Showing papers on "Traffic congestion reconstruction with Kerner's three-phase theory published in 2009"

Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

Abstract: Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML.

Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis.

Abstract: Recent work has shown that properties of network traffic that remain observable after encryption, namely packet sizes and timing, can reveal surprising information about the traffic’s contents (e.g., the language of a VoIP call [29], passwords in secure shell logins [20], or even web browsing habits [21, 14]). While there are some legitimate uses for encrypted traffic analysis, these techniques also raise important questions about the privacy of encrypted communications. A common tactic for mitigating such threats is to pad packets to uniform sizes or to send packets at fixed timing intervals; however, this approach is often inefficient. In this paper, we propose a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class. Through the use of convex optimization techniques, we show how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding. Our evaluation of this technique against two published traffic classifiers for VoIP [29] and web traffic [14] shows that morphing works well on a wide range of network data—in some cases, simultaneously providing better privacy and lower overhead than naive

Automatic Calibration of the Fundamental Diagram and Empirical Observations on Capacity

Abstract: This paper will present a method for automated, empirical calibration of freeway traffic flow characteristics. The method uses 5-min flow and density values for a section of freeway and rapidly and reliably estimates key parameters such as free flow speed, capacity, critical density, congestion wave speed and jam density, which are key inputs to many macroscopic traffic simulation models. The method consists of data filtering, capacity identification, and approximate quantile regression steps. The method was used to calibrate a cell transmission model of Interstate-880 in San Francisco Bay Area, California, a 40-mile long urban freeway with lots of recurrent and non-recurrent congestion and with dozens of loop detector stations. The calibrated model reproduced the observed traffic congestion behavior within 9% error for performance measures VMT (vehicle miles traveled), VHT (vehicle hours traveled) and total flow. Also, the empirical results suggest that capacity, defined as the maximum observed 5-minute flow rate over several days, differs from breakdown flow, defined as the flow that is observed just before the freeway section becomes congested.

Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks

Abstract: It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphesized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents vehicle classification and traffic density calculation methods using neural networks. The paper also reports results from real traffic videos obtained from Istanbul Traffic Management Company (ISBAK).

Understanding stop-and-go traffic in view of asymmetric traffic theory

Abstract: Stop-and-go traffic is a frequently observed phenomenon in congested highway traffic, but it has not been accurately modeled in existing traffic models. Car-following models based on kinematic flow theory cannot model stop-and-go traffic. Other approach assumed traffic states deviating from the equilibrium curve in the fundamental diagram, and the transitions between them, but no explanation was provided on the reason for the existence of different states. There is a need to understand the mechanism of stop-and-go traffic in terms of generation, propagation and dissipation in order to accurately model traffic dynamics. We propose an asymmetric traffic theory and explain the stop-and-go traffic phenomenon in light of the developed theory. The proposed theory is verified using individual vehicle trajectories from two freeway sites in California, US, collected as part of the Next Generation Simulation (NGSIM) project.

V2X-Based Traffic Congestion Recognition and Avoidance

Abstract: Vehicular traffic congestion is a global phenomenon that has increased in importance in the last decades and has caused economically and ecologically negative effects. Thus, finding a way to improve traffic efficiency is a high-frequented problem to be solved by scientists and politicians worldwide. One new promising approach is the usage of decentralized wireless vehicle to vehicle communication based on the Vehicle-2-X (V2X) technology. The idea is that vehicles share information about the current local traffic situation and use this information to optimize their routes. In this paper, we introduce a new algorithm that can be used by navigation systems to calculate routes circumnavigating congested roads. For this purpose, each vehicle transmits its average speed of a road segment to vehicles in the neighbourhood. As a result, vehicles receiving this information can recalculate their routes based on the knowledge about the current possible speeds in the road segments of their neighbourhood. To evaluate the improvements that can be achieved by our algorithm, simulations have been done. Our results show that navigation systems using the V2X technology for a more intelligent route calculation can improve the traffic efficiency of future transport systems.

