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.     

Optimizing schedule for improving the traffic impact of work zone on roads

Many different types of construction projects set up work zones on roads. Especially in urban areas, lane closures as a result of work zones have a considerable impact on local traffic. However, for a construction project that consists of several work zones and several work crews, the traffic impact may be improved by appropriate scheduling. Therefore, this paper proposed a scheduling model based on the route-changing behavior of road users. The proposed model calculates the traffic delay of vehicles by microscopic simulation, and applies team ant colony optimization to search for a near-optimal schedule. The project planner then ensures that the contractor executes the activities according to the near-optimal schedule. The proposed model is applied to schedule a sewer system construction project in a city. The results of our study indicate that with our proposed model the total traffic delay is reduced by 11.1% when compared with a schedule proposed by the project planner.     

基于交通延误最小的道路养护策略优化

道路养护常常需要关闭相应的机动车道 ,因而会产生新的交通延误。为减少四车道道路养护时所带来的交通延误 ,论文建立了基于交通延误最小的道路养护策略的整数规划优化模型 ,其中目标函数为路网总的交通延误最小 ,约束条件为总的可用工作时间 ;并通过具体的实例对模型进行了验证。在整数规划优化的基础上 ,论文还讨论了通过引入交通标志对交通流进行诱导从而进一步减少交通延误的可能性     

道路平面交叉口次路远引几何参数设置及通行效率研究

次要车流的左转和直行对交叉口车辆运行影响较大,特别是主路交通流量较大时尤为明显,通过次要道路车流远引的方式可以减少交叉口车辆冲突,有利于提高交叉口运行效率。分析了平面交叉口次路远引适用条件与车流组织方法,以延误最小为目标,构建了远引回转车流掉头位置、远引车辆掉头排队车道长度、远引掉头处开口长度与宽度等关键几何参数计算模型,提出了次路远引交叉口延误和通行能力等计算方法。结合实例验证了上述方法的可行性,仿真显示,在一定条件下平面交叉口次路远引可有效减少信号相位数和车辆平均延误,表明了平面交叉口次路远引的交通组织方法对优化城市道路交叉口设计有较好的应用价值。     

A Methodology for Object Identification and Tracking in Construction Based on Spatial Modeling and Image Matching Techniques

Abstract:   Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.     

Bridge user cost estimation – a synthesis of existing methods and addressing the issues of multiple counting,workzones and traffic capacity limitation

Efforts to incorporate the concerns of bridge users in bridge investment evaluation are often stymied by lack of a comprehensive framework for assessing different user costs. There is a need to synthesise and update existing user cost estimation techniques so that the incorporation of user costs in bridge investment evaluation can be more consistent and streamlined. Secondly, a bridge detour may occur for more than one reason, thus there is a danger of multiple counting that could cause overestimation of user costs. Thirdly, user costs during bridge workzones have rarely been considered in the literature. To address these issues, this article presents a framework for comprehensive estimation of bridge user costs, an approach to address the multiple-counting problem, and a methodology for bridge workzone user cost estimation. Furthermore, the article develops a method to estimate bridge user delay cost due to traffic capacity limitation. The methodologies are demonstrated using a case study.     

The influence of the envelope on the thermal performance of ventilated and occupied houses

The main objective of this article is to investigate the influence of the thermal properties of the envelope on the thermal performance of occupied and naturally ventilated houses. A naturally ventilated house built in Southern Brazil was modelled and calibrated in the EnergyPlus computer programme. Based on this calibrated model, a reference model for computer simulations was defined, and variations in the materials of the envelope, occupation patterns and ventilation were carried out. Hourly air temperature and relative air humidity were the output data for the thermal zones of each model. By inputting such data in the Analysis Bio computer programme, the percentage of discomfort hours in the models was obtained. Correlations between the percentage of discomfort hours and the equivalent thermal transmittance, thermal capacity and thermal delay values of the components of the envelope were investigated. Such analysis made clear that there is an influence of the envelope on the thermal performance of the occupied and ventilated house, and that the number of discomfort hours is lower in the models with higher thermal capacity and thermal delay envelope values. It was also observed that thermal capacity was the thermal property with the best correlation to discomfort hours.     

Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

Abstract: Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real‐life data collected in a freeway in England.     

Parameter estimation of unknown air exchange rates and effective mixing volumes from tracer gas measurements for complex multi-zone indoor air models

The steepest descent and simulated annealing optimization techniques are used to simultaneously estimate the effective mixing volumes and air exchange rates of a large partitionless building exhibiting heterogeneous spatial air flow conditions. The optimization is conducted using varying quantities and qualities of simulated tracer gas measurements. A simulated three-compartment system is numerically investigated to assess the performance of the parameter estimation methods. When simulated tracer gases are released in each zone, both techniques estimate actual parameter values within 10–35 percent. When tracers are released in selected zones, reasonable estimates were obtained for those zones in which a simulated gas was released, but significant errors are evident for the non-release zones.     

Activities and Comments: Urban Ecology and Land Value

A traffic model was employed in the San Diego area for the purpose of designing the proper freeway system. Following the basic gravity concept, this model relates trips between zones to total trips generated by each zone and to the distance between those zones. The mathematical influence that distance and trip generation have on travel patterns is determined by analyzing the data obtained from a comprehensive home-interview study. Tests of the procedure reveal a maximum error of 15 per cent for large volumes of traffic.     

城市交通分区体系建构研究

按照城市交通规划建设体系以及与城市规划一致性要求．文章提出宏观交通分区、交通设施分区与运行管理分区三级交通分区体系．宏观交通分区重点分析不同分区交通方式发展政策．交通设施分区主要研究不同分区交通基础设施的规模控制,运行管理分区重点研究不同分区交通流速度控制标准以及管理方式。并将三级交通分区体系应用到南京主城区交通政策规划中。     

车道被占用对城市通行能力的影响

针对道路车道因事故被占用时对该路段通行能力的影响问题,利用Excel处理数据进行了分析,通过统计得到数据,分析同一横断面交通事故所占车道不同对该横断面实际通行能力影响的差异,对排队车辆长度问题,用线性规划与最小二乘法分别构建模型,求出道路实际通行能力、上游车流量、时间与排队车长度间的关系,得出较为合理的数学模型,并用函数拟合说明该模型的可行性。     

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.     

行人影响下的信号交叉口通行能力研究

为量化行人交通对信号交叉口通行能力的影响,分析了行人-机动车冲突点处的行人行为特征和人车运行规律,并描述了行人成群到达的现象。在此基础上,利用车队分析法建立冲突点车辆通行能力模型,并且利用实测数据标定模型参数,为计算行人影响下的信号交叉口通行能力提供了分析依据。模型计算的冲突车流通行能力很大程度取决于有效绿灯时间内冲突点处人车冲突的平均次数。模型计算通行能力总体上低于HCM 2000方法,结果随行人流量增加而呈现的下降趋势渐趋平缓。模型建立在冲突点运行规律基础之上,具有较广泛的适应性。研究为混合交通条件下信号交叉口配时设计与交通管理提供了理论基础。     

Fuzzy system modeling for forecasting water quality index in municipal distribution system

An attempt has been made to develop a fuzzy expert system capable of establishing a criterion for predicting water quality index (WQI) in the various zones of municipal distribution system using pH, alkalinity, hardness, dissolved oxygen (DO), total solids (TS) and most probable number (MPN). The proposed expert system includes a fuzzy model consisting of IF-THEN rules to determine WQI based on water quality characteristics. The fuzzy models are developed using triangular and trapezoidal membership functions, with centroid, bisector and mean of maxima (MOM) methods for defuzzification. Further, the performance of fuzzy models is compared with adaptive neuro fuzzy inference (ANFIS) models. ANFIS models are developed by using triangular, trapezoidal, bell and Gaussian membership function. The study reveals that fuzzy models outperform ANFIS models for all water quality classes. Out of twenty nine zones in the study area, for twenty two zones fuzzy model with triangular membership function performs better than trapezoidal membership function and, for sixteen zones, the centroid method, for seven zones bisector and for remaining six zones MOM method of defuzzification performs better.     

