Roadside System Research Articles

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a Stacked Sparse Autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

Operational Wheel Flat Detector in Railway Vehicles

Maintenance of railway systems is shifting from being based on scheduled interventions to a continuous regime based on the actual status of assets. This change is supported mainly on three pillars: the development of new sensors and signal processing techniques, the capability to store and analyze all the information gathered by this huge amount of new sensors, and the capability of modifying dynamically the maintenance plans. This paper presents a new wayside system for detecting flats whose development has been based on combining physical models with Machine Learning Techniques. Physical models are used to understand the phenomena, define the key indicators to characterize the phenomena and generate synthetic data to train Machine Learning algorithms. Subsequently, regression models are generated to relate the key parameters with the flat severity. The last part of the paper is focused on validating the proposed methodology in a real environment.

A condition monitoring approach for the detection of defects in the primary suspension of railway vehicles

This work presents the main problems in the primary suspension of metro vehicles and a methodology to detect those using wayside systems. The primary suspension is usually based on rubber elements, which are space saving and have damping properties. However, rubber characteristics change with time, often in a different way in each element. Differences between elements can cause irregular transmission of loads to the wheels, and thus, a reduction or loss of guidance forces. The ageing of rubber can also lead to a general stiffening of the primary suspension, which increases the risk of derailment. This paper presents a wayside system that uses a few strain gauge bridges to detect those problems independently of the vehicle load and speed. Firstly, the system is developed by means of a multibody model. Secondly, a demonstrator is installed in an operating metro exploitation. Finally, the accuracy of the predictions is proven by testing some of the elements in the laboratory.

Objects Detection With 3-D Roadside LIDAR Under Snowy Weather

Rain and snowfall will increase noise, change the resolution of objects in the point cloud and present great challenges to the accurate recognition of traffic objects. Accordingly, this article proposes an efficient real-time method for roadside 3-D light detection and ranging (LIDAR) background point cloud extraction and object segmentation under snowy weather. We first use a historical point cloud sequence to quickly construct a background model, extract the background point cloud from the current frame by using a background difference method and update the background model in real-time. Then, the noise caused by snowfall in the non-background point cloud is filtered based on the beam density difference of the object point clouds. Finally, the remaining object point cloud is accurately segmented based on the proposed hierarchical object clustering method. We use an intelligent roadside system equipped with 3-D roadside LIDAR to collect point cloud data in a snowfall environment and evaluate the proposed method qualitatively and quantitatively. Experimental results show that the proposed method effectively avoids the problem of under-segmentation and over-segmentation of object point clouds under snowy conditions, and the precision and recall rate of traffic object segmentation reached 96.41% and 95.02%, respectively, thereby indicating a significant improvement in the accuracy and reliability of traffic object detection using roadside LIDAR under snowy conditions.

MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance.

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

Cooperative control strategies to stabilise the freeway mixed traffic stability and improve traffic throughput in an intelligent roadside system environment

Previous studies used an adaptive time gap strategy to stabilise the head-to-tail string stability by connected and automated vehicles (CAVs). However, this strategy can bring an adverse effect on traffic throughput. Moreover, at a lower market penetration rate of CAVs, it is also difficult to implement a larger adaptive time gap to dampen the unstable traffic conditions. To this end, this study proposes cooperative control strategies to improve the mixed traffic throughput and stabilise the head-to-tail string stability in an intelligent road system (IRS) environment. The mixed traffic was composed of autonomous vehicles (AVs) with string instability/stability, and CAVs with low penetration. The feasible stable control parameters of AVs and CAVs are computed and implemented into the cooperative control strategies. The results show that the cooperative control strategies have better performances in improving traffic throughput and stabilising the overall head-to-tail string stability. Moreover, analytical solutions and simulation experiments are conducted to verify the effectiveness of the proposed control strategies subject to exogenous disturbances. The cooperative control strategies can be potentially applied to alleviate the mixed traffic problems, with the advanced connection and automation technologies in an IRS environment.