Continuum modeling of cooperative traffic flow dynamics

Abstract: This paper presents a continuum approach to model the dynamics of cooperative traffic flow. The cooperation is defined in our model in a way that the equipped vehicle can issue and receive a warning massage when there is downstream congestion. Upon receiving the warning massage, the (up-stream) equipped vehicle will adapt the current desired speed to the speed at the congested area in order to avoid sharp deceleration when approaching the congestion. To model the dynamics of such cooperative systems, a multi-class gas-kinetic theory is extended to capture the adaptation of the desired speed of the equipped vehicle to the speed at the downstream congested traffic. Numerical simulations are carried out to show the influence of the penetration rate of the equipped vehicles on traffic flow stability and capacity in a freeway.

Traffic pattern detection in a partially deployed vehicular Ad Hoc network of vehicles

Abstract: Knowledge about traffic conditions on the road play an important role in route planning and avoiding traffic jams. With recent developments in technology, it is possible for vehicles to be equipped with communication and GPS systems. Equipped vehicles on the road can act as nodes to form a vehicular ad hoc network. These nodes can collect information regarding traffic conditions such as position, speed, and direction from other participating nodes. Depending on the number of participating nodes in the ad hoc network, this collected information can provide useful information on driving conditions to the node collecting this information. With proper analysis this information can be used in detecting and/or predicting traffic jam conditions on freeways. In this article the traffic information gathered by a node in an ad hoc network is viewed as a snapshot in time of the current traffic conditions on the road segment. This snapshot is considered as a pattern in time of the current traffic conditions. The pattern is analyzed using pattern recognition techniques. A weight-of-evidence-based classification algorithm is presented to identify different road traffic conditions. The algorithm is tested using data generated by microscopic modeling of traffic flow for simulation of vehicle or node mobility in ad hoc networks. Test results are presented depicting different percentage levels of vehicles equipped with communication capability.

Incorporating Weather Impacts in Traffic Estimation and Prediction Systems

Abstract: Dynamic Traffic Simulation-Assignment models are gaining wider acceptance and use to support transportation network planning and traffic operations decision-making. Significant improvements in traffic estimation capabilities and overall utilities of these systems for traffic management can be achieved by upgrading or adjusting them to account for the impacts of weather. This report presents the results of a study to develop weather-sensitive dynamic traffic assignment (DTA) models for Traffic Estimation and Prediction (TrEPS) application, which addresses both supply and demand aspects of the response to adverse weather, including user responses to various weather-specific interventions such as advisory information and control actions.

Traffic Data Collection under Mixed Traffic Conditions Using Video Image Processing

Abstract: Traffic data collection under mixed traffic conditions is one of the major problems faced by researchers as well as traffic regulatory authorities. Study and analysis of traffic behavior is critically dependent on the availability of observed traffic data. For mixed traffic observed in developing countries, no suitable tool is available for this purpose. Keeping in view these necessities and problems in data collection, a novel offline image processing-based data collection system, suitable for mixed traffic conditions, is developed. Its underlying ability to detect, track, and classify vehicles makes it useful in collecting traffic data under varying traffic conditions. This system can automatically analyze traffic videos and provide macroscopic traffic characteristics such as classified vehicle flows, average vehicle speeds and average occupancies, and microscopic characteristics such as individual vehicle trajectories, lateral, and longitudinal spacing. It is observed that this new system is working well even under congested mixed traffic conditions.

Research on Traffic Monitoring Network and Its Traffic Flow Forecast and Congestion Control Model Based on Wireless Sensor Networks

Abstract: In this paper, we have made a comprehensive study about the key technologies including applying wireless sensor networks to the traffic monitoring network, its traffic flow forecast based on gray forecasting model and traffic congestion control. According to the features that wireless sensor networks have no space constraints, flexible distribution, mobile convenience and quick reaction, we present a scheme that uses wireless sensor networks to monitor city transport vehicles and have designed a traffic monitoring system based on wireless sensor network that is applicable to all types of city environment. With the system, we can monitor the important roads that are easily blocked and find out the time changing law of traffic congestion, and then put the focus on monitoring them in order to greatly reduce the investment and achieve high efficiency. As far as the traffic flow forecasting methods concerned, we use Adaptive GM (1, 1) Model which have a real-time rolling forecast for city traffic and have a better forecast results. Because of the fewer study about the traffic congestion control in the current academia, we make a deep study about the traffic congestion control issues in this article. Learn from mature congestion control algorithm in computer network, we have designed an algorithm of traffic flow congestion control and scheduling for traffic network, which is called TRED. We have used it for real-time traffic scheduling and have opened up a new way to study and solve traffic congestion control problems.

