A Demand-Centric Repositioning Strategy for Bike-Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station's capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

Effect of the COVID-19 pandemic on bike-sharing demand and hire time: Evidence from Santander Cycles in London.

The COVID-19 pandemic has been influencing travel behaviour in many urban areas around the world since the beginning of 2020. As a consequence, bike-sharing schemes have been affected-partly due to the change in travel demand and behaviour as well as a shift from public transit. This study estimates the varying effect of the COVID-19 pandemic on the London bike-sharing system (Santander Cycles) over the period March-December 2020. We employed a Bayesian second-order random walk time-series model to account for temporal correlation in the data. We compared the observed number of cycle hires and hire time with their respective counterfactuals (what would have been if the pandemic had not happened) to estimate the magnitude of the change caused by the pandemic. The results indicated that following a reduction in cycle hires in March and April 2020, the demand rebounded from May 2020, remaining in the expected range of what would have been if the pandemic had not occurred. This could indicate the resiliency of Santander Cycles. With respect to hire time, an important increase occurred in April, May, and June 2020, indicating that bikes were hired for longer trips, perhaps partly due to a shift from public transit.

Quantifying the impact of COVID-19 on e-bike safety in China via multi-output and clustering-based regression models.

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries.

Effect of COVID-19 on epidemiological characteristics of road traffic injuries in Suzhou: a retrospective study.

BACKGROUND: To present the new trends in epidemiology of road traffic injuries (RTIs) during the Coronavirus disease 2019 (COVID-19) pandemic in Suzhou. METHODS: Pre-hospital records of RTIs from January to May in 2020 and the same period in 2019 were obtained from the database of Suzhou pre-hospital emergency center, Jiangsu, China. Data were extracted for analysis, including demographic characteristics, pre-hospital vital signs, transport, shock index, consciousness, pre-hospital death. A retrospective study comparing epidemiological characteristics of RTIs in Suzhou during the 5-month period in 2020 to the parallel period in 2019 was performed. RESULTS: A total of 7288 RTIs in 2020 and 8869 in 2019 met inclusion criteria. The overall volume of RTIs has statistical difference between the 2 years (p < 0.001), with fewer RTIs in 2020 compared with 2019. Electric bicycle related RTIs increased during the pandemic (2641, 36.24% vs 2380, 26.84%, p < 0.001), with a higher incidence of RTIs with disorder of consciousness (DOC) (7.22% vs 6.13%, p = 0.006). CONCLUSIONS: Under the impact of COVID-19, the total number of RTIs in Suzhou from January to May 2020 decreased. This observation was coupled with a rise in electric bicycle related injuries and an increase in the incidence of RTIs with DOC.

Provisional COVID-19 infrastructure induces large, rapid increases in cycling.

The bicycle is a low-cost means of transport linked to low risk of transmission of infectious disease. During the COVID-19 crisis, governments have therefore incentivized cycling by provisionally redistributing street space. We evaluate the impact of this new bicycle infrastructure on cycling traffic using a generalized difference in differences design. We scrape daily bicycle counts from 736 bicycle counters in 106 European cities. We combine these with data on announced and completed pop-up bike lane road work projects. Within 4 mo, an average of 11.5 km of provisional pop-up bike lanes have been built per city and the policy has increased cycling between 11 and 48% on average. We calculate that the new infrastructure will generate between $1 and $7 billion in health benefits per year if cycling habits are sticky.

Social Distancing and Outdoor Physical Activity During the COVID-19 Outbreak in South Korea: Implications for Physical Distancing Strategies.

The novel coronavirus disease (COVID-19) outbreak has put the entire world in a pandemic situation. In response, strict screening, quarantine protocols, and contact tracing have been conducted in South Korea. The purpose of this study was to examine effects of social distancing on the Public Bicycle Sharing System (PBSS) during the COVID-19 outbreak. We used the PBSS public dataset of Seoul, South Korea. Difference-in-differences (DID) analysis was used. In the DID approach, the 2 groups are distinguished based on designated year. Cases of PBSS use were observed in 2 time periods: pre- and post-strict social distancing in Seoul, Korea. Average PBSS usage per day doubled during 2019-2020 (30 697 vs 77 996, P < .001). Commuters and weekend users increased during the social distancing period in 2020 compared with the same period in 2019. DID analysis showed statistically significant positive effects of high levels of social distancing on PBSS usage, commuters, weekend users, and new subscribers. In conclusion, social distancing during the COVID-19 outbreak increased outdoor physical activity. Meaningful outdoor physical activity during the COVID-19 pandemic can be safe from infection and psychologically stabilized as long as keeping meticulous physical distancing, such as hand hygiene, wearing facial masks, and surface cleaning of public resources.