Arriving on time: estimating travel time distributions on large-scale road networks

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area

Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles

International audienceEstimating and analyzing traffi c conditions on large arterial networks is an inherently diffi cult task. The fi rst goal of this article is to demonstrate how arterial tra c conditions can be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage of vehicles. Traffi c signals, stop signs, and other flow inhibitors make estimating arterial traffi c conditions significantly more diffi cult than estimating highway traffi c conditions. To address these challenges, we propose a statistical modeling framework that leverages a large historical database and relies on the fact that tra ffic conditions tend to follow distinct patterns over the course of a week. This model is operational in North California, as part of the Mobile Millennium tra ffic estimation platform. The second goal of the article is to provide a global network-level analysis of tra ffic patterns using matrix factorization and clustering methods. These techniques allow us to characterize spatial tra ffic patterns in the network and to analyze traffi c dynamics at a network scale. We identify tra ffic patterns that indicate intrinsic spatio-temporal characteristics over the entire network and give insight into the traffi c dynamics of an entire city. By integrating our estimation technique with our analysis method, we achieve a general framework for extracting, processing and interpreting traffi c information using GPS probe vehicle data

LEARNING AND ESTIMATION APPLICATIONS OF AN ONLINE HOMOTOPY ALGORITHM FOR A GENERALIZATION OF THE LASSO

Abstract. The LASSO is a widely used shrinkage and selection method for linear regression. We propose a generalization of the LASSO in which the l 1 penalty is applied on a linear transformation of the regression parameters, allowing to input prior information on the structure of the problem and to improve interpretability of the results. We also study time varying system with an l 1 -penalty on the variations of the state, leading to estimates that exhibit few "jumps". We propose a homotopy algorithm that updates the solution as additional measurements are available. The algorithm takes advantage of the sparsity of the solution for computational efficiency and is promising for mining large datasets. The algorithm is implemented on three experimental data sets representing applications to traffic estimation from sparsely sampled probe vehicles, flow estimation in tidal channels and text analysis of on-line news. Least-squares regression with l 1 -norm regularization is known as the LASSO algorith

A hybrid approach of physical laws and data-driven modeling for estimation: the example of . . .

Mathematical models are a mathematical abstraction of the physical reality which is of great importance to understand the behavior of a system, make estimations and predictions and so on. They range from models based on physical laws to models learned empirically, as measurements are collected, and referred to as data-driven models. A model is based on a series of choices which influence its complexity and realism. These choices represent trade-offs between different competing objectives including interpretability, scalability, accuracy, adequation to the available data, robustness or computational complexity. The thesis investigates the advantages and disadvantages of models based on physical laws versus data-driven models through the example of signalized queuing networks such as urban transportation networks.The dynamics of conservation flow networks are accurately represented by a first order partial differential equation. Using Hamilton-Jacobi theory, the thesis underlines the importance to leverage physical laws to reconstruct missing information (\emph{e.g.} signal or bottleneck characteristics) and estimate the state of the network at any time and location. Noise and uncertainty in the measurements can be integrated in the model. When measurements are sparse, the state of the network cannot be estimated at every time and location on the network. Instead, the thesis shows how to leverage other characteristics, such as periodicity. From deterministic dynamics, the thesis derives the probability distribution functions of physical entities (\emph{e.g.} waiting time, density) by marginalizing the periodic variable. Using a Dynamic Bayesian Network formulation and exploiting the convexity structure of the system, the thesis shows how this modeling leads to realistic estimations and predictions, even when little measurements are available. Finally, the thesis investigates how sparse modeling and dimensionality reduction can provide insights on the large scale behavior of the network. Large scale dynamics and patterns are hard to model accurately based on physical laws. They can be discovered through data mining algorithms and integrated into physical models

Développement d'un modèle d'estimation des variables de trafic urbain basé sur l'utilisation des technologies de géolocalisation

Sustainable mobility development requires the optimization of existing transportation infrastructure. In particular, ubiquitous traffic information systems have the potential to optimize the use of the transportation network. The system must provide accurate and reliable traffic information in real-time to optimize mobility choices. Successful implementations are also valuable tools for traffic management agencies. The thesis studies how the emergence of Internet services and location based services on mobile devices enable the development of novel Intelligent Transportation Systems which estimate and broadcast traffic conditions in arterial networks. Sparsely sampled probe data is the main source of arterial traffic data with the prospect of broad coverage in the near future. The small number of vehicles that report their position at a given time and the low sampling frequency require specific models and algorithms to extract valuable information from the available data. On the one hand, the variability of traffic conditions in urban networks, caused mainly by the presence of traffic lights, motivates a statistical approach of arterial traffic dynamics. On the other hand, an accurate modeling of the physics of arterial traffic from hydrodynamic theory (formation and dissolution of horizontal queues) ensures the physical validity of the model. The thesis proposes to integrate the dynamical model of arterial traffic in a statistical framework to integrate noisy measurements from probe vehicle data and estimate physical parameters, which characterize the traffic dynamics. In particular, the thesis derives and estimates the probability distributions of vehicle location and of travel time between arbitrary locations. The thesis leverages the data and the infrastructure developed by the Mobile Millennium project at the University of California, Berkeley to validate the models and algorithms. The results underline the importance to design statistical models for sparsely sampled probe vehicle data in order to develop the next generation of operation large-scale traffic information systemsFace à l’augmentation de la mobilité, les politiques de développement durable cherchent à optimiser l’utilisation des infrastructures de transport existantes. En particulier, les systèmes d’information du trafic à large échelle ont le potentiel d’optimiser l’utilisation du réseau de transport. Ils doivent fournir aux usagers une information fiable en temps réel leur permettant d’optimiser leurs choix d’itinéraires. Ils peuvent également servir d’outils d’aide à la décision pour les gestionnaires du réseau. La thèse étudie comment l’émergence des services Internet sur les téléphones portables et la rapide prolifération des systèmes de géolocalisation permet le développement de nouveaux services d’estimation et d’information des conditions de trafic en réseau urbain. L’utilisation des données provenant de véhicules traceurs nécessite le développement de modèles et d’algorithmes spécifiques, afin d’extraire l’information de ces données qui ne sont envoyées, jusqu’à présent, que par une faible proportion des véhicules circulant sur le réseau et avec une fréquence faible. La variabilité des conditions de circulations, due à la présence de feux de signalisation, motive une approche statistique de la dynamique du trafic, tout en intégrant les principes physiques hydrodynamiques (formation et dissolution de files d’attentes horizontales). Ce modèle statistique permet d’intégrer de façon robuste les données bruitées envoyées par les véhicules traceurs, d’estimer les paramètres physiques caractérisant la dynamique du trafic et d’obtenir l’expression paramétrique de la loi de probabilité des temps de parcours entre deux points quelconques du réseau. La thèse s’appuie sur les données et les infrastructures développées par le projet Mobile Millennium à l’Université de Californie, Berkeley pour valider les modèles et algorithmes proposés. Les résultats soulignent l’importance du développement de modèles statistiques et d’algorithmes adaptés aux données disponibles pour développer un système opérationnel d’estimation du trafic à large échell