Vehicle traffic congestion management in vehicular ad-hoc networks

Abstract: Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the Adaptive Proportional Integral rate controller, a congestion control algorithm designed for the Internet, to deal with the problem of vehicle traffic congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data traffic congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle traffic congestion in a VANET.

Road Traffic Noise Prediction Model under Interrupted Traffic Flow Condition

Abstract: The objective of this study is to develop an empirical traffic noise prediction model under interrupted traffic flow conditions using two analytical the approaches, the first being the acceleration lane approach and second being the deceleration approach. The urban road network of Bangalore city has been selected as the study area. Sixteen locations are chosen in major traffic junctions of the study area. The traffic noise data collected from the study locations were analyzed separately for both acceleration and deceleration lanes when vehicles leave an intersection on a green traffic light and come to a stop on red traffic light. Based on the study, a regression noise prediction model has been developed for both acceleration and deceleration lanes.

Traffic Networks and Flows over Time

Abstract: In view of the steadily growing car traffic and the limited capacity of our street networks, we are facing a situation where methods for better traffic management are becoming more and more important. Studies [92] show that an individual "blind" choice of routes leads to travel times that are between 6% and 19% longer than necessary. On the other hand, telematics and sensory devices are providing or will shortly provide detailed information about the actual traffic flows, thus making available the necessary data to employ better means of traffic management.

Real-Time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method

Abstract: The occurrence of a highway traffic accident is associated with the short-term turbulence of traffic flow. In this paper, we investigate how to identify the traffic accident potential by using the k-nearest neighbor method with real-time traffic data. This is the first time the k-nearest neighbor method is applied in real-time highway traffic accident prediction. Traffic accident precursors and their calculation time slice duration are determined before classifying traffic patterns. The experimental results show the k-nearest neighbor method outperforming the conventional C-means clustering method.

Traffic light control for a single intersection based on wireless sensor network

Abstract: In view of the complicated traffic condition in the traffic lights monitoring-control system, the current conventional fixed-cycle traffic light control is unable to make full use of lanes. In this paper, a novel simple and economic way used the fuzzy theory to optimize the control of traffic lights for a single intersection is designed and presented based on Wireless Sensor Network. The traffic flow can be detected by the single-axis magnetic sensors and transmitted by wireless sensor network. In order to realize a real-time dynamic control of traffic lights and to reduce the vehicles' average trip waiting time (ATWT), the time for vehicles passing during the green lights would be dynamically adjusted through the fuzzy algorithm according to the current volume of vehicles. The test results show the lanes would be more unobstructed and the efficiency of vehicles traffic at an intersection would be enhanced. Compared with the traditional fixed-cycle system, it is more suitable to the complexity of current traffic conditions.

Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data

Abstract: We present a novel concept, Virtualized Traffic, to reconstruct and visualize continuous traffic flows from discrete spatio-temporal data provided by traffic sensors or generated artificially to enhance a sense of immersion in a dynamic virtual world. Given the positions of each car at two recorded locations on a highway and the corresponding time instances, our approach can reconstruct the traffic flows (i.e. the dynamic motions of multiple cars over time) in between the two locations along the highway for immersive visualization of virtual cities or other environments. Our algorithm is applicable to high-density traffic on highways with an arbitrary number of lanes and takes into account the geometric, kinematic, and dynamic constraints on the cars. Our method reconstructs the car motion that automatically minimizes the number of lane changes, respects safety distance to other cars, and computes the acceleration necessary to obtain a smooth traffic flow subject to the given constraints. Furthermore, our framework can process a continuous stream of input data in real time, enabling the users to view virtualized traffic events in a virtual world as they occur.

The effects of bus stop on traffic flow

Abstract: In this paper, we use the traffic flow model proposed by Tang et al. [Physica A387, 6845 (2008)] to study the effects of bus stop on traffic flow. Our numerical tests show that bus stop will have great effects on the stability of traffic flow and that the effects are related to the initial density and the number of bus stops. The numerical results are accordant with the real traffic, which shows that the model proposed by Tang et al. can describe some complex traffic phenomena resulted by bus stop.

