Measuring Traffic

A traffic performance measurement system, PeMS, currently functions as a statewide repository for traffic data gathered by thousands of automatic sensors. It has integrated data collection, processing and communications infrastructure with data storage and analytical tools. In this paper, we discuss statistical issues that have emerged as we attempt to process a data stream of 2 GB per day of wildly varying quality. In particular, we focus on detecting sensor malfunction, imputation of missing or bad data, estimation of velocity and forecasting of travel times on freeway networks.Comment: Published in at http://dx.doi.org/10.1214/07-STS238 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A review of travel time estimation and forecasting for advanced traveler information systems

Providing on line travel time information to commuters has become an important issue for Advanced Traveler Information Systems and Route Guidance Systems in the past years, due to the increasing traffic volume and congestion in the road networks. Travel time is one of the most useful traffic variables because it is more intuitive than other traffic variables such as flow, occupancy or density, and is useful for travelers in decision making. The aim of this paper is to present a global view of the literature on the modeling of travel time, introducing crucial concepts and giving a thorough classification of the existing tech- niques. Most of the attention will focus on travel time estimation and travel time prediction, which are generally not presented together. The main goals of these models, the study areas and methodologies used to carry out these tasks will be further explored and categorized

Study of real-time traffic state estimation and short-term prediction of signalized arterial network considering heterogeneous information sources

Compared with a freeway network, real-time traffic state estimation and prediction of a signalized arterial network is a challenging yet under-studied field. Starting from discussing the arterial traffic flow dynamics, this study proposes a novel framework for real-time traffic state estimation and short-term prediction for signalized corridors. Particle filter techniques are used to integrate field measurements from different sources to improve the accuracy and robustness of the model. Several comprehensive numerical studies based on both real world and simulated datasets showed that the proposed model can generate reliable estimation and short-term prediction of different traffic states including queue length, flow density, speed and travel time with a high degree of accuracy. The proposed model can serve as the key component in both ATIS (Advanced Traveler's Information System) and proactive traffic control system

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

The effects of traffic mix (the percentage of cars, trucks, buses and so on) are of particular interest in the speed-volume relationship in urban signalized arterials under various geometric and control characteristics. The paper presents some empirical observations on the relation between travel speed, traffic volume and traffic composition in urban signalized arterials. A methodology based on emerging self-organizing structures of neural networks to identify regions in the speed-volume relationship with respect to traffic composition and Bayesian networks to evaluate the effect of different types of motorized vehicles on prevailing traffic conditions is proposed. Results based on data from a large urban network indicate that the variability in traffic conditions can be described by eight regions in speed-volume relationship with respect to traffic composition. Further evaluation of the effect of motorized vehicles in each region separately indicates that the effect of traffic composition decreases with the onset of congestion. Moreover, taxis and motorcycles are the primary affecting parameter of the form of the speed-volume relationship in urban arterials. Document type: Articl

Evaluating Human Driving Behavior and Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the western United States, and the communities in wildland-urban interface (WUI) areas are at the greatest risk of fire. Understanding the driving behavior of individuals during evacuating fire-affected WUI areas is important because the evacuees may encounter difficult driving conditions and traffic congestions due to proximity to flammable vegetation and limited exit routes. Existing studies lack empirical data on evacuee driving behavior and traffic operation conditions during a wildfire evacuation. This study used two distinct connected vehicles (CV) datasets that contain lane-level precision historical vehicle trajectory and driving events datasets to investigate the traffic delays and driving behavior of individuals during historical wildfire evacuation events. The results of the study showed that the CV-datasets are a valuable source to accurately evaluate human driving behavior and calculate traffic delays in wildfire-caused evacuations

Speed estimation using single loop detector outputs

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way