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

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

Ανασκόπηση της αλτικής μεθόδου bosco και της δονητικής μεθόδου power plate

Ο σκοπός της μελέτης ήταν ανασκόπηση και σύγκριση της αξιολόγησης των αλτικών και δονητικών παραμέτρων σε σχέση με την υγεία, με το κατακόρυφο άλμα και με τις πλειομετρικές κινήσεις χρησιμοποιώντας τη μέθοδο Bosco και τη μέθοδο Power Plate. Το κριτήριο επιλογής της μελέτης ήταν τα κοινά ετερογενείς χαρακτηριστικά της μεθόδου Bosco και της μεθόδου Power Plate, τους οποίους χαρακτηρίζουν οι αλτικές και δονητικές παράμετροι αντίστοιχα. Οι πηγές αναζήτησης ήταν οι βάσεις δεδομένων όπως: Scopus, Google scholar, Research Gate, Pub med, Πέργαμος κ.α. Περιορισμένη ήταν η συλλογή των πληροφοριών και βιβλιογραφίας στην κατεύθυνση της χιονοδρομίας. Οι αλτικές μετρήσεις με τη μέθοδο Bosco γίνονται μέσω του λογισμικού και του πρωτοκόλλου περιγράφοντας την ανάλυση και τον στόχο των δοκιμασιών (τεστ) με τρείς τρόπους: α) με εξίσωση Bosco, β) με Bosco Ergo Jump System (πλατφόρμα δύναμης Kistler), γ) με φορητό ηλεκτρονικό τάπητα Chronojump - Bosco system. Με τη μέθοδο Bosco εφαρμόζονται τα εξής είδη αλμάτων: Squat Jump (SJ), Squat Jump με επιπλέον βάρος (SJ +), Counter Movement Jump (CMJ), Abalakov Jump (ABK), Drop Jump (DJ), Repetitive Jump (RJ) 5sec, 15sec, 30sec, 60sec και συνδυασμοί αλμάτων. Οι δονητικές μετρήσεις με τη μέθοδο Power Plate γίνονται μέσω του λογισμικού και του πρωτοκόλλου περιγράφοντας την ανάλυση και τον στόχο των δοκιμασιών (τεστ) στη δονούμενη πλατφόρμα, η οποία μπορεί να δονείται πάνω και κάτω περίπου 1 έως 2 χιλιοστά, 25 έως 50 φορές ανά δευτερόλεπτο. Με τη μέθοδο Power Plate εφαρμόζονται τα εξής είδη ασκήσεων και δοκιμασιών: α) ανάλογα την στάση και την θέση του σώματος του ατόμου, β) ανάλογα την χρονική διάρκεια της προσπάθειας, γ) ανάλογα τη συχνότητα της δονούμενης πλατφόρμας σε Hz. Με βάση την ανασκόπηση η αξιολόγηση των αλτικών παραμέτρων είναι πιο αποτελεσματική και εφαρμόσιμη στην πράξη σε σχέση με τις δονητικές παραμέτρους σχετικά με την υγεία, με το κατακόρυφο άλμα και με τις πλειομετρικές κινήσεις. Οι αθλητές χιονοδρόμοι των αγωνισμάτων του σλάλομ και της ελεύθερης κατάβασης (SL & DH) έχουν τα καλύτερα αποτελέσματα αλτικών παραμέτρων σε σχέση με τους αθλητές του άλματος με σκι και τους αθλητές της παγοδρομίας.N

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion