Search

Urban rail transit has been commonly regarded as an efficient transportation mode for alleviating the negative impacts of rapid urbanization. However, the complex and comprehensive built environment caused by rapid urbanization prevents policymakers and planners from understanding the performance of urban rail transit, particularly around transit stations. Developing station area typologies plays an important role in describing the characteristics of station areas. Numerous studies have developed typologies of station areas over the past decades; however, few have attempted to estimate the distribution of station types per station. To fill this gap, this paper first proposed the importance of a land use type (ILUT) index based on the term frequency-inverse document frequency method. This method avoids mutual concealment among land use types, to extract key built environment features based on built area datasets of the buffer areas of 337 selected urban rail stations in Beijing. Subsequently, the ILUT index was used to estimate the distribution of the station types per station using the latent Dirichlet allocation model. The 12 identified types of urban rail station areas in Beijing demonstrated that land use types consist of station types and presented the distribution of the station types with their probabilities, revealing the current performance of station areas. This paper proposes a description approach that will help policymakers and planners to effectively and efficiently identify and analyze built environment features. • Probability-based typology approach is proposed to describe built environments. • Index for extracting relatively important built environment features is developed. • Twelve types of station areas are identified using built area datasets in Beijing. • Distribution of 12 station types per station is estimated and visualized. [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]

• Trips by dockless bike-sharing show the highest travel satisfaction. • The longer assess time is related to lower satisfaction with dockless bike-sharing. • Travelers have higher satisfaction with dockless bike-sharing as the primary mode. • Pro-public transit attitude relates to higher satisfaction of dockless bike-sharing. • Residential road network has no effect on satisfaction with dockless bike-sharinga. Using survey data collected among residents in Beijing, this paper presents an investigation of the difference in travel satisfaction between dockless bike-sharing and other travel modes. The effects of individual, spatial and trip attributes on travel satisfaction with dockless bike-sharing are also identified. Our analysis adds to the empirical support for higher satisfaction with trips by dockless bike-sharing than trips by private bicycles. The assess time for shared bicycles is negatively associated with travel satisfaction with dockless bike-sharing. In addition, travelers have higher satisfaction with dockless bike-sharing when used as the primary mode than as the first-mile/last-mile solution. Travel-related attitudes tend to play a more significant role in travel satisfaction with dockless bike-sharing than residential built environment. Travelers with a preference for bicycles and public transport and those who value the health or environmental influence of travel tend to evaluate dockless bike-sharing travel as more satisfying. [ABSTRACT FROM AUTHOR]

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services. [ABSTRACT FROM AUTHOR]