Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

Abstract:   Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.     

A Dantzig‐Wolfe Decomposition‐Based Heuristic for Off‐line Capacity Calibration of Dynamic Traffic Assignment

Abstract: One of the critical elements in considering any real‐time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. Dynamic traffic assignment (DTA) is therefore gaining wider acceptance among agencies and practitioners as a more realistic representation of traffic phenomena than static traffic assignment. Though it is imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies, DTA calibration is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. To compliment other research on behavioral and trip table issues, this work focuses on DTA capacity calibration and presents an efficient Dantzig‐Wolfe decomposition‐based heuristic that decomposes the problem into a restricted master problem and a series of pricing problems. The restricted master problem is a capacity manipulation problem, which can be solved by a linear programming solver. The pricing problem is the user optimal DTA which can be optimally solved by an existing combinatorial algorithm. In addition, the proposed set of dual variable approximation techniques is one of a very limited number of approaches that can be used to estimate network‐wide dual information in facilitating algorithmic designs while maintaining scalability. Two networks of various sizes are empirically tested to demonstrate the efficiency and efficacy of the proposed heuristic. Based on the results, the proposed heuristic can calibrate the network capacity and match the counts within a 1% optimality gap.     

Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow

Abstract:   This article addresses the problem of the accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)-based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rule-based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real-world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors .     

A deep marked graph process model for citywide traffic congestion forecasting

Forecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data-driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the occurrence time of traffic congestion events for complex signalized road networks. Traffic congestion is considered as a nonrigorous spatiotemporal extreme event. We extend the traditional point process model by integrating a specially designed spatiotemporal graph convolutional network. This hybrid strategy takes advantage of the simple form of the point process model as well as the ability of graph neural networks to emulate the evolution of congestion. Experiments on real-world congestion data sets show that the proposed method outperforms state-of-the-art baseline methods, yielding satisfactory prediction results on a large signalized road network with superior computational efficiency.     

A neuro-wavelet technique for seismic damage identification of cantilever structures

A method based on artificial neural networks and wavelet transform is proposed for identifying seismic-induced damage of cantilever structures. In the proposed method, response accelerations are measured at strategically selected locations. To extract damage-induced sharp transitions from the measured signals, they are decomposed by continuous wavelet transform. The size of the decomposed signals is reduced by principal component analysis (PCA). Principal components obtained from PCA are fed to a set of neural networks to identify damage. The proposed algorithm is applied to a tall airport traffic control tower by means of numerical simulations. The obtained results show that the proposed method effectively identifies seismic-induced damage, and the noise intensity has a negligible effect on the predicted results. Moreover, the trained neural network system is able to predict the seismic-induced damage of unseen samples well.     

Data–Driven Methodology for Signal Timing Plan Development: A Computational Approach

Traffic signal systems serve as one of the most powerful control tools available to improve the efficiency of surface transportation travel. A large number of signal systems currently operate using the time–of–day (TOD) approach. In TOD systems, a day is segmented into a number of intervals in which a different timing plan is used. Thus, the challenge in operating a TOD system effectively is to (1) identify appropriate TOD intervals, and (2) develop optimal timing plans for each interval. The existing procedures used by traffic engineers to address these challenges are time consuming and use relatively small sets of data. This research effort developed a new timing plan development methodology that takes advantage of the large sets of archived traffic data (volume and occupancy) that modern systems are equipped to compile. Based upon statistical cluster analysis, this methodology (1) automates the identification of TOD intervals using a high–resolution definition of system state, and (2) provides representative volumes for plan optimization based on the set of archived data. The results of a case study reported in this paper demonstrate that the methodology supports the development of a TOD system that provides benefits when considering performance measures such as delay, when compared to currently used techniques.     

Case-Based Reasoning for Real-Time Traffic Flow Management

Real-time traffic management is a promising approach for alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that optimize the performance of highway networks. This article explores the potential for using case-based reasoning (CBR), an emerging artificial intelligence (AI) paradigm, to overcome the limitations of existing traffic-management decision support systems. To illustrate the feasibility of the approach, the article develops and evaluates a prototype CBR routing system for a real-world network in Hampton Roads, Virginia. Cases for building the system's case base are generated using a heuristic dynamic traffic assignment (DTA) model specifically designed for the region. Using a set of 25 new independent cases, the performance of the prototype system is evaluated by comparing its solutions with those of the DTA model. The evaluation results demonstrate the feasibility of the CBR approach. The prototype system was capable of running in real time and produced high-quality solutions using case bases of reasonable size.     

