An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

Wavelet-Clustering-Neural Network Model for Freeway Incident Detection

Abstract:   An improved freeway incident-detection model is presented based on speed, volume, and occupancy data from a single detector station using a combination of wavelet-based signal processing, statistical cluster analysis, and neural network pattern recognition. A comparative study of different wavelets (Haar, second-order Daubechies, and second- and fourth-order Coifman wavelets) and filtering schemes is conducted in terms of efficacy and accuracy of smoothing. It is concluded that the fourth-order Coifman wavelet is more effective than other types of wavelets for the traffic incident detection problem. A statistical multivariate analysis based on the Mahalanobis distance is employed to perform data clustering and parameter reduction to reduce the size of the input space for the subsequent step of classification by the Levenberg–Marquardt backpropagation (BP) neural network. For a straight two-lane freeway using real data, the model yields an incident detection rate of 100%, false alarm rate of 0.3%, and detection time of 35.6 seconds.     

Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis

To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident-related features from the traffic patterns. A robust feature-extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature-extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection.     

A PC-Based Decision Tool for Roadway Incident Management

A quick-response PC tool has been developed to address a number of crucial transportation needs following major road incidents or urban disasters. Known as TEMPO (Transportation Emergency Management of Post-Incident Operations), the tool is capable of instantaneously identifying near-optimal traffic-diversion strategies around disruptions in an urban traffic network. TEMPO utilizes heuristic approaches to estimating the origin-destination (O-D) of the traffic on the closed links and reassigning the estimated O-D to the remainder of the network.
This article describes the following features of TEMPO: (1) the GIS-based data structure that allows graphic user interaction and network editing, including closed street links, changes in number of lanes, cordoned off areas, changes in street directionality, speed limits, and parking regulations, and (2) the algorithm for traffic diversion around the incident based on cordoning the affected region around the closure, estimation of the O-D matrix for the traffic on the closed links, and reassignment of the O-D to the network. Finally, a simulation-based calibration procedure is conducted to compare the TEMPO results with those generated by a popular planning software, TRANPLAN. Thirty-three incident scenarios in a generic test network are simulated by both TEMPO and TRANPLAN, and the results are compared statistically.     

Traffic Volume Time-Series Analysis According to the Type of Road Use

Problems related to highway traffic operation and congestion management can be alleviated with the use of modern intelligent transportation systems (ITSs). Advanced Traveler Information Systems (ATIS) is one of the emerging technologies that will help travelers plan routes and schedules of their trips so as to redistribute the traffic over the highway network. Such redistribution will try to maximize the use of available highway capacity. Collections of real-time data and short-term predictions of traffic volumes are among the critical needs of an ATIS. This article studies characteristics of different traffic volume time series. In particular, time-series analysis is applied to the prediction of daily traffic volumes. The daily traffic volume is estimated by using the previous 13 daily traffic volumes. The study involves a comparison of statistical and neural network techniques for time series analysis. The analysis is applied to different types of road groups according to the trip purpose and trip length distribution. It is hoped that this study will provide a better understanding of various issues involved in the short-term prediction of traffic volumes on different types of highways.     

Evaluation of Information Applications of a Self‐Organizing Distributed Traffic Information System for a Large‐Scale Real‐World Traffic Network

Abstract: This article presents an evaluation of the system performance of a proposed self‐organizing, distributed traffic information system based on vehicle‐to‐vehicle information‐sharing architecture. Using microsimulation, several information applications derived from this system are analyzed relative to the effectiveness and efficiency of the system to estimate traffic conditions along each individual path in the network, to identify possible incidents in the traffic network, and to provide rerouting strategies for vehicles to escape congested spots in the network. A subset of vehicles in the traffic network is equipped with specific intervehicle communication devices capable of autonomous traffic surveillance, peer‐to‐peer information sharing, and self‐data processing. A self‐organizing traffic information overlay on the existing vehicular roadway network assists their independent evaluation of route information, detection of traffic incidents, and dynamic rerouting in the network based both on historical information stored in an in‐vehicle database and on real‐time information disseminated through intervehicle communications. A path‐based microsimulation model is developed for these information applications and the proposed distributed traffic information system is tested in a large‐scale real‐world network. Based on simulation study results, potential benefits both for travelers with such equipment as well as for the traffic system as a whole are demonstrated.     

Graph‐Based Modeling of Information Flow Evolution and Propagation under V2V Communications‐Based Advanced Traveler Information Systems

A vehicle equipped with a vehicle‐to‐vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications‐based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time‐dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph‐based multilayer network framework to model the V2V‐based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time‐dependent locations of vehicles in the traffic flow layer and intervehicle communication‐related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time‐dependent vehicle knowledge of the traffic network conditions using a simple graph‐based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V‐based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph‐based approach to a simulation‐based approach, and illustrate both memory usage and computational time efficiencies.     

