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Faisal Habib M, Motuba D, Huang Y. Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models. ACCIDENT; ANALYSIS AND PREVENTION 2024; 205:107650. [PMID: 38965029 DOI: 10.1016/j.aap.2024.107650] [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: 12/15/2023] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 07/06/2024]
Abstract
An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.
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Affiliation(s)
- Muhammad Faisal Habib
- Department of Transportation, Logistics & Finance, College of Business, North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA.
| | - Diomo Motuba
- Department of Transportation, Logistics & Finance, College of Business, North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA.
| | - Ying Huang
- Civil, Construction and Environmental Engineering Department, College of Engineering, North Dakota State University, PO Box 6050, Fargo, ND 58108-6050, USA.
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He S, Fu H, Wang J, Yang J, Yao Y, Kuang J, Xiao X. Exploring road safety using alignment perspective features in real driving images: A case study on mountain freeways. PLoS One 2024; 19:e0305241. [PMID: 38885243 PMCID: PMC11182566 DOI: 10.1371/journal.pone.0305241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/27/2024] [Indexed: 06/20/2024] Open
Abstract
INTRODUCTION While driving, drivers frequently adapt their driving behaviors according to their perception of the road's alignment features. However, traditional two-dimensional alignment methods lack the ability to capture these features from the driver's perspective. METHOD This study introduces a novel method for road alignment recognition, employing image recognition technology to extract alignment perspective features, namely alignment perspective skewness (APS) and alignment perspective kurtosis (APK), from in-real driving images. Subsequently, the K-means clustering algorithm is utilized for road segment classification based on APS and APK indicators. Various sliding step length for clustering are employed, with step length ranging from 100m to 400m. Furthermore, the accident rates for different segment clusters are analyzed to explore the relationship between alignment perspective features and traffic safety. A 150 km mountain road section of the Erlianhaote-Guangzhou freewway from Huaiji to Sihui is selected as a case study. RESULTS The results demonstrate that using alignment perspective features as classification criteria produces favorable clustering outcomes, with superior clustering performance achieved using shorter segment lengths and fewer cluster centers. The road segment classification based on alignment perspective features reveals notable differences in accident rates across categories; while traditional two-dimensional parameters-based classification methods fail to capture these differences. The most significant differences in accident rates across categories are observed with segment length of 100m, with the significance gradually diminishing as segment length increases and disappearing entirely when the length exceeds 300m. IMPLICATION These findings validate the reliability of using alignment perspective features (APS and APK) for road alignment classification and road safety analysis, providing valuable insights for road safety management.
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Affiliation(s)
- Shijian He
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China
| | - Hongmei Fu
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China
| | - Jie Wang
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China
| | - Jiacheng Yang
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China
| | - Yanqing Yao
- International College of Engineering, Changsha University of Science and Technology, Changsha, China
| | - Jiaojiao Kuang
- Hunan Communications Research Institute Co., Ltd, Changsha, Hunan, China
| | - Xiangliang Xiao
- Hunan Communications Research Institute Co., Ltd, Changsha, Hunan, China
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Sun Z, Cui K, Qi X, Wang J, Han L, Gu X, Lu H. How do drunk-driving events escalate into drunk-driving crashes? An empirical analysis of Beijing from a spatiotemporal perspective. Int J Inj Contr Saf Promot 2024; 31:256-272. [PMID: 38279202 DOI: 10.1080/17457300.2023.2300459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 12/24/2023] [Indexed: 01/28/2024]
Abstract
Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of 'the severity of drunk-driving event' and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.
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Affiliation(s)
- Zhiyuan Sun
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Keqi Cui
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Xin Qi
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Jianyu Wang
- Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Lu Han
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Xin Gu
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Huapu Lu
- Institute of Transportation Engineering, Tsinghua University, Beijing, China
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Kar P, Venthuruthiyil SP, Chunchu M. Crash risk estimation of Heavy Commercial vehicles on horizontal curves in mountainous terrain using proactive safety method. ACCIDENT; ANALYSIS AND PREVENTION 2024; 199:107521. [PMID: 38428243 DOI: 10.1016/j.aap.2024.107521] [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: 05/13/2023] [Revised: 02/11/2024] [Accepted: 02/21/2024] [Indexed: 03/03/2024]
Abstract
Heavy commercial vehicles (HCVs) face elevated crash risks in mountainous terrains due to the challenging topography and intricate geometry, posing a significant challenge for transportation agencies in mitigating these risks. While safety studies in such terrains traditionally rely on historical crash data, the inherent issues associated with crash data have led to a shift towards proactive safety studies using surrogate safety measures (SSM) in recent years. However, the scarcity of accurate microscopic data related to HCV drivers has limited the application of proactive safety studies in mountainous terrains. This study addresses this gap by employing an SSM known as anticipated collision time (ACT) to explore the impact of horizontal curves on the crash risk of HCVs in mountainous terrain. To perform the crash risk analysis, a collection of videos was gathered from horizontal curves in the mountainous terrain along the Guwahati-Shillong bypass in the Northeastern region of India. Subsequently, trajectories were extracted from these videos using semi-automated image processing software. Traffic conflicts were identified using ACT, and the crash risk was estimated through the Peak-Over Threshold (POT) approach of the Extreme Value Theory (EVT). The findings indicate that Run-Off-Road (ROR) traffic events happen more frequently on or near the horizontal curves falling in mountainous terrain. However, the frequency of severe ROR traffic events is lower, indicating the lower propensity for such collisions on the selected curves. The threshold for the safety margin of ROR traffic events involving HCVs was 2 s. The study revealed that stationary models exhibit an overestimation of crash frequency (0, 6) compared to the observed crash frequency (0, 2). Consequently, non-stationary crash risk models were developed, incorporating road geometry and the braking and yaw rates of HCVs as covariates. The results demonstrate that the estimated confidence bounds (1, 2) align with the observed crash frequency (0, 2), emphasizing the applicability of POT models for safety analysis in mountainous terrains in India. The study identified curve radius, length of the approach tangent, and the distance between the center points of horizontal and vertical curves as influential factors affecting the Run-Off-Road (ROR) crash risk of HCVs. Notably, sharp curves with radii less than 200 m or more are associated with a significantly higher crash risk. Additionally, an increased distance between the midpoints of horizontal and vertical curves beyond 1 m was found to escalate the ROR crash risk of HCVs. To mitigate these risks, it is recommended to reduce the length of the approach tangent to prevent high-speed travel on sharp curves. Furthermore, proper signage should be strategically placed to warn drivers and avert potential hazards.
