Showing papers on "Traffic congestion reconstruction with Kerner's three-phase theory published in 2014"

Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory

Abstract: Three-Phase Traffic Theory.- Definitions of The Three Traffic Phases.- Nature of Traffic Breakdown at Bottleneck.- Infinite Number of Highway Capacities of Free Flow at Bottleneck.- Nature of Moving Jam Emergence.- Origin of Hypotheses and Terms of Three-Phase Traffic Theory.- Spatiotemporal Traffic Congested Patterns.- II Impact of Three-Phase Traffic Theory on.- to Part II:Compendium of Three-Phase Traffic Theory.- Freeway Traffic Control based on Three-Phase Traffic Theory.- Earlier Theoretical Basis of Transportation Engineering: Fundamental Diagram Approach.- Three-Phase Traffic Flow Models.- Linking of Three-Phase Traffic Theory and Fundamental Diagram Approach to Traffic Flow Modeling.- Conclusions and Outlook.

Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections

Abstract: The forecasting of short-term traffic flow is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting could be a challenging task. Artificial Neural Network (ANN) could be a good solution to this issue as it is possible to obtain a higher forecasting accuracy within relatively short time through this tool. Traditional methods for traffic flow forecasting generally based on a separated single point. However, it is found that traffic flows from adjacent intersections show a similar trend. It indicates that the vehicle accumulation and dissipation influence the traffic volumes of the adjacent intersections. This paper presents a novel method, which considers the travel flows of the adjacent intersections when forecasting the one of the middle. Computational experiments show that the proposed model is both effective and practical.

On the impacts of locally adaptive signal control on urban network stability and the Macroscopic Fundamental Diagram

Abstract: Urban traffic networks are inherently unstable when congested. This instability causes a natural tendency towards spatially inhomogeneous vehicle distributions and less consistent and reproducible relationships between urban traffic variables. It is important to find ways to mitigate this unstable behavior since well-defined relationships between average network flow and density – the MFD – are useful to aid network design and control. This paper examines the impacts of locally adaptive traffic signals – e.g., those that allocate green times proportionally to upstream approach densities – on network stability and the MFD. A family of adaptive signal control strategies is examined on two abstractions of an idealized grid network using an analytical model and an interactive simulation. The results suggest that locally adaptive traffic signals provide stability when the network is moderately congested, which increases average flows and decreases the likelihood of gridlock. These benefits increase with the overall adaptivity of the signals. However, adaptive signals appear to have little to no effect on network stability or the MFD in heavily congested networks as vehicle movement becomes more constrained by downstream congestion and queue spillbacks. Under these conditions, other strategies should be used to mitigate the instability, such as adaptively routing drivers to avoid locally congested regions. These behaviors are verified using more realistic micro-simulations and are consistent with other observations documented in the literature.

DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration

Abstract: A number of emerging dynamic traffic analysis applications, such as regional or statewide traffic assignment, require a theoretically rigorous and computationally efficient model to describe the propagation and dissipation of system congestion with bottleneck capacity constraints. An open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. This paper describes its three major modeling components: (1) a light-weight dynamic network loading simulator that embeds Newell’s simplified kinematic wave model; (2) a mesoscopic agent-based DTA procedure to incorporate driver’s heterogeneity; and (3) an integrated traffic assignment and origin–destination demand calibration system that can iteratively adjust path flow volume and distribution to match the observed traffic counts. A number of real-world test cases are described to demonstrate the effectiveness and performance of the prop...

Smart Driving of a Vehicle Using Model Predictive Control for Improving Traffic Flow

Abstract: Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.