An Acoustic-Based Recognition Algorithm for the Unreleased Braking of Railway Wagons in Marshalling Yards

The abnormal braking of wagons is a challenging safety problem for operators at railway marshalling yard. This paper develops an acoustic-based technology to detect the unreleased braking of wagons during the uncoupling operation. Experiments have been conducted to collect the acoustic waves of wagons abnormal braking, as well as the background sounds like train whistling and wheel vibration. Before data collection, a wayside recording system and an experimental train composed of 5 different wagons have been prepared in the marshalling yard. The recognition algorithm consists of fast Fourier transform (FFT), feature extraction, template matching and support vector machine (SVM) classification. Based on the sample data of different acoustic waves, the FFT is firstly performed to obtain the frequency spectrum from original time-domain signals. Then the major spectrum features of different sounds are carefully extracted for SVM training through a newly-devised algorithm, where the features include the spectrum center, spectrum flux, energy peak and corresponding frequency. During the SVM training, classifiers are designed under the one-against-one strategy to guarantee the recognition accuracy. Given a test data, at most 3 SVM classifiers will be activated according to the decision matrix of template matching. Meanwhile, rules have been made to regulate the classification result considering different activation cases. Finally, a case study of all 12 sound categories has been performed to illustrate the application of proposed algorithm. Results show that the acoustic-based recognition algorithm is indeed reliable to identify wagons unreleased braking, with the global warning accuracy over 98%.

Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance.The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string.Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations.

Simulating a Multi-Vehicle Traffic Sensing System Based on Radio Tomographic Imaging

One of the most recently developed wireless technologies is Radio Tomographic Imaging (RTI). RTI employs a wireless sensor network that produces images of the change in the electromagnetic field of a monitored area using Received Signal Strength (RSS) measurements. This allows the tracking of device-free objects such as humans and cars. This paper is the first to propose and validate a simulation model that simulates RSS measurements for arbitrary RTI networks, based on the ZigBee communication protocol. The simulation model allows the specification of an RTI network from the ground up, including node positions, network size and geometry and RSS measurement processing. Furthermore, this paper demonstrates the implementation of the simulation model to an enhancement of a recently proposed RTI system, which acts as a roadside surveillance system. The enhancement includes three newly proposed techniques, namely a new weight matrix calculation method, a new node spacing setup and a new vehicle detection method. The simulation results indicate that it is possible to detect both one or two family sized cars simultaneously. Using techniques that reduce RSS variance due to multipath effects and the newly proposed methods, simulated vehicle detection performance is demonstrated to be between 95% and 100%.

Lessons Learned from Field Operational Test of Integrated Network Management in Amsterdam

The Amsterdam Practical Trial (APT) is a multiyear program initiated by the Dutch Ministry of Transport and the Environment and is carried out in close cooperation by Rijkswaterstaat, the City of Amsterdam, the Province of North Holland, and the Amsterdam Metropolitan Region. The APT aims to integrate innovative roadside and in-car developments networkwide. After the proof of concept was finished in 2009, two parallel tracks started in the Amsterdam region: the roadside track (aiming at an innovative, automated working system with ramp metering and traffic lights) and the in-car track (aiming at individualized travel and traffic information in the car for commuter traffic and for large-event traffic). This paper aims specifically at the innovative APT roadside system. Development of this system began in 2012, with the system becoming operational in April 2014. After all technical and functional tests were completed, the system parameters were tuned and finalized the implementation phase. Starting April 14, 11 weeks were taken to collect the data for the ex post assessment. In doing so, the authors have used an alternating assessment design, implemented by switching the system on and off every week so that a fair comparison could be made. From the collected data, it could be concluded that while the system caused additional delays on the urban network and ramps, the throughput on the freeway arterial was improved. Detailed analysis of the data showed the system performed as expected and which improvements can be made to further increase its effectiveness. These optimizations are discussed, as are the expected effects of implementing them in the system. How they are operationalized in Phase 2 of this innovative project will also be discussed.

