Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety

• URL: http://arxiv.org/abs/2003.07672v1
• Date: Thu, 5 Mar 2020 23:57:47 GMT
• Title: Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety
• Authors: Rateb Jabbar, Mohammed Shinoy, Mohamed Kharbeche, Khalifa Al-Khalifa, Moez Krichenz and Kamel Barkaouiy
• Abstract summary: Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. To tackle this problem, Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system, including traffic data infrastructure, safety planning, and engineering practices and standards. In this paper, an IoT based solution to facilitate such a study in Qatar is proposed. Different data points from a driver are collected and recorded in an unobtrusive manner, such as trip data, GPS coordinates, compass heading, minimum, average, and maximum speed and his driving behavior, including driver's drowsiness level. Analysis of these data points will help in prediction of crashes and road infrastructure improvements to reduce such events. It will also be used for drivers risk assessment and to detect extreme road user behaviors. A framework that will help to visualize and manage this data is also proposed, along with a Deep Learning-based application that detects drowsy driving behavior that netted an 82 percent accuracy.

Related papers

• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey [0.0]
  Risky drivers account for 70% of fatal accidents in the United States. This paper examines the various techniques used to analyze driver behavior using visual and vehicular data.
  arXiv  Detail & Related papers  (2023-08-25T14:33:59Z)
• AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities [0.0]
  This paper presents a comprehensive analysis of traffic accidents in different regions across the United States. To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems.
  arXiv  Detail & Related papers  (2023-07-22T17:08:13Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Driving Safety Prediction and Safe Route Mapping Using In-vehicle and Roadside Data [3.8103848718367597]
  The Safe Route Mapping (SRM) model is extended to consider driver behaviors when making predictions. An Android App is designed to gather drivers' information and upload it to a server. dynamic traffic information is captured by a roadside camera and uploaded to the same server. A LightGBM model is introduced to predict conflict indices for drivers in the next one or two seconds.
  arXiv  Detail & Related papers  (2022-09-12T20:39:33Z)
• A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure [1.9967512860886603]
  Single-vehicle accidents are the most common type of fatal accidents in Sweden. This study investigates the feasibility, implementation, limitations and scaling up of data processing pipelines for roadside object detection. The goal of this report is to investigate how to implement a scalable roadside object detection system towards safe road infrastructure and Sweden's Vision Zero.
  arXiv  Detail & Related papers  (2022-04-17T16:27:26Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor [6.291501119156943]
  Pedestrians are exposed to risk of death or serious injuries on roads, especially unsignalized crosswalks. We propose an automated and simpler system for effectively extracting object behavioral features from video sensors deployed on the road. This study demonstrates the potential for a network of connected video sensors to provide actionable data for smart cities.
  arXiv  Detail & Related papers  (2021-07-08T01:11:31Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.