101 results on '"Chenfeng Xiong"'
Search Results
2. Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach
- Author
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Weiyu Luo, Chenfeng Xiong, Jiajun Wan, Ziteng Feng, Olawole Ayorinde, Natalia Blanco, Man Charurat, Vivek Naranbhai, Christina Riley, Anna Winters, Fati Murtala-Ibrahim, and Alash’le Abimiku
- Subjects
SARS-CoV-2 ,human mobility ,trips ,policy ,Nigeria ,Science ,Science (General) ,Q1-390 ,Social Sciences ,Social sciences (General) ,H1-99 - Abstract
We employed emerging smartphone-based location data and produced daily human mobility measurements using Nigeria as an application site. A data-driven analytical framework was developed for rigorously producing such measures using proven location intelligence and data-mining algorithms. Our study demonstrates the framework at the beginning of the SARS-CoV-2 pandemic and successfully quantifies human mobility patterns and trends in response to the unprecedented public health event. Another highlight of the paper is the assessment of the effectiveness of mobility-restricting policies as key lessons learned from the pandemic. We found that travel bans and federal lockdown policies failed to restrict trip-making behaviour, but had a significant impact on distance travelled. This paper contributes a first attempt to quantify daily human travel behaviour, such as trip-making behaviour and travelling distances, and how mobility-restricting policies took effect in sub-Saharan Africa during the pandemic. This study has the potential to enable a wide spectrum of quantitative studies on human mobility and health in sub-Saharan Africa using well-controlled, publicly available large data sets. Significance: • The mobility measurements in this study are new and have filled a major data gap in understanding the change in travel behaviour during the SARS-CoV-2 pandemic in Nigeria. These measurements are derived from high-quality data samples by state-of-the-art data-driven methodologies and could be further adopted by other quantitative research related to human mobility. • Additionally, this study evaluates the impact of mobility-restricting policies and the heterogeneous effects of socio-economic and socio-demographic factors by a time-dependent random effect model on human mobility. The quantitative model provides a decision-making basis for the Nigerian government to provide travel-related guidance and make decisions in future public health events.
- Published
- 2023
- Full Text
- View/download PDF
3. How social distancing, mobility, and preventive policies affect COVID-19 outcomes: Big data-driven evidence from the District of Columbia-Maryland-Virginia (DMV) megaregion
- Author
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Jina Mahmoudi and Chenfeng Xiong
- Subjects
Medicine ,Science - Abstract
Many factors play a role in outcomes of an emerging highly contagious disease such as COVID-19. Identification and better understanding of these factors are critical in planning and implementation of effective response strategies during such public health crises. The objective of this study is to examine the impact of factors related to social distancing, human mobility, enforcement strategies, hospital capacity, and testing capacity on COVID-19 outcomes within counties located in District of Columbia as well as the states of Maryland and Virginia. Longitudinal data have been used in the analysis to model county-level COVID-19 infection and mortality rates. These data include big location-based service data, which were collected from anonymized mobile devices and characterize various social distancing and human mobility measures within the study area during the pandemic. The results provide empirical evidence that lower rates of COVID-19 infection and mortality are linked with increased levels of social distancing and reduced levels of travel—particularly by public transit modes. Other preventive strategies and polices also prove to be influential in COVID-19 outcomes. Most notably, lower COVID-19 infection and mortality rates are linked with stricter enforcement policies and more severe penalties for violating stay-at-home orders. Further, policies that allow gradual relaxation of social distancing measures and travel restrictions as well as those requiring usage of a face mask are related to lower rates of COVID-19 infections and deaths. Additionally, increased access to ventilators and Intensive Care Unit (ICU) beds, which represent hospital capacity, are linked with lower COVID-19 mortality rates. On the other hand, gaps in testing capacity are related to higher rates of COVID-19 infection. The results also provide empirical evidence for reports suggesting that certain minority groups such as African Americans and Hispanics are disproportionately affected by the COVID-19 pandemic.
- Published
- 2022
4. Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach
- Author
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Songhua Hu, Weiyu Luo, Aref Darzi, Yixuan Pan, Guangchen Zhao, Yuxuan Liu, and Chenfeng Xiong
- Subjects
Medicine ,Science - Abstract
Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.
- Published
- 2021
5. Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic.
- Author
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Weiyu Luo, Wei Guo, Songhua Hu, Mofeng Yang, Xinyuan Hu, and Chenfeng Xiong
- Subjects
Medicine ,Science - Abstract
During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.
- Published
- 2021
- Full Text
- View/download PDF
6. Human mobility trends during the early stage of the COVID-19 pandemic in the United States.
- Author
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Minha Lee, Jun Zhao, Qianqian Sun, Yixuan Pan, Weiyi Zhou, Chenfeng Xiong, and Lei Zhang
- Subjects
Medicine ,Science - Abstract
In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.