Iowa's Prenatal Care Barriers Project Data From 2014: Marshall County, April 4, 2016

The purpose of the fact sheet is the survey results from Iowa's Prenatal Care Barriers Project for women with Medicaid reimbursed birth. This information will be used to guide decision makers in implementing programs that improve the health outcomes of the women and infants who rely on Medicaid coverage

Leveraging geolocalization technologies to model and estimate urban traffic

Face à l augmentation de la mobilité, les politiques de développement durable cherchent à optimiser l utilisation des infrastructures de transport existantes. En particulier, les systèmes d information du trafic à large échelle ont le potentiel d optimiser l utilisation du réseau de transport. Ils doivent fournir aux usagers une information fiable en temps réel leur permettant d optimiser leurs choix d itinéraires. Ils peuvent également servir d outils d aide à la décision pour les gestionnaires du réseau. La thèse étudie comment l émergence des services Internet sur les téléphones portables et la rapide prolifération des systèmes de géolocalisation permet le développement de nouveaux services d estimation et d information des conditions de trafic en réseau urbain. L utilisation des données provenant de véhicules traceurs nécessite le développement de modèles et d algorithmes spécifiques, afin d extraire l information de ces données qui ne sont envoyées, jusqu à présent, que par une faible proportion des véhicules circulant sur le réseau et avec une fréquence faible. La variabilité des conditions de circulations, due à la présence de feux de signalisation, motive une approche statistique de la dynamique du trafic, tout en intégrant les principes physiques hydrodynamiques (formation et dissolution de files d attentes horizontales). Ce modèle statistique permet d intégrer de façon robuste les données bruitées envoyées par les véhicules traceurs, d estimer les paramètres physiques caractérisant la dynamique du trafic et d obtenir l expression paramétrique de la loi de probabilité des temps de parcours entre deux points quelconques du réseau. La thèse s appuie sur les données et les infrastructures développées par le projet Mobile Millennium à l Université de Californie, Berkeley pour valider les modèles et algorithmes proposés. Les résultats soulignent l importance du développement de modèles statistiques et d algorithmes adaptés aux données disponibles pour développer un système opérationnel d estimation du trafic à large échelleSustainable mobility development requires the optimization of existing transportation infrastructure. In particular, ubiquitous traffic information systems have the potential to optimize the use of the transportation network. The system must provide accurate and reliable traffic information in real-time to optimize mobility choices. Successful implementations are also valuable tools for traffic management agencies. The thesis studies how the emergence of Internet services and location based services on mobile devices enable the development of novel Intelligent Transportation Systems which estimate and broadcast traffic conditions in arterial networks. Sparsely sampled probe data is the main source of arterial traffic data with the prospect of broad coverage in the near future. The small number of vehicles that report their position at a given time and the low sampling frequency require specific models and algorithms to extract valuable information from the available data. On the one hand, the variability of traffic conditions in urban networks, caused mainly by the presence of traffic lights, motivates a statistical approach of arterial traffic dynamics. On the other hand, an accurate modeling of the physics of arterial traffic from hydrodynamic theory (formation and dissolution of horizontal queues) ensures the physical validity of the model. The thesis proposes to integrate the dynamical model of arterial traffic in a statistical framework to integrate noisy measurements from probe vehicle data and estimate physical parameters, which characterize the traffic dynamics. In particular, the thesis derives and estimates the probability distributions of vehicle location and of travel time between arbitrary locations. The thesis leverages the data and the infrastructure developed by the Mobile Millennium project at the University of California, Berkeley to validate the models and algorithms. The results underline the importance to design statistical models for sparsely sampled probe vehicle data in order to develop the next generation of operation large-scale traffic information systemsPARIS-EST-Université (770839901) / SudocSudocFranceF

Estimating arterial traffic conditions using sparse probe data

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse probe data represents the vast majority of the data available on arterial roads in most major urban environments. This article proposes a probabilistic modeling framework for estimating and predicting arterial travel time distributions using sparsely observed probe vehicles. We evaluate our model using data from a fleet of 500 taxis in San Francisco, CA, which send GPS data to our server every minute. The sampling rate does not provide detailed information about where vehicles encountered delay or the reason for any delay (i.e. signal delay, congestion delay, etc.). Our model provides an increase in estimation accuracy of 35 % when compared to a baseline approach for processing probe vehicle data. 1