Smart traffic lights switching and traffic density calculation using video processing

Abstract: Congestion in traffic is a serious problem nowadays Although it seems to pervade everywhere, mega cities are the ones most affected by it And it's ever increasing nature makes it imperative to know the road traffic density in real time for better signal control and effective traffic management There can be different causes of congestion in traffic like insufficient capacity, unrestrained demand, large Red Light delays etc While insufficient capacity and unrestrained demand are somewhere interrelated, the delay of respective light is hard coded and not dependent on traffic Therefore the need for simulating and optimizing traffic control to better accommodate this increasing demand arises In recent years, video monitoring and surveillance systems have been widely used in traffic management for traveler's information, ramp metering and updates in real time The traffic density estimation and vehicle classification can also be achieved using video monitoring systems This paper presents the method to use live video feed from the cameras at traffic junctions for real time traffic density calculation using video and image processing It also focuses on the algorithm for switching the traffic lights according to vehicle density on road, thereby aiming at reducing the traffic congestion on roads which will help lower the number of accidents In turn it will provide safe transit to people and reduce fuel consumption and waiting time It will also provide significant data which will help in future road planning and analysis In further stages multiple traffic lights can be synchronized with each other with an aim of even less traffic congestion and free flow of traffic

Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields

Abstract: Real-time road traffic congestion monitoring is an important and challenging problem. Most existing monitoring approaches require the deployment of infrastructure sensors or large-scale probe vehicles. Their installation is often expensive and temporal-spatial coverage is limited. Probe vehicle data are oftentimes noisy on urban arterials, and therefore insufficient to provide accurate congestion estimation. This paper presents a novel social-media based approach to traffic congestion monitoring, in which pedestrians, drivers, and passengers a retreated as human sensors and their posted tweets in Twitter as observations of nearby ongoing traffic conditions. There are three technical challenges for road traffic monitoring based on Twitter, namely: 1) language ambiguity in the usage of traffic related terms, 2) uncertainty and low resolution of geographic location mentions, and 3) interactions between traffic-related events such as accidents and congestion. We propose a topic modeling based language model to address the first challenge and a collaborative inference model based on probabilistic soft logic (PSL) to address the second and third challenges. We present a unified statistical framework that combines those two models based on hinge loss Markov random fields (HLMRFs). In order to address the computational challenges incurred by the non-analytical integral of latent variables (factors) and the MAP estimation of a large number of location-dependent traffic congestion variables, we propose a fast approximate inference algorithm based on maximization expectation (ME) and the alternating directed method of multipliers (ADMM). Extensive evaluations over a variety of metrics on real world Twitter and INRIX probe speed datasets in two U.S. Major cities demonstrate the efficiency and effectiveness of our proposed approach.

Empirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-Scale Systems

Abstract: Research on congestion propagation in large urban networks has been based mainly on microsimulations of link-level traffic dynamics. However, both the unpredictability of travel behavior and the complexity of accurate physical modeling present challenges, and simulation results may be time-consuming and unrealistic. This paper explores empirical data from large-scale urban networks to identify hidden information in the process of congestion formation. Specifically, the spatiotemporal relation of congested links is studied, congestion propagation is observed from a macroscopic perspective, and critical congestion regimes are identified to aid in the design of peripheral control strategies. To achieve these goals, the maximum connected component of congested links is used to capture congestion propagation in the city. A data set of 20,000 taxis with global positioning system (GPS) data from Shenzhen, China, is used. Empirical macroscopic fundamental diagrams of congested regions observed during propagation are presented, and the critical congestion regimes are quantified. The findings show that the proposed methodology can effectively distinguish congestion pockets from the rest of the network and efficiently track congestion evolution in linear time O(n).

A method and system to dynamically detect traffic anomalies in a network

Abstract: Methods implemented in a network are disclosed for dynamically distributing tasks of traffic anomaly monitoring and detecting traffic anomalies. The method starts collecting traffic statistics of large blocks of traffic flows as traffic aggregates. Based on the traffic statistics of traffic aggregates, a traffic anomaly is detected. Then for a traffic aggregate with a traffic anomaly, increased traffic sampling rate is applied to a smaller set of traffic flows within the traffic aggregate. If the smaller set of traffic flows does not contain a percentage of the traffic within the traffic aggregate, the sampling rate is further increase to an even smaller set of traffic flows until a small set of traffic flows are identified as the ones cause the traffic anomaly.