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- 2020
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7. A Big-Data Driven Framework to Estimating Vehicle Volume based on Mobile Device Location Data.
- Author
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Mofeng Yang, Weiyu Luo, Mohammad Ashoori, Jina Mahmoudi, Chenfeng Xiong, Jiawei Lu, Guangchen Zhao, Saeed Saleh Namadi, Songhua Hu, and Aliakbar Kabiri
- Published
- 2023
- Full Text
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8. An integrated modeling framework for active traffic management and its applications in the Washington, DC area.
- Author
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Chenfeng Xiong, Xianfeng Terry Yang, Lei Zhang 0118, Minha Lee, Weiyi Zhou, and Mohammed Raqib
- Published
- 2021
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9. A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management.
- Author
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Xiqun Michael Chen, Xiang He, Chenfeng Xiong, Zheng Zhu, and Lei Zhang 0118
- Published
- 2019
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10. Dynamic traffic assignment integration with real-time ramp metering for large-scale network management.
- Author
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Minha Lee, Zheng Zhu, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2017
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11. A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data.
- Author
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Chenfeng Xiong, Aref Darzi, Yixuan Pan, Sepehr Ghader, and Lei Zhang 0118
- Published
- 2020
12. Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 outbreak before reopening United States.
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Jun Zhao, Minha Lee, Sepehr Ghader, Hannah Younes, Aref Darzi, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2020
13. Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States.
- Author
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Yixuan Pan, Aref Darzi, Aliakbar Kabiri, Guangchen Zhao, Weiyu Luo, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2020
14. Quantifying the influence of inter-county mobility patterns on the COVID-19 outbreak in the United States.
- Author
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Qianqian Sun, Yixuan Pan, Weiyi Zhou, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2020
15. An Interstate Trips Analysis during COVID-19 in the United States.
- Author
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Weiyi Zhou, Minha Lee, Qianqian Sun, Weiyu Luo, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2020
16. A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data.
- Author
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Mofeng Yang, Yixuan Pan, Aref Darzi, Sepehr Ghader, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2020
17. A High-Order Hidden Markov Model and Its Applications for Dynamic Car Ownership Analysis.
- Author
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Chenfeng Xiong, Di Yang, and Lei Zhang 0118
- Published
- 2018
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18. Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland
- Author
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Jina Mahmoudi, Chenfeng Xiong, Mofeng Yang, and Weiyu Luo
- Subjects
Mechanical Engineering ,Civil and Structural Engineering - Abstract
This study leverages big location-based service data collected from mobile devices in 2019 to conduct a pedestrian and bicyclist safety analysis. Statistical models are estimated for pedestrian and bicyclist crash frequency at Maryland intersections using location-based service data as risk exposure data. The analysis is performed by employing prominent frequency modeling methodologies, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression techniques. The findings indicate that inclusion of big location-based service exposure data in the analysis improves the performance of the models. Further, the results suggest that key contributing factors to pedestrian and bicyclist crashes at Maryland intersections include the following: (i) intersection design- and traffic-related attributes, such as the number of intersection legs, presence of a traffic signal, and average level of traffic stress rating, as well as safety risk exposure measures, such as the average daily pedestrian, bicyclist, and vehicle volumes at the intersection; (ii) travel-related attributes, including public transportation and nonmotorized mode shares within the intersection’s census block group; (iii) land use and built environment attributes, such as road network density, activity density, and extent of walkability within the census block group; (iv) socioeconomic and sociodemographic attributes, including the percentage of low-income workers, households with no vehicles, African American population, and senior population within the census block group. The findings of the study show how big location-based service exposure data can be utilized to identify pedestrian and bicyclist safety risks and guide data-driven, evidence-based policy decision-making to improve the safety of vulnerable road users.
- Published
- 2022
19. Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives: A Simulation-Based Optimization Approach.
