the potential to promote a shift from short-distance driving to AT. The quantification of correlation thresholds provides precise empirical evidence for built environment interventions that promote short-distance AT.

Due to its flexibility, pro-environmental characteristics, and its contribution to solving the first or last kilometer’ problem, sharing bike has become a popular travel option for many residents, especially for short trips. This study analyses the impact of extreme temperatures on residents' shared travel and the bike-sharing industry by combining the full-sample travel data, meteorological data, and POI data in Hohhot, China. Based on the temporal and spatial characteristics of residents' travel, five patterns are distinguished by K-means clustering: basic trips, short-distance trips, round trips, time tolerance trips and distance tolerance trips. The main conclusions of the research are as follows: i) extremely low temperatures can increase the possibility of short-distance trips and round trips but reduce long-duration or long-distance travel. Extremely high temperature has a positive effect on residents' round trips and time tolerance trips but suppress short-distance trips and distance tolerance trips; ii) for long-duration or long-distance shared travel, extremely low temperature enhance the connection between sharing bikes and other modes of transportation, but the extremely high temperature has the opposite impact; iii) extreme temperature causes less substitution of sharing bikes for other vehicles in round trips. However, residents in basic trip mode prefer to use sharing bikes instead of vehicles at extremely high temperatures; iv) the total annual economic loss of shared travel caused by extreme temperatures reached 31,393.82 CNY. In addition, many residents abandon sharing bikes in favor of cars at extreme temperatures, leading to additional carbon emissions of 2585.29 kg/year. The study also provides profound policy implications for the bike-sharing industry and green travel.