Macroscopic traffic flow model validation at congested freeway off-ramp areas

Abstract: The reported study tests, validates and compares two well-known macroscopic traffic flow models in the special, but quite frequently occurring case, where congestion is created due to saturated freeway off-ramps. In particular, the comparison includes the first-order model CTM (Cell Transmission Model) and the second-order model METANET. In order to enable a reliable and fair comparison, the traffic flow models are first calibrated by use of real traffic data from Attiki Odos freeway in Athens, Greece. The resulting models are validated using various traffic data sets, different than the one used for their calibration; the models are then evaluated and compared with respect to their accuracy in the reproduction of congestion created at freeway off-ramp areas.

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

Abstract: This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.

A macro model for traffic flow on road networks with varying road conditions

Abstract: SUMMARY In this paper, we develop a macro traffic flow model with consideration of varying road conditions. Our analytical and numerical results illustrate that good road condition can enhance the speed and flow of uniform traffic flow whereas bad road condition will reduce the speed and flow. The numerical results also show that good road condition can smooth shock wave and improve the stability of traffic flow whereas bad road condition will lead to steeper shock wave and reduce the stability of traffic flow. Our results are also qualitatively accordant with empirical results, which implies that the proposed model can qualitatively describe the effects of road conditions on traffic flow. These results can guide traffic engineers to improve the road quality in traffic engineering. Copyright © 2012 John Wiley & Sons, Ltd.

Flow rate and time mean speed predictions for the urban freeway network using state space models

Abstract: Short-term predictions of traffic parameters such as flow rate and time mean speed is a crucial element of current ITS structures, yet complicated to formulate mathematically. Classifying states of traffic condition as congestion and non-congestion, the present paper is focused on developing flexible and explicitly multivariate state space models for network flow rate and time mean speed predictions. Based on the spatial–temporal patterns of the congested and non-congested traffic, the NSS model and CSS model are developed by solving the macroscopic traffic flow models, conservation equation and Payne–Whitham model for flow rate and time mean speed prediction, respectively. The feeding data of the proposed models are from historical time series and neighboring detector measurements to improve the prediction accuracy and robustness. Using 2-min measurements from urban freeway network in Beijing, we provide some practical guidance on selecting the most appropriate models for congested and non-congested conditions. The result demonstrates that the proposed models are superior to ARIMA models, which ignores the spatial component of the spatial–temporal patterns. Compared to the ARIMA models, the benefit from spatial contribution is much more evident in the proposed models for all cases, and the accuracy can be improved by 5.62% on average. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Our findings suggest that the NSS model is a better alternative for flow rate prediction under non-congestion conditions, and the CSS model is a better alternative for time mean speed prediction under congestion conditions.

Distributed road traffic congestion quantification using cooperative VANETs

Abstract: The well-known traffic congestion problem in urban environments has negative impact on many areas including economy, environment, health and lifestyle. Recently, a number of solutions based on vehicle-to-vehicle communications were proposed for traffic congestion detection and management. In this paper we present an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. Based on observations of traffic congestion by every vehicle, and by adapting the broadcast interval, it enables dissemination of the traffic information to other vehicles. The algorithm also makes every vehicle aware about the congestion level on the streets that are spatially separated from their current location by several streets. Its robustness keeps the vehicle's overall knowledge about congestion consistent, despite the short-term changes in vehicle's motion. Since the quantification of congestion is based on per-vehicle basis, the algorithm is able to operate even when only 10% of vehicles in the network are VANET enabled. Data aggregation and adaptive broadcasting are used to ensure that vehicles do not send redundant information about the traffic congestion. The simulations are conducted in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator.

