Article
Computer Science, Software Engineering
Wei Zeng, Chengqiao Lin, Juncong Lin, Jincheng Jiang, Jiazhi Xia, Cagatay Turkay, Wei Chen
Summary: This paper addresses the modifiable areal unit problem in urban traffic prediction using unit visualization techniques and collaborative design with domain experts. The study reveals the significant impact of geographical scale variations on prediction performances, highlighting the benefits of interactive visual exploration for experts in developing deep traffic prediction models.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2021)
Article
Environmental Sciences
Andrei Mirt, Johannes Reiche, Jan Verbesselt, Martin Herold
Summary: The article introduces a downsampling method to address the modifiable areal unit problem by keeping the data distribution constant. Experimental results show that this method outperforms classical downsampling methods in typical remote sensing scenarios within a 95% confidence level.
Article
Computer Science, Information Systems
Yuhao Yao, Haoran Zhang, Defan Feng, Jinyu Chen, Wenjing Li, Ryosuke Shibasaki, Xuan Song
Summary: This study analyzed the modifiable areal unit problem of the error in crowd density estimation from big mobility data. An optimization model-based restoration method was proposed by regarding the error as the result of a convolution operation, and the restoration effect was analyzed under different circumstances through several simulation experiments. A real application for grided population distribution map construction and restoration from Call Detail Record was conducted to prove the reliability of the whole analysis, which demonstrated that the restoration method can reduce the error by nearly 40% under certain conditions.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Geosciences, Multidisciplinary
Alvaro Briz-Redon
Summary: The modifiable areal unit problem (MAUP) refers to the effects caused by modifying the units of analysis in spatial statistical analyses. Despite being studied for decades, it is still of interest due to its complexity. Usually, research focuses on analyzing the variation of model estimates or statistical properties when changing the units of analysis, with limited theoretical advancements in this area. This paper proposes a Bayesian shared-effects modeling framework for quantifying the MAUP globally and locally. The method is applied to traffic accident and COVID-19 death count datasets to demonstrate its effectiveness.
SPATIAL STATISTICS
(2022)
Article
Economics
Reyhane Javanmard, Jinhyung Lee, Junghwan Kim, Luyu Liu, Ehab Diab
Summary: This study examines the sensitivity of social equity analysis on public transit reliability to the choice of spatial unit of analysis. Using Winnipeg as an example, the study investigates the social equity of bus on-time performance and pass-up distribution at multiple levels. The results show disparities in the distribution of on-time performance and pass-ups, highlighting the importance of considering spatial aggregation levels in social equity analysis. The findings provide insights for transit authorities, planners, and policymakers in diagnosing and addressing inequities in transit reliability.
JOURNAL OF TRANSPORT GEOGRAPHY
(2023)
Article
Geography
Xiang Ye, Peter Rogerson
Summary: The article analyzes the impact of the MAUP on omission error, showing that the expectation of coefficient estimates at the aggregate level can be decomposed into three parts: the true coefficient, individual-level bias, and aggregate-level bias. The findings bridge the gap between empirical studies in geography and theoretical results in econometrics.
GEOGRAPHICAL ANALYSIS
(2022)
Article
Computer Science, Information Systems
Maurici Ruiz-Perez, Joana Maria Segui-Pons
Summary: The study used GIS, simulation, and optimization tools to analyze the effects of bus frequency changes on service levels and horizontal equity. Results showed that smaller zoning units are more sensitive in detecting imbalances between population and bus service levels, and orthogonal zoning units are useful for identifying service and population concentration.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Engineering, Civil
Xingang Zhou, Anthony G. O. Yeh
Summary: The study focuses on the effect of the modifiable areal unit problem (MAUP) on employment self-containment (ESC) and jobs-housing balance (JHB) using mobile positioning data from Shenzhen, China. It found that the average ESC increases with larger spatial areal units, and the relationship between JHB and ESC is amplified with spatial aggregation. Additionally, a 2 km grid is identified as the ideal spatial unit for analyzing ESC in Shenzhen, as it marks a turning point in ESC increase and decrease in coefficient of variation.
Article
Computer Science, Information Systems
Feng Gao, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Xiaoming Zhang, Guanping Huang, Ziwei Huang
Summary: This study examines the modifiable areal unit problem (MAUP) and interactive effects of built environment factors in dockless bike-sharing usage using the geographical detector method. The results show that most built environment variables are sensitive to spatial areal units. The study provides scientific basis for temporal rebalance strategy in China's innovative and high-density metropolis.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2021)
Article
Transportation
Xingang Zhou, Chenchen Sun, Xinyi Niu, Cheng Shi
Summary: Jobs-housing balance is a transport policy aimed at reducing commuting distance and alleviating traffic congestion, but its measurement is influenced by the spatial areal unit used in analysis. Cellphone data have advantages in identifying commuting trips and can be integrated into different analysis units. The study shows that the effect of jobs-housing balance on commuting distance and different socio-demographic attributes changes unpredictably with different analysis units, and the uniformity of the spatial analysis unit also plays a role in this relationship.