무선통신기반 열차제어시스템에서의 운전시격 계산과 간격제어 성능개선을 위한 열차간격제어 알고리즘

Radio based train control system performs train safe interval control by receiving in realtime the position information of trains driving in the control area of the wayside system and providing onboard system in each train with updated movement authority. The performance of the train control system is evaluated to calculate the minimum operation headway, which reflects the operation characteristics and the characteristics of the train as well as the interval control performance of the train control system. In this paper, we propose the operation headway calculation for radio based train control system and a new train interval control algorithm to improve the operation headway. The proposed headway calculation defines line headway and station headway by the estimation the safety margin distance reflecting the performance of the train control system. Furthermore the proposed Enhanced Train Interval Control(ETIC) algorithm defines a new movement authority including both distance and speed, and improves the train operation headway by using braking distance occurring inevitably in the preceding train. The proposed operation headway calculation is simulated with Korean Radio-based Train Control System(KRTCS) and the simulated result is compared to improved train interval control algorithm. According to the simulated results, the proposed operation headway calculation can be used as performance indicator for radio based train control system, and the improved train control algorithm can improve the line and station headway of the conventional radio based train control system.

열차 차상신호 인터페이스 검사 시스템 개발

차내 연산 열차제어시스템은 검수고에 지상신호장치가 설치되지 않은 경우 본선 및 시험선으로 출고하였을 때에만 점검이 가능하여 업무의 효율성 저하 및 안전운행에 위해요인으로 작용한다. 열차의 운행 전 또는 운행 후 안전운행 확보를 위한 측면에서 차상 지상신호 설비 간 인터페이스 동작시험이 반드시 필요하다. 본 논문에서는 지상에서 차상시스템으로 전송되는 가상의 열차 운행정보에 따른 열차 차상신호 장치의 동작상태 확인과 차상에서 지상시스템으로 전송되는 열차 상태정보에 대한 실시간 자동 분석을 기반으로 한 열차 차상신호 인터페이스 검사시스템을 제안하여 대전도시철도 판암기지에 구축, 차상 지상신호 간 인터페이스시험 알고리즘을 검증하였다. 이에 따라 차상 열차제어시스템 점검을 위해 약 30분간의 시험선 운행이 불필요하여 업무의 효율성을 크게 향상시켰다. The inspection of distance to go train control system conducted only when train is in main line or inspecting line, that affects low work efficiency and hazard elements to safe operation. Before or after train operation, the interface test between on-board and wayside signal system must be conducted. In this paper, onboard signal interface inspection system based on the real-time automatic analysis of onboard system status information that is transmitted to the wayside system and the operation status check of the onboard signal device operates as a virtual train information that is transmitted to the onboard system have been proposed. And after building the inspection system to the inspecting track of Daejeon Subway Line 1, Panam Depot, the verification algorithm of proposed interface test and the results was presented. therefore, it has enhanced work efficiency to do not carry train operation of a half hour in test line to test on-board system.

3105 路車協調型自動運転システムの研究(OS10-3 自動車の自動運転・運転支援,OS10 アドバンティシンポジウム,オーガナイズド・セッション(OS))

Various road-vehicle cooperative systems have been developed and demonstrated vigorously in the field of driver assistance systems. Considering road-vehicle cooperation for automatic-driving vehicles, a lot of new possibilities arise because a vehicle system can treat time-varying information and probabilistic information and cooperation in motion-control level is possible. This paper presents the three studies on road-vehicle cooperation for automatic-driving vehicle. The first is the speed control using the probabilistic information on pedestrian's movement sent from a road-side system. The second is the obstacle avoidance control using the maneuver information from a road-side system. The last is the automatic vehicle guidance control by a road-side system.

