Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Abstract:   This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.     

The Effects of Detector Spacing on Traffic Forecasting Performance Using Neural Networks

An investigation was made as to how short-term traffic forecasting on motorways and other trunk roads is related to the density of detectors. Forecasting performances with respect to different detector spaces have been investigated with both simulated data and real data. Pruning techniques to the input variables used for neural networks were applied to the simulated data. The real data were collected from the M25 motorway and included flow, speed, and occupancy. With the data used in our study, the forecasting performances decrease with the increase of detector spaces. However, by taking the assumed costs of detector infrastructure into account, it may be concluded from this study that increasing coverage to a spacing of 500 m gives little extra benefit and may actually be counter productive in certain circumstances. It was concluded that, on the basis of current evidence, a detector spacing of between 1 and 1.5 km might be optimal.     

Real-Time Flood Forecasting Using Neural Networks

Real-time forecasting of stream flows during storms provides an essential input to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of the underlying phenomena. The technique of neural networks therefore was applied to model it. Forecasting of flood values during storms with a lead time of one and more hours was made using a selected sequence of past flood values observed at a specific location. Training of the network was done with the help of three alternative methods, viz., error backpropagation, conjugate gradient, and cascade correlation. Resulting flood forecasts were found to be satisfactory—especially when warning time was the least.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Mobile Active-Vision Traffic Surveillance System for Urban Networks

Abstract:   This article discusses the development of a mobile bus-mounted machine vision system for transit and traffic monitoring in urban corridors, as required by intelligent transportation systems. In contrast to earlier machine vision technologies used for traffic management, which rely mainly on fixed-point detection and simpler algorithms to detect certain traffic characteristics, the new proposed approach makes use of a recent trend in computer vision research; namely, the active vision paradigm. Active vision systems have mechanisms that can actively control camera parameters such as orientation, focus, zoom, and vergence in response to the requirements of the task and external stimuli. Mounting active vision systems on buses will have the advantage of providing real-time feedback of the current traffic conditions, while possessing the intelligence and visual skills that allow them to interact with a rapidly changing dynamic environment, such as moving traffic and continuously changing image background.     

Temporal Evolution of Short-Term Urban Traffic Flow: A Nonlinear Dynamics Approach

Abstract:   Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified .     

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 .     

城市地下空间利用与地下交通规划

越来越拥挤的城市市内环境,使人们的生活空间越来越狭小,人们向地下要空间的愿望也越来越强烈。作为对人类社会影响最大的交通问题,如何在地下得到最优化的发展,如何规划,更好的为人民服务,是关注的焦点。     

城市地下道路交通安全评价与优化设计研究

为在城市地下道路设计阶段开展交通评价,通过优化设计提高城市地下道路的交通安全水平,在考虑城市地下道路特征、交通安全影响因素等基础上,结合专家意见征询与打分,从几何线形与行车视距、出入洞口、分合流与地下互通、路面与内部环境以及交通安全设施等五方面建立适合城市地下道路设计阶段的安全评价。其成果应用于苏州星港街地下道路设计之中,结果表明在城市地下道路设计阶段根据安全评价对设计方案进行系统的安全检查,能够及时发现潜在的安全隐患,通过优化设计加以改善。     

Traffic Estimation and Optimal Counting Location Without Path Enumeration Using Bayesian Networks

Abstract:   This article deals with the problem of estimating and updating the origin-destination matrix and link flows from traffic counts and its optimal location. A combination (bi-level) of an OD-pair matrix estimation model based on Bayesian networks, and a Wardrop-minimum-variance model, which identifies origins and destinations of link flows, is used to estimate OD-pair and unobserved link flows based on some observations of links and/or OD-pair flows. The Bayesian network model is also used to select the optimal number and locations of the links counters based on maximum correlation. Finally, the proposed methods are illustrated by their application to the Nguyen–Dupuis and the Ciudad Real networks.     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.