Fiber-optic interferometric sensor for monitoring automobile and rail traffic

This article describes a fiber-optic interferometric sensor and measuring scheme including input-output components for traffic density monitoring. The proposed measuring system is based on the interference in optical fibers. The sensor, based on the Mach-Zehnder interferometer, is constructed to detect vibration and acoustic responses caused by vehicles moving around the sensor. The presented solution is based on the use of single-mode optical fibers (G.652.D and G.653) with wavelength of 1550 nm and laser source with output power of 1 mW. The benefit of this solution lies in electromagnetic interference immunity and simple implementation because the sensor does not need to be installed destructively into the roadway and railroad tracks. The measuring system was tested in real traffic and is characterized by detection success of 99.27% in the case of automotive traffic and 100% in the case of rail traffic.Web of Science2662995298

Effect on Speed Distribution due to Intrusive and Non-Intrusive Portable Speed Measurement Devices

Accurate traffic data are essential for supporting a multitude of transportation related decisions which affect transportation system operations, management, and planning. Advanced technology offers us various alternatives for accurately collecting traffic data. But accuracy of data is not just about the mechanical accuracy of the device, but it also about how people react when they see these devices installed either on roads or off roads. It is very important that the drivers should not get affected by the presence of these devices as these devices are not always to control the speeds but they are also installed to measure the true speed of the drivers. Such studies are the basis for important decisions, such as setting speed limits, timing traffic signals, placing traffic signs, and determining the effectiveness of the countermeasures. To evaluate the effectiveness on speed distribution due to the presence of various intrusive and non-intrusive portable speed measurement devices, automated traffic counters with pneumatic tubes, Smartsensor, Autoscope with camera trailer and Lidar gun were compared. Results showed that drivers did not react to pneumatic tubes and continued driving at the same speed; there was no significant difference in speeds at different locations while pneumatic tubes were installed. Drivers tend to react most by reducing their speeds when a Lidar gun was used, the Autoscope with camera trailer also effected driver behavior to a considerable amount. There was slight increase in speeds when the Smartsensor was installed. Similar driver behavior was observed when effect on the speeds of faster drivers was evaluated. For this analysis drivers driving above 85th percentile speeds were picked and tracked throughout the test site. Drivers reacted most to Lidar guns and then to the Autoscope with camera trailer. There was no significant difference in speeds when pneumatic tubes were installed

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

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

Vehicle classification in intelligent transport systems: an overview, methods and software perspective

Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods

Vehicle Trajectory Tracking Through Magnetic Sensors

Traffic surveillance is an important issue in Intelligent Transportation Systems(ITS). In this paper, we propose a novel surveillance system to detect and track vehicles using ubiquitously deployed magnetic sensors. That is, multiple magnetic sensors, mounted roadside and along lane boundary lines, are used to track various vehicles. Real-time vehicle detection data are reported from magnetic sensors, collected into data center via base stations, and processed to depict vehicle trajectories including vehicle position, timestamp, speed and type. We first define a vehicle trajectory tracking problem. We then propose a graph-based data association algorithm to track each detected vehicle, and design a related online algorithm framework respectively. We finally validate the performance via both experimental simulation and real-world road test. The experimental results demonstrate that the proposed solution provides a cost-effective solution to capture the driving status of vehicles and on that basis form various traffic safety and efficiency applications

Real-Time Vehicle Classification System Using a Single Magnetometer

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set

Speed data collection methods: a review

Various studies have been focusing on a wide range of techniques to detect traffic flow characteristics, like speed and travel times. Therefore, a key aspect to obtain statistically significant set of data is to observe and record driver behaviours in real world. To collect traffic data, traditional methods of traffic measurement - such as detection stations, radar guns or video cameras - have been used over the years. Other innovative methods refer to probe vehicles equipped with GPS devices and/or cameras, which allow continuous surveys along the entire road route. While point-based devices provide information of the entire flow, just in the section in which they are installed and only in the time domain, probe vehicles data are referred both to temporal and space domains but ignore traffic conditions. Obviously, it is necessary that the data collected refer to representative samples, by number and composition, of the user population. The paper proposes a review of the most used methods for speed data collection, highlighting the advantages and disadvantages of each experimental approach. Accordingly, the comparison illustrates the best relief method to be adopted depending on the research and investigation that will be performed

A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%