A Schematic Urban Street Network-Display Algorithm

The link-node network is the most commonly used model for representing a large-scale urban street system. It is easy to formulate a mathematical model which describes the complicated spatial and temporal urban-street-related problems on such a simplified network representation. However, recently developed microscopic network traffic models contain important details which cannot be depicted on the link-node diagram. This paper describes a computer-graphics algorithm which can generate a lane-detailed schematic urban street network display. The algorithm is very comprehensive, and it can accommodate various types of urban street configurations. The regular four-approach, right-angle intersection can be displayed most accurately. However, intersections with more or less than four approaches and/or links intersecting at an angle other than 90-deg can be generated satisfactorily. One-way streets intersecting two-way or one-way streets can also be depicted. Furthermore, roadways where the number of lanes changes (for example, from two lanes to three lanes, or from two lanes to one lane) can also be plotted. The overall quality of the display generated by this algorithm is satisfactory. The required input data is relatively simple, although the time associated with the input data preparation is short. In addition, applications using this algorithm are also discussed.     

Single-Station Algorithm Using Video-Based Data for Detecting Expressway Incidents

Abstract:  Most automatic incident detection algorithms were successfully developed using loop-detector-based traffic measurements collected from their own localities. But their detection performances were not satisfactory when applied on data collected using a video-based detector system. The video-based detector system is gaining popularity as it was reported to be cost-effective, less prone to damage compared to loop detectors embedded in road pavement, and possesses surveillance capability. It is able to provide the homogeneity of traffic measurements with greater reliability in non-incident situations. In this study, a simple detection rule was used to develop algorithms that use video-based data for detecting lane-blocking incidents. A set of 96 incidents from Singapore's Central Expressway was used for calibrating these algorithms, with another 64 incidents for validation. Two single-station algorithms, named dual-variable (DV) and flow-based DV algorithms were developed. They have similar detection logic, but the latter includes a pre-incident traffic flow condition in its detection framework. On average, the flow-based DV algorithm outperformed the DV algorithm, and both proved to be effective techniques when compared to some existing loop-detector-based algorithms.     

Analysis of Bridge Condition Rating Data Using Neural Networks

Currently bridges are evaluated using either a visual inspection process or a detailed structural analysis. When bridge evaluation is conducted by a visual inspection, a subjective rating is assigned to a bridge component. With analytical evaluation, the rating is computed based on the load applied and the resistance of the bridge component. There have been several attempts to correlate the subjective rating to the analytical rating. The conventional statistical analyses, as well as methods based on fuzzy logic, have not been very successful in providing a clear relationship between the two rating systems. This paper describes the application of neural network systems in developing the relation between subjective ratings and bridge parameters as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on bridge parameters. The specific application problem for railroad bridges in the commuter rail system in the Chicago metropolitan area is presented. The study showed that a successful training of a network can be achieved, especially if the input data set contains parameters with a diverse combination of intercorrelation coefficients. When the relationship between the bridge subjective rating and bridge parameters was investigated, the network had a prediction rating of about 73%. The study also investigated the relation between the subjective and analytical rating. In this case, the prediction rate was about 43%. Compared with conventional statistical methods and the fuzzy‐logic approach, the neural network system had a much better performance ratio in establishing the relation between the bridge rating and bridge parameters.     

Data driven models for compressive strength prediction of concrete at high temperatures

The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and K nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.     

A data‐driven approach to determining freeway incident impact areas with fuzzy and graph theory‐based clustering

Determining spatiotemporal impact areas of incidents plays a significant role in incident impact analysis. Although existing empirical methods have proven to be promising, they suffer from the drawbacks that limit their wide applications in automated freeway safety management. This study presents a data‐driven approach to automatically determining the spatiotemporal impact areas of freeway incidents. The spatiotemporal contour plots were first constructed using three representative traffic measures. Next, a nonrecurrent congestion area identification method based on fuzzy clustering was developed. To distinguish possible multiple independent blocks in the nonrecurrent congestion area, a clustering algorithm based on graph theory was adopted. The incident impact areas were then determined by conducting a postprocessing strategy. The incident records and the associated traffic flow data, collected on I‐5 freeway segments in San Diego Region, CA, were used to evaluate the proposed approach. Experimental results show the proposed approach can automatically and properly determine incident impact areas while accounting for the uncertainty resulting from traffic variations.     

Collecting comprehensive traffic information using pavement vibration monitoring data

Traffic data is essential for intelligent traffic management and road maintenance. However, the enormous effort used for data collection and analysis, combined with conventional approaches for traffic monitoring, is inefficient due to its high energy consumption, high cost, and the nonlinear relationships among various factors. This article proposes a new approach to obtain traffic information by processing raw data on pavement vibration. A large amount of raw data was collected in real time by deploying a vibration‐based in‐field pavement monitoring system. The data was processed with an efficient algorithm to achieve the monitoring of the vehicle speed, axle spacing, driving direction, location of the vehicle, and traffic volume. The vehicle speed and axle spacing were back‐calculated from the collected data and verified with actual measurements. The verification indicated that a reasonable precision could be achieved using the developed methods. Vehicle types and vehicles with an abnormal weight were identified by a three‐layer artificial neural network and the k‐means++ cluster analysis, respectively, which may help law enforcement in determining on an overweight penalty. A cost and energy consumption estimation of an acceleration sensing node is discussed. An upgraded system with low cost, low energy consumption, and self‐powered monitoring is also discussed for enabling future distributed computing and wireless application. The upgraded system might enhance integrated pavement performance and traffic monitoring.     