- Author
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Xiang He, Xiqun Michael Chen, Chenfeng Xiong, Zheng Zhu, and Lei Zhang 0118
- Published
- 2017
- Full Text
- View/download PDF
20. Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach
- Author
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Alash’le Abimiku, Fati Murtala-Ibrahim, Anna Winters, Christina Riley, Vivek Naranbhai, Man Charurat, Natalia Blanco, Olawole Ayorinde, Ziteng Feng, Jiajun Wan, Chenfeng Xiong, and Weiyu Luo
- Subjects
General Earth and Planetary Sciences ,General Agricultural and Biological Sciences ,General Biochemistry, Genetics and Molecular Biology - Abstract
We employed emerging smartphone-based location data and produced daily human mobility measurements using Nigeria as an application site. A data-driven analytical framework was developed for rigorously producing such measures using proven location intelligence and data-mining algorithms. Our study demonstrates the framework at the beginning of the SARS-CoV-2 pandemic and successfully quantifies human mobility patterns and trends in response to the unprecedented public health event. Another highlight of the paper is the assessment of the effectiveness of mobility- estricting policies as key lessons learned from the pandemic. We found that travel bans and federal lockdown policies failed to restrict trip-making behaviour, but had a significant impact on distance travelled. This paper contributes a first attempt to quantify daily human travel behaviour, such as trip-making behaviour and travelling distances, and how mobility-restricting policies took effect in sub-Saharan Africa during the pandemic. This study has the potential to enable a wide spectrum of quantitative studies on human mobility and health in sub-Saharan Africa using well-controlled, publicly available large data sets.Significance:• The mobility measurements in this study are new and have filled a major data gap in understanding the change in travel behaviour during the SARS-CoV-2 pandemic in Nigeria. These measurements arederived from high-quality data samples by state-of-the-art data-driven methodologies and could be further adopted by other quantitative research related to human mobility.• Additionally, this study evaluates the impact of mobility-restricting policies and the heterogeneous effects of socio-economic and socio-demographic factors by a time-dependent random effect model on human mobility. The quantitative model provides a decision-making basis for the Nigerian government to provide travel-related guidance and make decisions in future public health events.
- Published
- 2023
21. Setting up data science research in Africa and engagement of stakeholders
- Author
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Alash’ le Abimiku, Adetifa Ifedayo, Gambo Aliyu, Waasila Jassat, Shirley Collie, Patrick Dakum, Vivek Naranbhai, Chenfeng Xiong, Manhattan Charurat, Jibreel Jumare, and Fati Murtala-Ibrahim
- Subjects
General Earth and Planetary Sciences ,General Agricultural and Biological Sciences ,General Biochemistry, Genetics and Molecular Biology - Published
- 2023
22. Vaccination, Human Mobility, and COVID-19 Health Outcomes: Empirical Comparison Before and During the Outbreak of SARS‐Cov-2 B.1.1.529 (Omicron) Variant
- Author
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Songhua Hu, Chenfeng Xiong, Yingrui Zhao, Xin Yuan, and Xuqiu Wang
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Infectious Diseases ,General Veterinary ,General Immunology and Microbiology ,Public Health, Environmental and Occupational Health ,Molecular Medicine - Published
- 2023
23. Spatial Transferability of Neural Network Models in Travel Demand Modeling.
- Author
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Liang Tang, Chenfeng Xiong, and Lei Zhang 0118
- Published
- 2018
- Full Text
- View/download PDF
24. Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform
- Author
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Lei Zhang, Aref Darzi, Sepehr Ghader, Michael L. Pack, Chenfeng Xiong, Mofeng Yang, Qianqian Sun, Aliakbar Kabiri, and Songhua Hu
- Subjects
Location data ,2019-20 coronavirus outbreak ,Geography ,Coronavirus disease 2019 (COVID-19) ,Mechanical Engineering ,Social distance ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Population data ,Census ,Mobile device ,Data science ,Civil and Structural Engineering - Abstract
The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.
- Published
- 2021
25. Surrogate-Based Optimization of Expensive-to-Evaluate Objective for Optimal Highway Toll Charges in Transportation Network.
- Author
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Xiqun Michael Chen, Lei Zhang 0118, Xiang He, Chenfeng Xiong, and Zhiheng Li 0001
- Published
- 2014
- Full Text
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26. COVID-19 vaccine hesitancy cannot fully explain disparities in vaccination coverage across the contiguous United States
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Songhua Hu, Chenfeng Xiong, Qingchen Li, Zitong Wang, and Yuan Jiang
- Subjects
COVID-19 Vaccines ,Vaccination Coverage ,General Veterinary ,General Immunology and Microbiology ,Vaccination ,Public Health, Environmental and Occupational Health ,COVID-19 ,United States ,Infectious Diseases ,Molecular Medicine ,Humans ,Vaccination Hesitancy ,Pandemics ,Aged - Abstract
Vaccine hesitancy has been identified as a major obstacle preventing comprehensive coverage against the COVID-19 pandemic. However, few studies have analyzed the association between ex-ante vaccine hesitancy and ex-post vaccination coverage. This study leveraged one-year county-level data across the contiguous United States to examine whether the prospective vaccine hesitancy eventually translates into differential vaccination rates, and whether vaccine hesitancy can explain socioeconomic, racial, and partisan disparities in vaccine uptake. A set of structural equation modeling was fitted with vaccine hesitancy and vaccination rate as endogenous variables, controlling for various potential confounders. The results demonstrated a significant negative link between vaccine hesitancy and vaccination rate, with the difference between the two continuously widening over time. Counties with higher socioeconomic statuses, more Asian and Hispanic populations, more elderly residents, greater health insurance coverage, and more Democrats presented lower vaccine hesitancy and higher vaccination rates. However, underlying determinants of vaccination coverage and vaccine hesitancy were divergent regarding their different associations with exogenous variables. Mediation analysis further demonstrated that indirect effects from exogenous variables to vaccination coverage via vaccine hesitancy only partially explained corresponding total effects, challenging the popular narrative that portrays vaccine hesitancy as a root cause of disparities in vaccination. Our study highlights the need of well-funded, targeted, and ongoing initiatives to reduce persisting vaccination inequities.
