Introduction
Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike-sharing users are exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike-sharing.
Methods
Based on the trip data of bike-sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspectives of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike-sharing during the pandemic.
Results
Pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults may be more dependent on bike-sharing systems. The declined trips reduce user contacts and transmission risk. Central urban areas have more user close contacts and higher transmission risk than suburban areas. Besides, a new concept of
user distancing
is proposed to decrease transmission risk and the number of idle bikes.
Conclusions
This paper is the first research focusing on both user demand and transmission risk of bike-sharing during the COVID-19 pandemic. This study evaluates the mobility role of bike-sharing during the COVID-19 pandemic, and also provides insights into curbing the viral transmission within the city.
In high‐frequency transit, providing real‐time crowding information (RTCI) is a potential way to promote passenger satisfaction and reduce negative crowding externalities, by assisting passengers in choosing less crowded vehicles. To make RTCI convincing and reliable, it is necessary to provide predictive RTCI, in which bus passenger load (BPL) prediction is the primary problem. This paper proposes a novel two‐stage BPL prediction method using automatic passenger counting (APC) data. The first stage is to predict short‐term passenger flows at stops based on an adaptive Kalman filter approach. Using the outputs from the first stage as well as other variables directly from APC data, the second stage is to predict BPL based on a support vector regression algorithm. Several methods from the existing literature are used as benchmarks to test the relative performance of the proposed method. An empirical study on bus line 1 in Suzhou, China shows that the proposed method outperforms all the benchmarks, and shows significant superiority over other methods for stops with sharp increases in BPL and for multi‐step ahead prediction. This study contributes to the limited literature on BPL prediction and lays the foundation for providing accurate and reliable predictive RTCI in the future.
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