Field Experimental Analysis About the Effect of Road-Side Warning System at Stop Controlled Intersections

This study aims at developing a warning device from roadside for encounter collision avoidance at unsignalized small intersections by detecting dangerously approaching vehicles approaching from a non-priority road. This research focuses on budget-friendly, easy-to-setup, and necessary-warning devices. In this study, in order to ascertain the effect of our system at public intersections, trial experiments of the system were carried out at a stop controlled intersection which has a history of encounter collision accidents. A significant behavior improvement of the non-priority side vehicles was found. It was clearly shown that encounter collision accidents decreased by about 60 % using this system.

Wayside system for wheel–rail contact forces measurements

The system for assessment of the wheel–rail contact forces can be used for multiple purposes, among them for estimation of the train running safety, for train axle load measuring or wheel flats detection, as well as for the other research analyses. As the wheel–rail rolling contact moves along the track during train motion, it is very difficult to establish a reliable and accurate system for measuring of contact forces. Measurement principle of the wayside system, presented in this paper, is based on rail strains measurements using strain gauges, connected into the Wheatstone bridges in a smart way, in order to achieve signal proportional to applied load. This principle uses independent component analysis (ICA) model in combination with system calibration for successful separation of vertical and lateral contact forces from recorded strain signals. In addition, the system provides identification of the contact point position on the rail during train passing over it, which further expands the possibilities of its application to wheel–rail wear analysis, contact geometry optimization etc.

The Optimization Layout Method of Intelligent Roadside Sensor System in Traffic Management and Control

The vehicle sensor information and road side sensor information will be collaborative used in traffic management and control. In order to improve the comprehensiveness and economy of the traffic and road conditions’ information collection, we focus on the intelligent roadside system in this paper. Firstly, we analyse the functions of the intelligent roadside system. Through the analysis of the detection range, detection accuracy, price and applicable conditions of similar sensor, we delineate the selection range of the intelligent roadside sensor. Then we determine the layout scheme of the testing equipment sensors for different functions according to different types of network structure. Finally, we apply similarity analysis to optimize the configuration density to reduce system cost by selecting the sensor layout-intensive sections.

Design Guidelines and Full-Scale Verification for MSE Walls with Traffic Barriers

AbstractMillions of square meters of mechanically stabilized earth (MSE) retaining walls are constructed annually in the United States. For highway applications, a roadside barrier system is placed at the edge of the wall. This barrier system generally consists of an L-shape concrete structure so that if a vehicle hits the vertical part of the L shape (barrier), the horizontal part of the L (moment slab) provides the resisting moment during the impact. This impact must be resisted by the soil, the reinforcement, and the panels in the MSE wall. This paper gives guidelines on how to design the reinforcement for pullout and yielding and the panels for flexural and shear failure. It also gives the guidelines on how to ensure the stability of the barrier. These design guidelines are developed in terms of AASHTO load and resistance factor design procedures. In a second part, a full-scale crash test on an instrumented 2.79 m high MSE wall is described and analyzed. The wall and barrier behaved very satisfactoril...

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use.

Towards Effective Bus Lane Monitoring Using Camera Sensors

City administrators need to guarantee bus priority in urban public transportation. Building large-scale dedicated bus lanes is a cost-effective solution but it suffers from illegal utilization of dedicated bus lines by other non-permitted vehicles. In general, two systems can be utilized for bus lane monitoring: road-side system and bus mounted system. Although the former one has the advantage in terms of larger surveillance coverage, the investment cost makes it less feasible because of scalability issue. In this paper, we focus on bus mounted system to improve surveillance coverage without additional infrastructure cost. We introduce DoubleChecking, a cooperative violator identification scheme that can accurately pick out those non-permitted vehicles or violators. DoubleChecking is designed to improve the surveillance coverage of bus mounted system by using communications/cooperation between mounted camera sensors and existing camera sensors around intersections. Through theoretical analysis and simulation results, we show that DoubleChecking yields good performance for violator identification.