Freeway Traffic State Research Articles

A data-driven optimization-based approach for freeway traffic state estimation based on heterogeneous sensor data fusion

Accurate estimation of freeway traffic states is crucial for designing effective traffic management and operational strategies. The integration of various sensor data, such as data from the Electronic Toll Collection (ETC) system and traffic detectors, can significantly enhance the granularity and coverage of traffic state estimation. This study introduces a data-driven optimization-based approach for estimating freeway traffic states, leveraging the fusion of ETC data with detector data. This methodology capitalizes on the broad coverage provided by ETC data and the fine granularity offered by detector data. The probabilistic interdependence between the traffic state of a segment and its upstream and downstream counterparts is captured from real-world traffic state data. Two optimization models, based on the maximum likelihood and maximin likelihood principles, are developed to accurately depict the distribution patterns of freeway traffic states. To address the computational challenges of large-scale scenarios, the study proposes both a decomposition algorithm and a heuristic algorithm. A case study utilizing real-world data from the G92 freeway in Zhejiang, China, is conducted. The findings indicate that the two optimization models exhibit commendable accuracy, with mean absolute percentage errors of 0.9% and 2.3% during peak hours, and 0.9% and 1.4% during off-peak hours, respectively.

Real-time freeway traffic state estimation for inhomogeneous traffic flow

This paper addresses model-based approach considering online model parameters estimation to estimate the real-time freeway traffic state for inhomogeneous traffic flow. Its effectiveness is demonstrated through macroscopic simulation and the influence of detector configuration on the estimation performance is investigated. The results indicate that when a freeway is inhomogeneity, additional detectors need to be placed at the boundary of traffic flow inhomogeneity to achieve better estimation performance. Finally, it is confirmed that considering traffic flow inhomogeneity can achieve better estimation performance using real expressway data from Shanghai.

Spatio-temporal prediction of freeway congestion patterns using discrete choice methods

Predicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–time, such as stop-and-go waves or mega jams. These patterns are informative because they propagate in space–time in different ways, e.g., stop and go waves exhibit a typical propagation that can range far ahead in time. If these patterns and their propagation become predictable, this information can improve and enrich traffic state prediction. In this paper, we use a rich data set of congestion patterns on the A9 freeway in Germany near Munich to develop a mixed logit model to predict the probability and then spatio-temporally map the congestion patterns by analyzing the results. As explanatory variables, we use variables characterizing the layout of the freeway and variables describing the presence of previous congestion patterns. We find that a mixed logit model significantly improves the prediction of congestion patterns compared to the prediction of congestion with the average presence of the patterns at a given location or time.

Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

Highway traffic state data collected from a network of sensors can be considered as a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven method–anti-circulant dynamic mode decomposition with sparsity-promoting (circDMDsp)–to study the dynamics of highway traffic speed data. Particularly, circDMDsp addresses several issues that hinder the application of existing DMD models: limited spatial dimension, presence of both recurrent and non-recurrent patterns, high level of noise, and known mode stability. The proposed circDMDsp framework allows us to numerically extract spatial–temporal coherent structures with physical meanings/interpretations: the dynamic modes reflect coherent spatial bases, and the corresponding temporal patterns capture the temporal oscillation/evolution of these dynamic modes. Our result based on Seattle highway loop detector data showcases that traffic speed data is governed by a set of periodic components, e.g., mean pattern, daily pattern, and weekly pattern, and each of them has a unique spatial structure. The spatiotemporal patterns can also be used to recover/denoise observed data and predict future values at any timestamp by extrapolating the temporal Vandermonde matrix. Our experiments also demonstrate that the proposed circDMDsp framework is more accurate and robust in data reconstruction and prediction than other DMD-based models. The code for the algorithm and experiment is available at https://github.com/mcgill-smart-transport/circDMDsp.

A stochastic microscopic based freeway traffic state and spatial-temporal pattern prediction in a connected vehicle environment

Traffic state prediction forms the basis for effective and efficient traffic control and management strategies. A model-based traffic state prediction approach based on a stochastic microscopic three-phase model is developed to predict traffic flow, speed, and travel time in short prediction horizons consisting of multiple time steps ahead. The proposed model utilizes connected vehicles’ trajectory data including location and speed information and fuses this information with detector measurements using an Adaptive Kalman filter. Stochastic driver behaviors in merging, lane-changing, and over-acceleration are considered in the three-phase microscopic model, which allows for a precise prediction of macroscopic parameters for a relatively long stretch of freeway. Traffic flow and speed predictions are conducted for each lane individually and, for a whole segment. Per-lane predictions provide valuable information regarding different speed fluctuations in each lane for identifying congestion and applying proactive freeway controls. Predicted traffic parameters are used for tracking and predicting the spatial-temporal traffic patterns in real-time. The accuracy of the proposed model is examined and validated for various penetration rates of connected vehicles and prediction horizons and outperforms the baseline prediction methods.