TRAVEL BEHAVIOUR AND SOCIETY
(2022)
Article
Ergonomics
Dongyu Wu, Yingheng Zhang, Qiaojun Xiang
Summary: Machine learning models, such as random forests, have been widely used in the field of road safety. However, the traditional RF algorithm fails to capture spatial variability. To address this, a modified algorithm called geographically weighted random forest (GWRF) is employed. The results from analyzing London data show that GWRF outperforms RF and GWR, and is not affected by multicollinearity.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Geography
A. Stewart Fotheringham, M. Sachdeva
Summary: This article examines two scale issues in global versus local modeling: the modifiable areal unit problem (MAUP) and Simpson's paradox. It highlights that scale effects play different roles in considering local versus global modeling and reveals that the MAUP is influenced by the properties of processes rather than data. The study also discusses the extreme differences that can occur when calibrating global and local models and how Simpson's paradox can arise in this context.
JOURNAL OF GEOGRAPHICAL SYSTEMS
(2022)
Article
Ergonomics
Helai Huang, Xizhi Ding, Chen Yuan, Xinyuan Liu, Jinjun Tang
Summary: A copula-based model was developed to jointly model the severity of primary and secondary crashes on freeways by accounting for severity levels and correlation. The model considered various variables and was estimated using crash data from Los Angeles County. The copula model outperformed the traditional binary probit model and identified significant factors influencing crash severity. Countermeasures such as emergency services, engineering, law enforcement, and education were proposed based on the findings.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Emergency Medicine
Mei Ruu Kok, Matthew Tuson, Matthew Yap, Berwin Turlach, Bryan Boruff, Alistair Vickery, David Whyatt
Summary: The study examined the impact of the modifiable areal unit problem (MAUP) on factors associated with ED demand in Perth, Australia. Results showed that using different spatial units can lead to widely varying estimates, emphasizing the importance of considering the smallest geographic unit for reliable analysis.
EMERGENCY MEDICINE AUSTRALASIA
(2021)
Article
Chemistry, Multidisciplinary
Alamirew Mulugeta Tola, Tamene Adugna Demissie, Fokke Saathoff, Alemayehu Gebissa
Summary: The study presented a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using four years of crash data in Ethiopia. By incorporating various statistical tools, the research successfully identified and ranked crash hot spot areas, particularly focusing on the entrances and exits of Ethiopia's capital city, Addis Ababa.
APPLIED SCIENCES-BASEL
(2021)
Article
Transportation
Hongliang Ding, N. N. Sze, Yanyong Guo, Yuhuan Lu
Summary: This study evaluates the effect of the ultra-low emission zone (ULEZ) on the demand for public bike sharing in London. The results show that the introduction of ULEZ significantly increases bicycle demand, especially for short and intermediate trips. However, there is no significant change in overall bicycle trip duration.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Ergonomics
Tiantian Chen, Oscar Oviedo-Trespalacios, N. N. Sze, Sikai Chen
Summary: This study investigates the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers. The results show that distractions caused by mobile apps have a greater negative impact on driving performance compared to traditional radio systems. The study also finds that compensatory behavior is more prevalent when distracted by mobile apps while driving. Additionally, driver characteristics such as experience, driving records, and perception variables also influence driving performance.
ACCIDENT ANALYSIS AND PREVENTION
(2022)
Article
Green & Sustainable Science & Technology
Huitao Lv, Haojie Li, N. N. Sze, Ziqian Zhang, Gang Ren, Yingheng Zhang
Summary: This study investigates the factors affecting cycle counts at the road segment level based on a survey dataset in London. The results show that cycling facilities, public transit, minor roads, network continuity, and connectivity have a positive effect on both private and rental cycle counts. Furthermore, parking facilities also significantly impact cycle counts for both types of cyclists.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2023)
Article
Public, Environmental & Occupational Health
Penglin Song, N. N. Sze, Ou Zheng, Mohamed Abdel-Aty
Summary: In this study, correlated grouped random parameter logit models are used to analyze the association between conflict risk and influencing factors at toll plazas. A modified conflict risk indicator is developed to assess the safety of different traffic movements. The results show the impact of toll collection type, vehicle's location, and various factors on traffic conflict risk. The proposed analytic method can accommodate different conflict types and consider the correlation of unobserved heterogeneity.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2022)
Article
Ergonomics
Wenjing Zhao, Siyuan Gong, Dezong Zhao, Fenglin Liu, N. N. Sze, Helai Huang
Summary: A novel in-vehicle omni-direction collision warning system (OCWS) is developed with the emerging connected vehicle technologies, which can detect vehicles approaching from different directions and provide advanced collision warnings. This study assesses the effects of collision types and warning types on micro-level driver behaviors and safety performance, considering driver characteristics. Field tests with 51 drivers using an in-vehicle human-machine interface (HMI) that provides both visual and auditory warnings for different collision types are conducted. The results show that age, driving experience, collision type, and warning type can affect driving performance, providing guidance for the optimal design and customization of in-vehicle HMIs for collision warnings.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Ergonomics
Manman Zhu, N. N. Sze, Sharon Newnam, Dianchen Zhu