A Multi-Agent Based Vehicles Re-routing System for Unexpected Traffic Congestion Avoidance

Abstract: As urbanization has been spreading across the world for decades, the traffic congestion problem becomes increasingly serious in most of the major cities. Among the root causes of urban traffic congestion, en route events are the main source of the sudden increase of the road traffic load, especially during peak hours. The current solutions, such as on-board navigation systems for individual vehicles, can only provide optimal routes using current traffic data without considering any traffic changes in the future. Those solutions are thus unable to provide a better alternative route quickly enough if an unexpected congestion occurs. Moreover, using the same alternative routes may lead to new bottlenecks that cannot be avoided. Thus a global traffic load balance cannot be achieved. To deal with these problems, we propose a Multi Agent System (MAS) that can achieve a trade-off between the individual and global benefits by giving the vehicles optimal turn suggestions to bypass a blocked road ahead. The simulation results show that our strategy achieves a substantial gain in average trip time reduction under realistic scenarios. Moreover, the negative impact of selfish re-routing is investigated to show the importance of altruistic re-routing applied in our strategy.

A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks

Abstract: The cooperative vehicle-infrastructure technologies have enabled vehicles to collect and exchange traffic information in real time. Therefore, it is possible to use Vehicular Ad-hoc NETworks (VANETs) for detecting traffic congestion on urban expressways. However, because of the special topology of urban expressways (consisting of both major and auxiliary roadways), the existing traffic congestion detection methods using VANETs do not work very well. In addition, the existing dissemination methods of congestion information lack the necessary control mechanism, so the information may be disseminated to irrelevant geographical areas. This paper proposes a congestion detection and notification scheme using VANETs for urban expressways. The scheme adopts a simplified Doppler frequency shift method to estimate and differentiate traffic conditions for major and auxiliary roadways. Vehicular cooperation and human cognition are introduced to improve the estimation accuracy and to describe the overall traffic conditions. Additionally, the scheme develops a spatial–temporal effectiveness model based on the potential energy theory to control the dissemination area and survival time of the congestion information. Meanwhile, the proposed scheme uses several broadcast control mechanisms to alleviate vehicular network congestion. Simulations through TransModeler indicate that our scheme ensures the accuracy of the estimation of congestion degree. Consequently, the scheme can provide effective references for driving decision-making and path-planning.

Smart traffic control system with application of image processing techniques

Abstract: In this paper we propose a method for determining traffic congestion on roads using image processing techniques and a model for controlling traffic signals based on information received from images of roads taken by video camera. We extract traffic density which corresponds to total area occupied by vehicles on the road in terms of total amount of pixels in a video frame instead of calculating number of vehicles. We set two parameters as output, variable traffic cycle and weighted time for each road based on traffic density and control traffic lights in a sequential manner.

Traffic Origins: A Simple Visualization Technique to Support Traffic Incident Analysis

Abstract: Traffic incidents such as road accidents and vehicle breakdowns are a major source of travel uncertainty and delay, but the mechanism by which they cause heavy traffic is not fully understood. Traffic management controllers are tasked with routing repair and clean up crews to clear the incident and often have to do so under time pressure and with imperfect information. To aid their decision making and help them understand how past incidents affected traffic, we propose Traffic Origins, a simple method to visualize the impact road incidents have on congestion. Just before a traffic incident occurs, we mark the incident location with an expanding circle to uncover the underlying traffic flow map and when it ends, the circle recedes. This not only directs attention to upcoming events, but also allows us to observe the impact traffic incidents have on vehicle flow in the immediate vicinity and the cascading effect multiple incidents can have on a road network. We illustrate this technique using road incident and traffic flow data from Singapore.

