Travel Time Estimation from Multiple Data Sources

Travel time is the best indicator of the level of service in a road link, and perhaps the most important variable for measuring congestion. This paper presents a method for estimating accurate travel times in toll highways using data from multiple sources, as loop detectors and toll tickets. The proposed methodology consists of a data fusion technique using different travel time estimations in order to obtain a more accurate fused value with less error than individual estimations by itself. Finally results obtained in the application of the methodology to the AP-7 highway, near Barcelona in Spain, are presented.Peer Reviewe

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Link Travel Time Estimation Based on Network Entry/Exit Time Stamps of Trips

This dissertation studies the travel time estimation at roadway link level using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation-Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Towards a robust slam framework for resilient AUV navigation

Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern navies, to the point that the control of oceans will soon be decided by their strategic use. In face of more complex missions occurring in potentially hostile environments, the resilience of such systems becomes critical. In this study, we investigate the following scenario: how does a lone AUV could recover from a temporary breakdown that has created a gap in its measurements, while remaining beneath the surface to avoid detection? It is assumed that the AUV is equipped with an active sonar and is operating in an uncharted area. The vehicle has to rely on itself by recovering its location using a Simultaneous Localization and Mapping (SLAM) algorithm. While SLAM is widely investigated and developed in the case of aerial and terrestrial robotics, the nature of the poorly structured underwater environment dramatically challenges its effectiveness. To address such a complex problem, the usual side scan sonar data association techniques are investigated under a global registration problem while applying robust graph SLAM modelling. In particular, ways to improve the global detection of features from sonar mosaic region patches that react well to the MICR similarity measure are discussed. The main contribution of this study is centered on a novel data processing framework that is able to generate different graph topologies using robust SLAM techniques. One of its advantages is to facilitate the testing of different modelling hypotheses to tackle the data gap following the temporary breakdown and make the most of the limited available information. Several research perspectives related to this framework are discussed. Notably, the possibility to further extend the proposed framework to heterogeneous datasets and the opportunity to accelerate the recovery process by inferring information about the breakdown using machine learning.PH