Traffic State Estimation Research Articles

A self-regulation traffic-condition-based route guidance strategy with realistic considerations: Overlapping routes, stochastic traffic, and signalized intersections

ABSTRACTVariable message signs that provide various types of route guidance information have been widely deployed in large cities. To release proper information only using easily collected data, a simple traffic-condition-based (TCB) route guidance strategy was recently proposed. The strategy works based on the estimation of free-flow and congested traffic conditions and is capable of approximating user optimal equilibrium stably. Due to little consideration of the complexity of urban road networks, the TCB strategy is still away from field applications in urban areas. To further push the strategy toward field tests, this article improves the TCB strategy in the following aspects: supplementing the strategy with a self-regulation ability by considering existing traffic conditions; decomposing link capacity to solve the problem of overlapping routes by comparing link capacity on alternative routes; coping with stochastic traffic; and the impact of signalized intersections by utilizing aggregated data. A scenario for an urban road network in Beijing, China, is simulated to test the improved strategy, and the simulation results clearly indicate the effectiveness of the proposed improvements. The improvements extend the TCB strategy on moderately complicated urban road networks, and still have the advantages of simple diversion rules, easily obtained input data, and stable and effective diversion processes.

An extended generalized filter algorithm for urban expressway traffic time estimation based on heterogeneous data

ABSTRACTTravel time estimation and its variation for urban expressways are vital to both the information provision to road users, and the system evaluation and management for traffic administrators. Fruitful research efforts have been made to develop methodologies of reconstructing spatiotemporal traffic states mainly for freeways based on one or multiple data sources. However, few studies specifically focused on urban expressways. There are more intensive merging and diverging traffic due to short distances between ramps, for example, 300–500 m. Based on the empirical analysis of traffic data collected on a typical segment of a congested urban expressway, this study proposes an extended generalized filter algorithm for the urban expressway traffic state estimation based on heterogeneous data. More specifically, the multiple sources of data include both fixed sensor data (e.g., inductive loops or radar data) and global positioning system (GPS) probe vehicle data. This study compares the proposed algorithm and the traditional algorithm for freeways using data collected on the segment of expressway in Beijing, China. Results demonstrate the advantage of the proposed method, as well as its feasibility and effectiveness.

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.

Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach

The real-time traffic state estimation we propose uses a state-space model considering the variability of the fundamental diagram (FD) and sensing data. Serious congestion was caused by vehicle evacuation in many Sanriku coast cities following the great East Japan earthquake on March 11, 2011. Many of the vehicles in these congested queues were caught in the enormous tsunami after the earthquake [1]. Safe, efficient evacuation and rescue and restoration require that dynamic traffic states be monitored in real time especially in natural disasters. Variational theory (VT) based on kinematic wave theory is used for the system model, with probe vehicle and traffic detector data used to for measurement data. Our proposal agrees better with simulated benchmark traffic states than deterministic VT results do.

RTS: road topology‐based scheme for traffic condition estimation via vehicular crowdsensing

SummaryUrban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle‐sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net into road sections and junction areas. Based on this division, we introduce a two‐phased data collection and processing scheme named road topology‐based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data. Copyright © 2016 John Wiley & Sons, Ltd.

Collecting floating car data with smartphones: results from a field trial in Austria

This work reports on results from a field trial regarding the collection of floating car data with smartphones in Austria. The field trial has been conducted within Austria’s National Floating Car Data Testbed pursuing the goal to test different aspects of floating car data technology for traffic data collection, traffic state estimation and traffic prediction. The test bed collects, processes and analyses FCD from several thousand vehicles. The field trial for smartphone-based data collection has been conducted within the Federal State of Salzburg covering 1500 kilometres of major road network. Between the launch of the Android-based smartphone application in March 2014 and the end of the field trial in February 2015, the application has been downloaded by more than 2100 users. One year after launch the app is still installed on 650 devices and attracts around 15 users daily. The work gives insights into the application’s concepts and discusses app usage statistics, usage patterns and user feedback in the context of community-driven traffic data collection. On the one hand, results from the field trial confirm that community-driven traffic data collection is still not a phenomenon of the masses due to various challenges discussed throughout the work. On the other hand, results contribute to a deeper understanding of community-driven data collection in the traffic domain and help to learn for future trials.

