Article
Geography
Junghwan Kim, Mei-Po Kwan
Summary: The Neighborhood Effect Averaging Problem (NEAP) can lead to erroneous assessments when studying mobility-dependent exposures such as air or noise pollution. A study conducted in the Los Angeles metropolitan statistical area on 2,737 individuals showed that high-income, employed, younger, and male participants tend to have higher levels of neighborhood effect averaging compared to low-income, nonworking, older, and female participants.
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2021)
Article
Environmental Sciences
Junghwan Kim, Mei-Po Kwan
Summary: The paper explores the neighborhood effect averaging problem and its impact on assessments of individual exposure to air pollution, highlighting different manifestations of the problem for different social/racial groups. Non-workers are found to not experience neighborhood effect averaging, leading to potentially higher exposures while traveling, highlighting the importance of considering mobility in studies related to environmental disparities.
ENVIRONMENTAL RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Matteo Bohm, Mirco Nanni, Luca Pappalardo
Summary: This study used GPS traces and a microscopic model to analyze the emissions from thousands of private vehicles in three European cities, identifying gross polluters and grossly polluted roads. The research showed that emissions reduction policies targeting gross polluters are more effective than general vehicle restrictions.
NATURE SUSTAINABILITY
(2022)
Article
Geography, Physical
Yufeng He, Barbara Hofer, Yehua Sheng, Yue Yin, Hui Lin
Summary: This study introduces a new approach for detecting the location and direction of traffic congestion using GPS data from taxis. The approach utilizes a dynamic model that represents events, processes, and states. The model is implemented as a graph database, which captures the relationships between states, events, processes, and things like points of interest and road grid. Algorithms for constructing and updating these relationships, as well as a dynamic retrieval method for taxi behaviors in Neo4j, are presented. An implementation in Shanghai in 2015 demonstrates the capabilities of congestion direction detection and cause searching.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Computer Science, Information Systems
Donghoun Lee, Sehyun Tak, Sari Kim
Summary: The research emphasizes the importance of developing a reinforcement learning-based traffic predictive vehicle routing algorithm, which can mitigate the impact of uncertainty in non-recurrent traffic congestion on travel time. The algorithm outperforms existing algorithms in different traffic scenarios with various demand patterns.
Article
Chemistry, Multidisciplinary
Yazed Alsaawy, Ahmad Alkhodre, Adnan Abi Sen, Abdullah Alshanqiti, Wasim Ahmad Bhat, Nour Mahmoud Bahbouh
Summary: Traffic congestion is a global challenge, and this paper proposes a comprehensive framework for solving the problem. The framework consists of four layers and includes various technologies and algorithms for efficient and reliable traffic management.
APPLIED SCIENCES-BASEL
(2022)
Article
Environmental Studies
Abdelfettah Laouzai, Rachid Ouafi
Summary: This paper proposes an enhanced approach to reduce atmospheric pollution in urban areas by considering additional constraints in traffic congestion analysis through a bi-level optimization program. The approach effectively respects eco-friendly threshold constraints, categorizes travel demand into two classes, and verifies its validity through optimality conditions. Two network examples are discussed to show that the proposed optimal solution outperforms common route choice policies in terms of reducing traffic congestion and minimizing air pollution.
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
Ghada Alturif, Wafaa Saleh
Summary: Car ownership and use are high in Saudi Arabia due to high income, low fuel prices, and lack of public transportation. The country aims to reduce car dependency and promote sustainable travel options, such as public transportation. To achieve this, decent public transportation options need to be provided and travel behavior needs to be influenced. An online survey was conducted in Riyadh to assess Saudi nationals' attitudes towards pricing measures and their impact on travel behavior, with the highest support found for measures improving road safety, reducing travel times, and reducing congestion in the city.
