Traffic State Estimation Research Articles

Toward a Cost-Effective Motorway Traffic State Estimation From Sparse Speed and GPS Data

In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.

Fuzzy Inference System for Congestion Index Estimation Based on Speed Probability Distributions

The increased development of the urban areas consequently results in a larger number of vehicles on the road network, which inevitably leads to traffic congestion, especially in the rush hours. Intelligent transport systems solutions present the applications that can be useful in detecting and dealing with the problems that are related to congestion. This paper presents a method for the congestion index estimation using the speed transition matrix and the corresponding center of mass. The congestion index is estimated using a Fuzzy Inference System optimized by adopting the Genetic Algorithm. In this paper, the large real-world Global Navigation Satellite System data are used to evaluate the proposed method for the traffic state estimation of most relevant road segments in the largest city in Croatia, the City of Zagreb. The validation of results is performed using the domain knowledge presented in the Highway Capacity Manual, which resulted in the model’s precision of 94.6%. The result indicates a possible application of the method for the congestion estimation in urban centers.

Urban network traffic state estimation using a data-based approach

In this paper we propose an estimator of vehicle density in every road section of a large urban traffic network. We assume a limited number of flow and turning ratio sensors can be installed, and that aggregate floating car data (FCD) is available, such that the space-mean speed of each road can be estimated. We propose a method to locate turning ratio sensors, which takes as input previous low-quality estimates of the turn rates, and then assigns to each intersection a weight according to the effect on the total density reconstruction error caused by perturbations between a priori and actual turning ratio values. We evaluate the models and estimator using data from the urban traffic network of Grenoble in France.

Traffic flow prediction by an ensemble framework with data denoising and deep learning model

Accurate traffic flow data is important for traffic flow state estimation, real-time traffic management and control, etc. Raw traffic flow data collected from inductive detectors may be contaminated by different noises (e.g., sharp data increase/decrease, trivial anomaly oscillations) under various unexpected interference (caused by roadway maintenance, loop detector damage, etc.). To address the issue, we introduced data denoising schemes (i.e., Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet (WL)) to suppress the potential data outliers. After that, the Long Short-Term Memory (LSTM) neural network was introduced to fulfill the traffic flow prediction task. We have tested the proposed framework performance on three traffic flow datasets, which were downloaded from Caltrans Performance Measurement System (PeMS). The experimental results showed that the LSTM+EEMD scheme obtained higher accuracy considering that the average Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are 0.79, 0.60 and 2.14.

Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning

Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain, it is challenging to model the citywide traffic dynamics under a structural pattern of critical features such as hours of days, days of weeks and weather conditions. To address this challenge, this article develops a disentangled representation learning framework to learn an interpretable factorized representation of the independent data generative factors. In order to make full use of the knowledge on generative factors, this article proposes spatial-temporal generative adversarial network (ST-GAN) to assign the generative factors of traffic flow to the feature vector in latent space and reconstructs the high-dimensional citywide traffic flow from the given factors. With the help of the disentangled representations, the decomposed feature vector in latent space discloses the relationship between underlying patterns and citywide traffic dynamics. Several comprehensively experiments show that ST-GAN not only effectively improves the prediction accuracy but also promisingly characterize structural properties of the traffic evolution process.

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

MULTI-SENSOR TRAFFIC DATA FUSION FOR CONGESTION DETECTION AND TRACKING

Abstract. Traffic management applications including congestion detection and tracking rely on traffic from multiple sources to model the traffic conditions. The sources are either stationary sensors which include inductive loop detectors (ILD), radar stations and Bluetooth/WiFi/BLE sensors or Floating Car Data (FCD) from moving vehicles which transmit their locations and speeds. The different sources have their inherent strengths and weaknesses but when used together, they have the potential to provide traffic information with increased robustness. Multi-sensor data fusion has the potential to enhance the estimation of traffic state in real-time by reducing the uncertainty of individual sources, extending the temporal and spatial coverage and increasing the confidence of data inputs. In this study, we fuse data from different FCD providers to improve travel time and average segment speeds estimation. We use data from INRIX, HERE and TomTom FCD commercial services and fuse the speeds based on their confidence values and granularity on virtual sub-segments of 250 m. Speeds differences between each pair of datasets are evaluated by calculating the absolute mean and standard deviation of differences. The evaluation of systematic differences is also performed for peak periods depending on the day of the week. INRIX FCD speeds are compared with ground truth spot speeds where both datasets are measured at a 1-minute interval which show good agreement with an error rate of between 8–20%. Some issues that affect FCD accuracy which include data availability and reliability problems are identified and discussed.

Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data

License-plate recognition (LPR) data are emerging data sources in urban transportation systems which contain rich information. Large-scale LPR systems have seen rapid development in many parts of the world. However, limited by privacy considerations, LPR data are seldom available to the research community, which lead to huge research gap in data-driven applications. In this study, we propose a complete solution using LPR data for link-based traffic state estimation and prediction for arterial networks. The proposed integrative data-driven framework provides the inference of both cycle maximum queue length states and average travel times of links using LPR data from a subset of intersections in an arterial network. The framework contains three novel data-driven sub-components that are highly customized based on the characteristics of LPR data, including: a traffic signal timing inference model to find signal timing information from the LPR timestamp sequences; a light-weighted queue length approximation model to estimate lane-based cycle maximum queue lengths and a network-wide traffic state inference model to perform network-level estimation and prediction using partially observed data. This study exploits and utilizes the unique features of LPR data and other similar vehicle re-identification data for urban network-wide link-based traffic state estimation and prediction. A six days’ LPR dataset from a small road network in the city of Langfang in China and a more comprehensive link-level field experiment dataset are used to validate the model. Numerical results show that the framework provides good estimation and prediction accuracy. The proposed framework is efficient and calibration-free, which can be easily implemented in urban networks for various real-time traffic monitoring and control applications.

Fault-tolerant control of variable speed limits for freeway work zone using likelihood estimation

Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.

GE-GAN: A novel deep learning framework for road traffic state estimation

Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems (ITS). However, the collected traffic state data are often incomplete in the real world. In this paper, a novel deep learning framework is proposed to use information from adjacent links to estimate road traffic states. First, the representation of the road network is realized based on graph embedding (GE). Second, with this representation information, the generative adversarial network (GAN) is applied to generate the road traffic state information in real-time. Finally, two typical road networks in Caltrans District 7 and Seattle area are adopted as cases study. Experimental results indicate that the estimated road traffic state data of the detectors have higher accuracy than the data estimated by other models.

Evaluation of freeway travel speed estimation using anonymous cellphones as probes: a field study in China

The high penetration rate of mobile phones among drivers and passengers indicates an opportunity for obtaining detailed information on vehicle spatial movement and estimating traffic state at a lower cost than traditional traffic monitoring techniques. In this study, we examine and assess the use of cellphones as probes to measure vehicle speeds. Network-based wireless location technology is adopted and calibrated, and the cellphone signalling data are used for traffic state estimation. Then, we introduce the relationship between the cellular network and road network, and explain how it can be used to estimate link-based average travel speed. Cellular probe-based measurements are compared with those obtained by microwave detectors for each five-minute time interval on busy road links along a freeway in Zhejiang, China, on weekdays, weekends, and holidays respectively. The analysis results show that the proposed cellphone-based system can effectively estimate travel speed on freeways with high cellphone penetration rates.

Towards real-time density estimation using vehicle-to-vehicle communications

Traffic state estimation is a fundamental task of Intelligent Transportation Systems (ITS). Recent advances in sensor technology and emerging computer and vehicular communications paradigms have brought the task of estimating traffic state parameters in real time within reach. Recognizing this, the US-DOT started promoting the Connected Vehicles (CV) initiative. By using wireless connectivity between the vehicles participating in the traffic, the CV initiative aims to promote an increased awareness of real-time traffic conditions and, as a result, to reduce the number and severity of crashes.A number of recent papers have proposed CV-based approaches to estimating traffic state parameters including density and flow. However, virtually all the CV-based approaches for density estimation also rely on communication with stationary detectors and other pre-deployed roadside infrastructure. This assumption is problematic since such infrastructure is often not available.The main contribution of this paper is to propose a simple and easy to implement real-time traffic density estimation method that uses only vehicle-to-vehicle communications and the on-board sensing capabilities of present-day vehicles. In our method, using their on-board devices, vehicles maintain a tally that keeps track of the difference between the number of times other vehicles pass them and the number of times they pass other vehicles. Notice that since vehicles may vary their speed as they please, they may pass and be passed by the same vehicle multiple times and, consequently, maintaining a correct tally is a non-trivial task. We show that the tallies computed by vehicles relate, in an interesting way, to traffic density.We provide a detailed proof of our method using techniques that avoid the use of common simplifications inherent to visual time-space traffic diagrams. Furthermore, we demonstrate the accuracy of our method through extensive simulations using real NGSIM traffic traces along with SUMO-generated synthetic traffic traces.

PDE Traffic Observer Validated on Freeway Data

This article develops a boundary observer for the estimation of congested freeway traffic states based on the Aw-Rascle-Zhang (ARZ) partial differential equations (PDEs) model. Traffic state estimation refers to the acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a model-driven approach in which the estimation of aggregated traffic states in a freeway segment is obtained simply from the boundary measurement of flow and velocity without knowledge of the initial states. The macroscopic traffic dynamics is represented by the ARZ model, a 2 ×2 coupled nonlinear hyperbolic PDEs for traffic density and velocity. Using the PDE backstepping method, we construct a boundary observer consisting of a copy of the nonlinear plant with output injections from boundary measurement errors. The exponential stability of the estimation error system in the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm and finite-time convergence to zero is guaranteed. Numerical simulation and data validation are conducted to validate the boundary observer design with vehicle trajectory data.

