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.     

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.     

Spreading Patterns of Malicious Information on Single‐Lane Platooned Traffic in a Connected Environment

The connected vehicle can be easily attacked by cyber threats due to its communication through the wireless network in an open‐access environment. But very few studies have paid attention to the spreading of malicious information such as denial of service, message delay/replay, and eavesdropping generated by cyberattacks. To this end, this article introduces an analytical model, named vehicular malicious information propagation (VMIP), which integrates a classical epidemic model through two‐layer system structure, in which the upper layer describes the malicious information spreading and the lower describes the traffic flow dynamics. The proposed VMIP model is designated for platooned (one‐lane, particularly) traffic. Numerical experiments show the proposed model can efficiently describe the interactions between traffic dynamics and malicious information spreading; and the information propagation highly depends on traffic flow patterns. This article is expected to contribute to a better understanding of the impacts of cyberattacks on traffic and lays a foundation for future development of control strategies on mitigating the disastrous effects of cyberattacks.     

Collecting comprehensive traffic information using pavement vibration monitoring data

Traffic data is essential for intelligent traffic management and road maintenance. However, the enormous effort used for data collection and analysis, combined with conventional approaches for traffic monitoring, is inefficient due to its high energy consumption, high cost, and the nonlinear relationships among various factors. This article proposes a new approach to obtain traffic information by processing raw data on pavement vibration. A large amount of raw data was collected in real time by deploying a vibration‐based in‐field pavement monitoring system. The data was processed with an efficient algorithm to achieve the monitoring of the vehicle speed, axle spacing, driving direction, location of the vehicle, and traffic volume. The vehicle speed and axle spacing were back‐calculated from the collected data and verified with actual measurements. The verification indicated that a reasonable precision could be achieved using the developed methods. Vehicle types and vehicles with an abnormal weight were identified by a three‐layer artificial neural network and the k‐means++ cluster analysis, respectively, which may help law enforcement in determining on an overweight penalty. A cost and energy consumption estimation of an acceleration sensing node is discussed. An upgraded system with low cost, low energy consumption, and self‐powered monitoring is also discussed for enabling future distributed computing and wireless application. The upgraded system might enhance integrated pavement performance and traffic monitoring.     

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.     

Effect of Pulse‐and‐Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles

The fuel consumption of ground vehicles is significantly affected by how they are driven. The fuel‐optimized vehicular automation technique can improve fuel economy for the host vehicle, but their effectiveness on a platoon of vehicles is still unknown. This article studies the performance of a well‐known fuel‐optimized vehicle automation strategy, i.e., Pulse‐and‐Glide (PnG) operation, on traffic smoothness and fuel economy in a mixed traffic flow. The mixed traffic flow is assumed to be a single‐lane highway on flat road consisting of both driverless and manually driven vehicles. The driverless vehicles are equipped with fuel economy‐oriented automated controller using the PnG strategy. The manually driven vehicles are simulated using the Intelligent Driver Models (IDM) to mimic the average car‐following behavior of human drivers in naturalistic traffics. A series of simulations are conducted with three scenarios, i.e., a single car, a car section, and a car platoon. The simulation results show that the PnG strategy can significantly improve the fuel economy of individual vehicles. For traffic flows, the fuel economy and traffic smoothness vary significantly under the PnG strategy.     

Real‐Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

Traffic‐related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic‐related air pollution remains a challenge. Obtaining high‐resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic‐induced air pollution emissions, and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real‐time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real‐time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real‐time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks.     

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.     

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.     

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.     

Two‐Dimensional Simulation of Turning Behavior in Potential Conflict Area of Mixed‐Flow Intersections

The potential conflict area of intersection is the space where conflicting traffic flows pass through in the same signal phase. At this area, turning vehicles interact with most traffic flows, which introduce complex features including variation of trajectories and shared‐priority phenomenon. The traditional one‐dimensional simulation oversimplifies these features with lane‐based assumption. This study integrates the modified social force model with behavior decision and movement constraints to reproduce the two‐dimensional turning process. The method is framed into a three‐layered mathematical model. First, the decision layer dynamically makes decision for turning patterns. Then the operation layer uses the modified social force model to initially generate vehicle movements. Finally, the constraint layer modifies the vehicular motion with vehicle dynamics constraints, boundary of intersection and the collision avoidance rule. The proposed model is validated using trajectories of left‐turn vehicles at a real‐world mixed‐flow intersection with nonprotected signal phases, resulting in a more realistic simulation than previous methods. The distributions of decision points and travel time in simulation are compared with the empirical data in statistics. Moreover, the spatial distribution of simulated trajectories is also satisfactory.     

