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Yu R, He Y, Li H, Li S, Jian B. RiskFormer: Exploring the temporal associations between multi-type aberrant driving events and crash occurrence. ACCIDENT; ANALYSIS AND PREVENTION 2024; 206:107698. [PMID: 38964139 DOI: 10.1016/j.aap.2024.107698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/16/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024]
Abstract
With the development of driving behavior monitoring technologies, commercial transportation enterprises have leveraged aberrant driving event detection results for evaluating crash risk and triggering proactive interventions. The state-of-the-art applications were established based upon instant associations between events and crash occurrence, which assumed crash risk surged with aberrant events. Consequently, the generated crash risk monitoring results merely contain discrete abrupt changes, failing to depict the time-varying trend of crash risk and posing challenges for interventions. Given the multiple types of aberrant events and their various temporal combinations, the key to depict crash risk time-varying trend is the analysis of multi-type events' temporal coupling influence. Existing studies employed event frequency to model combined influence, lacking the capability to differentiate the temporal sequential characteristics of events. Hence, there is an urgent need to further explore multi-type events' temporal coupling influence on crash risk. In this study, the temporal associations between multi-type aberrant driving events and crash occurrence are explored. Specifically, a contrastive learning method, fusing prior domain knowledge and empirical data, was proposed to analyze the single event temporal influence on crash risk. After that, a novel Crash Risk Evaluation Transformer (RiskFormer) was developed. In the RiskFormer, a unified encoding method for different events, as well as a self-attention mechanism, were established to learn multi-type events' temporal coupling influence. Empirical data from online ride-hailing services were employed, and the modeling results unveiled three distinct time-varying patterns of crash risk, including decay, increasing, and increasing-decay pattern. Additionally, RiskFormer exhibited remarkable crash risk evaluation performance, demonstrating a 12.8% improvement in the Area Under Curve (AUC) score compared to the conventional instant-association-based model. Furthermore, the practical utility of RiskFormer was illustrated through a crash risk monitoring sample case. Finally, applications of the proposed methods and their further investigations have been discussed.
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Affiliation(s)
- Rongjie Yu
- College of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804, Shanghai, China.
| | - Yang He
- Didi Chuxing, Xixigu International Business Center, No.80 Zixia Street, Xihu District, Hangzhou, China.
| | - Hao Li
- Didi Chuxing, Xixigu International Business Center, No.80 Zixia Street, Xihu District, Hangzhou, China.
| | - Shoubo Li
- Didi Chuxing, Xixigu International Business Center, No.80 Zixia Street, Xihu District, Hangzhou, China.
| | - Bowen Jian
- College of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804, Shanghai, China.
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2
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Tian H, Cai H, Hu L, Qiang Y, Zhou B, Yang M, Lin B. Unveiling community adaptations to extreme heat events using mobile phone location data. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 366:121665. [PMID: 39032252 DOI: 10.1016/j.jenvman.2024.121665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/15/2024] [Accepted: 06/30/2024] [Indexed: 07/23/2024]
Abstract
The escalating frequency, duration, and intensity of extreme heat events have posed a significant threat to human society in recent decades. Understanding the dynamic patterns of human mobility under extreme heat will contribute to accurately assessing the risk of extreme heat exposure. This study leverages an emerging geospatial data source, anonymous cell phone location data, to investigate how people in different communities adapt travel behaviors responding to extreme heat events. Taking the Greater Houston Metropolitan Area as an example, we develop two indices, the Mobility Disruption Index (MDI) and the Activity Time Shift Index (ATSI), to quantify diurnal mobility changes and activity time shift patterns at the city and intra-urban scales. The results reveal that human mobility decreases significantly in the daytime of extreme heat events in Houston while the proportion of activity after 8 p.m. is increased, accompanied with a delay in travel time in the evening. Moreover, these mobility-decreasing and activity-delaying effects exhibited substantial spatial heterogeneity across census block groups. Causality analysis using the Geographical Convergent Cross Mapping (GCCM) model combined with correlation analyses indicates that people in areas with a high proportion of minorities and poverty are less able to adopt heat adaptation strategies to avoid the risk of heat exposure. These findings highlight the fact that besides the physical aspect of environmental justice on heat exposure, the inequity lies in the population's capacity and knowledge to adapt to extreme heat. This research is the first of the kind that quantifies multi-level mobility for extreme heat responses, and sheds light on a new facade to plan and implement heat mitigations and adaptation strategies beyond the traditional approaches.