A Dynamic Bus-Arrival Time Prediction Model Based on APC Data

Abstract:   Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-of-week, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route .     

卷积神经网络在结构损伤诊断中的应用

对卷积神经网络(CNN)在工程结构损伤诊断中的应用进行了深入探讨; 以多层框架结构节点损伤位置的识别问题为研究对象,构建了可以直接从结构动力反应信号中进行学习并完成分类诊断的基于原始信号和傅里叶频域信息的一维卷积神经网络模型和基于小波变换数据的二维卷积神经网络模型; 从输入数据样本类别、训练时间、预测准确率、浅层与深层卷积神经网络以及不同损伤程度的影响等多方面进行了研究。结果表明:卷积神经网络能从结构动力反应信息中有效提取结构的损伤特征,且具有很高的识别精度; 相比直接用加速度反应样本,使用傅里叶变换后的频域数据作为训练样本能使CNN的收敛速度更快、更稳定,并且深层CNN的性能要好于浅层CNN; 将卷积神经网络用于工程结构损伤诊断具有可行性,特别是在大数据处理和解决复杂问题能力方面与其他传统诊断方法相比有很大优势,应用前景广阔。     

A High-Resolution Cellular Automata Traffic Simulation Model with Application in a Freeway Traffic Information System

Abstract: In this article, we describe a novel traffic information system for the freeway traffic in North Rhine‐Westphalia (NRW), the most populous German state. It consists of more than 4,000 loop detectors, a simulator, and a microscopic and macroscopic graphical interface. These should be considered as “data input,”“data processing,” and “data output,” respectively. First, we discuss the loop detectors: Their mode of operation, how and where they are located, and the quality of their measurements. Next, we describe the simulator, especially its high‐resolution cellular automaton model of traffic flows, the abstraction of the road network into tracks and nodes, how the data from the loop detectors is integrated, and we give some details on an efficient implementation of the dynamics. Finally, we discuss the graphical interfaces, which display the simulated traffic states, and we give some concluding remarks. In particular, we present the traffic information web page http://www.autobahn.nrw.de , where the simulated actual traffic state on the freeway network in NRW can be sighted.     

Neural network analysis of construction safety management systems: a case study in Singapore

A neural network analysis was conducted on a quantitative occupational safety and health management system (OSHMS) audit with accident data obtained from the Singapore construction industry. The analysis is meant to investigate, through a case study, how neural network methodology can be used to understand the relationship between OSHMS elements and safety performance, and identify the critical OSHMS elements that have significant influence on the occurrence and severity of accidents in Singapore. Based on the analysis, the model may be used to predict the severity of accidents with adequate accuracy. More importantly, it was identified that the three most significant OSHMS elements in the case study are: incident investigation and analysis, emergency preparedness, and group meetings. The findings imply that learning from incidents, having well-prepared consequence mitigation strategies and open communication can reduce the severity and likelihood of accidents on construction worksites in Singapore. It was also demonstrated that a neural network approach is feasible for analysing empirical OSHMS data to derive meaningful insights on how to improve safety performance.     

交通意外事件紧急疏导配流方案生成方法研究

城市交通意外事件易造成交通拥挤、诱发新的交通意外事件,严重影响城市道路网的正常运行,首要之策是分析评估交通意外事件造成的影响范围,实施交通紧急控制与诱导。因此,本文提出交通意外事件紧急疏导配流方案生成方法。该方法由交通意外事件疏导范围确定和多目标交通疏导配流模型两部分组成,通过寻找合理的交通区域进行交通流的二次分配,根据事发前后区域出行总时间的变化寻找合理的疏导范围;在确定的疏导范围内,应用多目标交通疏导配流模型对交通流实施动态分配。该方法在方案生成过程中,兼顾疏导区域、总行驶时间、道路交通流量、道路交通流增量等诸多因素,形成交通意外事件紧急疏导配流方案,保障疏导范围内配流方案的可行性、平稳性和安全性。     

实时交通数据的筛选与恢复研究

实时交通数据的质量非常重要,因此本文全面地分析影响交通特性参数精度的因素,总结实时处理交通数据的必要性;探讨交通管理系统中筛选实时交通数据的四种方法;提出目前常用交通数据检测方法中存在的问题,对比分析后提出解决方案;并对未来信息采集和数据管理提出建议;研究实时交通数据问题诊断和恢复方法,即数据限定法和统计相关法,最后通过沪宁高速公路实例验证其可行性与科学性。     

高速公路外场视频监控系统方案设计

高速公路的外场高清数字化视频监控系统主要是利用监控设备重点对高速公路路面进行监控,为高速公路运行提供现代化管理手段,指挥调节交通流;快速反应发生的意外事件、交通事故：防止交通阻塞、减少交通延误、充分发挥高速公路的功能,及时为道路利用人员提供有效的交通信息。本文详细介绍了视频监控系统的前端监控设计、视频管理平台的设计、系统存储设计及网络传输设计等。高级工程师,国家一级注册建造师（机电）,国家高级项目管理师（CPMP）。