Dynamic origin‐destination flow estimation using automatic vehicle identification data: A 3D convolutional neural network approach

Dynamic origin‐destination (OD) flow estimation is one of the most fundamental problems in traffic engineering. Despite numerous existing studies, the OD flow estimation problem remains challenging, as there is large dimensional difference between the unknown values to be estimated and the known traffic observations. To meet the needs of active traffic management and control, accurate time‐dependent OD flows are required to understand time‐of‐day traffic flow patterns. In this work, we propose a three‐dimensional (3D) convolution‐based deep neural network, “Res3D,” to learn the high‐dimensional correlations between local traffic patterns presented by automatic vehicle identification observations and OD flows. In this paper, a practical framework combining simulation‐based model training and few‐shot transfer learning is introduced to enhance the applicability of the proposed model, as continuously observing OD flows could be expensive. The proposed model is extensively tested based on a realistic road network, and the results show that for significant OD flows, the relative errors are stable around 5%, outperforming several other models, including prevalent neural networks as well as existing estimation models. Meanwhile, corrupted and out‐of‐distribution samples are generated as real‐world samples to validate Res3D's transferability, and the results indicated a 60% improvement with few‐shot transfer learning. Therefore, this proposed framework could help to bridge the gaps between traffic simulations and empirical cases.     

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 .     

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.     

Probabilistic simulations of TBM tunnelling in highly fractured and faulted rocks

Despite the potential excellent performance of TBMs in favourable ground conditions, the presence of fault zones or heavily jointed rocks represents important geological hazards encountered during tunnel excavation. The effects of these challenging environments on the final tunnel construction time and costs can be investigated through a specific computer code: the Decision Aids for Tunnelling (DAT). In this framework the DAT simulate the tunnel excavation in several geological profiles, where changing ground scenarios are described in terms of different “fault zone” classes (from highly fractured rocks, to faulted and crushed material). For each class a certain reduction of the TBM advance rate is specified based on real data analyses. Although the great uncertainty, the results give a reliable estimation of the effect of degrading rock mass conditions on the tunnelling performance. Finally, a real case-study has been simulated by DAT in order to validate the use of the “fault zone” classes (and the relative advance rate reductions) in the estimation of the final time of tunnel construction. The predicted time values prove to be very close to the ones recorded on the field, confirming the importance of a more detailed and comprehensive characterisation of difficult ground conditions such as fault and highly fractured zones.     

Neural Network for Rapid Depth Evaluation of Shallow Cracks in Asphalt Pavements

Abstract:   Rapid and nondestructive evaluation of pavement crack depths is a major challenge in pavement maintenance and rehabilitation. This article presents a computer-based methodology with which one can estimate the actual depths of shallow, surface-initiated fatigue cracks in asphalt pavements based on rapid measurement of their surface characteristics. It is shown that the complex overall relationship among crack depths, surface geometrical properties of cracks, pavement properties, and traffic characteristics can be learnt effectively by a neural network (NN). The learning task is facilitated by a database that includes relevant traffic and pavement characteristics of Florida's state highway network. In addition, the specific data used for the NN model development also contained laser-scanned microscopic surface geometrical properties of cracks in 95 pavement sections and pavement core samples scattered within five counties of Florida. Relatively advanced training algorithms were investigated in addition to the Standard Backpropagation algorithm to determine the optimal NN architecture. In terms of optimizing the NN training process, the "early stopping method" was found to be effective. The crack depth evaluation model was validated based on an unused portion of the database and fresh core samples. The results indicate the promise of NN usage in nondestructive estimation of shallow crack depths based on crack-surface geometry and pavement and traffic characteristics .