Sensors For Traffic Monitoring Research Articles

Toward Self-Powered Sensors for Traffic Monitoring and Energy Harvesting—Fundamental Principles and Results Based on SbSI/Epoxy Resin Nanocomposite

Toward Self-Powered Sensors for Traffic Monitoring and Energy Harvesting—Fundamental Principles and Results Based on SbSI/Epoxy Resin Nanocomposite

Road Network-Guided Fine-Grained Urban Traffic Flow Inference.

Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitoring sensors for cost savings. In this work, we note that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works. To facilitate this problem, we propose a novel road-aware traffic flow magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the road-aware spatial distribution of fine-grained traffic flow. Specifically, a multidirectional 1-D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios. Our code and datasets are released at https://github.com/luimoli/RATFM.

Visual extensions and anomaly detection in the pNEUMA experiment with a swarm of drones

The usage of Unmanned Aerial Systems (UAS) in traffic monitoring has advantages such as broader vision, flexibility, privacy, and cost-efficiency compared to other traffic monitoring sensors like loop detectors or fixed surveillance cameras. UAS has been instrumental in the recent pNEUMA experiment, where a swarm of drones collected a large-scale urban traffic dataset containing vehicle trajectories. These trajectory data are subject to challenges such as noise due to visual restrictions, perspective distortions, and human-induced errors. While the pNEUMA dataset is missing its imagery part, we present an extended version of it named pNEUMA Vision, which incorporates imagery data and annotations of vehicles in the form of image coordinates along with newly added vehicle trajectory features like azimuth. Moreover, we demonstrate that visually restricted trajectories are also highly prone to become anomalies, and vice-versa, through novel anomaly detection proposed in this literature. Specifically, we distinguish between stationary and non-stationary errors and argue that the latter account for the largest part of the noise. Furthermore, we analyze the new visual dataset with two different computer vision methods for estimating the number of vehicles on the roads from the input images. Particularly, we show that using a density map to count vehicles from drone images achieves comparable results to conventional vehicle counting methods via detection. Results show that the time–space diagrams plotted from density map predictions could identify the congested urban roads’ queues better than a widely used vehicle detection method.

Sustainable tunnel lighting system based on mechanical-regulated and soft-contact hybridized nanogenerator

Tunnel lighting consumes nearly 30 % of the energy in the whole infrastructure, so optimizing the lighting control strategy could significantly increase the energy utilization efficiency. However, there is still lack of an intelligent and cost-effective lighting control system. Here, we report a low-cost, intelligent, and self-powered tunnel lighting control system based on the mechanical-regulated and soft-contact hybridized nanogenerator (MS-HNG) as both the power supply and distributed traffic monitoring sensor. Benefited from the mechanical regulation and frequency up-conversion design, the MS-HNG can effectively convert the energy into electricity from intermittent stimulation of the moving vehicle and thus offers a maximum output power of 108 mW to transmit the traffic information wirelessly. The introducing of soft fur also endows HNG with excellent stability and durability, showing a negligible decrease in the output over 100,000 cycles. Moreover, an “illumination traveling with the vehicle” sustainable tunnel lighting system which could save energy by 72 % at a traffic volume of 20 veh/h compared with the traditional one is successfully demonstrated. This work may pave the way for industrial applications of nanogenerators and shows great potential in energy-efficient and intelligent transportation systems (ITS).

Acoustic Detector of Road Vehicles Based on Sound Intensity.

A method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions: parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement. The algorithm was tested on a continuous 24-h recording made in real-world conditions. The overall results were: recall 0.95, precision 0.95, F-score 0.95. In the analysis of one-hour slots, the worst results obtained in dense traffic were: recall 0.9, precision 0.93, F-score 0.91. The proposed method is intended for application in a network of traffic monitoring sensors, such as a smart city system. Its advantages include using a small, low cost and passive sensor, low algorithm complexity, and satisfactory detection accuracy.

Exploring Resilient Observability in Traffic-Monitoring Sensor Networks: A Study of Spatial–Temporal Vehicle Patterns

Vehicle mobility generates dynamic and complex patterns that are associated with our day-to-day activities in cities. To reveal the spatial–temporal complexity of such patterns, digital techniques, such as traffic-monitoring sensors, provide promising data-driven tools for city managers and urban planners. Although a large number of studies have been dedicated to investigating the sensing power of the traffic-monitoring sensors, there is still a lack of exploration of the resilient performance of sensor networks when multiple sensor failures occur. In this paper, we reveal the dynamic patterns of vehicle mobility in Cambridge, UK, and subsequently, explore the resilience of the sensor networks. The observability is adopted as the overall performance indicator to depict the maximum number of vehicles captured by the deployed sensors in the study area. By aggregating the sensor networks according to weekday and weekend and simulating random sensor failures with different recovery strategies, we found that (1) the day-to-day vehicle mobility pattern in this case study is highly dynamic and decomposed journey durations follow a power-law distribution on the tail section; (2) such temporal variation significantly affects the observability of the sensor network, causing its overall resilience to vary with different recovery strategies. The simulation results further suggest that a corresponding prioritization for recovering the sensors from massive failures is required, rather than a static sequence determined by the first-fail–first-repair principle. For stakeholders and decision-makers, this study provides insightful implications for understanding city-scale vehicle mobility and the resilience of traffic-monitoring sensor networks.

Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network

Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic flows. In this paper, we propose a novel Convolutional LSTM neural network architecture for multi-lane short-term traffic prediction. Compared to existing methods, we highlight the importance of (1) applying multiple features to characterize traffic conditions; (2) explicitly considering the routing between neighbouring lanes and downstream/upstream traffics; and (3) predicting multiple time-step traffic in a rolling-prediction manner. Experiments on 10 months 5-minute interval observations of the US I-101 Northern freeway at California Bay Area verify the proposed model. The results show that our model has considerable advantages in predicting multi-lane short-term traffic flow.