A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data

This paper proposes a streaming-data-driven method for freeway traffic state estimation based on probe vehicle trajectory data, which are represented by a series of timestamps, spatial locations, and instantaneous speeds. The flow and density of a freeway section are reconstructed by estimating the numbers of normal vehicles between consecutive probe vehicles. Specifically, freeway traffic process is divided into different episodes based on the occurrence of shockwaves. The speed of a shockwave is assumed stochastic, and its posterior distribution is estimated via Bayesian regression. Based on the estimated shockwave speed, the number of vehicles between the most downstream and most upstream probe vehicles in an episode is estimated based on Newell’s simplified car-following theory. Then the penetration rate of probe vehicles can be obtained and the numbers of normal vehicles among the probe vehicles that are not captured by shockwaves can also be estimated. Finally, the trajectories of the normal vehicles are reconstructed using linear interpolation. The proposed approach is demonstrated by a simulation experiment and a real-world case study. A good estimation accuracy is achieved even when the penetration rates are as low as 5%–20%. The proposed method is also compared with a state-of-the-art method in the simulation study, which also estimates freeway traffic state solely based on probe vehicle trajectory data. It achieves a comparable performance without spacing information in the trajectory data.

Generic Approaches to Estimating Freeway Traffic State and Percentage of Connected Vehicles With Fixed and Mobile Sensing

Three filtering-based approaches to freeway traffic state estimation are studied using measurements from connected vehicles and also a minimum number of fixed detectors. These approaches are: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method 1</i> based on EKF and the second-order traffic flow model METANET, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods 2</i> and 3 based on KF and the conservation equation that is driven by mean speed data of connected vehicles under a speed-uniformity assumption. Each method is capable of estimating segment traffic flow variables (speeds, densities, and flows) as well as segment market penetration rates (MPRs) of connected vehicles. The three methods are evaluated and compared in depth using NGSIM data with respect to their traffic state estimator design, data requirements, capabilities, limitations in the mixed sensing case. Recommendations are given about the choice of methods over the range of MPR.

Freeway traffic safety state classification method based on multi-parameter fusion clustering

In order to deeply analyze the quantitative relationship between traffic flow state and crash risk, a highway traffic safety state classification method based on multi-parameter fusion clustering was proposed. First, attribute data of highway traffic crashes and corresponding upstream and downstream traffic flow data were extracted, and matched with paired case-control method. Secondly, considering the different roles of traffic volume, speed and occupancy in traffic state classification, the weight optimization algorithm is introduced to calculate the weight of the three parameters. Therefore, the comprehensive evaluation index of traffic state with the fusion of three parameters is obtained and used as the input index of traffic safety state clustering. Finally, [Formula: see text]-means clustering method is used to classify the highway traffic safety status. The result of the case study shows that the proposed method can achieve reasonable and effective traffic safety state division. The classification results are helpful to quantitatively evaluate highway crash risk levels under different traffic safety states.

Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: State-of-the-art overview, methods, and case studies

This paper addresses real-time joint traffic state and model parameter estimation on freeways using data from fixed sensors and connected vehicles. It investigates how the combined usage of both types of sensing data improves the performance of traffic state estimation (TSE) and what role the online model parameter estimation (OMPE) plays therein. The paper first presents a state-of-the-art overview for freeway TSE with mixed sensing, focusing on a few critical issues such as filtering methods, Eulerian and Lagrangian formulation for traffic flow modeling/sensing/estimation, OMPE, and fusion of disparate sensing data, to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. Three representative approaches to the design of freeway traffic state estimators using mixed sensing data are then investigated, which are based on a first-order, a second-order traffic flow model, and a speed-uniformity assumption, respectively. The paper intends to check if the gradual richness of mobile sensing data (in the era of connected vehicles) would compensate the deficiency of first-order models (as compared to second-order models) for TSE; if OMPE would still be essential for TSE in the mixed sensing case compared to the fixed sensing case; if the increasing usage of mobile sensing data would reduce the necessity of OMPE for TSE? The designed traffic state estimators have been evaluated thoroughly using NGSIM data, with the above questions answered.

