Predicting Motorway Traffic Performance by Data Fusion of Local Sensor Data and Electronic Toll Collection Data

Abstract: This article proposes data fusion from different sources to improve estimation and prediction accuracy of traffic states on motorways. This is demonstrated in two case studies on an intraurban and an interurban motorway section in Austria. Data fusion in this case combines local detector data and speed data from the Electronic Toll Collection (ETC) system for heavy goods vehicles (HGV). A macroscopic model for open motorway sections has been used to estimate passenger car and HGV density, applying a standard state‐space model and a linear Kalman filter. The resulting historical database of 4 months of speed‐density patterns has been used as a basis for pattern recognition. A nonparametric kernel predictor with memory length of 9 and 18 hours has been used to predict HGV speed for a prediction horizon of 15 minutes to 2 hours. Results show good overall prediction accuracy. Correlation analysis showed little bias of predicted speed for free flow and congested time intervals, whereas transition states between free flow and congestion were frequently biased. Prediction accuracy can be improved by applying a combination of different prediction methods. On the other hand, computational performance of the prediction has to be further improved prior to implementation in a traffic management center.     

A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways

Abstract: Fusing freeway traffic data such as spot speeds and travel times from a variety of traffic sensors (loops, cameras, automated vehicle identification systems) into a coherent, consistent, and reliable picture of the prevailing traffic conditions (e.g., speeds, flows) is a critical task in any off‐ or online traffic management or data archival system. This task is challenging as such data differ in terms of spatial and temporal resolution, accuracy, reliability, and most importantly in terms of spatiotemporal semantics. In this article, we propose a data fusion algorithm (the extended generalized Treiber‐Helbing filter [the EGTF]) which, although heuristic in nature, uses basic notions from traffic flow theory and is generic in the sense that it does not impose any restrictions on the way the data are structured in a temporal or spatial way. This implies that the data can stem from any data source, given they provide a means to distinguish between free flowing and congested traffic. On the basis of (ground truth and sensor) data from a micro‐simulation tool, we demonstrate that the EGTF method results in accurate reconstructed traffic conditions and is robust to increasing degrees of data corruption. Further research should focus on validating the approach on real data. The method can be straightforwardly implemented in any traffic data archiving system or application which requires consistent and coherent traffic data from traffic sensors as inputs.     

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.     

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.     

Allocation of Link Flow Detectors for Origin‐Destination Matrix Estimation—A Comparative Study

Abstract: Origin‐destination (OD) matrices are essential for various analyses in the field of traffic planning, and they are often estimated from link flow observations. We compare methods for allocating link flow detectors to a traffic network with respect to the quality of the estimated OD‐matrix. First, an overview of allocation methods proposed in the literature is presented. Second, we construct a controlled experimental environment where any allocation method can be evaluated, and compared to others, in terms of the quality of the estimated OD‐matrix. Third, this environment is used to evaluate and compare three fundamental allocation methods. Studies are made on the Sioux Falls network and on a network modeling the city of Linköping. Our conclusion is, that the most commonly studied approach for detector allocation, maximizing the coverage of OD‐pairs, seems to be unfavorable for the quality of the estimated OD‐matrix.     

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.     

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.     

An Approach to Dynamic Classification of Traffic Flow Patterns

Abstract: Analyses on the dynamics of traffic flow, ranging from intersection flows to network‐wide flow propagation, require accurate information on time‐varying local traffic flows. To effectively determine the flow performance measures and consequently the congestion indicators of segmented road pieces, the ability to process such data in real time is out of the question. In this article, a dynamic approach to specify flow pattern variations is proposed mainly concentrating on the incorporation of neural network theory to provide real‐time mapping for traffic density simultaneously in conjunction with a macroscopic traffic flow model. To deal with the noise and the wide scatter of raw flow measures, a filtering is applied prior to modeling processes. Filtered data are dynamically and simultaneously input to processes of neural density mapping and traffic flow modeling. The classification of flow patterns over the fundamental diagram, which is dynamically plotted with the outputs of the flow modeling subprocess, is obtained by considering the density measure as a pattern indicator. Densities are mapped by selected neural approximation method for each simulation time step considering explicitly the flow conservation principle. Simultaneously, mapped densities are matched over the fundamental diagram to specify the current corresponding flow pattern. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized within a series of intelligent management strategies including especially nonrecurrent congestion effect detection and control.     

