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Since the outbreak of COVID-19, it has rapidly evolved into a sudden and major public health emergency globally. With the variants of COVID-19, the difficulty of pandemic control continues to increase, which has brought significant costs to the society. The existing pandemic control zoning method ignores the impact on residents'lives. In this study, we propose a refined and low-cost pandemic control method by scientifically delineating zoning areas. First, a spatial interaction network is built up based on the multimodal transport travel data in Nanjing, China, and an improved Leiden community detection method based on the gravity model is used to obtain a preliminary zoning scheme. Then, we use spatial constraints to correct the results with the discrete spatial distribution. Finally, reasonable zones for pandemic control are obtained. The modularity of the algorithm results is 0.4185, proving that the proposed method is suitable for pandemic control zoning. The proposed method is also demonstrated to be able to minimize traffic flows between pandemic control areas and only 24.8% of travel connections are cut off, thus reducing the impact of pandemic control on residents'daily life and reducing the cost of pandemic control. The findings can help to inform sustainable strategies and suggestions for the pandemic control., Comment: 20pages 7080words

As a rapidly expanding service, bike sharing is facing severe problems of bike over-supply and demand fluctuation in many Chinese cities. This study develops a large-scale method to determine the minimum fleet size under uncertainty, based on the bike sharing data of millions of trips in Nanjing. It is found that the algorithm of minimizing fleet size under the incomplete-information scenario is effective in handling future uncertainty. For a dockless bike sharing system, supplying 14.5% of the original fleet could meet 96.8% of trip demands. Meanwhile, the results suggest that providing a integrated service platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the COVID-19 pandemic, this study proposes a social distancing policy that maintains a suitable usage interval. These findings provide useful insights for improving the resource efficiency and operational service of bike sharing and shared mobility., Comment: 31 pages,10 figures

The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations between different demand time series due to general demand flow patterns across the network. It is expectable that cross-modal ripple effects become more prevalent, with Mobility as a Service. Therefore, by propagating demand data across modes, a better demand prediction could be obtained. To this end, this study explores various machine learning models and transfer learning strategies for cross-modal demand prediction. The trip data of bike-share, metro, and taxi are processed as the station-level passenger flows, and then the proposed prediction method is tested in the large-scale case studies of Nanjing and Chicago. The results suggest that prediction models with transfer learning perform better than unimodal prediction models. Furthermore, stacked Long Short-Term Memory model performs particularly well in cross-modal demand prediction. These results verify our combined method's forecasting improvement over existing benchmarks and demonstrate the good transferability for cross-modal demand prediction in multiple cities., Comment: 27 pages, 4 figures

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 will be 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. 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 perspective 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. The results show that 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 are more dependent on bike sharing systems. Besides, the trip decrease reduces user contact and increases idle bikes. And a new concept of user distancing is proposed to avoid transmission risk and activate idle bikes. This study evaluates the role of shared micro-mobility during the COVID-19 pandemic, and also inspires the blocking of viral transmission within the city., Comment: 30 pages, 7 figures, 6 tables