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The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents' commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers' commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples' jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents' travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments. [ABSTRACT FROM AUTHOR]

Concerns over traffic congestion and environmental pollution have prompted more studies into the impacts of built environment on commuters' mode choice behavior. This paper aims to make a comprehensive analysis of commuters' mode choice behavior with the 5th Household Travel Survey in Beijing. A mixed nested logit model was employed to jointly explore the impacts of mode attributes and built environment variables on commuters' mode choice. The results show that the proposed mixed nested logit model outperforms the contrastive models, i.e., standard multinomial logit model and nested logit model. Estimated results indicate that non-motorized mode commuters pay more attention to OD distance. Land-use mix and density have significant influence on reducing car preference, and only show significant effect in working zone. Furthermore, public modes including bus and subway are regarded as one group that the convenient of either one of them changes will influence commuters' preference on the whole public transportation system. [ABSTRACT FROM AUTHOR]

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]

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]

Travel mode and distance choices are not independent decisions, and individual choices are usually made in the knowledge of the preference and needs of other household members. To represent the dependency between mode choice and travel distance and the role of intra-household interactions in travel behavior, we develop a joint discrete–continuous model with intra-household interactions. In this model, we use joint household tour as the unit of analysis and characterize the dependency between mode choice and travel distance using flexible copula functions. Then, we apply the proposed model to analyze tour-based mode choice and travel distance in Beijing, China. The results indicate that unobserved factors contribute to positive dependency between mode choice and travel distance. The choice of walk is more dependent on travel distance than other travel modes, and the choice of travel modes except public transit shows a higher correlation with travel distance in complex individual tours than in simple individual and joint household tours. Further, a comparison between the proposed model, the independent model, and the model without intra-household interactions reveals that ignoring the dependency between mode choice and travel distance, or not considering intra-household interactions, could lead to over- or under-estimation of the effects of changes in exogenous variables. [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]