- Published
- 2022
27. An integrated modeling framework for active traffic management and its applications in the Washington, DC area
- Author
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Weiyi Zhou, Xianfeng Terry Yang, Minha Lee, Mohammed Raqib, Lei Zhang, and Chenfeng Xiong
- Subjects
050210 logistics & transportation ,Computer science ,Applied Mathematics ,05 social sciences ,Aerospace Engineering ,02 engineering and technology ,Computer Science Applications ,Modeling and simulation ,Transport engineering ,Travel behavior ,Active traffic management ,Control and Systems Engineering ,0502 economics and business ,Automotive Engineering ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Software ,Information Systems - Abstract
Developing appropriate modeling and simulation tools with the capability of analyzing traffic patterns, travel demand, and traveling/driving behavior responses at the regional level is important fo...
- Published
- 2021
28. High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer
- Author
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Chenfeng Xiong and Songhua Hu
- Subjects
Automotive Engineering ,Transportation ,Management Science and Operations Research ,Civil and Structural Engineering - Published
- 2023
29. Residency and worker status identification based on mobile device location data
- Author
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Yixuan Pan, Qianqian Sun, Mofeng Yang, Aref Darzi, Guangchen Zhao, Aliakbar Kabiri, Chenfeng Xiong, and Lei Zhang
- Subjects
Automotive Engineering ,Transportation ,Management Science and Operations Research ,Civil and Structural Engineering - Published
- 2023
30. A Descriptive Bayesian Approach to Modeling and Calibrating Drivers' En Route Diversion Behavior.
- Author
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Chenfeng Xiong and Lei Zhang 0118
- Published
- 2013
- Full Text
- View/download PDF
31. High-Dimensional Population Flow Time Series Forecasting Via an Interpretable Hierarchical Transformer
- Author
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SONGHUA HU and Chenfeng Xiong
- Subjects
History ,Polymers and Plastics ,Business and International Management ,Industrial and Manufacturing Engineering - Published
- 2022
32. A Web GIS for Sea Ice Information and an Ice Service Archive.
- Author
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Songnian Li, Chenfeng Xiong, and Ziqiang Ou
- Published
- 2011
- Full Text
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33. An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems
- Author
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Xuesong Zhou, Mehrdad Shahabi, Jun Zhao, Chenfeng Xiong, Lei Zhang, and Yafeng Yin
- Subjects
Scheme (programming language) ,050210 logistics & transportation ,Operations research ,Computer science ,05 social sciences ,Control (management) ,Transportation ,Congestion management ,010501 environmental sciences ,01 natural sciences ,System model ,Behavioral modeling ,Computer Science Applications ,Travel behavior ,Incentive ,0502 economics and business ,Automotive Engineering ,computer ,0105 earth and related environmental sciences ,Efficient energy use ,computer.programming_language ,Civil and Structural Engineering - Abstract
Recently, the employment of different types of incentives in transportation systems to form advanced transportation congestion management solutions has garnered significant attention. Instead of using presumed or fixed-amount incentives, this paper develops an integrated and personalized traveler information and incentive scheme to incentivize toward a more energy-efficient travel and mobility decisions. We have developed a behavior research and empirical modeling system to quantify the personalized monetary incentives. Then, it is integrated with a control optimizer for optimized incentive allocation. This scheme innovatively integrates behavioral modeling and optimization for travel incentive design. Through a demonstrative case study for a large-scale transportation system in the Washington D.C. and Baltimore regions, the capability of the proposed scheme is highlighted with significant system-level energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency.
- Published
- 2020
34. Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach
- Author
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Guangchen Zhao, Weiyu Luo, Songhua Hu, Yixuan Pan, Aref Darzi, Yuxuan Liu, and Chenfeng Xiong
- Subjects
Viral Diseases ,Epidemiology ,Ethnic group ,Social Sciences ,Medical Conditions ,Outcome Assessment, Health Care ,Medicine and Health Sciences ,Ethnicity ,Ethnicities ,Public and Occupational Health ,African American people ,Hispanic People ,Minority Groups ,Multidisciplinary ,Geography ,Population groupings ,Middle Aged ,Socioeconomic Aspects of Health ,Infectious Diseases ,Research Design ,Income ,Medicine ,Research Article ,Mediation (statistics) ,Census ,Coronavirus disease 2019 (COVID-19) ,Science ,Health outcomes ,Human Geography ,Research and Analysis Methods ,Structural equation modeling ,Racism ,Population Metrics ,Urbanization ,Humans ,Pandemics ,Aged ,Population Density ,Survey Research ,Mediation Analysis ,Population Biology ,SARS-CoV-2 ,Racial Groups ,Biology and Life Sciences ,COVID-19 ,Covid 19 ,Health Status Disparities ,Infection rate ,Health Care ,Black or African American ,Earth Sciences ,Household income ,Human Mobility ,People and places ,Demography - Abstract
Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.
