AUTOMATED VEHICLE COUNTING AND CLASSIFICATION SYSTEM FOR TRAFFIC CENSUS

Traffic census is important for the purpose of upgrading and widening the road. The information gained from the traffic census can be used in the budget planning for road maintenance. Traffic census can be done automatically or by counting and classifying the vehicles manually using human labor. Most of the automatic traffic census system used nowadays focus on counting the vehicles by using devices called magnetic loop detector. This device is costly and once installed, it cannot be removed. To overcome this problem, an automated traffic census system based on image processing is introduced which can be used to count and to classify the classes of the vehicle. Computer vision technology is used to achieve this objective. For the vehicle detection, background subtraction and approximate median algorithm are used. The system uses the length of the vehicle for the purpose of classification. The chosen algorithm for vehicle detection is called approximate median as it is more accurate compared to background subtraction method. On the other hand, although the results gained by using approximate median method is more accurate than a simple background subtraction method, it has its drawback too which is more complex calculation hence taking more time to execute the algorithm. Some optimizations have been done on the approximate median algorithm and the result is very promising as it has shortened the execution time while the accuracy of the detection remains the same. In conclusion, this project is a success since it can count and classify the vehicles, but further works need to be done to achieve better accuracy

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Video based vehicle detection for advance warning Intelligent Transportation System

Video based vehicle detection and surveillance technologies are an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and capability or capturing global and specific vehicle behavior data. The initial goal of this thesis is to develop an efficient advance warning ITS system for detection of congestion at work zones and special events based on video detection. The goals accomplished by this thesis are: (1) successfully developed the advance warning ITS system using off-the-shelf components and, (2) Develop and evaluate an improved vehicle detection and tracking algorithm. The advance warning ITS system developed includes many off-the-shelf equipments like Autoscope (video based vehicle detector), Digital Video Recorders, RF transceivers, high gain Yagi antennas, variable message signs and interface processors. The video based detection system used requires calibration and fine tuning of configuration parameters for accurate results. Therefore, an in-house video based vehicle detection system was developed using the Corner Harris algorithm to eliminate the need of complex calibration and contrasts modifications. The algorithm was implemented using OpenCV library on a Arcom\u27s Olympus Windows XP Embedded development kit running WinXPE operating system. The algorithm performance is for accuracy in vehicle speed and count is evaluated. The performance of the proposed algorithm is equivalent or better to the Autoscope system without any modifications to calibration and lamination adjustments

A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks

Traffic management centers (TMCs) need high-quality data regarding the status of roadways for monitoring and delivering up-to-date traffic conditions to the traveling public. Currently this data is measured at static points on the roadway using technologies that have significant maintenance requirements. To obtain an accurate picture of traffic on any road section at any time requires a real-time probe of vehicles traveling in that section. We envision a near-term future where network communication devices are commonly included in new vehicles. These devices will allow vehicles to form vehicular networks allowing communication among themselves, other vehicles, and roadside units (RSUs) to improve driver safety, provide enhanced monitoring to TMCs, and deliver real-time traffic conditions to drivers. In this dissertation, we contribute and develop a framework for dynamic trafficmonitoring (DTMon) using vehicular networks. We introduce RSUs called task organizers (TOs) that can communicate with equipped vehicles and with a TMC. These TOs can be programmed by the TMC to task vehicles with performing traffic measurements over various sections of the roadway. Measurement points for TOs, or virtual strips, can be changed dynamically, placed anywhere within several kilometers of the TO, and used to measure wide areas of the roadway network. This is a vast improvement over current technology. We analyze the ability of a TO, or multiple TOs, to monitor high-quality traffic datain various traffic conditions (e.g., free flow traffic, transient flow traffic, traffic with congestion, etc.). We show that DTMon can accurately monitor speed and travel times in both free-flow and traffic with transient congestion. For some types of data, the percentage of equipped vehicles, or the market penetration rate, affects the quality of data gathered. Thus, we investigate methods for mitigating the effects of low penetration rate as well as low traffic density on data quality using DTMon. This includes studying the deployment of multiple TOs in a region and the use of oncoming traffic to help bridge gaps in connectivity. We show that DTMon can have a large impact on traffic monitoring. Traffic engineers can take advantage of the programmability of TOs, giving them the ability to measure traffic at any point within several km of a TO. Most real-time traffic maps measure traffic at midpoint of roads between interchanges and the use of this framework would allow for virtual strips to be placed at various locations in between interchanges, providing fine-grained measurements to TMCs. In addition, the measurement points can be adjusted as traffic conditions change. An important application of this is end-of-queue management. Traffic engineers are very interested in deliver timely information to drivers approaching congestion endpoints to improve safety. We show the ability of DTMon in detecting the end of the queue during congestion

AUTOMATED VEHICLE COUNTING AND CLASSIFICATION SYSTEM FOR TRAFFIC CENSUS

Traffic census is important for the purpose of upgrading and widening the road. The information gained from the traffic census can be used in the budget planning for road maintenance. Traffic census can be done automatically or by counting and classifying the vehicles manually using human labor. Most of the automatic traffic census system used nowadays focus on counting the vehicles by using devices called magnetic loop detector. This device is costly and once installed, it cannot be removed. To overcome this problem, an automated traffic census system based on image processing is introduced which can be used to count and to classify the classes of the vehicle. Computer vision technology is used to achieve this objective. For the vehicle detection, background subtraction and approximate median algorithm are used. The system uses the length of the vehicle for the purpose of classification. The chosen algorithm for vehicle detection is called approximate median as it is more accurate compared to background subtraction method. On the other hand, although the results gained by using approximate median method is more accurate than a simple background subtraction method, it has its drawback too which is more complex calculation hence taking more time to execute the algorithm. Some optimizations have been done on the approximate median algorithm and the result is very promising as it has shortened the execution time while the accuracy of the detection remains the same. In conclusion, this project is a success since it can count and classify the vehicles, but further works need to be done to achieve better accuracy