Article
Economics
Ding Wang, Mohammad Tayarani, Brian Yueshuai He, Jingqin Gao, Joseph Y. J. Chow, H. Oliver Gao, Kaan Ozbay
Summary: The study addresses the challenges COVID-19 poses to transportation in the post-pandemic era, highlighting how social distancing requirements may worsen traffic congestion and emissions. While effective in reducing contact exposure, social distancing policies negatively impact congestion and emissions in Manhattan and surrounding areas. Telework aids in reducing citywide congestion and emissions, but has greater negative impacts in Manhattan due to social distancing and behavioral inertia.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Economics
Juliette Ugirumurera, Joseph Severino, Karen Ficenec, Yanbo Ge, Qichao Wang, Lindy Williams, Junghoon Chae, Monte Lunacek, Caleb Phillips
Summary: This study presents a modeling framework to analyze the impact of airport traffic scenarios and policies on airport operations. Simulation results show that increasing public transit ridership can postpone the need for airport curbside expansion, shared mobility can reduce curbside congestion delays, and automation and electrification can save fuel consumption and emissions.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Economics
Lei Zhao, Xinhua Bi, Gendao Li, Zhaohui Dong, Ni Xiao, Anni Zhao
Summary: This paper introduces a robust traveling salesman problem with multiple drones, in which a truck coordinates with a heterogeneous fleet of drones to make deliveries under uncertain navigation environments.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Transportation Science & Technology
Nicolas Chiabaut, Remi Faitout
Summary: This paper introduces a new method for real-time estimation of traffic conditions and travel times on highways by utilizing principal component analysis and clustering of historical dataset. The clustering results show similarity in traffic conditions and dynamics of days in the same group, and a consensual day is identified as the most representative day of each cluster. This method uses past observations to predict future traffic conditions and travel times based on the closest consensual day to a new day, showing promising results on a French freeway dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Engineering, Civil
Yanli Wang, Yuning Jin, Sabyasachee Mishra, Bing Wu, Yajie Zou
Summary: The study proposes an integrated travel demand and accessibility model to examine the impact of new infrastructures on accessibility for households and employment. Using various accessibility measures and considering travel behavior and network traffic congestion, the study demonstrates the importance of improving public transit facilities to enhance accessibility and reduce the gap between peak and off-peak hours.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2022)
Article
Mathematics, Applied
Yuchen Li, Dan Liu, Ibrahim Kucukkoc
Summary: This paper studies the mixed-model assembly line balancing problem, considering the impact of learning effect and uncertain demand on the level of production. A novel model is proposed to optimize the total expected cost and average cycle time, and two algorithms are proposed to solve the model under different system response time requirements.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Green & Sustainable Science & Technology
Susmita Dasgupta, David Wheeler, M. Khaliquzzaman, Mainul Huq
Summary: This article uses new global data sources to revisit the siting problem in Dhaka, Bangladesh, combining spatiotemporal data with an econometric model. It finds significant divergences in siting priorities when focusing exclusively on local congestion, citywide travel time, vehicular pollution, or vulnerable-resident pollution exposure.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
Article
Construction & Building Technology
Longxu Yan, Rui Zhu, Mei-Po Kwan, Wei Luo, De Wang, Shangwu Zhang, Man Sing Wong, Linlin You, Bisheng Yang, Biyu Chen, Ling Feng
Summary: This study develops a detail-oriented deep learning approach to construct 3D building models from high-resolution satellite images and estimate PV potential. Two convolutional neural networks were developed and trained on datasets targeting Shanghai, and accuracy assessments suggest satisfactory results. The proposed model is novel and effective for constructing 3D building models that can facilitate PV penetration and urban studies.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Computer Science, Interdisciplinary Applications
Junghwan Kim, Kee Moon Jang
Summary: In this study, we investigated the spatial coverage and temporal variability of Google Street View (GSV) images using a people-based approach. We focused on walk commute trajectories of 97,505 people from 45 small and medium-sized cities in the U.S., which have received little attention in previous studies. Our findings indicate that 44% of commute routes lack sufficient GSV image spatial coverage. We also observed significant variability in their temporal ranges. The average monthly variation in timestamps of GSV images on commute trajectories is approximately seven years, and only about 10% of samples contain GSV images taken within one year.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2023)
Article
Environmental Studies
Jue Wang, Xue Zhang, Yanwei Chai, Mei-Po Kwan
Summary: This study proposes an innovative context-based approach for neighbourhood life circle delineation, considering accessibility and walkability. With a case study of three neighbourhoods in Beijing, China, the approach successfully identifies the context-based neighbourhood life circles and their internal spatial utilization and shared spaces. The findings provide a practical way for identifying the spatial scope of neighbourhood life circles and understanding their internal spatial utilization and neighbourhood spatial sharing relationships. The proposed method will also serve as a fundamental method for neighbourhood life circle research and shed light on urban planning practices.