A novel model for intersections of vehicular traffic flow

Abstract: This paper deals with intersections' modeling for vehicular traffic flow governed by the Lighthill $\&$ Whitham [24] and Richards [26] model. We present a straightforward reformulation of recent intersections' models, introduced in [19] and [4], using a description in terms of supply and demand functions [22, 6]. This formulation is used to state the new model which takes into account a possible storage capacity of an intersection as seen in roundabouts or highway on-ramps. We discuss the Riemann problem at the junction and present numerical simulations.

The Use of Genetic Algorithm for Traffic Light and Pedestrian Crossing Control

Abstract: Summary The increase in urban traffic has resulted in traffic congestions, long travel times and increase hazards to pedestrians due to inefficient traffic light controls. These scenarios necessitate the use of new methods in the design of traffic light control for vehicles and pedestrian crossings. In a conventional traffic light controller, the traffic lights change at constant cycle times which are clearly not optimal. The preset cycle time regardless of the dynamic traffic load only adds to the problem. It would be more feasible and sensible to pass more vehicles at the green interval if there are fewer vehicles waiting behind the red lights or vice versa. We apply the genetic algorithm technology in the traffic control system and pedestrian crossing to provide intelligent green interval responses based on dynamic traffic load inputs, thereby overcoming the inefficiencies of the conventional traffic controllers. We apply such technology to a four-way, two-lane junction based on two sets of parameters: vehicles and pedestrians queues behind a red light and number of vehicles and pedestrians that passes through a green light. The algorithms dynamically optimize the red and green times to control the flow of both the vehicles and the pedestrians. To represent a typical traffic flow system, we use the Cellular Automata for modeling vehicular motion behind the traffic lights. We developed an algorithm to model the situation of a four-way two-lane junction based on this technology. We compare the performance between the genetic algorithms controller and a conventional fixed time controller and the results show that the genetic algorithms controller performs better than the fixed-time controller.

A New Macro Model for Traffic Flow on a Highway with Ramps and Numerical Tests

Abstract: In this paper, we present a new macro model for traffic flow on a highway with ramps based on the existing models. We use the new model to study the effects of on-off-ramp on the main road traffic during the morning rush period and the evening rush period. Numerical tests show that, during the two rush periods, these effects are often different and related to the status of the main road traffic. If the main road traffic flow is uniform, then ramps always produce stop-and-go traffic when the main road density is between two critical values, and ramps have little effect on the main road traffic when the main road density is less than the smaller critical value or greater than the larger critical value. If a small perturbation appears on the main road, ramp may lead to stop-and-go traffic, or relieve or even eliminate the stop-and-go traffic, under different circumstances. These results are consistent with real traffic, which shows that the new model is reasonable.

Transportation and Traffic Theory 2009: Golden Jubilee: Papers Selected for Presentation at Isttt 18, a Peer Reviewed Series Since 1959