Data Infrastructure for Connected Vehicle Applications

Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

Sparse Bayesian Learning Assisted Approaches for Road Network Traffic State Estimation

Real-time (online) road network traffic state estimation plays a vital role in enhancing the services offered by Intelligent Transportation Systems (ITS). Spatio-temporal data vacancies of contemporary acquisition systems considerably limit the reliability of online traffic state estimation. Online estimation insists smaller temporal frame widths inhibiting the accuracies of low rank matrix reconstruction approaches, while matrix imputation methods do not capture the non-trivial traffic data relationships. Alternatively, this paper investigates traffic state estimation by constructing sparse representations via Sparse Bayesian Learning (SBL) and Block SBL (BSBL) approaches to accommodate under-sampled data, independent of the acquisition type. Appropriate kernel matrices are determined by leveraging historical spatio-temporal correlations among the road network traffic data. Subsequently, unavailable traffic states are estimated from predictive distributions. The estimates are further pruned by kalman filter that corroborates online processing. With the SBL approach, experiments on PeMS traffic data demonstrate less than 6% Normalized Mean Absolute Error (NMAE) for a Signal Integrity (SI) of 0.5. Compared to the state-of-the-art approaches, this value is significantly better. BSBL approach gives similar error performance as SBL despite a SI of 0.26, at the cost of increased computational time. The NMAE from kalman filtered SBL (SBL+K) approach is less than 3.5% in contrast to 4.5% from kalman filtered BSBL (KBSBL) approach, thus demonstrating SBL+K approach as a good compromise between NMAE and computational time, facilitating more accurate online traffic state estimation.

Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces

The ability to obtain accurate estimates of city-wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms. This paper focuses on the network-wide traffic speed estimation, using trajectory data generated by a city-wide fleet of ride-sourcing vehicles equipped with GPS-capable smartphones. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. Data limitations are addressed using the Schatten p-norm matrix completion algorithm, which can minimize speed estimation errors even with high rates of data unavailability. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.

Traffic State Estimation of Bus Line With Sparse Sampled Data

The traffic state of the bus line is the information basis for the bus company to make bus dispatch and travel time prediction. However, the bus GPS data is severely sparse in time and space coverage of traffic state, due to the long data sampling time interval and low bus departure frequency. Because of ignoring the severe sparseness of the bus data, the existing traffic state methods cannot reconstruct the traffic state accurately. To deal with this problem, a new traffic state estimation method for the bus line, named GAN_BS, is proposed. First, an improved generative adversarial network (GAN-I) is used to generate reasonable bus data. GAN-I aims to find the probability space of the data distribution under sparse sampling. And to reduce the size of the latent space of data, the traffic knowledge is introduced as prior information layers. Then, a traffic adaptive bilateral smoothing method (BS) is used to map discrete bus data into the continuous traffic state. The BS convolves data with a bilateral kernel, which multiplies the local action kernel with a mask of traffic state similarity. Therefore, the BS can maintain transitions between different traffic patterns while separating noise from traffic state. Finally, a set of numerical experiments are performed on the real bus data set in Changchun. The results show that the GAN-I can accurately reproduce the traffic state when the missing rate of data exceeds 50%. And the BS can eliminate the noise better compared with other methods.

A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and Its Statistical Meaning

This paper presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical statistical theories, utilizes statistical tests against the system's observation model. We discuss the application of the two major statistical testing frameworks, Fisherian significance testing and Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has a reliable model of faulty data, while the Fisher one is applicable when one may not have a model of faults, which may occur when dealing with third-party data, like GNSS data of transportation system users. These statistical tests can be combined with a particle filter to obtain a Monte Carlo state estimation scheme that is robust to faulty or outlier data. We present a synthetic freeway traffic state estimation problem where the filters are able to reject simulated faulty GNSS measurements. The fault-model-free Fisher filter, while underperforming the Neyman-Pearson one when the latter has an accurate fault model, outperforms it when the assumed fault model is incorrect.

First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

ABSTRACTUnbalanced lane usage on motorways might lead to the reduction in capacity of the motorway. Lane-level traffic management present new opportunities to balance the lane-flow distribution and help reduce congestion. In order to come up with efficient traffic management strategies on a lane-level, there is a need for accurate lane-specific traffic state estimation models. This paper presents a first-order lane-level traffic flow model. The proposed model differs from the existing models in the following areas: (i) incentive-based motivation for lane changes and consideration of downstream conditions (ii) transfer of lateral flows among cells. The model is tested against real-world data. It is observed that the model is able to capture the lane-level dynamics in terms of the lane flow distribution. The model results are compared to a linear regression model and results show that the developed model performs better than the regression model on the test sections.

Extended Observer for Urban Traffic Control Based on Limited Measurements From Connected Vehicles

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.