Short‐Term Traffic Speed Prediction for an Urban Corridor

Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.     

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.     

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.     

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.     

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.     

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.     

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.     

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.     

Methodology for spatio‐temporal predictions of traffic counts across an urban road network and generation of an on‐road greenhouse gas emission inventory

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.     

Development of a Data‐Driven Framework for Real‐Time Travel Time Prediction

Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long‐term prediction in a real‐time manner have been lacking. Existing methods do not fully utilize the advantages of the state‐of‐the‐art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real‐time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long‐term (at least 6‐hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k‐nearest neighbor (Mk‐NN) method which is compared with the conventional k‐NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long‐term travel time with shorter computation time.     

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.     

Time‐Varying Lane‐Based Capacity Reversibility for Traffic Management

Abstract: This article presents a new bi‐level formulation for time‐varying lane‐based capacity reversibility problem for traffic management. The problem is formulated as a bi‐level program where the lower level is the cell‐transmission‐based user‐optimal dynamic traffic assignment (UODTA). Due to its Non‐deterministic Polynomial‐time hard (NP‐hard) complexity, the genetic algorithm (GA) with the simulation‐based UODTA is adopted to solve multiorigin multidestination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4 with a jam‐density factor parameter (JDF) employ time‐dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs best. The GA with the appropriate inclusion of problem‐specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.     

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.     

Surrogate‐Based Optimization of Expensive‐to‐Evaluate Objective for Optimal Highway Toll Charges in Transportation Network

This article adopts a family of surrogate‐based optimization approaches to approximate the response surface for the transportation simulation input–output mapping and search for the optimal toll charges in a transportation network. The computational effort can thus be significantly reduced for the expensive‐to‐evaluate optimization problem. Meanwhile, the random noise that always occurs through simulations can be addressed by this family of approaches. Both one‐stage and two‐stage surrogate models are tested and compared. A suboptimal exploration strategy and a global exploration strategy are incorporated and validated. A simulation‐based dynamic traffic assignment model DynusT (Dynamic Urban Systems in Transportation) is utilized to evaluate the system performance in response to different link‐additive toll schemes implemented on a highway in a real road transportation network. With the objective of minimizing the network‐wide average travel time, the simulation results show that implementing the optimal toll predicted by the surrogate model can benefit society in multiple ways. The travelers gain from the 2.5% reduction (0.45 minutes) of the average travel time. The total reduction in the time cost during the extended peak hours would be around US$65,000 for all the 570,000 network users assuming a US$15 per hour value of time. Meanwhile, the government benefits from the 20% increase of toll revenue compared to the current situation. Thus, applying the optimized pricing scheme in real world can be an encouraging policy option to enhance the performance of the transportation system in the study region.     

Recurrent Neural Networks for Uncertain Time‐Dependent Structural Behavior

Abstract: In this article, an approach is introduced which permits the numerical prediction of future structural responses in dependency of uncertain load processes and environmental influences. The approach is based on recurrent neural networks trained by time‐dependent measurement results. Thereby, the uncertainty of the measurement results is modeled as fuzzy processes which are considered within the recurrent neural network approach. An efficient solution for network training and prediction is developed utilizing α‐cuts and interval arithmetic. The capability of the approach is demonstrated by means of the prediction of the long‐term structural behavior of a reinforced concrete plate strengthened by a textile reinforced concrete layer.     

Freeway Travel Time Prediction Using Takagi–Sugeno–Kang Fuzzy Neural Network

This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno–Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US‐290 in Houston, Texas are used to train and validate the network. The prediction performance of the TSKFNN is investigated with different combinations of traffic count, occupancy, and speed as input options. The comparison between online TSKFNN, offline TSKFNN, the back propagation neural network (BPNN) and the time series model (ARIMA) is made to evaluate the performance of TSKFNN. The results show that using count, speed, and occupancy together as input produces the best TSKFNN predictions. The online TSKFNN outperforms other commonly used models and is a promising tool for reliable travel time prediction on a freeway corridor.     

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.     

Optimizing Coordinated Ramp Metering: A Preemptive Hierarchical Control Approach

Abstract: This article describes a coordinated ramp metering algorithm for systematically mitigating freeway congestion. A preemptive hierarchical control scheme with a three‐priority‐layer structure is employed in this algorithm. Ramp metering is formulated as a multiobjective optimization problem to enhance system performance. These optimization objectives include promptly tackling freeway congestion, sufficiently utilizing on‐ramp storage capacities, and preventing on‐ramp vehicles from overflowing to local streets, balancing on‐ramp vehicle equity, and maximizing traffic throughputs for the entire system. Instead of relying heavily on accurate estimates of freeway traffic flow evolvement, this new approach models ramp meter control as a linear program and uses real‐time traffic sensor measurements for minimizing the indeterminate impacts from the mainstream flow capacities. VISSIM‐based simulation experiments are performed to examine its practicality and effectiveness using geometric and traffic demand data from one real‐world freeway segment. The simulation test results show that the proposed ramp metering approach performed well in optimizing overall freeway system operations under various traffic conditions. The system‐wide optimal control performance can be achieved to quickly mitigate freeway congestion, prevent traffic from overflowing to local streets, and maximize overall traffic throughputs. The proposed ramp metering approach can dynamically assemble relevant ramp meters to work together and effectively coordinate the individual meter rates to leverage their response strengths for minimizing time to clear the congestion. This study demonstrates that utilization of existing freeway infrastructure can be optimized through the proposed algorithm.     

Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion

Abstract: Passing rate measurements of backward‐moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane‐changing and impact of lane‐changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane‐changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane‐changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane‐changing activities do not induce a long‐term change in traffic states; traffic streams are perturbed temporarily by lane‐changing maneuvers but return to the initial states after relaxations.     

A Spatial‐Bayesian Technique for Imputing Pavement Network Repair Data

Abstract: Pavement construction and repair history is necessary for several pavement management functions such as developing pavement condition prediction models and developing maintenance and rehabilitation (M&R) trigger values based on past repair frequencies. It is often difficult to integrate M&R data with condition data since these data are often stored in disparate heterogeneous databases. This article provides a computational technique for estimating construction and M&R history of a pavement network from the spatiotemporal patterns of its condition data. The technique is founded on Bayesian and spatial statistics and searches pavement condition data in groups of adjacent pavement sections for evidence of repair. The developed technique was applied to a pavement network in Texas and has been found to have a 74% precision and a 95% accuracy in estimating repair history data.