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Sadeghi M, Aghabayk K, Quddus M. A hybrid Machine learning and statistical modeling approach for analyzing the crash severity of mobility scooter users considering temporal instability. ACCIDENT; ANALYSIS AND PREVENTION 2024; 206:107696. [PMID: 38964138 DOI: 10.1016/j.aap.2024.107696] [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: 04/12/2024] [Revised: 06/29/2024] [Accepted: 06/29/2024] [Indexed: 07/06/2024]
Abstract
One of the main objectives in improving the quality of life for individuals with disabilities, especially those experiencing mobility issues such as the elderly, is to enhance their day-to-day mobility. Enabling easy mobility contributes to their independence and access to better healthcare, leading to improvements in both physical and mental well-being. Mobility Scooters have become increasingly popular in recent years as a means of facilitating mobility, yet traffic safety issues such as crash severity have not been adequately investigated in the literature. This study addresses this knowledge gap by employing a hybrid method that combines a machine learning approach using the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and an advanced statistical model called Random Parameters Binary Logit accounting for heterogeneity in means and variances. Analyzing the United Kingdom mobility scooter crash data from 2018 to 2022, the study examined temporal instability using a likelihood ratio test. The results revealed that there was instability over the three distinct periods of time based on the coronavirus (COVID) pandemic, namely, pre-COVID, during COVID, and post-COVID. Moreover, the results revealed that mobility scooter crashes occurring at a give-way or uncontrolled junctions has a random effect on the severity, while factors such as mobility scooter riders aged over 80, rear-end and sideswipe crashes, and crashes during winter months increase the risk of severe injuries. Conversely, mobility scooter riders involved in crashes while riding on the footway are less likely to experience severe injuries. These findings offer valuable insights for enhancing road safety measures that can be utilized to effectively reduce the crash severity of mobility scooter riders.
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Affiliation(s)
- Matin Sadeghi
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Kayvan Aghabayk
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Mohammed Quddus
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
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Zhou Y, Fu C, Jiang X, Liu H. Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach? ACCIDENT; ANALYSIS AND PREVENTION 2024; 207:107752. [PMID: 39180851 DOI: 10.1016/j.aap.2024.107752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 07/26/2024] [Accepted: 08/17/2024] [Indexed: 08/27/2024]
Abstract
The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.
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Affiliation(s)
- Yue Zhou
- Flight Technology College, Civil Aviation Flight University of China, Guanghan, China
| | - Chuanyun Fu
- School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China; Department of Civil Engineering, University of British Columbia, Vancouver, Canada.
| | - Xinguo Jiang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
| | - Haiyue Liu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
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Zhang S, Sze NN. Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances. ACCIDENT; ANALYSIS AND PREVENTION 2024; 207:107739. [PMID: 39151252 DOI: 10.1016/j.aap.2024.107739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 07/24/2024] [Accepted: 08/04/2024] [Indexed: 08/19/2024]
Abstract
Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.
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Affiliation(s)
- Shile Zhang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - N N Sze
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
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Samerei SA, Aghabayk K. Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering. ACCIDENT; ANALYSIS AND PREVENTION 2024; 202:107603. [PMID: 38701559 DOI: 10.1016/j.aap.2024.107603] [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: 01/31/2024] [Revised: 04/02/2024] [Accepted: 04/27/2024] [Indexed: 05/05/2024]
Abstract
Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.
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Affiliation(s)
- Seyed Alireza Samerei
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Kayvan Aghabayk
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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Zhu Y, Qian Y, Xu J, Hu W. Young novice drivers' road crash injuries and contributing factors: A crash data investigation. TRAFFIC INJURY PREVENTION 2024:1-8. [PMID: 38917367 DOI: 10.1080/15389588.2024.2367504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
OBJECTIVE Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers' crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. METHODS Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. RESULTS The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). CONCLUSIONS The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.
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Affiliation(s)
- Yansong Zhu
- School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Yubin Qian
- School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Jiejie Xu
- Shanghai Intelligent Vehicle Fusion Innovation Center Co., Shanghai, China
| | - Wenhao Hu
- Key Laboratory of Product Defect and Safety for State Market Regulation, Beijing, China
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Rangam H, Sivasankaran SK, Balasubramanian V. Visual hazardous models: A hybrid approach to investigate road hazardous events. ACCIDENT; ANALYSIS AND PREVENTION 2024; 200:107556. [PMID: 38531281 DOI: 10.1016/j.aap.2024.107556] [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: 11/18/2023] [Revised: 02/10/2024] [Accepted: 03/21/2024] [Indexed: 03/28/2024]
Abstract
Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009-2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 82% more chance of fatal/grievous injuries on expressways (posted speed limit: 100 km per hour) in annual hazardous zone locations. Working pedestrian age group (25-64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.
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Affiliation(s)
- Harikrishna Rangam
- RBG Labs, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Sathish Kumar Sivasankaran
- RBG Labs, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Venkatesh Balasubramanian
- RBG Labs, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.
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Sun Z, Wang Z, Qi X, Wang D, Gu X, Wang J, Lu H, Chen Y. Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP. Int J Inj Contr Saf Promot 2024; 31:273-293. [PMID: 38284989 DOI: 10.1080/17457300.2023.2300479] [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: 03/16/2023] [Accepted: 12/24/2023] [Indexed: 01/30/2024]
Abstract
Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.
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Affiliation(s)
- Zhiyuan Sun
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
| | - Zhicheng Wang
- 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
| | - Duo Wang
- 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
| | - Jianyu Wang
- Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Huapu Lu
- Institute of Transportation Engineering, Tsinghua University, Beijing, China
| | - Yanyan Chen
- Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
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