- Published
- 2021
35. Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections
- Author
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Chenfeng Xiong, Songhua Hu, Mofeng Yang, Weiyu Luo, and Lei Zhang
- Subjects
Coronavirus disease 2019 (COVID-19) ,Virus transmission ,mobile device location data ,Pneumonia, Viral ,Social Sciences ,Inflow ,partial reopening ,03 medical and health sciences ,Betacoronavirus ,Simultaneous equations ,0502 economics and business ,Econometrics ,Humans ,Pandemics ,030304 developmental biology ,Location data ,050210 logistics & transportation ,0303 health sciences ,Travel ,Multidisciplinary ,SARS-CoV-2 ,05 social sciences ,COVID-19 ,Models, Theoretical ,Data availability ,mobility ,United States ,Geography ,Positive relationship ,Coronavirus Infections ,Mobile device ,Environmental Sciences ,Cell Phone - Abstract
Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data of over 100 million monthly active samples, we compute origin–destination travel demand and aggregate mobility inflow at each US county from March 1 to June 9, 2020. Then, we quantify the change of mobility inflow across the nation and statistically model the time-varying relationship between inflow and the infections. We find that external travel to other counties decreased by 35% soon after the nation entered the emergency situation, but recovered rapidly during the partial reopening phase. Moreover, our simultaneous equations analysis highlights the dynamics in a positive relationship between mobility inflow and the number of infections during the COVID-19 onset. This relationship is found to be increasingly stronger in partially reopened regions. Our study provides a quick reference and timely data availability for researchers and decision makers to understand the national mobility trends before and during the pandemic. The modeling results can be used to predict mobility and transmissions risks and integrated with epidemics models to further assess the public health outcomes.
- Published
- 2020
36. Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States
- Author
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Aref Darzi, Zhiyu Catherine Jin, Chenfeng Xiong, Hannah Younes, Mofeng Yang, and Songhua Hu
- Subjects
Variables ,Human mobility ,Coronavirus disease 2019 (COVID-19) ,Renewable Energy, Sustainability and the Environment ,Mobile device location data ,Social distance ,media_common.quotation_subject ,Non-pharmaceutical interventions ,Generalized additive model ,Geography, Planning and Development ,Ethnic group ,COVID-19 ,Transportation ,Health equity ,Article ,Racial/ethnic disparities ,Countermeasure ,Geography ,Pandemic ,Built environment ,Civil and Structural Engineering ,Demography ,media_common - Abstract
Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.
- Published
- 2021
37. A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
- Author
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Lei Zhang, Yixuan Pan, Aref Darzi, Mofeng Yang, Chenfeng Xiong, and Sepehr Ghader
- Subjects
FOS: Computer and information sciences ,Physics - Physics and Society ,Computer science ,Mobile device location data ,Population ,Travel surveys ,FOS: Physical sciences ,Transportation ,Physics and Society (physics.soc-ph) ,Travel mode share ,Development ,computer.software_genre ,Article ,Data-driven ,Computer Science - Computers and Society ,Classifier (linguistics) ,Computers and Society (cs.CY) ,Machine learning ,education ,Civil and Structural Engineering ,Ground truth ,education.field_of_study ,Random forest ,Travel behavior ,Data mining ,Noise (video) ,Mobile device ,computer - Abstract
Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper tends to study the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from the MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes (drive, rail, bus, bike and walk) with a Random Forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making., Transportation (2021)
- Published
- 2021
38. Analyzing Simulation-Based Active Traffic Management Impact on a Large-Scale Regional Network
- Author
-
Weiyi Zhou, Chenfeng Xiong, Lei Zhang, Minha Lee, and Zheng Zhu
- Subjects
Transport engineering ,050210 logistics & transportation ,Active traffic management ,Scale (ratio) ,Computer science ,Mechanical Engineering ,0502 economics and business ,05 social sciences ,0501 psychology and cognitive sciences ,Simulation based ,050107 human factors ,Civil and Structural Engineering - Abstract
A vast number of real-time corridor management strategies have been introduced because the dynamics of traffic patterns and increased congestion result in challenging problems on road systems. Although these strategies can offer positive impacts on regional traffic, their evaluation tools are often limited to the scope of one specific corridor. To fill this gap, this study integrates a mesoscopic dynamic traffic assignment simulation model with an existing traffic-responsive ramp metering strategy. This integrated model is suitable for network-wide analysis and large-scale simulation of integrated corridor management strategies. The integrated modeling platform is demonstrated as a practice-ready tool. We present a case study that explores the benefits of metering control under various traffic conditions in a real-world network in Maryland. Both local and network-wide impacts are illustrated in the case study. This is one of the first attempts to simultaneously analyze network-wide traffic impacts and capture minute-by-minute demand–supply interactions under managed corridor strategies. The results indicate that ramp metering is beneficial even under non-recurrent traffic conditions at multiple spatial resolutions.