Highway traffic state prediction method considering risky driving behavior

In recent years, with the continuous development of the national economy, the state and people have a higher demand for the service level of the highway, which also requires highway managers to have a more accurate prediction of the running state of the highway. So this paper proposes an Elman neural network highway state prediction method considering the risky driving behavior. In this method, expert scoring method and analytic hierarchy process are used to calculate the weight of highway risk driving behavior, and then Elman neural network is used to establish the highway state recognition model based on the weight and traffic flow data. Finally, the data of expressways within 4 days is used as the sample training model, and the data of the fifth day is used as the test sample for testing. The results show that the model has a high accuracy for predicting the future traffic status, and it can also provide reference and support for the active traffic early warning and management.

Eco-Driving System for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage the advances of connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve both fuel and operational efficiency of vehicles on the freeways. The proposed algorithm optimizes CAVs’ trajectories with three objectives, including travel time minimization, fuel consumption minimization, and traffic safety improvement, following a two-stage control logic. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The first model functions to predict the freeway traffic states in the near future and accordingly optimize CAVs’ desired speed profile to minimize total freeway travel time. Notably, the interactions between CAVs and human-driven vehicles (HVs) are described in the embedded traffic flow model and the optimization can fully account for CAVs’ impact to HVs’ speeds. Then grounded on the obtained speed profile, the second eco-driving model would further update it so as to platoon CAVs and minimize their fuel consumption. The second stage, for real-time control purpose, is developed to ensure the operational safety of CAVs. Particularly, based on the speed profile from the first stage, real-time adaptions would be placed on CAVs to dynamically adjust speeds, in response to local driving conditions. To evaluate the proposed algorithms, this study selects a freeway segment of I-15 in Salt Lake City as the study site. The extensive numerical simulation results confirmed the effectiveness of the proposed framework in both mitigating freeway congestion and reducing vehicles’ fuel consumption.

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.

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.

A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing

As the backbone and arteries of a comprehensive transportation network, highways play an important role in improving people’s living standards and promoting economic growth. However, globally, there is limited quantifiable data evaluating the highway traffic state, characteristics, and performance. From the 1960s to the present, remote sensing has been regarded as the most effective technology for long-term and large-scale monitoring of surface information. However, how to reflect the dynamic “flow” information of traffic with a static remote sensing image has always been a difficult problem that is hard to solve in the field. This study aims to construct a method of evaluating highway traffic prosperity using nighttime remote sensing. First, based on nighttime light data that indicate social and economic activities, a highway-oriented method was proposed to extract highway nighttime light data from 2015 annual nighttime light data of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (SNPP-VIIRS). Subsequently, Pearson correlation analysis was used to fit the relationship between freeway traffic flow volume and freeway nighttime light at the provincial level. The results showed that Pearson Correlation Coefficient of freeway nighttime light and freeway traffic flow volume for coach and truck are 0.905 and 0.731, respectively, which are higher than between freeway traffic flow volume for coach and truck and total nighttime light (0.593 and 0.516, respectively). A new index—Highway Nighttime Traffic Prosperity Index (HNTPI)—was proposed to evaluate highway traffic across China. The results showed that HNTPI has a strong correspondence with socio-economic parameters. The Pearson Correlation Coefficient of HNTPI and gross domestic product (GDP) per capita, consumption per capita, and population are 0.772, 0.895, and 0.968, respectively. There is a huge spatial heterogeneity in China nighttime traffic, the prosperity degree of highway traffic in developed coastal areas is obviously higher than that inland. The national general highway is the most prosperous highway at night and the national general highway nighttime prosperity of Shanghai reached 22.34%. This research provides basic data for the long-term monitoring and evaluation of regional traffic operation at night and research on the correlation between regional highway construction and the economy.

Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test.

Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

Boundary Observer for Congested Freeway Traffic State Estimation via Aw-Rascle-Zhang model

Abstract This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to 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 boundary observer design so that estimates of aggregated traffic states in a freeway segment are obtained simply from boundary measurement of flow and velocity. The macroscopic traffic dynamics is represented by the ARZ model, consisting of 2 × 2 coupled nonlinear hyperbolic PDEs for traffic density and velocity. Analysis of the linearized ARZ model leads to the study of a hetero-directional hyperbolic PDE model for congested traffic regime. Using spatial transformation and PDE backstepping method, we construct a boundary observer with a copy of the nonlinear plant and output injection of boundary measurement errors. The output injection gains are designed for the error system of the linearized ARZ model so that the exponential stability of error system in the L2 norm and finite-time convergence to zero are guaranteed. Simulations are conducted to validate the boundary observer design for nonlinear ARZ model without knowledge of initial conditions.