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Affiliation(s)
- Hao Tian
- Department of Geography, Texas A&M University, College Station, TX 77840, USA
| | - Heng Cai
- Department of Geography, Texas A&M University, College Station, TX 77840, USA.
| | - Leiqiu Hu
- Department of Atmospheric Science, University of Alabama in Huntsville, AL 35899, USA
| | - Yi Qiang
- School of Geosciences, University of South Florida, Tampa, FL 33620, USA
| | - Bing Zhou
- Department of Geography, Texas A&M University, College Station, TX 77840, USA
| | - Mingzheng Yang
- Department of Geography, Texas A&M University, College Station, TX 77840, USA
| | - Binbin Lin
- Department of Geography, Texas A&M University, College Station, TX 77840, USA
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3
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He Z, Hu Y, Duan LL, Michailidis G. Returners and explorers dichotomy in the face of natural hazards. Sci Rep 2024; 14:13184. [PMID: 38851774 PMCID: PMC11162431 DOI: 10.1038/s41598-024-64087-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/05/2024] [Indexed: 06/10/2024] Open
Abstract
Understanding human mobility patterns amid natural hazards is crucial for enhancing urban emergency responses and rescue operations. Existing research on human mobility has delineated two primary types of individuals: returners, who exhibit a tendency to frequent a limited number of locations, and explorers, characterized by a more diverse range of movement across various places. Yet, whether this mobility dichotomy endures in the context of natural hazards remains underexplored. This study addresses this gap by examining anonymized high-resolution mobile phone location data from Lee County, Florida residents, aiming to unravel the dynamics of these distinct mobility groups throughout different phases of Hurricane Ian. The results indicate that returners and explorers maintained their distinct mobility characteristics even during the hurricane, showing increased separability. Before the hurricane, returners favored shorter trips, while explorers embarked on longer journeys, a trend that continued during the hurricane. However, the hurricane heightened people's inclination to explore, leading to a notable increase in longer-distance travel for both groups, likely influenced by evacuation considerations. Spatially, both groups exhibited an uptick in trips towards the southern regions, away from the hurricane's path, particularly converging on major destinations such as Miami, Fort Lauderdale, Naples, and West Palm Beach during the hurricane.
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Affiliation(s)
- Zeyu He
- Department of Geography, University of Florida, Gainesville, FL, 32611, USA
| | - Yujie Hu
- Department of Geography, University of Florida, Gainesville, FL, 32611, USA.
| | - Leo L Duan
- Department of Statistics, University of Florida, Gainesville, FL, 32611, USA
| | - George Michailidis
- Department of Statistics and Data Science, University of California Los Angeles, Los Angeles, CA, 90095, USA
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Tang J, Zhao P, Gong Z, Zhao H, Huang F, Li J, Chen Z, Yu L, Chen J. Resilience patterns of human mobility in response to extreme urban floods. Natl Sci Rev 2023; 10:nwad097. [PMID: 37389148 PMCID: PMC10306362 DOI: 10.1093/nsr/nwad097] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 07/01/2023] Open
Abstract
Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented '720' Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, 'reverse bathtub', 'ever-increasing' and 'ever-decreasing' patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people's gender or age. In view of the common association between travel behaviors and travelers' socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.