A blockchain and gamification approach for smart parking

City parking is increasingly complex and available parking spaces are scarce. Being able to identify a space to park their cars can lead many drivers to drive around the intended parking area several times, increasing traffic density and pollution. In this research we propose a collaborative blockchain solution with gamification for parking. Users collaborate to report free spaces and receive free parking minutes for their service to the community. In parallel, this approach can be used to collect beacon information from the parked vehicles and create a low-cost collaborative approach for managing a parking control process platform Blockchain that can handle this distributed process and the gamification platform increases users’ participation.info:eu-repo/semantics/acceptedVersio

Accurate vehicle classification including motorcycles using piezoelectric sensors

Thesis (M.S. ECE)--University of Oklahoma, 2012.Includes bibliographical references (leaves 88-90).State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifier devices currently employ inductive loops, piezoelectric sensors, or some combination of both, to aid in the identification of 13 Federal Highway Administration (FHWA) classifications. However, systems using inductive loops have proven unable to accurately classify motorcycles and record pertinent data. Previous investigations undertaken to overcome this problem have focused on classification techniques utilizing inductive loops signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. Most were off-line classification studies with results not directly intended for product development. Vision, infrared, and acoustic classification systems among others have also been explored as possible solutions. This thesis presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally on the roadway to accurately identify motorcycles from among other vehicles, as well as identify vehicles in the remaining 12 FHWA classifications. An algorithm was formulated and deployed in an embedded system for field testing. Both single element and multi-element piezoelectric sensors were investigated for use as part of the vehicle classification system. The piezoelectric sensors and vehicle classification system reported in this thesis were subsequently tested at the University of Oklahoma-Tulsa campus. Various vehicle types traveling at limited vehicle speeds were investigated. The newly developed vehicle classification system demonstrated results that met expectation for accurately identifying motorcycles

3D Modelling for Improved Visual Traffic Analytics

Advanced Traffic Management Systems utilize diverse types of sensor networks with the goal of improving mobility and safety of transportation systems. These systems require information about the state of the traffic configuration, including volume, vehicle speed, density, and incidents, which are useful in applications such as urban planning, collision avoidance systems, and emergency vehicle notification systems, to name a few. Sensing technologies are an important part of Advanced Traffic Management Systems that enable the estimation of the traffic state. Inductive Loop Detectors are often used to sense vehicles on highway roads. Although this technology has proven to be effective, it has limitations. Their installation and replacement cost is high and causes traffic disruptions, and their sensing modality provides very limited information about the vehicles being sensed. No vehicle appearance information is available. Traffic camera networks are also used in advanced traffic monitoring centers where the cameras are controlled by a remote operator. The amount of visual information provided by such cameras can be overwhelmingly large, which may cause the operators to miss important traffic events happening in the field. This dissertation focuses on visual traffic surveillance for Advanced Traffic Management Systems. The focus is on the research and development of computer vision algorithms that contribute to the automation of highway traffic analytics systems that require estimates of traffic volume and density. This dissertation makes three contributions: The first contribution is an integrated vision surveillance system called 3DTown, where cameras installed at a university campus together with algorithms are used to produce vehicle and pedestrian detections to augment a 3D model of the university with dynamic information from the scene. A second major contribution is a technique for extracting road lines from highway images that are used to estimate the tilt angle and the focal length of the camera. This technique is useful when the operator changes the camera pose. The third major contribution is a method to automatically extract the active road lanes and model the vehicles in 3D to improve the vehicle count estimation by individuating 2D segments of imaged vehicles that have been merged due to occlusions

Real-time vehicle speed estimation using Unmanned Aerial Vehicles for traffic surveillance

Drones are an emerging tool for traffic surveillance; however, they inherently lack the capability to solely obtain vehicle speed on the road. This Bachelor's thesis presents the design, implementation and study of a system to detect the position, velocity and type of vehicles using the video stream obtained from drones. The solution is created to be used with any kind of aerial vehicle but is tailored for the drones in the European project LABYRINTH, of which the thesis has been a part. The tool utilizes the video feed from a sole camera and the telemetry data from the drone to detect, track and project the objects present on the road from the image into reality. This allows for an estimation of their position and speed. The detection and tracking algorithm implemented is the Simple Online Real Time algorithm, which is often referred to as SORT. Once the position has been acquired, another stream is generated that displays the same video, but with the bounding boxes, velocity and confidence ratings of all identified vehicles, with an overall computing time lower than the frame rate. After implementation, the tool underwent testing in a simulated environment to determine its assets and shortcomings, and was used during the LABYRINTH traffic monitoring flight tests. The Bachelor's thesis achieves the aimed objectives with minimum resource utilization, using readily available logic and open-source software to strike an optimal balance between real-time functionality and precise detection of vehicle position.Outgoin