APPLIED SPATIAL ANALYSIS AND POLICY
(2023)
Article
Computer Science, Information Systems
Chanwoo Jin, Sohyun Park, Hui Jeong Ha, Jinhyung Lee, Junghwan Kim, Johan Hutchenreuther, Atsushi Nara
Summary: This study utilizes an explainable GeoAI method called GLIME to explore human mobility patterns in the United States from 2012 to 2019. By developing a two-layer LSTM model, the researchers are able to predict individual-level residential mobility patterns. The use of GLIME provides geographical perspectives and interpretations of deep neural networks, revealing the spatial impacts attributed to different variables.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2023)
Article
Plant Sciences
Tiantian Xu, Shiyi Wang, Qing Liu, Junghwan Kim, Jingyi Zhang, Yiwen Ren, Na Ta, Xiaoliang Wang, Jiayu Wu
Summary: This study explores the neighborhood effect averaging problem (NEAP) in the context of green inequality, specifically focusing on vegetation color diversity. The findings suggest that while communities with high economic levels had significantly higher exposure to vegetation color diversity, the exposure at the individual level tended to be average. The study highlights the importance of enhancing the ornamental value and quality of green spaces in addressing green inequality.
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Plant Sciences
Yang Liu, Mei-Po Kwan, Changda Yu
Summary: This study compares the differences in green space exposure obtained from different geographic contexts using residence-based and mobility-based methods, multiple spatial resolutions, and buffer zones. The results indicate that mobility-based measurements of exposure to green space are significantly higher than those of residence-based measurements, highlighting the uncertain geographic context problem (UGCoP).
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Public, Environmental & Occupational Health
Jiannan Cai, Mei-Po Kwan, Zihan Kan, Jianwei Huang
Summary: Annoyance caused by environmental noise is a significant health burden. However, our understanding of noise's health impacts is limited by fixed contextual units and limited sound characteristics used in exposure assessments, as well as assumptions about exposure-response relationships. To address these limitations, this study analyzes the complex relationships between noise annoyance and real-time noise exposure in various activity microenvironments and times of day, considering individual mobility, multiple sound characteristics, and nonstationary relationships. The results show that sound level and sound increment have nonlinear effects on momentary noise annoyance, and different sound characteristics can have a joint effect. Additionally, daily activity microenvironments and individual sociodemographic attributes can also affect noise annoyance and its relationship with sound characteristics. These findings provide scientific evidence for creating acoustically comfortable living environments.
Article
Public, Environmental & Occupational Health
Wanying Song, Mei-Po Kwan
Summary: Perception biases of air pollution hinder the public's awareness of actual air quality. This study used real-time air pollutant sensors and ecological momentary assessment to investigate the association and disparity between momentary air pollution exposure and perceived air quality. The results showed that exposure to air pollution is higher in residential and transportation land-use areas compared to commercial areas, and factors such as activity type, travel mode, spatial and temporal contexts, and social factors contribute to this disparity.
Article
Multidisciplinary Sciences
Bingbo Gao, Jianyu Yang, Ziyue Chen, George Sugihara, Manchun Li, Alfred Stein, Mei-Po Kwan, Jinfeng Wang
Summary: This paper presents a causal inference model based on cross-sectional Earth System data for revealing complex nonlinear causal associations. The model performs well in detecting weak to moderate causations between variables with insignificant correlations and limited temporal variations. It is also advantageous in identifying the primary causation direction and revealing bidirectional asymmetric causation.