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Affiliation(s)
- Junqing Tang
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
- Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | | | - Zhaoya Gong
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
- Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Hongbo Zhao
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization, Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China
| | - Fengjue Huang
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jiaying Li
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Zhihe Chen
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Ling Yu
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jun Chen
- Key National Geomatics Center of China, Beijing 100830, China
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Zhang Y, Cheng S, Li Z, Jiang W. Human mobility patterns are associated with experienced partisan segregation in US metropolitan areas. Sci Rep 2023; 13:9768. [PMID: 37328538 PMCID: PMC10276023 DOI: 10.1038/s41598-023-36946-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 06/13/2023] [Indexed: 06/18/2023] Open
Abstract
Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways: place-level partisan segregation based on the partisan composition of its daily visitors and community-level experienced partisan segregation based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation.
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Affiliation(s)
- Yongjun Zhang
- Department of Sociology and Institute for Advanced Computational Science, Stony Brook University, Stony Brook, USA.
| | - Siwei Cheng
- Department of Sociology, New York University, New York, NY, USA
| | - Zhi Li
- Department of Sociology, New York University, New York, NY, USA
- Center for Applied Social and Economic Research, NYU Shanghai, Shanghai, China
| | - Wenhao Jiang
- Department of Sociology, New York University, New York, NY, USA
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Wu M, Li C, Shen Z, He S, Tang L, Zheng J, Fang Y, Li K, Cheng Y, Shi Z, Sheng G, Liu Y, Zhu J, Ye X, Chen J, Chen W, Li L, Sun Y, Chen J. Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data. COMMUNICATIONS PHYSICS 2022; 5:270. [PMID: 36373056 PMCID: PMC9638278 DOI: 10.1038/s42005-022-01045-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.
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Affiliation(s)
- Mincheng Wu
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Chao Li
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Zhangchong Shen
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Shibo He
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Lingling Tang
- Shulan (Hangzhou) Hospital Affiliated to Shulan International Medical College, Zhejiang Shuren University, Hangzhou, 310015 China
| | - Jie Zheng
- Zhejiang Institute of Medical-care Information Technology, Hangzhou, 311100 China
| | - Yi Fang
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Kehan Li
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Yanggang Cheng
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Zhiguo Shi
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Guoping Sheng
- Shulan (Hangzhou) Hospital Affiliated to Shulan International Medical College, Zhejiang Shuren University, Hangzhou, 310015 China
| | - Yu Liu
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Jinxing Zhu
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Xinjiang Ye
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Jinlai Chen
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Wenrong Chen
- Westlake Institute for Data Intelligence, Hangzhou, 310012 China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Hangzhou, 310027 China
| | - Youxian Sun
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
| | - Jiming Chen
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 China
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 China
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7
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Du J, Ye X, Newman G, Retchless D. Network Science-based Urban Forecast Dashboard. ARIC 2022 : PROCEEDINGS OF THE 5TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND INTELLIGENT CITIES (ARIC 2022) : 1ST NOV 2022, SEATTLE, WA, USA. ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND IN... 2022; 2022:7-10. [PMID: 38098514 PMCID: PMC10719900 DOI: 10.1145/3557916.3567822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
The urban environment is a highly dynamic and complex system. Urban dynamics in this complex system is largely reflected by the movement of people to and from Places of Interest (POIs) in the urban area. To better understand and plan for the city's various scenarios, there is a need to forecast urban dynamic conditions in terms of the possible movements of people across POIs. However, such predictions are not easy because an interdependent and living system is hard to forecast. In addition, the commuting and shopping of individuals in urban environments will show distinct patterns at various stages of disasters as compared to normal situations. This paper presents a network science-based urban forecast dashboard, in order to monitor urban events and identify the interdependencies that characterize urban dynamics. Behind the dashboard is a deep learning model that incorporates the network dynamics between POIs. The dashboard powers the prediction of urban dynamics from a network science perspective. This research calls for a unified framework to model the flow and network in the city. The dashboard visualizes how network science and urban science can mutually benefit from each other.
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Affiliation(s)
- Jiaxin Du
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - Galen Newman
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77843, USA
| | - David Retchless
- Department of Marine and Coastal Environmental Science Texas A&M University at Galveston, Galveston, Texas
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