Fusing Loop and GPS Probe Measurements to Estimate Freeway Density

In an age of ever-increasing penetration of GPS-enabled mobile devices, the potential of real-time "probe" location information for estimating the state of transportation networks is receiving increasing attention. Much work has been done on using probe data to estimate the current speed of vehicle traffic (or equivalently, trip travel time). While travel times are useful to individual drivers, the state variable for a large class of traffic models and control algorithms is vehicle density. Our goal is to use probe data to supplement traditional, fixed-location loop detector data for density estimation. To this end, we derive a method based on Rao-Blackwellized particle filters, a sequential Monte Carlo scheme. We present a simulation where we obtain a 30\% reduction in density mean absolute percentage error from fusing loop and probe data, vs. using loop data alone. We also present results using real data from a 19-mile freeway section in Los Angeles, California, where we obtain a 31\% reduction. In addition, our method's estimate when using only the real-world probe data, and no loop data, outperformed the estimate produced when only loop data were used (an 18\% reduction). These results demonstrate that probe data can be used for traffic density estimation

Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

Book of abstract

Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents

Grenoble Traffic Lab: An experimental platform for advanced traffic monitoring and forecasting

International audienceThis paper describes the main features of the "Grenoble Traffic Lab" (GTL), a new experimental platform for the collection of traffic data coming from a dense network of wireless sensors installed in the south ring of Grenoble, in France. The main challenges related to the configuration of the platform and data validation are discussed, and two relevant traffic monitoring and forecasting applications are presented to illustrate the operation of GTL

Travel time estimation in congested urban networks using point detectors data

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models

Modeling and Probing Strategy for Intelligent Transportation System Utilizing Lagrangian Traffic Data.

Traffic flow model that provides accurate traffic prediction can be beneficial for traffic congestion management. Macroscopic traffic flow models were used in the past to incorporate probe vehicle data and to provide real-time traffic information, but the data collection has not been done efficiently. Also, prediction of traffic state, especially for unexpected traffic jam, is needed to compensate latency in data processing and to provide advance warning to the driver. The objective of this dissertation is to develop an analytical tool which predicts congested highway traffic by utilizing macroscopic traffic flow model and strategically collecting data from probing vehicles with real-time update. First, Newtonian relaxation method is used to incorporate probing data into the LWR model in Eulerian coordinates for traffic status estimation. An investigation of probe vehicle deployment optimization is used to reveal the trade-off between the quality of traffic estimation and the probing cost. Synthetic data is used for numerical experiment, and Genetic algorithm is used to solve the optimization problem. The result indicates that optimal deployment of probe vehicle can reduce probing cost and estimation error by efficient usage of probe vehicles. It is possible to decrease probing data for congested traffic with negligible degradation on the quality of traffic estimation. Second, a stochastic Lagrangian macroscopic traffic flow model is formulated which update the prediction of model parameters and traffic state with unscented Kalman filter in real-time. The proposed probing method tracks vehicles in pairs and utilizes loop detector data for additional information as needed. The model is validated with two sets of empirical data to demonstrate its capability of providing short-term prediction and using model parameter to detect traffic jam i advance. An adaptive probing scheme is presented to show that adjusting probing cell size based on the variance from stochastic model can improve the prediction accuracy. This dissertation proposed a stochastic Lagrangian traffic flow model with the capability of traffic prediction and traffic jam detection, and also demonstrated the benefit of using adaptive probing. Future research interests include performance bounds investigation, optimization of adaptive probing, traffic information distribution, and probing commercial vehicle with optimal operation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133332/1/kcchu_1.pd