Link travel time estimation in urban areas by detectors and probe vehicles fusion

International audienceThis paper presents an approach to estimate link travel time in urban areas. This approach consists of a data fusion from underground loop detectors and probe vehicles equipped with global positioning system (GPS). This method is expected to be more accurate, reliable and robust than using either of these data sources alone. In this approach, an algorithm is developed. This algorithm is based on the unscented Kalman filter using vehicle counts and flows from loop detectors located at the end of every link, and travel time from probe vehicles. From these counts the average travel time is calculated using the "cumulative plot" method. Furthermore, in order to incorporate the GPS data, a map-matching method is used to associate a travel time to the appropriate link

Estimation du temps de parcours d’un réseau urbain par fusion de données de boucles magnétiques et de véhicules traceurs : Une approche stochastique avec mise en oeuvre d’un filtre de Kalman sans parfum

Travel time information is easy to be perceived by users and has the potential to reduce congestion on both temporal and spatial scales thanks to traveller assistance systems. This thesis addresses the problem of estimating the travel time in city, where traffic is characterized by variable flow and also significant perturbation from/to mid-link source/sink that are not equipped with traffic sensors. This flow disturbs the equation of conservation of vehicles. The bibliography contains deterministic and stochastic approaches, using, in specific experimental situations, either inductive loops or probe vehicles, or both. We propose a stochastic approach based on unscented Kalman filtering. Its dynamic model is based on the classical analytical procedure that considers the time difference between the total of incoming and outgoing vehicles for each segment of the network. The formulation of this model is not explicit, which justifies the use of an unscented filter. The observations are both loop counters at the exit sections and probe vehicles map-matched to the network. The algorithm is applicable in real time, without prior information on the percentage of turning movements at intersections, and under any condition of perturbation. The variance estimation output by the filter measures the confidence in the estimated travel time and allows the rejection of outliers. The validation was shown based on simulations on a simple network with three intersections, variable entering flows, variable percentage of perturbation, and a variable percentage of probe vehicles.La notion de temps de parcours est une information simple à intégrer par les usagers des transports et a le potentiel, via des systèmes d’assistance, de réduire la congestion aussi bien de manière temporelle que spatiale. Cette thèse porte sur la problématique de l’estimation du temps de parcours en ville, où la circulation se caractérise par un débit variable et un flux significatif de/vers des voies transversales non équipées de capteurs de trafic, flux qui agit comme perturbation de l’équation de conservation des véhicules. La bibliographie présente des approches déterministes et stochastiques, utilisant, selon les situations expérimentales, soit des boucles inductives, soit des véhicules traceurs, soit les deux. Nous proposons une méthode stochastique du type filtrage de Kalman sans parfum. Son modèle dynamique est basé sur la méthode analytique classique des stocks qui considère le décalage temporel entre les cumuls de véhicules entrants et sortants dans chaque tronçon du réseau. La formulation de ce modèle n’est pas explicite, ce qui justifie l’utilisation d’un filtre sans parfum. Les observations utilisées sont à la fois les boucles magnétiques à la sortie des tronçons et les véhicules traceurs après map-matching sur le réseau. L’algorithme est applicable en temps réel, sans connaissance a priori des affectations aux carrefours, et sous conditions éventuelles de perturbation. En sortie de filtre, on dispose des variances d’estimation, ce qui mesure la confiance dans le temps de parcours estimé et permet de rejeter des mesures aberrantes. La validation a été montrée sur la base de simulations sur un réseau simple à trois intersections, avec des flux variables et des perturbations, et pour quelques pourcents de véhicules traceurs

Unscented Kalman filter for urban link travel time estimation with mid-link sinks and sources

International audienceTo estimate the link travel time, the classical analytical procedure uses vehicles counts at upstream and downstream locations. This procedure is vulnerable in urban networks mainly due to significant flow to and from mid-link sinks and sources. One of the important developments recently done on this topic has yielded to the CUPRITE methodology. This method is derived from the classical analytical procedure. It integrates probe vehicle data to correct deterministically the upstream cumulative plot to match the information of probe vehicles travel times, whilst the downstream cumulative plot is kept unchanged. The algorithm proposed and validated in this research estimates urban links travel times based on an unscented Kalman filter (UKF). This algorithm integrates stochastically the vehicle count data from underground loop detectors at the end of every link and the travel time from probe vehicles. The proposed methodology, which can be used for travel time estimation in real-time, is compared to the classical analytical procedure and to the CUPRITE method in case of mid-link perturbation. Along to its lower sensitivity than CUPRITE, the UKF algorithm makes it possible detection and exclusion of outliers from both data sources

Unscented Kalman filter for urban network travel time estimation

International audienceTo estimate urban network travel time, the classical analytical procedure uses cumulative counts at upstream and downstream locations of links. This procedure is vulnerable in urban networks mainly due to significant flow to and from mid-link sinks and sources. Moreover, most urban network links are only equipped with detectors at their end. Therefore without information on the percentage of turning movement at crossroads, the classical analytical procedure is not applicable. The algorithm proposed and validated in this research estimates urban links travel times based on an unscented Kalman filter (UKF). This algorithm integrates stochastically the vehicle count data from underground loop detectors at the end of every link and the travel times from probe vehicles. The proposed methodology can be used for estimating travel time in real-time. Moreover, with this methodology the number of upstream vehicles as well as the number of mid-link sink/source vehicles is estimated for each link