Virtual Patrolling

Approximately 25 per cent of all congestion on motorways is caused by incidents. By virtual patrolling, incidents e.g. queues, accidents and car breakdowns on a road network, can be predicted or detected in an early stage. This early detection and prediction of an incident likely to happen, offers the opportunity to act faster and therefore reduce congestion compared to current practice. A module is developed capable of virtual patrolling and based on four key features: data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and artificial neural networks. In CHARM, the cooperation between the Highways England (UK) and Rijkswaterstaat (NL), the potential of this module has been demonstrated in a simulation environment and in 2015 further developed, deployed and tested in real life.Extended Kalman filtering and traffic flow theory algorithms are used to fuse data from loop detectors with FVD (floating vehicle data) to provide detailed traffic state data (i.e. flow and speed for 10seconds for every segment of approximately 200 meters). For the prediction of the future traffic state, information on the actual road capacity is necessary. Road capacity varies for weather, lightness, number of heavy vehicles et cetera. A self-adapting module is used to make a real time estimation of the fundamental diagram and road capacity. The traffic state estimation itself is based on fuzzy logic to determine to what extent the traffic state is free flow or congested. If only speed data is available, alternative simplified fuzzy logic rules are applied. The fuzzy traffic state estimations for separate segments is combined into a combined traffic state estimation. This traffic state estimation is used as input for artificial neural networks (ANN) to predict whether it is likely that a queue will form within the short-term future (within 10minutes) on a relevant segment. This is defined as a segment that suffers from queues on a frequent basis.The results of these estimations will be used for virtual patrolling to distinguish between normal and expected congestion and other situations. Information from the modules can also be used for the detection of prediction of shockwaves. This can be of help to act faster when incidents happen and to prevent or solve shockwaves by giving (in-car) advices on speed and lane choice to car users.

Acoustic signature-based vehicular traffic density state estimation in developing regions

In developing regions like Asia, where the traffic conditions are chaotic and non-lane driven, the intrusive techniques may be inapplicable. The vehicular acoustic signals and the occurrence and mixture weighting of these signals are determined by the prevalent traffic density state condition. This research work considers the problem of vehicular traffic density state estimation, based on the information present in the acoustic signal acquired from roadside-installed microphone. In this work a visual analytic for consideration of frame size and shift size, while extracting feature vectors using Mel Frequency Cepstral Coefficients (MFCC) for traffic density state estimation and corresponding experimental validation is provided. Different kernel functions of support vector machine (SVM) from single acoustic frame to multiple contiguous frames were used to classify the density state as low, medium and heavy. The system results in enhanced classification performance when observed time increases or when multiple contiguous frames were considered.

Highway Traffic State Estimation with Mixed Connected and Conventional Vehicles

Abstract: A macroscopic model-based approach for estimation of the traffic state, specifically of the (total) density and flow of vehicles, is developed for the case of mixed traffic, i.e., traffic comprising both ordinary and connected vehicles. The development relies on the following realistic assumptions: (i) The density and flow of connected vehicles are known at the (local or central) traffic monitoring and control unit on the basis of their regularly reported positions; and (ii) the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by merely estimating the percentage of connected vehicles with respect to the total number of vehicles. A model is derived, which describes the dynamics of the percentage of connected vehicles, utilizing only well-known conservation law equations that describe the dynamics of the density of connected vehicles and of the total density of all vehicles. Based on this model, which is a linear parameter-varying system, an estimation algorithm for the percentage of connected vehicles is developed employing a Kalman filter. The estimation methodology is validated through simulations, using a second-order macroscopic traffic flow model as ground truth for the traffic state, as well as using real microscopic traffic data collected within the Next Generation SIMulation (NGSIM) program.

