An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Zafar Iqbal,Asad Habib,Shahid Hussain,Majid I Khan,Átila Bueno

doi:10.1155/2021/5543698

Zafar Iqbal, Asad Habib + Show 3 more

Open Access

https://doi.org/10.1155/2021/5543698

Copy DOI

Abstract

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

Highlights

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders in smart cities
While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. is necessitates that the AID system detects and classifies all the popular traffic incident types, but severity as well that is associated with these incidents. erefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. e experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers
(4) e proposed model stacking approach shows improvements in both severity classification based on specialized incident severity predictors at level 1 and incident type classification based on intermediary predictions at level 2 that narrow down the domain of incident type

Summary

Introduction

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. According to report [5], reckless driving causes 33% of all deaths involving major car accidents, which are more than 13,000 each year Another challenging type of on-road traffic incident is Complexity congestion that relates to an excess of vehicles on a portion of a roadway at a time resulting in slower speeds, sometimes much slower than normal or “free flow” speeds, the Federal Highway Administration (FHWA) [6]. The interdependency of these RTIs makes the boundary of incident classification blurred, which increases false alarm rate (FAR) and misclassification of the incident type (IT), and makes it hard to guess the risk factor (RF) known as incident severity (IS) associated with the RTIs. e risk of secondary incidents can be reduced by deploying an incident management system (IMS) to timely and accurately detect the primary incident type (accident, congestion, and reckless driving) and its associated severity (low, medium, and high) and reporting the RTI with its details to appropriate rescue departments for timely clearance. The ML-based AID system can be characterized as a singlefeature classification (SFC) which classifies the single feature or one aspect of the underlying scenario while multifeature classification (MFC) can classify or predict multiple features or multiple aspects about the underlying problem. erefore, a context-free AID system equipped with MFC can extract multiple aspects of the incident, such as type and severity

Objectives

Methods

Results

Conclusion