- Published
- 2019
39. A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management
- Author
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Lei Zhang, Xiqun Michael Chen, Xiang He, Chenfeng Xiong, and Zheng Zhu
- Subjects
050210 logistics & transportation ,Mathematical optimization ,Heteroscedasticity ,021103 operations research ,Computer science ,05 social sciences ,Bayesian probability ,0211 other engineering and technologies ,Transportation ,02 engineering and technology ,Advanced Traffic Management System ,Simulation-based optimization ,Kriging ,Simulation noise ,0502 economics and business ,Joint (building) ,Civil and Structural Engineering - Abstract
Since advanced traveler information and traffic management systems have become popular, it is vital to capture the joint impact of various strategies on transportation systems. Developing analytical models to incorporate traffic dynamics and travel behavior is challenging. Based on observation of the heteroscedasticity of the simulation noise in the stochastic simulator, this paper develops the Bayesian stochastic Kriging (BSK) model to adapt for the heteroscedastic noise and metamodel parameter uncertainty in a Bayesian framework, which enhances the existing surrogate-based optimization methods. This paper presents a metamodel for large scale simulation-based freeway traffic management optimization problems. Simulation-based optimization combines the advantages of simulation models and mathematical optimization methods. The parameter estimation of the BSK model is accomplished by the Bayesian inference. The proposed methodology enables the efficient use of large scale high-resolution traffic simulation models for simulation-based optimization, while accounting for travelers’ behavioral responses to information provision. We demonstrate the advantages of BSK compared to other existing metamodels using a numerical example of a synthetic network and a mathematical example. In a work zone scenario on a real-world freeway/arterial corridor of I-270 and MD-355 in the State of Maryland, the BSK model is applied to the freeway traffic management via optimizing the high-occupancy/toll rate and deploying dynamic message signs. Field traffic measurements by loop/microwave detections are used to calibrate travel demand and to supply the simulation parameters. The optimization results are promising in reducing the corridor-wide travel delay and enhancing the vehicle throughput. The online appendix is available at https://doi.org/10.1287/trsc.2018.0819 .
- Published
- 2019
40. Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach
- Author
-
Fang Zhou, Yi Guo, Xiaopeng Li, Chenfeng Xiong, Jiaqi Ma, and Wei Hao
- Subjects
050210 logistics & transportation ,Mathematical optimization ,Optimization problem ,Intersection (set theory) ,Heuristic (computer science) ,Computer science ,Subroutine ,05 social sciences ,Transportation ,Trajectory optimization ,010501 environmental sciences ,Signal timing ,01 natural sciences ,7. Clean energy ,Computer Science Applications ,Dynamic programming ,Control theory ,0502 economics and business ,Automotive Engineering ,0105 earth and related environmental sciences ,Civil and Structural Engineering - Abstract
Connected and automated vehicle (CAV) technologies offer promising solutions to challenges that face today’s transportation systems. Vehicular trajectory control and intersection controller optimization based on CAV technologies are two approaches that have significant potential to mitigate congestion, lessen the risk of crashes, reduce fuel consumption, and decrease emissions at intersections. These two approaches should be integrated into a single process such that both aspects can be optimized simultaneously to achieve maximum benefits. This paper proposes an efficient DP-SH (dynamic programming with shooting heuristic as a subroutine) algorithm for the integrated optimization problem that can simultaneously optimize the trajectories of CAVs and intersection controllers (i.e., signal timing and phasing of traffic signals), and develops a two-step approach (DP-SH and trajectory optimization) to effectively obtain near-optimal intersection and trajectory control plans. Also, the proposed DP-SH algorithm can also consider mixed traffic stream scenarios with different levels of CAV market penetration. Numerical experiments are conducted, and the results prove the efficiency and sound performance of the proposed optimization framework. The proposed DP-SH algorithm, compared to the adaptive signal control, can reduce the average travel time by up to 35.72% and save the consumption by up to 31.5%. In mixed traffic scenarios, system performance improves with increasing market penetration rates. Even with low levels of penetration, there are significant benefits in fuel consumption savings. The computational efficiency, as evidenced in the case studies, indicates the applicability of DP-SH for real-time implementation.