Computer vision‐based recognition of 3D relationship between construction entities for monitoring struck‐by accidents

Struck‐by accidents often cause serious injuries in construction. Monitoring of the struck‐by hazards in terms of spatial relationship between a worker and a heavy vehicle is crucial to prevent such accidents. The computer vision‐based technique has been put forward for monitoring the struck‐by hazards but there exists shortages such as spatial relationship distortion due to two‐dimensional (2D) image pixels‐based estimation and self‐occlusion of heavy vehicles. This study is aimed to address these problems, including the detection of workers and heavy vehicles, three‐dimensional (3D) bounding box reconstruction for the detected objects, depth and range estimation in the monocular 2D vision, and 3D spatial relationship recognition. A series of experiments were conducted to evaluate the proposed method. The proposed method is expected to estimate 3D spatial relationship between construction worker and heavy vehicle in a real‐time and view‐invariant manner, thus enhancing struck‐by hazards monitoring at the construction site.     

A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision

Spatiotemporal information of the vehicles on a bridge is important evidence for reflecting the stress state and traffic density of the bridge. A methodology for obtaining the information is proposed based on computer vision technology, which contains the detection by Faster region‐based convolutional neural network (Faster R‐CNN), multiple object tracking, and image calibration. For minimizing the detection time, the ZF (Zeiler & Fergus) model with five convolutional layers is selected as the shared part between Region Proposal Network and Fast R‐CNN in Faster R‐CNN. An image data set including 1,694 images is established about eight types of vehicles for training Faster R‐CNN. Combined with the detection of each frame of the video, the methods of multiple object tracking and image calibration are developed for acquiring the vehicle parameters, including the length, number of axles, speed, and the lane that the vehicle is in. The method of tracking is mainly based on the judgment of the distances between the vehicle bounding boxes in virtual detection region. As for image calibration, it is based on the moving standard vehicles whose lengths are known, which can be regarded as the 3D template to calculate the vehicle parameters. After acquiring the vehicles' parameters, the spatiotemporal information of the vehicles can be obtained. The proposed system has a frame rate of 16 fps and only needs two cameras as the input device. The system is successfully applied on a double tower cable‐stayed bridge, and the identification accuracies of the types and number of axles are about 90 and 73% in the virtual detection region, and the speed errors of most vehicles are less than 6%.     

基于WIM的典型城市桥梁车辆荷载状况分析

基于安装在杭州石祥路留石高架上的动态称重系统（WIM）采集的交通流和车辆荷载数据,通过对交通流量、车辆构成、轴重、车辆总重等进行分析,获取了其概率统计特性及分布规律,建立了轴载谱和车辆总重谱,并与相关文献、规范进行了比较。结果表明：一天中各时段的交通流量具有很强的潮汐规律性;2.86%的车辆轴重超过了10 t的超载标准,最大的单轴重量达39.5 t,最大车辆总重达115 t,后半夜车辆恶性超载严重;2轴车车辆总重表现为单峰分布,3轴以上车辆的总重均表现为多峰分布,5轴车和6轴车的总重表现为3峰分布;轴重大的车辆主要是超载的2轴、3轴车辆,这部分车辆对桥梁等基础设施的危害较大。这些车辆荷载特征的获取有助于区域既有桥梁安全性的合理评估,为超重车辆治理和桥梁安全管理提供帮助。     

Empirical Innovation of Computational Dual‐Loop Models for Identifying Vehicle Classifications against Varied Traffic Conditions

Clarifying traffic flow phases is a primary requisite for applying length‐based vehicle classifications with dual‐loop data under various traffic conditions. One challenge lies in identifying traffic phases using variables that could be directly calculated from the dual‐loop data. This article presents an innovative approach and associated algorithm for identifying traffic phases through a hybrid method that incorporates level of service method and K‐means clustering method. The “phase representative variables” are identified to represent traffic characteristics in the traffic flow phase identification algorithm. The traffic factors influencing the vehicle classification accuracy under non‐free traffic conditions are successfully identified using video‐based vehicular trajectory data, and the innovative length‐based vehicle classification models are then developed. The result of the concept‐of‐evidence test with use of sample data indicates that compared with the existing model, the accuracy of the estimated vehicle lengths is increased from 42% to 92% under synchronized and stop‐and‐go conditions. The results also foster a better understanding of the traffic stream characteristics and associated theories to lay out a good foundation for further development of relevant microscopic simulation models with other sensing traffic data sources.     

