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Due to its flexibility and door-to-door service, taxis are an integral part of the urban transportation system. They have become an essential solution to the first/last mile problem. Even though much research has been conducted on the effects of built environment variables on taxi passengers' travel behaviors, few have accounted for the spatial heterogeneity embedded in multiscale spatial processes. This study applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the gaps. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial non-stationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. Specifically, increased residential density induces more taxi demand in areas with less access to public transportation than their surrounding units. Increasing bus coverage where bus coverage is relatively low may attract more commuters to adopt taxi plus bus mode for commuting. Road network density has a more substantial effect on taxi ridership in the south end of the city than in the north. The former is characterized by lower road density. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation and urban governance. [ABSTRACT FROM AUTHOR]

Transit-oriented development (TOD) is an effective mode for sustainable land use in megacities. Owing to the diverse geographical, socioeconomic, and cultural landscapes prevailing in distinct nations, the manifestations of TOD can significantly differ, potentially leading to outcomes that do not belong to TOD. In the Chinese context, the implementation of TOD is not synchronized with rapid urbanization. Consequently, several subway stations have been constructed in well-developed areas to meet the large volume of traffic demand, namely development-oriented transit (DOT). Therefore, we propose a novel method to better capture land use patterns around subway stations on a high-resolution spatial scale. The contributions of this study are threefold. First, we enrich the theory pertaining to TOD by incorporating the concepts of sustainable development and the interrelationship between land use and transport. Second, we develop a method utilizing the density gradient of land use and a self-organizing map (SOM) to quantitatively identify TOD and non-TOD. Third, we discuss the development policies for specific types of TOD stations and modes other than TOD. We conduct an empirical study in Beijing using point of interest (POI) data. The results show that 44.9 % of the subway stations exhibit TOD characteristics. Depending on the situations of their respective development, they are categorized into distinct types and only 18 stations (approximately 5 %) have successfully employed TOD. Notably, some subway stations in suburban areas show the significance of residential suburbanization and mixed-use commercial development in TOD implementation. However, approximately half of the subway stations are non-TOD stations. A diversified, flexible, and context-specific planning approach is important in urban development. • We enrich the theory pertaining to TOD with the interrelationship between land use and transport. • We develop a method utilizing the density gradient of land use and a self-organizing map to quantitatively identify TOD. • We conduct an empirical analysis in Beijing and find that 44.9 % of the subway stations exhibit TOD characteristics. [ABSTRACT FROM AUTHOR]

In the era of big data, multiple data sources have been employed in the study of land use and transportation for urban and regional planning purposes. This paper offers an example of how multiple data sources (e.g., mobile signalling data, taxi trips and transit trips from smartcard data) can be used to estimate the spatial pattern of commute mode preference in Beijing, China. The comparative analysis investigates the spatial pattern of commute mode preference by taxi at a fine resolution in Beijing. This work indicates how the preference for taxis can be seen in the north-east of the inner city, but not around employment centres. Equally, a complementary relationship is found between a preference for taxis and public transit that provides useful insights into modal choice at an intra-urban scale. These findings are useful in urban planning and transport management. [ABSTRACT FROM AUTHOR]

In urban transportation systems, taxis are regarded as flexible, convenient, and time-saving. Taxi demand is affected by various built-environment factors and by the time of the day. Although many studies have investigated correlations between taxi demand and the built environment, the direct and spillover effects of built environment factors on taxi demand have not been examined at a fine spatial scale. To address this gap in the literature, this paper employs spatial econometric models using GPS-tracked taxi trips, mobile signaling data, and points of interest (POIs) to study taxi demand in Beijing at a 1-kilometer square grid resolution. The results show that, in the morning and evening peak hours, road network density has the strongest (positive) direct and indirect impact on taxi ridership. A relationship is also found between public transportation and taxi ridership: bus coverage has positive direct effects and insignificant indirect effects on taxi pick-ups and drop-offs, while subway coverage has negative indirect effects, suggesting that it may absorb taxi demand from surrounding grids. Results also indicate that various built-environment factors affect taxi demand differently at morning and evening peak times. This study reveals the complex nature of taxi ridership and has important implications for policymakers, transport planners, and other stakeholders in megacities around the world. • Use spatial autoregressive models to investigate the relation between built environment, socioeconomics and taxi ridership. • Conduct the analysis for morning and evening peak hours. • Built environment characteristics and socioeconomics influence taxi ridership via both local and spillover effects. • Offer a framework to incorporate various types of big data, including taxi data, mobile signaling data, points of interest. [ABSTRACT FROM AUTHOR]

Hospital accessibility is a vital factor in identifying areas with a shortage of healthcare. Due to a lack of data, however, few measures of hospital accessibility in existing studies have considered individual preferences for certain hospitals. To address this issue, we propose the concept of "observed hospital accessibility" (OHA) incorporating trips to the hospital by taxi in Beijing. A comparative analysis was conducted between OHA and the potential hospital accessibility (PHA) method adopted in previous studies. The results suggest that PHA overestimates the accessibility of hospitals in the periphery of the city center where there is more traffic congestion, but underestimates accessibility in the suburbs, as suburban healthcare needs can be met by nearby hospitals. In summary, the OHA method offers a novel approach to employing trip records in the investigation of hospital access behavior when the availability of patient register data is limited. It also provides insights for other activity-based travel behavior studies. • This study first proposed the concept of observed hospital accessibility (OHA) and conducted an enhanced 2SFCA method with taxi trip data for evaluation. • OHA method offers a novel approach to employ trip records for researching out-patient treatment activities. • OHA spatially fitted a polycentric structure, whereas potential hospital accessibility (PHA) showed a monocentric structure with the case study of Beijing. • The paper provides insights for other activity-based travel behavior study. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]