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Adib K, Mazouzi M, Elyoussoufi S. Investigation of annual trucks fatal accidents in El Hajeb province of Morocco using TRIZ-Ishikawa diagram. Heliyon 2024; 10:e26295. [PMID: 38390066 PMCID: PMC10882120 DOI: 10.1016/j.heliyon.2024.e26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 02/03/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
A stretch of road in the province of EL HAJEB, located in the central-south region of Morocco, is classified among the roadways experiencing an accumulation of fatal traffic accidents, with a particular involvement of freight transport vehicles. Investigation reports elaborated for these fatal accidents specify that these accidents occurred when drivers lost control of their vehicles due to brake system failures, resulting in multiple fatalities. However, these investigation reports did not provide root causes of this phenomena. Scientific research efforts in this field are directed toward preventive solutions and proposing a comprehensive analytical approach. This study aims to elucidate the mechanisms behind these specific accident phenomena on the identified stretch in the city of EL HAJEB. To achieve the study's objective and identify the triggering or contributing factors of these failures, we employed a novel approach combining the TRIZ and Ishikawa tools. This is a systematic methodology for analyzing potential causes of accidents, allowing us to clarify the intricacies of the specific phenomena leading to accidents while systematizing the analysis process, thus contributing to enhancing the effectiveness of investigative teams. This article contributes to introducing a new analytical tool in the field of accident analysis.
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Affiliation(s)
- Karim Adib
- ENSEM School, Hassan II University, Morocco
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2
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Wu D, Zhang Y, Xiang Q. Geographically weighted random forests for macro-level crash frequency prediction. ACCIDENT; ANALYSIS AND PREVENTION 2024; 194:107370. [PMID: 37939418 DOI: 10.1016/j.aap.2023.107370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/29/2023] [Accepted: 11/01/2023] [Indexed: 11/10/2023]
Abstract
Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.
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Affiliation(s)
- Dongyu Wu
- Jiangsu Key Laboratory of Urban ITS, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China; School of Transportation, Southeast University, China
| | - Yingheng Zhang
- Jiangsu Key Laboratory of Urban ITS, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China; School of Transportation, Southeast University, China
| | - Qiaojun Xiang
- Jiangsu Key Laboratory of Urban ITS, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China; School of Transportation, Southeast University, China.
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Niyogisubizo J, Liao L, Zou F, Han G, Nziyumva E, Li B, Lin Y. Predicting traffic crash severity using hybrid of balanced bagging classification and light gradient boosting machine. INTELL DATA ANAL 2023. [DOI: 10.3233/ida-216398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author’s knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes.
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Affiliation(s)
- Jovial Niyogisubizo
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
| | - Lyuchao Liao
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
| | - Fumin Zou
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
| | - Guangjie Han
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- College of Internet of Things Engineering, Hohai University, Nanjing, Jiangsu, China
| | - Eric Nziyumva
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
| | - Ben Li
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
| | - Yuyuan Lin
- Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fujian, China
- Fujian Provincial Universities Engineering Research Centre for Intelligent Self-Driving Technology, Fujian University of Technology, Fuzhou, Fujian, China
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Iranmanesh M, Seyedabrishami S, Moridpour S. Identifying high crash risk segments in rural roads using ensemble decision tree-based models. Sci Rep 2022; 12:20024. [PMID: 36414672 PMCID: PMC9681741 DOI: 10.1038/s41598-022-24476-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial-temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, time headway), road characteristics (e.g. road type, number of lanes), weather data (e.g. daily rainfall, snow depth, temperature), and calendar variables (e.g. day of the week, public holidays, month, year) are used for model calibration. Three different decision tree algorithms, including, Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been employed to predict crash frequency. Results show that based on the traditional evaluation measures, the XGBosst is better for the explanation and interpretation of the factors affecting crash frequency, while the RF model is better for detecting trends and forecasting crash frequency. According to the results, the traffic flow rate, road type, year of the crash, and wind speed are the most influencing variables in predicting crash frequency on rural roads. Forecasting the high and medium risk segment-day in the rural network can be essential to the safety management plan. This risk will be sensitive to real traffic data, weather forecasts and road geometric characteristics. Seventy percent of high and medium risk segment-day are predicted for the case study.
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Affiliation(s)
- Maryam Iranmanesh
- grid.412266.50000 0001 1781 3962Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
| | - Seyedehsan Seyedabrishami
- grid.412266.50000 0001 1781 3962Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sara Moridpour
- grid.1017.70000 0001 2163 3550Civil and Infrastructure Engineering Discipline, RMIT University, Melbourne, Australia
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Wang B, Zhang C, Wong YD, Hou L, Zhang M, Xiang Y. Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13693. [PMID: 36294267 PMCID: PMC9603763 DOI: 10.3390/ijerph192013693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.