NATURE COMMUNICATIONS
(2023)
Article
Public, Environmental & Occupational Health
Dong Liu, Mei-Po Kwan, Zihan Kan, Yang Liu
Summary: This study analyzes the associations between built-environment and socioeconomic factors and the tri-exposure to greenspace and air/noise pollution in Hong Kong. The findings show that higher transit nodal accessibility, building density, building height, and land-use mix are significantly associated with a higher likelihood of being disadvantaged in terms of tri-exposure to air/noise pollution and greenspace. Conversely, more advantageous tri-exposures are significantly related to higher median monthly household income and sky view factor.
SOCIAL SCIENCE & MEDICINE
(2023)
Article
Green & Sustainable Science & Technology
Zherong Wu, Xinyang Zhang, Peifeng Ma, Mei-Po Kwan, Yang Liu
Summary: Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. This study utilized remote sensing techniques and various analytical methods to investigate the spatiotemporal patterns and influencing factors of land surface temperature (LST) in Hong Kong. The findings reveal an increasing trend in LST, negative correlations with vegetation and water bodies, and a positive correlation with built-up areas. Built-up areas were identified as the dominant influence, contributing to a significant portion of elevated LST levels. These insights provide valuable guidance for policymakers and urban planners in promoting sustainable urban development.
Article
Public, Environmental & Occupational Health
Wataru Morioka, Mei-Po Kwan, Kimihiro Hino, Ikuho Yamada
Summary: This study aims to explore and analyze the relationship between the daily shopping environment and step counts of older adults in Yokohama City, as well as the impact of the reasonable distance and spatial configuration of fundamental amenities on the physical activity of older people. By using two indicators (shortest-path distance and proximity to specialized food shops), the study examined the effects of the shopping environment on the daily step counts of older people, revealing that living at a middle distance from the nearest supermarkets and high destination diversity can increase older women's daily walking. The study also identified disadvantaged areas in terms of grocery store accessibility through the map of the distance to the nearest supermarket. These findings contribute partially to promoting a walkable city.
JOURNAL OF TRANSPORT & HEALTH
(2023)
Article
Transportation
Xuefeng Li, Jiacong Xu, Mingyang Du, Dong Liu, Mei-Po Kwan
Summary: This paper aims to explore the spatio-temporal variation of ride-hailing demands under different travel distances, using operation data of ride-hailing in Chengdu, China. Firstly, the characteristics of ride-hailing demand under different travel distances during the morning and evening rush hours are analyzed. Secondly, geographically weighted regression (GWR) models are established to discern influential factors of ride-hailing demand under different travel distances and time periods. The study concludes that built environmental factors such as road density, population density, sports and leisure services, medical care services, and bus stops all have significant spatio-temporal heterogeneity on ride-hailing demand at different travel distances.
TRAVEL BEHAVIOUR AND SOCIETY
(2023)
Article
Environmental Sciences
Min Chen, Zhen Qian, Niklas Boers, Anthony J. Jakeman, Albert J. Kettner, Martin Brandt, Mei-Po Kwan, Michael Batty, Wenwen Li, Rui Zhu, Wei Luo, Daniel P. Ames, C. Michael Barton, Susan M. Cuddy, Sujan Koirala, Fan Zhang, Carlo Ratti, Jian Liu, Teng Zhong, Junzhi Liu, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhixin Zhang, Zhuo Sun, Jian Lin, Zaiyang Ma, Yuanqing He, Kai Xu, Chunxiao Zhang, Hui Lin, Guonian Lue
Summary: Methods to integrate Earth system modelling with deep learning offer promise for advancing understanding of Earth processes. This Perspective explores the development and applications of hybrid Earth system modelling, a framework that integrates neural networks into ESM throughout the modelling lifecycle. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development.
NATURE REVIEWS EARTH & ENVIRONMENT
(2023)
Article
Transportation
Dong Liu, Mei-Po Kwan, Jianying Wang
Summary: The 15-minute city concept has been adopted by many cities worldwide to create compact and livable urban environments where residents can access essential urban functions within a 15-minute active travel radius. However, there is a lack of comprehensive measures and understanding of the 15-minute city status. This study proposes a 15-minute city index that incorporates accessibility to five categories of POI locations using the latest population census and POI data in Hong Kong.
TRAVEL BEHAVIOUR AND SOCIETY
(2024)
