Low-Cost Portable Video-Based Queue Detection for Work- Zone Safety (original) (raw)
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Test a Queue Detection System for Special Events in Nevada
Journal of Transportation Technologies, 2014
Queue detection systems have been used in traffic management in work zones and have also been recommended for traffic control for special events like sports and conventions. However, they have not been tested in the field. This paper presents the results of tests for a queue detection system at two special events in Las Vegas, Nevada. The system consisted of two vision detectors, radio frequency communications and one changeable message sign. Two aspects of the system were evaluated: the effectiveness of the system in reducing speeds and the cost and effectiveness of its deployment. In the tests, traffic data such as queue length and vehicle operating speeds were collected and analyzed to see whether motorists respond congestion related message on the changeable message sign when they did not perceive the congestion. By this approach, the motorists' true responses to the system were identified. The results indicated that motorists did make positive responses to the messages provided by the system. However, it was found that the system may not be cost-effective because significant costs would be incurred in purchasing, installation and maintenance of the system. Recommendations were provided to utilize existing message signs and detectors to provide the same information to motorists as a queue detection system does.
Growing concern over traffic safety as well as rising congestion costs have been recently redirecting research effort from the traditional crash detection and clearance reactive traffic management towards online, proactive crash prevention solutions. In this project such a solution, specifically for high crash areas, is explored by identifying the most relevant real time traffic metrics and incorporating them in a crash likelihood estimation model. Unlike earlier attempts, this one is based on a unique detection and surveillance infrastructure deployed on the freeway section experiencing the highest crash rate in the state of Minnesota. This state-of-the-art infrastructure allowed video recording of 110 live crashes, crash related traffic events, as well as contributing factors while simultaneously measuring traffic variables such as individual vehicle speeds and headways over each lane in several places inside the study area. This crash rich database was combined with visual observations and analyzed extensively to identify the most relevant real-time traffic measurements for detecting crash prone conditions and develop an online crash prone conditions model. This model successfully established a relationship between fast evolving real time traffic conditions and the likelihood of a crash. Testing was performed in real time during 10 days not previously used in the model development, under varying weather and traffic conditions.
TECHNCIAL EVALUATION OF ROAD WORKING AREA SAFETY SYSTEMS AND TRAFFIC SENSORS
Speeding is a significant contributor to a significant portion of highway collisions. For work zones in particular, the speeding problem is compounded by on-site road re-configuration, narrowed lanes, or poor visibility. This paper describes a recent study in California that is designed to assess the technical performance of automated speed enforcement (ASE) equipment in the field. Several traffic monitoring systems were field tested with an automated speed enforcement system at a study site in California. The study site was located on a rural two-lane highway, where severe collisions occurred frequently and speeding appeared to be a significant factor. The ASE equipment and other devices were found to detect 2-5 % of passing vehicles to travel in excess of 65 mph in a highway with a posted speed limit of 55 mph.
Accident Prevention Based on Automatic Detection of Accident Prone Traffic Conditions: Phase I
Growing concern over traffic safety as well as rising congestion costs have been recently redirecting research effort from the traditional crash detection and clearance reactive traffic management towards online, proactive crash prevention solutions. In this project such a solution, specifically for high crash areas, is explored by identifying the most relevant real time traffic metrics and incorporating them in a crash likelihood estimation model. Unlike earlier attempts, this one is based on a unique detection and surveillance infrastructure deployed on the freeway section experiencing the highest crash rate in the state of Minnesota. This state-of-the-art infrastructure allowed video recording of 110 live crashes, crash related traffic events, as well as contributing factors while simultaneously measuring traffic variables such as individual vehicle speeds and headways over each lane in several places inside the study area. This crash rich database was combined with visual observations and analyzed extensively to identify the most relevant real-time traffic measurements for detecting crash prone conditions and develop an online crash prone conditions model. This model successfully established a relationship between fast evolving real time traffic conditions and the likelihood of a crash. Testing was performed in real time during 10 days not previously used in the model development, under varying weather and traffic conditions. We capitalize on the crash likelihood model methodology to use these additional traffic metrics in the development of an effective and efficient crash prone condition detection methodology. The process is outlined in the following steps:
Traffic Safety using Frame Extraction Through Time
2007 IEEE International Conference on System of Systems Engineering, 2007