Traffic State Estimation Using Floating Car Data

There is an increasing availability of floating car data both historic, in the form of trajectory datasets and real-time, in the form of continuous data streams. This paves the way for several advanced traffic management services such as current traffic state estimation, congestion and incident detection and prediction of the short-term evolution of traffic flow. In this paper, we present an analysis of using probe vehicles for reconstructing traffic state. We employ detailed agent-based microscopic simulations of a real world expressway to estimate the state from floating car data. The probe penetration required for accurate traffic state estimation is also determined.

Signal Timing Detection Based on Spatial–Temporal Map Generated from CCTV Surveillance Video

Signal timing data are a critical component in the estimation of arterial traffic states. Estimating arterial travel time, intersection delay, and queue length from either detector or probe data relies on accurate input or detection of signal timing. Moreover, the emerging eco-intersection approaching and departure applications also require reliable signal timing input. With the accelerated deployment of adaptive and smart signal systems, signal timing data have become more and more readily available at arterial management centers. However, of the thousands of intersections around the country, many still do not report real-time signal timing data because of various maintenance, hardware, software, communication, cost, and other problems. Some existing studies have proposed the use of probe trajectories to infer signal timing on the basis of shock wave theory; however, because of the limited sample sizes, coverage and reliability are also limited. A computer vision–based algorithm to detect signal timing from the regular low-resolution CCTV cameras at many major arterial intersections around the United States is proposed in this paper. The algorithm detects vehicle trajectories by evaluating the movement of pixel colors at a predefined scan line on CCTV video footage. By detecting static objects on the scan line and their stalling duration, the proposed algorithm can efficiently detect the starting and ending times of red lights. The algorithm is calibrated and evaluated by using arterial traffic video collected from the intersection of Henderson Road at U.S. Hwy 1 in North Brunswick, New Jersey. The results show the promising performance of the algorithm in becoming a cost-effective signal detection solution.

Simulation of Traffic Flows on the Road Network of Urban Area

This paper represents some investigation of models, criteria and methods of estimation of current traffic conditions on urban network. These data have been used for studying of reliability parameters of traffic flow for specific urban transport situations at deriving the information from traffic detectors and probe vehicles. Relationships between trip time, stop time, volume of traffic flow, occupancy and capacity have been obtained.

Use of Speed Measurements for Highway Traffic State Estimation: Case Studies on NGSIM Data and Highway A20, Netherlands

This paper presents two case studies in which a macroscopic model-based approach for the estimation of traffic conditions, which the authors have recently developed, is employed and tested. The estimation method is developed for a mixed traffic scenario, in which traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicle reports, and a minimal number (sufficient to guarantee observability) of spot sensor–based total flow measurements are utilized. In the first case study, NGSIM microscopic data are used to test the capability of estimating traffic conditions on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilized to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases, the performances are satisfactory and the obtained results demonstrate the effectiveness of the method in both qualitative and quantitative terms.

Big traffic data processing framework for intelligent monitoring and recording systems

In recent years, intelligent monitoring and recording system (IMRS) has been widely used in many cities. It is a networked system which includes the front-end image acquisition system and the back-end data processing platform. In the front-end acquisition system, after recording the image data the front-end processing system analyzes the image data and automatically extracts some information of the passing vehicles, such as time, location, direction, the number and color of license plate, etc. After that, the information is sent to the back-end data processing platform for deeper analysis, such as vehicle trajectory tracking and traffic state estimation. However, as the data scales to a great amount, it becomes difficult for traditional analysis framework and tools to meet the requirement of big data analysis. HBase is the column-oriented database based on Hadoop, along with Map-Reduce programming paradigm. It can deal with the problems in processing big data that intelligent monitoring and recording system faces nowadays. In this paper, a big traffic data processing framework using HBase to analyze the data of IMRS is proposed. The performance of the proposed framework is compared with the Oracle strategy on three different kinds of applications, and the experimental results show that the proposed strategy is with promising performance in computation latency.

