Wireless magnetic sensor network for road traffic monitoring and vehicle classification

Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification

Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Abstract This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

MagSpeed: A Novel Method of Vehicle Speed Estimation Through A Single Magnetic Sensor

© 2019 IEEE. Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, the velocity of vehicles provides important information for traffic management. However, the present methods for monitoring vehicle speed have many shortcomings. In this paper, we propose MagSpeed, a novel vehicle speed estimation method based on a small magnetic sensor. The developed magnetic sensor system is wireless, cost-effective, and environmental-friendly. Through modelling of local magnetic field perturbations caused by a moving vehicle, we extract the characteristics of magnetic waveforms for speed estimation. In addition, we compare the performance of the models with other speed estimation algorithms, which shows the superior accuracy of the proposed technique in speed estimation

Intelligent traffic monitoring and control system

Thesis (M.S.) University of Alaska Fairbanks, 2019This thesis presents an intelligent system for monitoring and controlling traffic by sensing vehicles' attributes and using communication between vehicles and roadside infrastructures. The goal of this system is to improve the safety of the commuters and help the drivers in making better decisions by providing them with additional information about the traffic conditions. A prototype system consisting of a roadside unit (RSU) and an on-board unit (OBU) was developed to test the functionalities of the proposed system. The RSU consists of sensors for detecting vehicles and estimating their attributes and a radio for communicating with the OBU. The OBU also has a radio for communication purpose. Afterward, a vehicle was used to test the functionalities of the system and the communication between OBU and RSU was evaluated by emulating the presence of a vehicle. A protocol for exchanging messages between the RSU and the OBU was developed to support effective communication. The efficiency of the communication process was further improved by varying the transmission range of different messages. A format for the message was proposed to convey all the necessary information efficiently. The process of collecting vehicle data, processing them and extracting useful information from the data was discussed here along with some limitations of the proposed system

Research of distorted vehicle magnetic signatures recognitions, for length estimation in real traffic conditions

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.Lietuvos Mokslo Taryba | Ref. S-MIP-21-3