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.     

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

Abstract: Currently, pavement instrumentation for condition monitoring is done on a localized and short‐term basis. Existing technology does not allow for continuous long‐term monitoring and network level deployment. Long‐term monitoring of mechanical loading for pavement structures could reduce maintenance costs, improve longevity, and enhance safety. In this article, on‐going research to develop and validate a smart pavement monitoring system is described. The system mainly consists of a novel self‐powered wireless sensor based on the integration of piezoelectric transduction with floating‐gate injection capable of detecting, storing, and transmitting strain history for long‐term monitoring and a novel passive temperature gauge. A technique for estimating full‐field strain distributions using measured data from a limited number of implemented sensors is also described. The ultimate purpose is to incorporate the traffic wander effect in the fatigue prediction algorithms. Preliminary results are shown and limitations are discussed.     

Analysis of microbiota in rhizosphere,water and gut of grass carp (Ctenopharyngodon idella) in ponds treated with artificial floating beds

With the increasing demand for environment friendly aquaculture, artificial floating beds have been now widely applied. However, there are few data available on the effect on microbiota in ponds. This study assessed the rhizobacteria community of artificial floating beds with three different kinds of aquatic plants [water spinach (Ipomoea aquatic), water hyacinth (Eichhornia crassipes) and alligator weed (Alternanthera philoxeroides)]. We used Polymerase Chain Reaction‐Denaturing Gradient Gel Electrophoresis to assess whether artificial floating beds would result in changes in microbiota of pond water and gut of grass carp (Ctenopharyngodon idella). The results showed that there existed no significant differences in rhizobacterial composition, and artificial floating beds would not greatly affect the microbiota of pond water. The dominant microbiota in the guts of grass carp changed from Aeromonas jandaei to Paenibacillus sp. Our results indicated that artificial floating beds may serve as a candidate of modulating fish gut microbiota.     

A Cell‐Based Model for Multi‐class Doubly Stochastic Dynamic Traffic Assignment

Abstract: This article proposes a cell‐based multi‐class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte–Carlo‐based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self‐regulated averaging method. The results illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: (1) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, (2) Using the self‐regulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, (3) The combination of the values of the step size parameters highly affects the speed of convergence, (4) A higher demand, a better information quality, or a higher degree of the risk aversion of drivers can lead to a higher computation time, (5) More driver classes do not necessarily result in a longer computation time, and (6) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes.     

A GEOGRAPHY‐SPECIFIC APPROACH TO ESTIMATING THE DISTRIBUTIONAL IMPACT OF HIGHWAY TOLLS: AN APPLICATION TO THE PUGET SOUND REGION OF WASHINGTON STATE

ABSTRACT: This study contributes to the debate about tolls’ equity impacts by examining the potential economic costs of tolling for low‐income and non‐low‐income households. Using data from the Puget Sound metropolitan region in Washington State and geographic information systems methods to map driving routes from home to work, we examine car ownership and transportation patterns among low‐income and non‐low‐income households. We follow standard practice of estimating tolls’ potential impact only on households with workers who would drive on tolled and nontolled facilities. We then redo the analysis including broader groups of households. We find that the degree of regressivity is quite sensitive to the set of households included in the analysis. The results suggest that distributional analyses of tolls should estimate impacts on all households in the relevant region in addition to impacts on just users of roads that are currently tolled or likely to be tolled.     

An Approach to Dynamical Classification of Daily Traffic Patterns

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.     

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.     