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Affiliation(s)
- Bo Wang
- School of Highway, Chang’an University, Xi’an 710064, China
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Chi Zhang
- School of Highway, Chang’an University, Xi’an 710064, China
- Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi’an 710000, China
| | - Yiik Diew Wong
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Lei Hou
- School of Engineering, STEM College, RMIT University, Melbourne, VIC 3001, Australia
| | - Min Zhang
- College of Transportation Engineering, Chang’an University, Xi’an 710064, China
| | - Yujie Xiang
- School of Highway, Chang’an University, Xi’an 710064, China
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Lei Y, Ozbay K, Xie K. Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic. ACCIDENT; ANALYSIS AND PREVENTION 2022; 173:106715. [PMID: 35623304 PMCID: PMC9125007 DOI: 10.1016/j.aap.2022.106715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/28/2022] [Accepted: 05/14/2022] [Indexed: 05/03/2023]
Abstract
With the advance of intelligent transportation system technologies, contributing factors to crashes can be obtained in real time. Analyzing these factors can be critical in improving traffic safety. Despite many crash models having been successfully developed for safety analytics, most models associate crash observations and contributing factors at the aggregate level, resulting in potential information loss. This study proposes an efficient Gaussian process modulated renewal process model for safety analytics that does not suffer from information loss due to data aggregations. The proposed model can infer crash intensities in the continuous-time dimension so that they can be better associated with contributing factors that change over time. Moreover, the model can infer non-homogeneous intensities by relaxing the independent and identically distributed (i.i.d.) exponential assumption of the crash intervals. To demonstrate the validity and advantages of this proposed model, an empirical study examining the impacts of the COVID-19 pandemic on traffic safety at six interstate highway sections is performed. The accuracy of our proposed renewal model is verified by comparing the areas under the curve (AUC) of the inferred crash intensity function with the actual crash counts. Residual box plot shows that our proposed models have lower biases and variances compared with Poisson and Negative binomial models. Counterfactual crash intensities are then predicted conditioned on exogenous variables at the crash time. Time-varying safety impacts such as bimodal, unimodal, and parabolic patterns are observed at the selected highways. The case study shows the proposed model enables safety analytics at a granular level and provides a more detailed insight into the time-varying safety risk in a changing environment.
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Affiliation(s)
- Yiyuan Lei
- C2SMART Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA.
| | - Kaan Ozbay
- C2SMART Center, Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA.
| | - Kun Xie
- Department of Civil and Environmental Engineering, Old Dominion University, 129C Kaufman Hall, Norfolk, VA 23529, USA.
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Geospatial Simulation System of Mountain Area Black Ice Accidents. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the development of mountain areas has recently increased in Korea, existing roads are being renovated, and new highways are being constructed, which increases driving speeds in mountainous areas. However, the mountainous region in northeastern Korea is more likely to form black ice due to higher humidity, frequent fog, and hillshade, depending on the terrain, which can cause serious traffic pileups. In this study, therefore, we present a method to build a more effective black ice prediction and warning system by linking spatial information to the existing road management system that estimates the road surface temperature based on real-time weather information. The spatial information enabled a prediction to be made of the risk level of black ice formation for each time zone by simulating changes in the shadow area based on precise 3D terrain information. Moreover, this information also presented slope and curvature information of the road to estimate the risk zone. The spatial information was integrated with weather data to predict road surface temperature. The proposed system was tested in two mountainous regions with weather data accumulated from 2017 to 2018. As a result, the proposed system anticipated 71% of traffic accidents caused by black ice during the testing period. The results show that the system can contribute significantly to preventing black-ice-related traffic accidents by providing reasonable predictions.
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Čubranić-Dobrodolac M, Švadlenka L, Čičević S, Trifunović A, Dobrodolac M. A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement. Soft comput 2021. [DOI: 10.1007/s00500-021-06516-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Abstract
Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.
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Narváez-Villa P, Arenas-Ramírez B, Mira J, Aparicio-Izquierdo F. Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18168327. [PMID: 34444076 PMCID: PMC8391987 DOI: 10.3390/ijerph18168327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022]
Abstract
Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.
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Affiliation(s)
- Paúl Narváez-Villa
- University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain; (B.A.-R.); (F.A.-I.)
- Transportation Engineering Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador
- Correspondence: or
| | - Blanca Arenas-Ramírez
- University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain; (B.A.-R.); (F.A.-I.)
| | - José Mira
- Statistics Department, Escuela Técnica Superior de Ingenieros Industriales (ETSII-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain;
| | - Francisco Aparicio-Izquierdo
- University Institute for Automobile Research Francisco Aparicio Izquierdo (INSIA-UPM), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain; (B.A.-R.); (F.A.-I.)
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Xiao T, Lu H, Wang J, Wang K. Predicting and Interpreting Spatial Accidents through MDLSTM. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041430. [PMID: 33546503 PMCID: PMC7913614 DOI: 10.3390/ijerph18041430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 11/16/2022]
Abstract
Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.
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Affiliation(s)
- Tianzheng Xiao
- Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China; (T.X.); (H.L.)
| | - Huapu Lu
- Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China; (T.X.); (H.L.)
| | - Jianyu Wang
- Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China; (T.X.); (H.L.)
- Correspondence: ; Tel.: +86-010-6277-2615
| | - Katrina Wang
- Division of Biosciences, University College London, London WC1E 6BT, UK;
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Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents. SUSTAINABILITY 2021. [DOI: 10.3390/su13010390] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Climate change and the extreme weather have a negative impact on road traffic safety, resulting in severe road traffic accidents. In this study, a negative binomial model and a log-change model are proposed to analyse the impact of various factors on fatal traffic accidents. The dataset used in this study includes the fatal traffic accident frequency, social development indicators and climate indicators in California and Arizona. The results show that both models can provide accurate fitting results. Climate variables (i.e., average temperature and standard precipitation 24) can significantly affect the frequency of fatal traffic accidents. Non-climate variables (i.e., beer consumption, rural Vehicle miles travelled ratio, and vehicle performance) also have a significant impact. The modelling results can provide decision-making guidelines for the transportation management agencies to improve road traffic safety.
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