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Author

Aoyong Li

Other affiliations: Chinese Academy of Sciences
Bio: Aoyong Li is an academic researcher from ETH Zurich. The author has contributed to research in topics: Transport engineering & Medicine. The author has an hindex of 7, co-authored 16 publications receiving 146 citations. Previous affiliations of Aoyong Li include Chinese Academy of Sciences.

Papers
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Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors employed the PageRank algorithm to evaluate the importance of cities on the migration network and divide the cities into five grades, and then the hierarchical structure of the migrant network is illustrated and analyzed.

74 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the changes in micromobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland, and evaluated and compared from the perspective of space, time and semantics.

55 citations

Journal ArticleDOI
TL;DR: In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization and the coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area.

45 citations

Journal ArticleDOI
TL;DR: An innovative trip-level inference approach is proposed for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling, and the relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined.
Abstract: Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.

38 citations

Journal ArticleDOI
TL;DR: A distinctive framework for assessing the environmental influences ofDLBS in high resolution based on DLBS transaction data is put forward and the empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DL BS at the trip level.

36 citations


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Journal ArticleDOI
01 Mar 2020
TL;DR: The development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GISShould be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.
Abstract: The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

384 citations

Journal ArticleDOI
TL;DR: In this paper, the influence of the COVID-19 pandemic on road users' perceptions, needs, and use of sustainable travel modes (i.e., public transport, walking, and cycling) was investigated.
Abstract: The COVID-19 pandemic has resulted in unprecedented measures changing travel habits in many countries. Many users have started to prefer traveling by private cars, which is against the sustainability policies of the European cities. The necessity of gaining a deeper understanding of road users’ travel habit changes, their feelings on public transport use, and their perceptions of using sustainable urban mobility modes has emerged for future transport planning. Considering these facts, the study in this paper aimed to investigate the influence of the COVID-19 pandemic on road users’ perceptions, needs, and use of sustainable travel modes (i.e., public transport, walking, and cycling). An online survey was carried out during the period from March to May 2020 in the case study area, Sicily of Southern Italy. Regarding the population of the case study, the survey was representative, with 431 individuals. The survey included variables, namely gender, age, city of residence, private car ownership, walking and cycling frequency before and during the pandemic, public transport use frequency for leisure activities before and during the pandemic, need for remote working, and the stress and anxiety perception of using public transport during the pandemic. The analysis started with descriptive statistics and it was followed by correlation analysis in order to explore the characteristics of the dataset and relationship between variables. It was found that these were not statistically significantly correlated at a 95% confidence level. An ordinal regression model was applied for determining the predictions. The results suggested that women were less likely to walk during the pandemic than men. Participants were more likely to resume remote work even after the second phase in order to reduce their daily travel needs and keep their isolation. Participants have expressed a positive opinion on the use of micromobility during pandemic situations. These results can be considered as a basis for sustainable urban planning and a guide for decision-makers who aim to encourage the use of public transport, walking, cycling, and micromobility.

159 citations

Journal Article
TL;DR: In this paper, the authors explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users and find that 93% potential predictability for user mobility across the whole user base.
Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

118 citations

Journal ArticleDOI
TL;DR: It is suggested that the bikeshare system provides resilience to the overall transportation system during disasters when public transit is considered dangerous or is disrupted, and that the subway ridership remains substantially below pre-COVID levels.

95 citations

Journal ArticleDOI
01 Jun 2021
TL;DR: In this paper, travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures were investigated.
Abstract: In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances.

71 citations