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.     

Modeling Vehicle Interactions during Lane‐Changing Behavior on Arterial Streets

Abstract: Lane‐changing algorithms have attracted increased attention during recent years in traffic modeling. However, little has been done to address the competition and cooperation of vehicles when changing lanes on urban streets. The main goal of this study is to quantify the vehicle interactions during a lane‐changing maneuver. Video data collected at a busy arterial street in Gainesville, Florida, were used to distinguish between free, forced, and competitive/cooperative lane changes. Models particularly for competitive/cooperative lane changes were developed, depending on whether the following vehicle cooperates with the subject vehicle or not. By referring to the “TCP/IP” protocol in computer network communications, a sequence of “hand‐shaking” negotiations were designed to handle the competition and cooperation among vehicles. The developed model was implemented and validated in the CORSIM microsimulator package, with the simulation capabilities compared against the original lane‐changing model in CORSIM. The results indicate that the new model better replicates the observed traffic under different levels of congestion.     

Applying Projection‐Based Methods to the Asymmetric Traffic Assignment Problem

This article examines the application of a path‐based algorithm to the static and fixed demand asymmetric traffic assignment problem. The algorithm is of the simplicial decomposition type and it solves the equilibration or master problem step by means of five existing projection methods for variational inequality problems to evaluate their performance on real traffic networks. The projection methods evaluated are: (1) a cost approximation‐based method for minimizing the Fukushima's gap function, (2) the modified descent method of Zhu and Marcotte ( 1988 ), (3) the double projection method of Khobotov ( 1987 ) and three of its recently developed variants (Nadezhkina and Takahashi, 2006 ; Wang et al., 2010 ; and He et al., 2012); (4) the method of Solodov and Svaiter ( 1999 ); and (5) the method of Solodov and Tseng ( 1996 ). These projection methods do not require evaluation of the Jacobians of the path cost functions. The source for asymmetries are link costs with interactions, as in the case of priority ruled junctions. The path‐based algorithm has been computationally tested using the previous projection methods on three medium to large networks under different levels of congestion and the computational results are presented and discussed. Comparisons are also made with the basic projection algorithm for the fixed demand asymmetric traffic assignment problem. Despite the lack of monotonicity properties of the test problems, the only method that failed to converge under heavy congestion levels was the basic projection algorithm. The fastest convergence was obtained in all cases solving the master problem step using the method of He et al. (2012), which is a variant of Khobotov's method.     

A Mixed User‐Equilibrium and System‐Optimal Traffic Flow for Connected Vehicles Stated as a Complementarity Problem

Connected vehicles (CVs), be they autonomous vehicles or a fleet of cargo carriers or Uber, are a matter of when they become a reality and not if. It is not unreasonable to think that CV technology may have a far‐reaching impact, even to the genesis of a completely new traffic pattern. To this end, the literature has yet to address the routing behavior of the CVs, namely traffic assignment problem (TAP) (perhaps it is assumed, they ought to follow the traditional shortest possible paths, known as user equilibrium [UE]). It is possible that real‐time data could be derived from the vehicles’ communications that in turn could be used to achieve a better traffic circulation. In this article, we propose a mathematical formulation to ensure the CVs are seeking the system optimal (SO) principles, while the remainder continue to pursue the old‐fashioned UE pattern. The model is formulated as a nonlinear complementarity problem (NCP). This article contributes to the literature in three distinct ways: (i) mathematical formulation for the CVs’ routing, stated as a mixed UE‐SO traffic pattern, is proposed; (ii) a variety of realistic features are explicitly considered in the solution to the TAP including road capacity, elastic demand, multiclass and asymmetric travel time; and (iii) formal proof of the existence and uniqueness of the solutions are also presented. The proposed methodology is applied to the networks of Sioux‐Falls and Melbourne.     

A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

New technologies have emerged to estimate the travel time on freeways by matching certain unique identifications of passing vehicles at different locations. These types of technologies share many similarities despite having different mechanisms. In this article, a generic method is presented to estimate freeway travel times using vehicle ID‐matching technologies. In particular, the new method addresses two long‐standing challenges: outlier screening and travel time estimation. Innovations include (1) using both statistical methods and traffic flow theory to screen outliers; and (2) accounting for mechanisms of various equipment measurement errors. The effectiveness of the proposed method is demonstrated using simulation and shown to be more accurate and responsive to travel time changes than methods based on the use of traditional inductive loops.