Linear complementarity system approach to macroscopic freeway traffic modelling: uniqueness and convexity

The modified cell transmission model (MCTM) is formulated as a linear complementarity system (LCS) in this paper. The LCS formulation presented here consists of a discrete time linear system and a set of complementarity conditions. The discrete time linear system corresponds to the flow conservation equations while the complementarity conditions govern the sending and receiving functions defined by a series of ‘min’ operations in the MCTM. Technical difficulties encountered in application of the CTM and its extensions such as the hard nonlinearity caused by the ‘min’ operator can be avoided by the proposed LCS model. Several basic properties of the proposed LCS formulation, for example, existence and uniqueness of solution, are analysed based on the theory of linear complementarity problem. By this formulation, the theory of LCS developed in control and mathematical programming communities can be applied to the qualitative analysis of the CTM/MCTM. It is shown that the CTM/MCTM is equivalent to a convex programme which can be converted into a constrained linear quadratic control problem. It is found that these results are irrelevant to the cell partition, that is, different cell partitions will not change the uniqueness and convexity of solution. This property is essential for stability analysis and control synthesis. The proposed LCS formulation makes the CTM/MCTM convenient for the design of traffic state estimators, ramp metering controllers.

Learning for an aesthetic model for estimating the traffic state in the traffic video

With the increasing number of vehicles running on the urban roads, the traffic jam becomes much more serious. Properly estimating the traffic jam level from traffic videos is essential for the department of transportation management and drivers. Currently, for estimating the traffic state on videos, most solutions are built on evaluating traffic flow by counting the running vehicles per time unit or detecting their moving speed. However, the main challenge of these solutions is on the vehicle tracking method, in which the vehicles are necessary to be effectively and integrally segmented from the scenes. The solutions should tradeoff the accuracy of the estimation results and the efficiency of the method. In this paper, we propose a learning-based aesthetic model to estimate the traffic state on videos. The model uses multiple video-based perceptual features about traffic state to train the random forest classifier with the labeled data, and estimates traffic state by data classification. The evaluation experiments are conducted on a testing image set, and the results show that the traffic state estimation accuracy of the proposed model is higher than 98% and the efficiency performance is achieved in real-time.

Automatic calibration of fundamental diagram for first‐order macroscopic freeway traffic models

SummaryDespite its importance in macroscopic traffic flow modeling, comprehensive method for the calibration of fundamental diagram is very limited. Conventional empirical methods adopt a steady state analysis of the aggregate traffic data collected from measurement devices installed on a particular site without considering the traffic dynamics, which renders the simulation may not be adaptive to the variability of data. Nonetheless, determining the fundamental diagram for each detection site is often infeasible. To remedy these, this study presents an automatic calibration method to estimate the parameters of a fundamental diagram through a dynamic approach. Simulated flow from the cell transmission model is compared against the measured flow wherein an optimization merit is conducted to minimize the discrepancy between model‐generated data and real data. The empirical results prove that the proposed automatic calibration algorithm can significantly improve the accuracy of traffic state estimation by adapting to the variability of traffic data when compared with several existing methods under both recurrent and abnormal traffic conditions. Results also highlight the robustness of the proposed algorithm. The automatic calibration algorithm provides a powerful tool for model calibration when freeways are equipped with sparse detectors, new traffic surveillance systems lack of comprehensive traffic data, or the case that lots of detectors lose their effectiveness for aging systems. Furthermore, the proposed method is useful for off‐line model calibration under abnormal traffic conditions, for example, incident scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

Modality of Adaptive Neuro-Fuzzy Classifier for Acoustic Signal-Based Traffic Density State Estimation Employing Linguistic Hedges for Feature Selection

Modality of adaptive neuro-fuzzy classifier (ANFC) for vehicular traffic density estimation using linguistic hedges (LH) and feature selection (FS) approach is proposed in this work. Cumulative vehicular acoustic signal is collected from roadside installed Omni-directional microphone followed by acoustic feature extraction using Mel frequency cepstral coefficients for varying combination of frame size and shift size. ANFC is modeled to classify traffic density states as low, medium, and heavy. Classification performance is further improved through modeling of ANFC with LH. Feature selection criteria are considered for varying combinations of frame size and shift, where linguistic hedges are employed for FS followed by ANFC to model traffic density states. We are getting better classification performance as compared to state of art literature for lower combinations of frame and shift size and even for consideration of single frame feature vector. Consideration of multiple contiguous frames will definitely increase the accuracy but with cost of computational time.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator.