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Sociopsychological factors associated with the adoption and usage of electric micromobility. A literature review

https://doi.org/10.1016/j.cities.2022.103932

Correlations between the urban built environmental factors and the spatial distribution at the community level in the reported COVID-19 samples: A case study of Wuhan

Abstract Though metro systems are established in many Chinese cities including Nanjing, they have yet covered every corner of a city. Bikeshare as a feeder mode to metro helps solve the last mile problem. Thus, it is necessary to monitor and analyze metro-bikeshare transfer characteristics. The primary objective of this study is to derive a reproducible methodology that isolates bicycle-metro transfer trips using smart card data. Two recognition rules proposed are a maximum transfer time of 10 min and a maximum transfer distance of 300 m. To explore the general characteristics of metro-bikeshare transfer trips, transfer stations served at less than 30 transfer trips during three consecutive weeks were eliminated to ensure that a non-typical transfer pattern would not distort the results. The results show that more than 89% passengers recognized have less than 6 transfers in 3 weeks, indicating that most users integrate bikeshare with metro impromptu. Two transfer peaks on workdays are during 7:00–9:00 and 17:00–19:00, especially in suburban areas, while at weekends, transfers show quite even during 8:00–19:00. As to “Return-Enter” and “Exit-Lease” transfer modes, the “time difference” phenomenon does exist, which means that the transfer peak of “Return-Enter”mode always happens one hour earlier than that of “Exit-Lease”. Furthermore, the demographic differences in metro-bikeshare usage pattern are revealed. Finally, policy implications are involved to improve the performance of metro-bikeshare integration for all kinds of people without creating inequality.

Understanding Bikeshare Mode as a Feeder to Metro by Isolating Metro–Bicycle Transfers from Smart Card Data

The free-floating bicycle sharing system (FFBSS) service has been becoming an important and popular mode for travel in the urban mobility ecosystem. Although many studies conducted surveys to understand the usage characteristics of FFBSS service for the general population, few studies focus on the usage behavior of university students. A total of 228 valid responses from Huai'yin Institute of Technology (HYIT) were collected to examine the usage behavior of FFBSS service and identify the key service attributes driving satisfaction levels among Chinese university students. Undergraduate and graduate university students are an important and novel population, as they are still forming their values and beliefs, and therefore are more open to engaging in sustainability efforts (e.g., not owning a car or choices towards green travel modes). Descriptive analysis was performed to analyze FFBSS service usage frequency, FFBSS trip characteristics, and the motivation to use FFBSS service. The results show that the socio-demographic information (e.g., age, gender, year of study, and monthly expense) and e-bike/bike ownership significantly influence the usage frequency. Time-saving and convenience are two of the main motivations driving FFBSS service usage. Multivariate regression and importance-performance analysis (IPA) are conducted to identify important FFBSS service attributes impacting user satisfaction levels and identify priority service attributes for improvement. It highlights the importance of the density of the designated location for bike returns, the number of available bicycles, the position accuracy of bicycles, and the good functioning condition of bicycles. The findings provide useful insights into the planning and management of FFBSS services on campus and in cities.

Usage behavior and satisfaction analysis of free-floating bicycle sharing system service: evidence from a Chinese university campus

Unplanned disruptions bring challenges to urban railway system operations because of their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its importance, few studies have been reported on unplanned disruption analysis in urban railway systems or they have been limited in their analysis and modeling because of the lack of disruption data. This paper collects a complete set of unplanned disruption data for 7 years in Hong Kong and explores important factors affecting operation delays and affected areas. Quantile regression (QR) models are developed to explore the causes of operation delays under unplanned disruptions. The significant factors include the time of day, weather condition, signal control system (moving/fixed block), line types (urban/suburban), line operation direction, disruption location (underground/ground/elevated), the number of affected stations, and disruption types (e.g., tracing, locomotive and rolling stock, passengers, and operation). A binary logit model is developed to explore the variables contributing to the affected areas (single or multiple stations). The results show that the affected area is significantly influenced by the signal control system, line types, line operation direction, disruption location, terminal/departure station involved or not, transfer station involved or not, and disruption types. The findings provide useful insights into unplanned disruptions and support the development of engineering and policy countermeasures to prevent and mitigate unplanned disruption effects on operations and services.

Xin Chen

Papers

Usage behavior and satisfaction analysis of free-floating bicycle sharing system service: evidence from a Chinese university campus

Unplanned Disruption Analysis and Impact Modeling in Urban Railway Systems