Introducing a risk estimation index for drivers: A case of Iran

Moghaddam, Abolfazl Mohammadzadeh; Ayati, Esmaeel

doi:10.1016/j.ssci.2013.08.004

Cited by 25 publications

(3 citation statements)

References 65 publications

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“…Spatiotemporal correlation profiles in car collisions can be explained with the assumption that several traffic-related factors exhibit some spatiotemporal features [ 26 , 27 , 28 ], e.g., since neighboring roads have similar traffic flow characteristics and, consequently, accidents are spatially clustered over defined temporal units. Results of a spatiotemporal analysis of traffic accidents may become a useful guide for accident management and prevention [ 20 , 29 , 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming spatiotemporal interactions as a basic factor shaping collision density [ 37 , 38 , 39 , 40 ], different approaches have been proposed for explicit analysis of processes interacting over space [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Given the benefits associated with a comprehensive knowledge of temporal and spatial correlation among individual events [ 31 , 32 , 33 ], crash (frequency) data were usually aggregated over space and time [ 16 ], which may produce unobserved heterogeneity, as crashes that occur close in space or time are likely to share some unobserved characteristics [ 21 , 41 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

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Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Kalantari

Shahraki

Yaghmaei

et al. 2021

IJERPH

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Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Kalantari

Shahraki

Yaghmaei

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…For example, some researchers defined high-risk drivers as those who were involved in crashes more than expected [5,6]. At-fault drivers were also considered to be risky and have been extensively studied [7,8,9,10,11]. Crash severity was another dimension of research interest [12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

Wang

Liu²,

et al. 2019

IJERPH

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The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively.

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An Intelligent Safety System for Human-Centered Semi-autonomous Vehicles

Khojasteh¹,

Alipour²,

Ansari³

et al. 2020

Data Science: From Research to Application

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Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.

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Introducing a risk estimation index for drivers: A case of Iran

Cited by 25 publications

References 65 publications

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework

An Intelligent Safety System for Human-Centered Semi-autonomous Vehicles

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