Jams and superjams: a systems approach to congested traffic networks

The road traffic engineer attempts to solve the problem of congestion and delay to road traffic in urban road networks by increasing capacity. He does this by computer control of traffic signals, by traffic management (one-way systems, banned turns etc.), by junction design, and to a limited extent by building new or improved roads. Such increases in capacity do not increase traffic speed if traffic demand is sufficiently high. However, they do increase the risk of traffic jams when blockages of the network, and especially of junctions occur, because there is increased traffic on essentially the same network as before. Traffic speed in congested road networks is set by an equilibrium with the best alternative public transport system. It can only be improved if public transport is improved and road space devoted to the more efficient user of space, the bus or tram, or, if demand is sufficiently high, by the construction of segregated rail systems.     

环境目标下城市交通综合网络设计的优化模型及求解算法

机动车尾气排放因子(emission factor)、路段行驶时间和交叉口等待时间等都是影响城市交通尾气总排放量的重要因素。而城市交通网络中路段通行能力的改善或者交叉口信号配时方案的调整都会直接影响机动车尾气排放因子和交通出行时间的变化,从而影响城市交通尾气总排放量的大小。为此,首先根据机动车在路段上的行驶状况,引入两个与出行时间关联的尾气排放因子,即路段正常行驶时的尾气排放因子和交叉口排队等待时的尾气排放因子,进而给出适用于城市交通网络设计问题的尾气总排放量计算公式;然后用双层规划方法综合优化信号灯配时方案和道路能力改善方案,以达到最大程度地降低交通尾气总排放量的目的。基于粒子群优化算法,设计了一个求解该双层模型的全局优化算法,该算法操作简单,易于实现,并用一个简单算例验证了本文模型与算法的有效性。     

Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural Network

Abstract:   This study presents a wavelet neural network-based approach to dynamically identifying and modeling a building structure. By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation.     

Life-cycle cost analysis of optimal timing of pavement preservation

Optimal application of pavement preservation or preventive maintenance is critical for highway agencies to allocate the limited budget for different treatments. This study developed an integrated life-cycle cost analysis (LCCA) model to quantify the impact of pavement preservation on agency cost and vehicle operation cost (VOC) and analyzed the optimal timing of preservation treatments. The international roughness index (IRI) data were extracted from the long-term pavement performance (LTPP) program specific pavement studies 3 (SPS-3) to determine the long-term effectiveness of preservation treatments on IRI deterioration. The traffic loading and the initial IRI value significantly affects life extension and the benefit of agency cost caused by pavement preservation. The benefit in VOC is one to two orders greater in magnitude as compared to the benefit in agency cost. The optimal timing calculated based on VOC is always earlier than the optimal timing calculated based on agency cost. There are considerable differences among the optimal timing of three preservation treatments.     

位移反分析的自适应神经模糊推理方法

现有各种位移反分析方法均存在着这种或那种不足之处：基于最优化理论的位移反分析方法，解的稳定性较差，易陷入局部极小，反演参数较多时收敛速度较慢，且难以搜索到最优解；基于人工神经网络的位移反分析方法，当解空间稍大时便难以收敛到所需要的精度，且训练结果不具有唯一性，因而很难获得与实际岩体相吻合的反演结果；基于遗传进化的位移反分析方法，需对搜索过程进行大量经验性干预才能搜索到最优解；基于遗传进化和神经网络的位移反分析方法，亦只在较小的解空间内才有效。针对这些不足之处，应用自适应神经模糊推理系统的原理，建立了位移反分析的自适应神经模糊推理方法，并应用该方法对所设定的某一标准弹塑性问题的力学参数进行了反演，反演结果表明，在较大的解空间内，这种位移反分析方法收敛速度快、解的稳定性好、反演结果精度高，是一种优异的位移反分析方法。     

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.     

环境污染限制及最优信号控制条件下的综合离散网络设计问题

现代城市综合交通网络设计问题研究的主要内容就是通过新建或改善道路网络,用以减少现代交通带来的环境污染、合理设置交通信号等诸方面的问题。将道路环境能力限制、最优交通信号设置问题与城市交通离散网络设计问题结合起来研究,一方面通过修建新的路段使交通需求量达到最大从而满足城市中不断增长的交通需求;另一方面通过道路环境能力限制使交通网络的最大需求量能符合现代城市环境保护的要求。给出了描述上述问题的一个双层规划模型及其启发式求解算法。最后,通过一个简单的算例,说明该算法是可行并且有效的。     

Increasing the Capacity of the Intersection Downstream of the Freeway Off‐Ramp Using Presignals

High volume from urban freeway off‐ramps coupled with extensive traffic weaving and limited capacity at downstream intersections create major bottlenecks in urban road networks. This article presents an integrated design model to eliminate traffic weaving and to maximize the section's overall capacity by using the presignal and sorting area concept. The selection of movements controlled by the presignal, the layout of the section, and the signal timing are optimized in a uniform framework by a mixed‐integer nonlinear program model. The mathematical model was linearized and solved using the standard branch‐and‐bound technique. Extensive numerical analysis and a case study validate the effectiveness of the proposed integrated model in improving capacity with the comparison of conventional design under various geometric configuration and traffic demand pattern scenarios. The proposed model has promising application at locations where the queuing space is long enough and the number of exit lanes is enough to receive the traffic stream from the sorting area.     