- Published
- 2019
41. Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States
- Author
-
Lei Zhang, Aliakbar Kabiri, Aref Darzi, Chenfeng Xiong, Guangchen Zhao, Yixuan Pan, and Weiyu Luo
- Subjects
Lifestyle modification ,0301 basic medicine ,Index (economics) ,Epidemiology ,Physical Distancing ,lcsh:Medicine ,Disease ,Article ,03 medical and health sciences ,0302 clinical medicine ,Development economics ,Humans ,030212 general & internal medicine ,Cooperative Behavior ,lcsh:Science ,China ,Pandemics ,Travel ,Government ,Models, Statistical ,Multidisciplinary ,SARS-CoV-2 ,Social distance ,lcsh:R ,COVID-19 ,Outbreak ,Health policy ,United States ,030104 developmental biology ,Geography ,Viral infection ,Epidemiological Monitoring ,Quarantine ,Government Regulation ,lcsh:Q ,Construct (philosophy) - Abstract
Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.
- Published
- 2020
42. Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems
- Author
-
Songhua Hu, Mingyang Chen, Yuan Jiang, Wei Sun, and Chenfeng Xiong
- Subjects
Geography, Planning and Development ,Transportation ,General Environmental Science - Published
- 2022
43. Human mobility trends during the early stage of the COVID-19 pandemic in the United States
- Author
-
Lei Zhang, Weiyi Zhou, Jun Zhao, Chenfeng Xiong, Minha Lee, Yixuan Pan, and Qianqian Sun
- Subjects
Viral Diseases ,Epidemiology ,Economics ,Psychological intervention ,Social Sciences ,Social Distancing ,Systems Science ,Medical Conditions ,0302 clinical medicine ,Order (exchange) ,Pandemic ,Medicine and Health Sciences ,Global health ,Public and Occupational Health ,030212 general & internal medicine ,education.field_of_study ,Multidisciplinary ,Geography ,Social distance ,Infectious Diseases ,Physical Sciences ,Medicine ,Coronavirus Infections ,Research Article ,Employment ,Computer and Information Sciences ,medicine.medical_specialty ,Infectious Disease Control ,Movement ,Science ,Pneumonia, Viral ,Population ,Human Geography ,Betacoronavirus ,03 medical and health sciences ,Spatio-Temporal Analysis ,Population Metrics ,Political science ,Development economics ,medicine ,Humans ,education ,Pandemics ,Population Density ,Electronic Data Processing ,Government ,Population Biology ,SARS-CoV-2 ,Public health ,COVID-19 ,Biology and Life Sciences ,Covid 19 ,United States ,Cell Phone Use ,Labor Economics ,Earth Sciences ,Human Mobility ,Mathematics ,030217 neurology & neurosurgery ,Dwell Time - Abstract
In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.
- Published
- 2020
44. An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region
- Author
-
Sepehr Ghader, Charles Barber, Carlos Carrion, Subrat Mahapatra, Thomas F Rossi, Di Yang, Lei Zhang, Martin Milkovits, and Chenfeng Xiong
- Subjects
050210 logistics & transportation ,Computer science ,Process (engineering) ,020209 energy ,Mechanical Engineering ,0502 economics and business ,05 social sciences ,Activity based modeling ,0202 electrical engineering, electronic engineering, information engineering ,02 engineering and technology ,Industrial engineering ,Civil and Structural Engineering - Abstract
The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.
- Published
- 2018
45. Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis
- Author
-
Xiang He, Chenfeng Xiong, Zheng Zhu, Xiqun Chen, and Lei Zhang
- Subjects
050210 logistics & transportation ,Operations research ,Calibration (statistics) ,business.industry ,Computer science ,05 social sciences ,Transferability ,Microsimulation ,Transportation ,Traffic dynamics ,Computer Science Applications ,Impact studies ,Travel behavior ,0502 economics and business ,Automotive Engineering ,Traffic conditions ,Land development ,business ,050212 sport, leisure & tourism ,Civil and Structural Engineering - Abstract
A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.
- Published
- 2018
46. AgBM-DTALite: An integrated modelling system of agent-based travel behaviour and transportation network dynamics
- Author
-
Chenfeng Xiong, Xuesong Zhou, and Lei Zhang
- Subjects
Agent-based model ,050210 logistics & transportation ,Transportation planning ,Engineering ,business.industry ,05 social sciences ,Control (management) ,Traffic simulation ,Transportation ,010501 environmental sciences ,Flow network ,01 natural sciences ,Advanced Traffic Management System ,Transport engineering ,0502 economics and business ,Mode choice ,business ,Perfect rationality ,0105 earth and related environmental sciences - Abstract
Advanced modelling methods and products, such as an integrated advanced travel demand model and a fine-grained time-sensitive network that can operate at statewide, metropolitan and subarea/corridor levels, are required by a number of transportation planning agencies to meet their objectives and address various key challenges. This research develops an application-ready integrated transportation model that can predict, in a future-year scenario or in a hypothetical scenario, both the changes in travel behavioural adjustments and the dynamics in traffic conditions. The integrated framework embeds theoretically sound behavioural foundation by incorporating agent-based searching, information acquisition, learning, knowledge updating and decision-making. Multidimensional travel behaviour, including mode choice, route choice, departure time choice and en-route diversion, is considered. Behavioural user equilibrium is defined without assuming perfect rationality. A dynamic traffic simulation engine is employed to model and simulate real-time traffic conditions. Data exchanges between the travel demand model and the traffic simulation are explained in detail. The integration is demonstrated using a real-world case study. Future applications should cover a wide spectrum of scenarios in transportation planning/policy and traffic operations/control analyses.