Improving traffic operations using real-time optimal lane selection with connected vehicle technology

Abstract: To better regulate traffic flow and reduce the potential impacts due to uncoordinated lane changes, we proposed a real-time optimal lane selection (OLS) algorithm by using the information available from connected vehicle (CV) technology. Such information includes the location, speed, lane and desired driving speed of individual vehicle agents (VA) on a localized roadway. Microscopic traffic simulation studies show that the proposed algorithm can result in both mobility and environmental benefits for the entire traffic system. Specifically, the application of the OLS algorithm reduces the average travel time by up to 3.8% and the fuel consumption by around 2.2%. In addition, the reduction in emissions of criteria pollutants, such as CO, HC, NOx and PM2.5 ranges from 1% to 19%, depending on the congestion level of the roadway segment. Potential extensions of the proposed OLS algorithm are discussed at the end of this paper.

A predictive road traffic management system based on vehicular ad-hoc network

Abstract: With an increasing number of vehicles on the road, demand for intelligent transportation systems is on the rise. In this paper, we present a predictive road traffic management system (PRTMS) based on the Vehicular Ad-hoc Network (VANET) architecture. The proposed PRTMS uses a novel communications scheme to estimate the future traffic intensities at different intersections based on a modified linear prediction algorithm. Based on the prediction, a central controller reduces the congestion level by re-routing the vehicles and adaptively changing the signaling cycles. An IEEE 802.11p based vehicle to-infrastructure communications system is used to collect trip information and transmit control signals to enforce multi-junction traffic flow control. Simulations are conducted using an integrated OPNET model comprised of road infrastructure, vehicular mobility management and communications networking to jointly examine the performances of the proposed PRTMS and the VANET. The results indicate that the proposed scheme provides a significant performance improvement in terms of total journey time and waiting time of the vehicles. In addition, the performance of the prediction algorithm is also investigated.

A new robust approach for highway traffic density estimation

Abstract: In this paper we present a robust mode selector for the uncertain graph-constrained Switching Mode Model (SMM), which we use to describe the highway traffic density evolution. Assuming an uncertain speed of the congestion wave, the proposed selector relies on a transition digraph suitably incorporating the present and historical statistical traffic information, to determine the most probable current mode of the SMM. Its effectiveness is demonstrated on the problem of traffic density reconstruction via a switching observer, in an instrumented 2.2 km highway section of Grenoble south ring in France.

Wavelets on graphs with application to transportation networks

Abstract: The technological advancements in Intelligent Transport Systems have made it possible to acquire large amounts of traffic data in real-time. As a result, various data-mining techniques are being used to extract useful traffic patterns. The research presented in this article focuses on the detection of disruptive traffic events such as congestion. In most transportation studies, traffic parameters are typically modeled as time series. However, these techniques fail to incorporate the spatial dependencies between different traffic variables. In this work, the traffic quantities such as speeds are considered as the signals defined at the vertices of a network line graph. Furthermore, the graph wavelet operators are applied to the spatial signals to generate the wavelet coefficients at different wavelet scales. By analyzing these wavelet coefficients, useful information such as origin, propagation, and the span of traffic congestion are inferred. For analysis, we consider two major expressways in Singapore. The analysis shows that the abrupt changes in the speed can be captured by using the wavelet coefficients at the higher scales. On the other hand, the high magnitude coefficients at the lower wavelet scales reflect the smooth flow of the traffic across the network. I. INTRODUCTION Intelligent Transport Systems (ITS) can play a vital role in developing sophisticated control strategies for optimal usage of the road infrastructure of a land-scarce city like Singapore. The technological advancements in ITS and sensor developments enabled the availability of extensive data related to the on ground traffic conditions. Consequently, data driven approaches are being widely used for applications such as traffic sensing, congestion control, traffic forecasting, and route guidance (1)-(5). In this work, we focus on detecting disruptive traffic events such as unexpected traffic speed fluctuations, traffic slowdown, and congestion that hinder normal traffic flow. The early detection of such traffic events can be useful in issuing early warnings that will eventually help the drivers to plan alternate routes. Previous studies have proposed various methods to detect congestion and other disruptive events. These methods include Principal Component Analysis (PCA), Robust Principal Component Analysis (RPCA), Fourier transform, and wavelets. However, such approaches typically model