In today's world the public understands video COMPASS is a freeway traffic management system surveillance systems as a series of Closed Circuit developed by the Ontario Ministry of Transportation Television (CCTV) systems. People imagine tens of (MTO) to respond to traffic congestion problems on urban cameras connected to tens of remote monitors, controlled freeways. This system helps the MTO increase road safety by multiple personal who pay attention to people, vehicles, by: and suspicious objects to improve overall public safety. Increasingly this view is incorrect as most systems are a Allowing for detection and removal of freeway controlled and monitored using some form of computer accidents and vehicle breakdowns. vision system. d Providing accurate freeway delay information to motorists, and, Transportation safety is a key area where video a Effective managing of rush hour traffic flow. surveillance is usedfor public safety. Canada's road safety vision is "to have the safest roads in the world". In order Improvements in image processing technologies have to provide this type of road safety a CCTV system called allowed traffic vision systems to detect more than just COMPASS has been deployed to monitor traffic. However, traffic flow and density [3]. These systems are now capable a computer vision monitoring system like this raises of collecting, analyzing, and recording the standard data as interesting and difficult problems for automated image well as more complex tasks like verifying incidents, processing. The changing lighting conditions have lead to algorithms which require a great deal of computational casfying ile m oni ir o, power to meet the needs of real-time operations and monitoring. In this paper we propose a new pre-processing approach to helping minimize nighttime lighting concerns Computer vision-based traffic safety monitoring systems by generating a nighttime view with daytime contrastfrom traditionally perform three main calculations; Vehicle ayseqernc o nimgetfmes wthroh time. c detection, motion detection [5] (including direction), and a sequence oy imagefJrames through time.* f calculation vehicle speed [6]. Traffic flow monitoring Keywords: Traffic Montobased on computer vision extracts information about the Kroeyword trafficMo r Vision flow of motion from multiple images acquired with cameras Processing, Computer Vision located along the freeway. Using the same sequence of images, the speed of the traffic can also be calculated to
Signal Warning Detector (SWAD) for Sustainable Working Environment at Highways
International Journal of Integrated Engineering
Work environment in highway construction are hazardous due to the particular dynamic and limited workspace availability. Within highway work zones, a variety of encounters involving employees, passing cars, and moving construction equipment exists, creating risky situations that may result in injury or death. Active approaches, such as the deployment of intrusion detection and alert devices in highway employees and the construction and maintenance of transportation infrastructure, can be effective in reducing these unforeseen situations. The study focus on development of the emergency signal system for the safety of highway workers was carried out to prevent accidents or danger from happening in the straight emergency line on the highway by developing a system of hazard detectors using a distance sensor. Therefore, the development of the Signal Warning Detector (SWAD) for the safety of highway workers was carried out to prevent accidents or danger from happening in the straight emer...
A Predictive Framework of Speed Camera Locations for Road Safety
Computer and Information Science
Road traffic crashes are a public health issue due to their terrible impact on individuals, communities, and countries. Studies affirmed that vehicle speed is a major contributor to crash likelihood and severity. At the same time, they identified Automated Speed Enforcement (ASE) systems, namely speed cameras, as a highly effective measure to reduce excessive and inappropriate speed, and thus improving road safety. However, identifying optimum sites for fixed speed camera placement stays an open issue in the literature, although it is a key factor that guarantees the efficiency of such ASE systems. This paper describes a predictive framework of speed camera locations using a classification algorithm that can predict, for each section of a given road network, its pertinence as a speed camera location. First, we identify a set of features as predictors of the classification algorithm, that we have argued their goodness through correlation tests. Second, for training our algorithm, dat...
2006
This report summarizes results from the Intelligent Vehicle Initiative (IVI) Road Departure Crash Warning System Field Operational Test (RDCW FOT) project. This project was conducted under a cooperative agreement between the U.S. Department of Transportation and the University of Michigan Transportation Research Institute, along with its partners, Visteon Corporation and AssistWare Technologies. Road departure crashes account for 15,000 fatalities annually in the U.S. This project developed, validated, and field-tested a set of technologies intended to warn drivers in real time when the driver was drifting from their lane, and a curve-speed warning system designed to provide alerts to help drivers slow down when approaching a curve too fast to safely negotiate the curve. This report describes the field operational test of the system and subsequent analysis of the data to address the suitability of similar systems for widespread deployment within the U.S. passenger-vehicle fleet. Two...
Video-based situation assessment for road safety
2016
In recent decades, situational awareness (SA) has been a major research subject in connection with autonomous vehicles and intelligent transportation systems. Situational awareness concerns the safety of road users, including drivers, passengers, pedestrians and animals. Moreover, it holds key information regarding the nature of upcoming situations. In order to build robust automatic SA systems that sense the environment, a variety of sensors, such as global positioning systems, radars and cameras, have been used. However, due to the high cost, complex installation procedures and high computational load of automatic situational awareness systems, they are unlikely to become standard for vehicles in the near future. In this thesis, a novel video-based framework for the automatic assessment of risk of collision in a road scene is proposed. The framework uses as input the video from a monocular video camera only, avoiding the need for additional, and frequently expensive, sensors. The ...