Search

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the cross-regional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation.

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly., Author(s): Hongtai Yang [1]; Zishuo Guo (corresponding author) [1]; Guocong Zhai [2]; Linchuan Yang [3]; Jinghai Huo [1] 1. Introduction The outbreak of COVID-19 has seriously threatened the lives of [...]

Transit offers stop-to-stop services rather than door-to-door services. The trip from a transit hub to the final destination is often entitled as the "last-mile" trip. This study innovatively proposes a hybrid approach by combining the data mining technique and multiple attribute decision making to identify the optimal travel mode for last-mile, in which the data mining technique is applied in order to objectively determine the weights. Four last-mile travel modes, including walking, bike-sharing, community bus, and on-demand ride-sharing service, are ranked based upon three evaluation criteria: travel time, monetary cost, and environmental performance. The selection of last-mile trip modes in Chengdu, China, is taken as a typical case example, to demonstrate the application of the proposed approach. Results show that the optimal travel mode highly varies by the distance of the "last-mile" and that bike-sharing serves as the optimal travel mode if the last-mile distance is no more than 3 km, whilst the community bus becomes the optimal mode if the distance equals 4 and 5 km. It is expected that this study offers an evidence-based approach to help select the reasonable last-mile travel mode and provides insights into developing a sustainable urban transport system. [ABSTRACT FROM AUTHOR]