Summary: Hong Kong is a compact and bustling city. Previous research rarely investigated the impact of pedestrian network configuration on pedestrian crashes. This study uses a three-dimensional digital map to assess the connectivity and accessibility of the pedestrian network and measures the relationship between network characteristics and pedestrian safety at a macroscopic level. The findings indicate that improving the accessibility of footbridges and underpasses can enhance pedestrian safety.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Public, Environmental & Occupational Health
Hongliang Ding, Yuhuan Lu, N. N. Sze, Constantinos Antoniou, Yanyong Guo
Summary: In this study, a novel data-driven approach is developed for the allocation of boundary crashes, considering crash features and cyclist characteristics. The results indicate that this method achieves high matching percentages and superior prediction performances compared to conventional methods. Moreover, it allows for the identification of more influencing factors for bicycle crash frequency at a macroscopic level.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2023)
Article
Public, Environmental & Occupational Health
Qiang Zeng, Fangzhou Wang, Tiantian Chen, N. N. Sze
Summary: In order to optimize incident management strategies, it is crucial to have a better understanding of the factors that affect accident clearance time. This study developed a grouped random parameters hazard-based duration model with time-varying covariates to address this gap in the literature. The results showed that certain factors, such as rear-end accidents, involvement of trucks or other vehicles, evening hours, and shoulder blockage, had heterogeneous effects on the hazard function. Additionally, time-varying covariates like wind speed, temperature, and humidity were found to have significant effects on accident clearance time.
ANALYTIC METHODS IN ACCIDENT RESEARCH
(2023)
Article
Ergonomics
Qiang Zeng, Qianfang Wang, Keke Zhang, S. C. Wong, Pengpeng Xu
Summary: This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections in Hong Kong based on high-resolution accident data recorded from 2010 to 2019. Spatiotemporal logistic regression models were developed to account for spatial and temporal correlations among crash data. The results showed that the model with the Leroux conditional autoregressive prior and random walk structure performed the best. Various factors such as pedestrian age, head injury, pedestrian location, and driver maneuvers significantly affected the severity of pedestrian injuries. Targeted countermeasures including safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed. The study provides a valuable toolkit for analyzing spatiotemporal correlations in crash modeling.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Ergonomics
Yunchao Zhang, Yanyan Chen, Xin Gu, N. N. Sze, Jianling Huang
Summary: Driving style plays an important role in traffic safety. This paper proposes a personalized risk lane-changing prediction framework that considers driving style, using driving volatility indices and a dynamic clustering method. The results show that the LightGBM algorithm outperforms other machine learning methods in personalized lane-changing risk prediction, and aggressive drivers have a higher lane-changing risk.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Economics
Umer Mansoor, Arshad Jamal, Junbiao Su, N. N. Sze, Anthony Chen
Summary: Motorcycle crashes cause a significant number of fatalities and severe injuries worldwide, especially in developing countries. Machine learning methods have been found to provide better prediction performance, but with weaker interpretability. This study aims to compare the consistency of risk factors identified by statistical models and machine learning methods in analyzing motorcycle crash severity.
Article
Economics
Dianchen Zhu, N. N. Sze, Zhongxiang Feng, Ho-Yin Chan
Summary: Crossing a road with multiple traffic lanes and busy traffic is a challenging task for pedestrians. Footbridges and underpasses have been built in metropolitan cities like Hong Kong to resolve the problem, but the choice behavior of pedestrians towards these crossing facilities is not fully understood. This study examines the relationship between influencing factors and the crossing choices of pedestrians in Hong Kong and highlights the importance of considering environmental conditions in understanding individual perception and choice. The findings provide insights for improving the walking environment and promoting walkability in future urban and transport planning strategies.
Article
Transportation
Ziqian Zhang, Haojie Li, N. N. Sze, Gang Ren
Summary: Due to the lack of instructions and regulations, pedestrian crossing areas and routes are usually uncertain at mid-blocks without crossing facilities, resulting in higher crossing risks. Using Unmanned Aerial Vehicle and roadside camera data, this study investigates pedestrian crossing behaviors at mid-blocks without crossing facilities. The study proposes metrics for characterizing pedestrian crossing routes and investigates the role of roadside environment in explaining pedestrian route choice, providing practical suggestions for street layout design and enhancing pedestrian crossing safety.
TRAVEL BEHAVIOUR AND SOCIETY
(2023)
Article
Ergonomics
Dongjie Liu, Dawei Li, N. N. Sze, Hongliang Ding, Yuchen Song
Summary: This study proposes an integrated data- and theory-driven crash-severity model, known as TVR-EF, which improves both predictive performance and interpretability by leveraging the strengths of both approaches.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Green & Sustainable Science & Technology
Hongliang Ding, Haojie Li, N. N. Sze
Summary: The study finds that implementing the London western charging zone (WCZ) scheme significantly reduces traffic volume, NOX, and CO2 emissions, while abolishing the scheme has the opposite effect.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