Consistent Point Clouds of Narrow Spaces Using Multiscan Domain Mapping

Abstract: Three‐dimensional (3D) range scanning of large spaces, such as civil structures, generates an immense cloud of 3D points with inconsistent data densities due to the limited positions of the stationary scanner, inaccessible surfaces, and narrow pathways. This density variation is the dominant detrimental factor in extracting accurate scanned shapes. This article introduces an effective scan planning methodology for capturing accurate geometry from long and narrow spaces, which minimizes the need for subsequent data approximations. The technique computes an optimum scanning range for each stationary position of the scanner that limits the density variation to a user‐defined value. Three cases are proposed to define the “limited data density” and a FARO®‐LS880 laser scanner is used to illustrate the proposed approach that achieves acceptable scanning results in terms of its critical shape capturing capability, overall point cloud density, and accurate point‐based visualization. The experimental observations confirm that the accuracy of the scanned data can be improved by registering multiple partial scans with restricted density and positioning the data acquisition device close to the critical features. The latter recommended step decreases the incident angle to the world domain, which, in turn, reduces the surface occlusions and data density variations.     

Impact of Trucks on Signalized Intersection Capacity

Abstract: The most common method used for the analysis of signalized intersections in the United States is contained in the Highway Capacity Manual (HCM). In this method, the base saturation flow rate of the signalized intersection is defined in units of passenger cars per hour green per lane (pc/hg/ln). To account for the presence of large trucks in the traffic stream, the HCM includes a Passenger Car Equivalency (PCE) value. In the current edition of the HCM, a PCE value of 2.0 is applied for all large trucks, with no distinction between different sizes of trucks. The HCM also recommends a single value of 2.0 seconds for startup lost time, regardless of queue composition. Many transportation professionals have questioned the validity of the PCE value and startup lost time recommended by the HCM. They are concerned that the impact of trucks at signalized intersections is being underestimated. If this is the case, then capacity is being overestimated and intersections are not being adequately designed. The objective of this study was to identify appropriate truck PCE values and a relationship for startup lost time as a function of truck percentage in the traffic stream. To accomplish this objective, a custom simulation tool was developed based on the modified Pitt car‐following model, calibrated with field data, and applied to a comprehensive experimental design. The PCE values determined from this study are 1.8, 2.2, and 2.8 for small, medium, and large trucks, respectively. A model for estimating startup lost time based on the same small, medium, and large truck classifications was also developed.     

Fuzzy Cellular Automata Model for Signalized Intersections

At signalized intersections, the decision‐making process of each individual driver is a very complex process that involves many factors. In this article, a fuzzy cellular automata (FCA) model, which incorporates traditional cellular automata (CA) and fuzzy logic (FL), is developed to simulate the decision‐making process and estimate the effect of driving behavior on traffic performance. Different from existing models and applications, the proposed FCA model utilizes fuzzy interface systems (FISs) and membership functions to simulate the cognition system of individual drivers. Four FISs are defined for each decision‐making process: car‐following, lane‐changing, amber‐running, and right‐turn filtering. A field observation study is conducted to calibrate membership functions of input factors, model parameters, and to validate the proposed FCA model. Simulation experiments of a two‐lane system show that the proposed FCA model is able to replicate decision‐making processes and estimate the effect on overall traffic performance.     

A graph deep learning method for short‐term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.     

Structural Disparities of Urban Traffic in Southern California: Implications for Vehicle‐Related Air Pollution Exposure in Minority and High‐Poverty Neighborhoods

Abstract: Structural inequalities provide an important context for understanding and responding to the impact of high traffic densities on disadvantaged neighborhoods. Emerging atmospheric science and epidemiological research indicates hazardous vehicle‐related pollutants (e.g., diesel exhaust) are highly concentrated near major roadways, and the prevalence of respiratory ailments and mortality are heightened in these high‐traffic corridors. This article builds on recent findings that low‐income and minority children in California disproportionately reside in high‐traffic areas by demonstrating how the urban structure provides a critical framework for evaluating the causes, characteristics, and magnitude of traffic, particularly for disadvantaged neighborhoods. We find minority and high‐poverty neighborhoods bear over two times the level of traffic density compared to the rest of the Southern California region, which may associate them with a higher risk of exposure to vehicle‐related pollutants. Furthermore, these areas have older and more multifamily housing, which is associated with higher rates of indoor exposure to outdoor pollutants, including intrusion of motor vehicle exhaust. We discuss the implications of these patterns on future planning and policy strategies for mitigating the serious health consequences of exposure to vehicle‐related air pollutants.