Broadband-Millimeter-Wave Absorber Based on ε-(TiIVCoII)xFeIII2−2xO3 for Advanced Driver Assistance Systems

Abstract High-speed wireless communication plays a significant role in the Internet of Things (IoT) era. 120-GHz and 140-GHz band millimeter waves have potential in broadcasting wireless communications, wireless data transmissions between cellular base stations, and traffic monitoring sensors in intersection areas for advanced driver assistance systems (ADAS). Herein we report a magnetic millimeter wave absorber composed of TiIV–CoII substituted ε-Fe2O3, which is a series of ε-(TiIVCoII)xFeIII2−2xO3 nanomagnets (0 ≤ x ≤ 0.048). ε-(TiIVCoII)xFeIII2−2xO3 shows magnetic hysteresis loops, and the coercive field changes from 20.0 kOe (x = 0) to 8.4 kOe (x = 0.048) with increasing x. Terahertz time domain spectroscopy (THz-TDS) measurements show that ε-(TiIVCoII)xFeIII2−2xO3 exhibits millimeter wave absorption peaks at 140 GHz with full width at half maximum (Δf) = 15 GHz for x = 0.033 and at 121 GHz with Δf = 24 GHz for x = 0.048. These broadband absorptions are useful for noise suppression over a wide frequency range.

Automated Incident Detection Using Real-Time Floating Car Data

The aim of this paper is to demonstrate the feasibility of a live Automated Incident Detection (AID) system using only Floating Car Data (FCD) in one of the first large-scale FCD AID field trials. AID systems detect traffic events and alert upcoming drivers to improve traffic safety without human monitoring. These automated systems traditionally rely on traffic monitoring sensors embedded in the road. FCD allows for finer spatial granularity of traffic monitoring. However, low penetration rates of FCD probe vehicles and the data latency have historically hindered FCD AID deployment. We use a live country-wide FCD system monitoring an estimated 5.93% of all vehicles. An FCD AID system is presented and compared to the installed AID system (using loop sensor data) on 2 different highways in Netherlands. Our results show the FCD AID can adequately monitor changing traffic conditions and follow the AID benchmark. The presented FCD AID is integrated with the road operator systems as part of an innovation project, making this, to the best of our knowledge, the first full chain technical feasibility trial of an FCD-only AID system. Additionally, FCD allows for AID on roads without installed sensors, allowing road safety improvements at low cost.

A new smart traffic monitoring method using embedded cement-based piezoelectric sensors

Cement-based piezoelectric composites are employed as the sensing elements of a new smart traffic monitoring system. The piezoelectricity of the cement-based piezoelectric sensors enables powerful and accurate real-time detection of the pressure induced by the traffic flow. To describe the mechanical-electrical conversion mechanism between traffic flow and the electrical output of the embedded piezoelectric sensors, a mathematical model is established based on Duhamel’s integral, the constitutive law and the charge-leakage characteristics of the piezoelectric composite. Laboratory tests show that the voltage magnitude of the sensor is linearly proportional to the applied pressure, which ensures the reliability of the cement-based piezoelectric sensors for traffic monitoring. A series of on-site road tests by a 10 tonne truck and a 6.8 tonne van show that vehicle weight-in-motion can be predicted based on the mechanical-electrical model by taking into account the vehicle speed and the charge-leakage property of the piezoelectric sensor. In the speed range from 20 km h−1 to 70 km h−1, the error of the repeated weigh-in-motion measurements of the 6.8 tonne van is less than 1 tonne. The results indicate that the embedded cement-based piezoelectric sensors and associated measurement setup have good capability of smart traffic monitoring, such as traffic flow detection, vehicle speed detection and weigh-in-motion measurement.

Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.

Collaborative Web-GIS Design: A Case Study for Road Risk Analysis and Monitoring

This article presents a methodology for designing a WebGIS framework intended for automatically analyzing spatial data and updating statistics of interest with new information inserted daily by multiple users via a Web portal. A practical example is used on vehicle accident data for assessing risk in specific road segments. Two main blocks integrated together will be described: the collaborative block and the data-analysis block. The former gives end-users computer-aided tools to view, insert, modify and manage data related to accidents and traffic monitoring sensors, whereas the latter is developed to automatically analyze the accident data coming from user's collaboration. Because different agencies can survey accident sites, a collaborative environment is necessary – and a Web-based solution is ideal – for permitting multi-user access and data insertion. A centralized approach to process the data in real time is described in all its components. Server-side Structured Query Language functions optimize performance by using dedicated libraries for spatial processing and re-structuring the attributes associated with elements which are consequently re-classified for correct color-scaling. The end-product is a system that provides a seamless integration of front-end tools for user collaboration and back-end tools to update accident risk statistics in real time and provide them to stakeholders.

Evolutionary engineering design synthesis of on-board traffic monitoring sensors

In this paper, a formal engineering design synthesis methodology based on evolutionary computation is presented, with special emphasis on the design and optimization of distributed independent systems. A case study concerned with design of a sensory system for traffic monitoring purposes is presented, along with simulations of traffic scenarios at several levels of abstraction. It is shown how the methodology introduced is able to deal with the engineering design challenges present in the case study, and effectively synthesize novel design solutions of good quality. Moreover, when the fitness function is formulated as an aggregation of design preferences with different weights and trade-off strategies, the complete Pareto optimal frontier can be determined by the evolutionary synthesis methodology. The results of this study suggest that the approach can be useful for designers to solve challenging engineering design synthesis problems.