Freeway traffic state estimation: A Lagrangian-space Kalman filter approach

Recent researches have shown the potential benefits of using Lagrangian coordinates in modeling mobile sensor data such as GPS, Bluetooth, Wi-Fi, and cellphone probe data. Research shows the numerical accuracy and convenience of Lagrangian traffic flow models in traffic state estimation. In this paper, a new traffic state estimation model by using Lagrangian-space Kalman filter is proposed based on the travel time transition model (TTM). The proposed methodology reformulates the TTM model into a state-space form to fit the Kalman filter framework. The corresponding state-updating matrices for various traffic conditions are also provided. A numerical experiment is conducted based on a simulation model calibrated with the field loop detector data on IH-894 in Milwaukee, Wisconsin for model evaluation. The proposed TTM-based method is compared with a CTM-based Kalman filter estimator on Eulerian coordinate under different penetration rates of the input Bluetooth, Wi-Fi, or Cellular probe vehicle data in which vehicles are re-identified between two consecutive physical or virtual readers. The evaluation results indicate that TTM-based estimation model performs well especially during congestion and can track traffic breakdowns and recovery effectively. The TTM-based estimator outperforms CTM-based methods at all penetration rates levels. Furthermore, the 4% penetration rate is found to be a threshold beyond which TTM-based estimation results improve significantly. With increased penetration rates, the TTM-based model can achieve a mean absolute percentage error around 10%; while CTM-based model remains higher than 13%.

A constrained filtering algorithm for freeway traffic state estimation

ABSTRACTA real-time traffic state estimation algorithm is developed and applied to a freeway. The evolution of the traffic is defined by a second-order macroscopic model which computes, for each section of the freeway, the density, and the mean speed according to several nonlinear equations. Different extensions of the Kalman method were already applied to this model, though none of them considers the natural constraints in the state variables. In this work, a new method that incorporates those natural constraints is applied to the macroscopic model obtaining better results. To validate the proposed method, a simulation over a freeway section was made using two different tools: the macroscopic simulator called METANET and the microscopic simulator called SUMO. Promising results were obtained using both approaches.

Bayesian Network for Freeway Traffic State Prediction

Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent a flow breakdown. Therefore, it is necessary to predict the traffic state in such a way as to reflect the stochastic process of traffic flow. To predict accurately the traffic state, machine learning-based models have been widely adopted, but they have difficulty in obtaining insights for traffic state prediction due to black-box procedures of the models. A Bayesian network (BN) is a methodology that is suitable for dealing with problems that involve uncertainty, and it can also improve the understanding of such problems. In this study, we develop a traffic state prediction model using a BN to reflect the dynamic and stochastic characteristics of traffic flow. To improve the BN, which has been used with a simple structure in transportation problems, we propose a modeling procedure using a mixture of Gaussians (MoGs). In the performance evaluation, the BN has better performance than a logistic regression, and it has the same level of performance as an artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provide the understanding of traffic state prediction and the guidelines for improving the model. The BN developed in this study can be considered as a traffic state prediction model with good prediction accuracy and interpretability.

A Novel Approach to Estimate Freeway Traffic State: Parallel Computing and Improved Kalman Filter

This paper presents a novel approach for freeway traffic state estimation. Although there is currently a wide variety of estimation methods, new designs and technologies can still be created and implemented to improve the accuracy and time efficiency. In this study, a parallel computing framework is developed. The parallel computing framework uses a genetic algorithm process to calibrate the parameters of the traffic model based on the freeway traffic data once an hour. Meanwhile, the framework uses a Kalman filter algorithm process to optimize the traffic model results with the real-time freeway traffic data. Under the framework, the operations of the two processes will not interfere with each other, thus reducing the time it takes to estimate, increasing the efficiency. Furthermore, an improved Kalman filter algorithm is proposed. The algorithm optimizes the traffic model results by balancing the ratio of detector measurements to model results based on their variances, instead of using the Taylor series expansion. Therefore, time efficiency and accuracy of the Kalman filter algorithm are improved. The effectiveness of the approach is evaluated using real field data. Experiments have shown that this approach has high real-time tracking accuracy, and is faster and more accurate than the extended Kalman filter. Results also indicated that the estimation accuracy can be enhanced by accessing additional data sources.