Abstract: A Game Theoretic Approach to the Determination of Hyperpaths in Transportation Networks.- Network Equilibrium under Cumulative Prospect Theory and Endogenous Stochastic Demand and Supply.- Estimation of Parameters of Network Equilibrium Models: A Maximum Likelihood Method and Statistical Properties of Network Flow.- Spatiotemporal Effects of Segregating Different Vehicle Classes on Separate Lanes.- Microscopic Traffic Behaviour near Incidents.- Understanding Stop-and-go Traffic in View of Asymmetric Traffic Theory.- A Stochastic #x03B1 -reliable Mean-excess Traffic Equilibrium Model with Probabilistic Travel Times and Perception Errors.- Equilibrium Trip Scheduling in Congested Traffic under Uncertainty.- Reliable a Priori Shortest Path Problem with Limited Spatial and Temporal Dependencies.- Risk Averse Second Best Toll Pricing.- Cordon Pricing Consistent with the Physics of Overcrowding.- Build-operate-transfer Schemes for Road Franchising with Road Deterioration and Maintenance Effects.- Equilibria and Inefficiency in Traffic Networks with Stochastic Capacity and Information Provision.- An Active-set Algorithm for Discrete Network Design Problems.- Multi-class Multi-modal Network Equilibrium with Regular Choice Behaviors: A General Fixed Point Approach.- Existence of Equilibrium in a Continuous Dynamic Queueing Model for Traffic Networks with Responsive Signal Control.- Harmonic Analysis and Optimization of Traffic Signal Systems.- A Two-direction Method of Solving Variable Demand Equilibrium Models with and without Signal Control.- Modeling Learning Impacts on Day-to-day Travel Choice.- A Probit-based Joint Discrete-continuous Model System: Analyzing the Relationship between Timing and Duration of Maintenance Activities.- Bayesian Learning, Day-to-day Adjustment Process, and Stability of Wardrop Equilibrium.- Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach.- The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Congested Highways.- Driver Behavior, Dilemma Zone, and Capacity at Red Light Camera Equipped Intersections.- Optimization of a Bus and Rail Transit System with Feeder Bus Services under Different Market Regimes.- Modelling Dynamic Generation of a Choice Set in Pedestrian Networks.- A Common Modeling Framework for Dynamic Traffic Assignment and Supply Chain Management Systems with Congestion Phenomena.- A Pedestrian Model Considering Anticipatory Behaviour for Capacity Evaluation.- A Comparative Assessment of Stochastic Capacity Estimation Methods.- Supply-demand Diagrams and a New Framework for Analyzing the Inhomogeneous Lighthill-Whitham-Richards Model.- Network Evaluation Based on Connectivity Vulnerability.- Reliability-based Dynamic Discrete Network Design with Stochastic Networks.- Flow Breakdown, Travel Reliability and Real-time Information in Route Choice Behavior.- Optimal Sensor Placement for Freeway Travel Time Estimation.- Updating Dynamic Origin-destination Matrices using Observed Link Travel Speed by Probe Vehicles.

Prediction of traffic density for congestion analysis under Indian traffic conditions

Abstract: Traffic congestion is a serious problem which traffic engineers all over the world are trying to solve. Congestion increases the uncertainty in travel times leading to human stress and unsafe traffic situations. Better management of traffic through Intelligent Transportation Systems (ITS) applications, especially by predicting the congestion on various roads and informing the travelers regarding the same is one possible solution. Accurate and quick prediction is one of the important factors on which the reliability of such a system depends. If one is able to predict congestion on a roadway, then the travelers can be warned of the same either pre-trip or enroute so that they can take well informed travel decisions. The number of vehicles in a given stretch of a roadway (usually referred to as “traffic density”) is one of the most commonly used congestion indicator. Also, the travelers in general will be more interested to know what they can expect when they make the trip in future rather than the present scenario. This makes the short term prediction to future time intervals important. In this study, some of the reported techniques for density prediction under homogeneous traffic conditions are attempted under heterogeneous traffic conditions in order to determine their feasibility under the Indian traffic scenario.

A macro-kinetic hybrid model for traffic flow on road networks

Abstract: Abstract We have developed a new hybrid model for an heterogeneous traffic flow, based on a coupling of the Lighthill — Whitham and Richards (LWR) macroscopic model and the kinetic model. On the highways of a road network, we consider the macroscopic description of the traffic flow and switch to the kinetic model to compute the mass flux through a junction. This new model reproduces the capacity drop phenomenon at a merge junction, for instance, without imposing any priority rule. We present some numerical simulations in which we compare the results of the hybrid model with those given by the fully macroscopic model. Furthermore, we illustrate the consequences of the velocity distribution on the flow through a merging junction.

Using vehicular networks to collect common traffic data

Abstract: State transportation departments are required to annually report various traffic statistics to the US government. Currently, this data is measured using older technologies that are susceptible to failure and are difficult to maintain and repair. In this paper, we propose using vehicular ad-hoc networks (VANETs) to measure this common traffic data. In addition to collecting information required for reporting purposes, VANET-based traffic monitoring can provide highly-desired metrics such as travel times, which cannot be directly measured using commonly used traffic monitoring approaches. We show that with 100% penetration rate, our methods can collect precise traffic data and that even with low penetration rates or low density traffic, we can collect high quality estimates of travel times, time mean speeds, and space mean speeds.