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Affiliation(s)
- Pranab Kar
- Indian Institute of Technology Guwahati, India.
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Se C, Champahom T, Jomnonkwao S, Chonsalasin D, Ratanavaraha V. Modeling of single-vehicle and multi-vehicle truck-involved crashes injury severities: A comparative and temporal analysis in a developing country. ACCIDENT; ANALYSIS AND PREVENTION 2024; 197:107452. [PMID: 38183691 DOI: 10.1016/j.aap.2023.107452] [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: 09/29/2023] [Revised: 12/07/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024]
Abstract
Truck-involved crashes persist as a significant concern, yielding noteworthy human casualties and causing economic ramifications, particularly in developing countries. This paper aims to undertake a comprehensive analysis of the associated factors influencing injury severity in truck-involved crashes, with a particular emphasis on discerning variations between single-vehicle and multi-vehicle incidents, as well as accounting for heterogeneity and temporal stability. The data analysis involves a meticulous examination of crash data spanning the entirety of Thailand from 2017 to 2020. Employing three distinct levels of injury severities, namely PDO injury, moderate injury, and severe injury, the study employs a series of mixed logit models that account for unobserved heterogeneity in both means and variances. Results revealed significant instability in injury risk determinants over time among both single and multi-vehicle events. Aligning predictive assessments further spotlighted fluctuations in projected burdens across models and years - collectively underscoring the imperative to integrate temporal considerations into modeling and prevention. Several crash-type distinctions and priorities emerged. For single-truck events, key risks included roadway alignments and geometry, speeding, fatigue, and lighting conditions. However multi-truck collisions concentrated around exposure factors like highway traits, sightline limitations, and vulnerable road users. Ultimately, the technique permitted responsive countermeasure targeting and recalibration opportunities keyed to each crash form's evolving landscapes. While it is indeed noteworthy that several variables have exhibited instability in their effects, it is equally important to acknowledge the existence of certain variables that maintain a relative degree of temporal stability. This underscores their pivotal role in shaping the foundation of enduring strategies aimed at enhancing traffic safety in the long run. The multifaceted investigation constitutes an invaluable reference for diverse transportation stakeholders seeking to curb rising truck fatalities through evidence-based improvements in policy, engineering, usage protocols, and technologies. It provides a blueprint for nimble safety planning within complex modernizing road systems.
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Affiliation(s)
- Chamroeun Se
- Institute of Research and Development, Suranaree University of Technology, 111, Maha Witthayalai Rd, Suranari, Mueang, Nakhon Ratchasima 30000, Thailand.
| | - Thanapong Champahom
- Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, 744 Sura Narai Rd, Nai-muang, Muang, Nakhon Ratchasima 30000, Thailand.
| | - Sajjakaj Jomnonkwao
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111, Maha Witthayalai Rd, Suranari, Mueang, Nakhon Ratchasima 30000, Thailand.
| | - Dissakoon Chonsalasin
- Faculty of Railway Systems and Transportation, Rajamangala University of Technology Isan, 744 Sura Narai Rd, Nai-muang, Muang, Nakhon Ratchasima 30000, Thailand.
| | - Vatanavongs Ratanavaraha
- School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111, Maha Witthayalai Rd, Suranari, Mueang, Nakhon Ratchasima 30000, Thailand.
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Zeng Q, Wang Q, Zhang K, Wong SC, Xu P. Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models. ACCIDENT; ANALYSIS AND PREVENTION 2023; 189:107119. [PMID: 37235968 DOI: 10.1016/j.aap.2023.107119] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 04/18/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023]
Abstract
This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.
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Affiliation(s)
- Qiang Zeng
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China.
| | - Qianfang Wang
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
| | - Keke Zhang
- Human Provincial Communications Planning, Survey & Design Institute Co., Ltd, Changsha, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.
| | - Pengpeng Xu
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China.
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