- Published
- 2018
47. Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis
- Author
-
Xiqun Chen, Di Yang, Lei Zhang, Jiaqi Ma, and Chenfeng Xiong
- Subjects
050210 logistics & transportation ,Series (mathematics) ,Computer science ,business.industry ,05 social sciences ,Bayesian probability ,Transferability ,0211 other engineering and technologies ,021107 urban & regional planning ,Transportation ,02 engineering and technology ,Development ,Demand forecasting ,Machine learning ,computer.software_genre ,System dynamics ,Travel behavior ,0502 economics and business ,Artificial intelligence ,Mode choice ,business ,Hidden Markov model ,computer ,Civil and Structural Engineering - Abstract
As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.
- Published
- 2018
48. Calibrating supply parameters of large‐scale DTA models with surrogate‐based optimisation
- Author
-
Lei Zhang, Xiqun Chen, Chenfeng Xiong, and Zheng Zhu
- Subjects
050210 logistics & transportation ,Mathematical optimization ,Matching (statistics) ,Scale (ratio) ,Computer science ,Mechanical Engineering ,05 social sciences ,Process (computing) ,Transportation ,010501 environmental sciences ,01 natural sciences ,Decision variables ,0502 economics and business ,Genetic algorithm ,Calibration ,Statistical analysis ,Kriging surrogate model ,Law ,0105 earth and related environmental sciences ,General Environmental Science - Abstract
This study is among the early attempts to employ a surrogate-based optimisation (SBO) approach to solve the large-scale dynamic traffic assignment (DTA) calibration problem that is characterised by an expensive-to-evaluate and non-closed-form objective function. This paper formulates the calibration of the large-scale DTA model as a bi-level optimisation problem with a non-closed objective function such that it can only be evaluated through simulation. The Kriging surrogate model is adopted to construct the response surface between the objective value and the decision variables. The SBO approach first evaluates a number of initial samples, then fits the response surface and searches for the optima via an infill process. It reduces the number of large-scale DTA runs for evaluating the objective values and saves much computational time. For demonstrative purposes, a real-world large-scale DTA model in the state of MD is calibrated with the proposed SBO approach. After 400 initial points and 100 infill points, the SBO approach reduces the calibration matching gap from 29.68 to 21.90%. It is also presented that the proposed SBO is significantly faster than the genetic algorithm in searching for better solutions. The results demonstrate the feasibility and capability of SBO in DTA calibration problems.
- Published
- 2018
49. The conditional probability of travel speed and its application to short-term prediction
- Author
-
Lei Zhang, Xiqun Chen, Liang Tang, Chenfeng Xiong, and Zheng Zhu
- Subjects
050210 logistics & transportation ,021103 operations research ,Computer science ,05 social sciences ,0211 other engineering and technologies ,Conditional probability ,Transportation ,02 engineering and technology ,Term (time) ,Work (electrical) ,Modeling and Simulation ,0502 economics and business ,Traffic conditions ,Econometrics ,Embedding ,Software - Abstract
Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered t...
- Published
- 2018
50. Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic
- Author
-
Wei Guo, Mofeng Yang, Xinyuan Hu, Songhua Hu, Chenfeng Xiong, and Weiyu Luo
- Subjects
Male ,Viral Diseases ,Epidemiology ,Psychological intervention ,Social Sciences ,Governments ,Medical Conditions ,Pandemic ,Medicine and Health Sciences ,Empirical evidence ,Aged, 80 and over ,Multidisciplinary ,Geography ,Age Factors ,Middle Aged ,Infectious Diseases ,Treatment Outcome ,Interactive effects ,Research Design ,Medicine ,Female ,Research Article ,Census ,2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,Death Rates ,Science ,Political Science ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Research and Analysis Methods ,Human Geography ,Antiviral Agents ,Population Metrics ,Humans ,State Governments ,Pandemics ,Aged ,Population Density ,Survey Research ,Models, Statistical ,Actuarial science ,Population Biology ,SARS-CoV-2 ,Biology and Life Sciences ,COVID-19 ,Covid 19 ,United States ,COVID-19 Drug Treatment ,Medical Risk Factors ,Communicable Disease Control ,Earth Sciences ,Linear Models ,Human Mobility ,Business ,Curfew - Abstract
During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.
- Published
- 2021
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