Traffic Prediction and Management via RBF Neural Nets and Semantic Control

The objective of this work has been to develop layers of control and optimization modules for the purpose of urban traffic management. We utilize the semantic control paradigm to model both the macrolevel (traffic control) and the microlevel (vehicle path planning and steering control). A semantic controller consists of three modules for identification, goal selection, and adaptation, respectively. This hierarchical structure has been used successfully at the Center for Optimization and Semantic Control to solve complex, nonlinear, and time-varying problems. In our previous work we have used a judicious combination of artificial intelligence, optimization, and control systems.
The focus of this paper is the identifier module, which performs "system identification," i.e., determines the road network congestion level. Traffic flow can be characterized as a nonlinear stochastic process where linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for data-based modeling and system identification. The radial basis function neural network (RBFNN) is an attractive tool for nonlinear time-series modeling and traffic-flow prediction. The goal selector module that finds the shortest path is also discussed in some detail.
A model of the highway system, based on historical data provided by Missouri Highway and Transportation Department (MoHTD), has been developed. The prediction and planning system is evaluated using the traffic-flow data from nine sensors located on the highway in the St. Louis metropolitan area.     

The use of artificial neural networks in adiabatic curves modeling

Adiabatic hydration curves are the most suitable data for temperature calculations in concrete hardening structures. However, it is very difficult to predict the adiabatic hydration curve of an arbitrary concrete mixture. The idea of modeling adiabatic temperature rise during concrete hydration with the use of artificial neural networks was introduced in order to describe the adiabatic hydration of an arbitrary concrete mixture, depending on factors which influence the hydration process of cement in concrete. The influence of these factors was determined by our own experiments. A comparison between experimentally determined adiabatic curves and adiabatic curves, evaluated by proposed numerical model shows that artificial neural networks can be used to predict adiabatic hydration curves effectively. This model can be easily incorporated in the computer programs for prediction of the thermal fields in young concrete structures, implemented in the finite element or finite difference codes. New adiabatic hydration curves with some other initial parameters of the concrete mixture can be easily included in this model in order to expand the range of suitability of artificial neural networks to predict the adiabatic hydration curves.     

Non-destructive evaluation of concrete physical condition using radar and artificial neural networks

This paper deals with the combination of radar technology and artificial neural networks (ANN) for the non-destructive evaluation of the water and chloride contents of concrete. Two networks were trained and tested to predict these concrete properties. Input data to the statistical models were extracted from time-domain signals of direct and reflected radar waves. ANN training and testing were implemented according to an experimental database of 100 radar measurements performed on concrete slabs having various water and chloride contents. Both networks were multi-layer-perceptrons trained according to back-propagation algorithm.The results of this research highlight the potential of artificial neural networks for solving the inverse problem of concrete physical evaluation using radar measurements. It was found that the optimized statistical models predicted water and chloride contents of concrete laboratory slabs with maximum absolute errors of about 2% and 0.5 kg/m³ of concrete, respectively.     

Data Fusion of Fixed Detector and Probe Vehicle Data for Incident Detection

An important feature of many advanced traveler information systems (ATIS) is real-time information about incidents on the street network. This paper describes a system for automatically detecting incidents for such an ATIS developed using artificial neural networks and statistical prediction methods. The system monitors traffic conditions using two types of data: inductive loop detectors (ILDs) and vehicle probes. For both neural network and statistical methods, incident detection is accomplished using two approaches: by processing traffic input data directly and by processing the output of specialized algorithms that detect incidents using information from each data source. Analysis data generated from a simulation of a typical suburban signalized major arterial street are used. Different model configurations are examined and tested to identify the input variables and methods that are the best predictors of incident occurrence. The neural network approaches consistently perform at least as well as the discriminant analysis models, especially when results are adjusted to avoid false alarms.     

基于BP人工神经元网络的臭氧生物活性炭系统建模研究

针对臭氧生物活性炭系统的特点和研究中的难点，创新地引入人工神经元网络的理论和思想，提出该过程的人工神经网络的分析方法。通过建立基于ＢＰ人工神经元网络的臭氧生物活性炭系统模型，考察该网络对水处理系统建模的适应性，探讨了臭氧生物活性炭系统中影响因素之间的关系，为提高臭氧生物活性炭系统的应用水平和实现水处理系统的在线控制 一条可行途径。