Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

Trajectory analysis at intersections for traffic rule identification

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.)

Smartphone Based Detection of Vehicle Encounters

Riding a bicycle in shared traffic alongside motor vehicles causes discomfort or even stress for many cyclists. Avoiding busy or crowded roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles for most roads. Acquiring a data set that combines smartphone sensor data with known vehicle encounters can become the foundation for a smartphone based moving vehicle detector. Therefore, readings from the omnipresent smartphone sensors magnetometer and barometer can be exploited as indicators of passing vehicles. In this paper, a novel approach is presented to detect vehicle encounters in smartphone sensor data. For this purpose, a modular mobile sensor platform is first constructed and set up to collect smartphone, camera and ultrasonic sensor data in real traffic scenarios. The platform is designed to be used with various sensor configurations to serve a broader set of use cases in the future. In the presented use case, the platform is constructed to create a reference data set of vehicle encounters consisting of location information, direction, distance, speed and further metadata. To this end, a methodology is presented to process the collected camera images and ultrasonic distance data. Furthermore, two smartphones are used to collect raw data from their magnetometer and barometric sensor. Based on both, the reference and the smartphones’ data set, a classifier for the detection of vehicle encounters is then trained to operate on pure smartphone sensor data. Experiments on real data show that a Random Forest classifier can be successfully applied to recorded smartphone sensor data. The results prove that the presented approach is able to detect overtaking vehicle encounters with a F1-score of 71.0 %, which is sufficient to rank different cycling routes by their ’stress factor’

Visuelle Kommunikation von Fahrradrouten mittels kartographischer Symbolisierung

Mit zunehmender Förderung nachhaltiger Mobilitätsformen im Rahmen der Verkehrswende stellt das Fahrrad eine umweltfreundliche Alternative zum motorisierten Individualverkehr dar - insbesondere zur Bewältigung innerstädtischer Wege. Im Gegensatz zu Autofahrenden hängt der empfundene Fahrkomfort und das Sicherheitsempfinden von Radfahrenden jedoch stark von Routenmerkmalen wie der Oberflächenbeschaffenheit oder der Verkehrsinfrastruktur ab. Aktuell verfügbare Systeme zur Fahrradnavigation berücksichtigen diese für die Routenwahl von Radfahrenden relevanten Faktoren jedoch kaum und die Visualisierung beschränkt sich vielfach auf eine einfache Hervorhebung der empfohlenen Route. In dieser Arbeit wird daher untersucht, inwieweit sich verschiedene kartographische Darstellungsvarianten für Fahrradrouten zur visuellen Kommunikation der Routenmerkmale Art des Untergrunds, Untergrundrauigkeit, Geländeneigung und Fahrtunterbrechungen, als angemessen erweisen. Im Rahmen einer Nutzerbefragung wird die Effektivität, Attraktivität, Eignung und Entbehrlichkeit einer Legende der verschiedenen Darstellungsvarianten für die unterschiedlichen Routenmerkmale überprüft. Die Ergebnisse der Umfrage zeigen auf, dass viele der vorgeschlagenen Visualisierungsvarianten angemessen für die visuelle Kommunikation von Fahrradrouten sind. Dies betrifft insbesondere Farbdarstellungen sowie Darstellungen mit Symbolen oder Signaturen. Hinsichtlich der getesteten Fahrradroutenmerkmale hängt die angemessenste Darstellung jedoch stark von der zu kommunizierenden Information ab. Die Erkenntnisse dieser Studie sollen zur Entwicklung von speziell auf die Bedürfnisse der Radfahrenden zugeschnittenen Routenvisualisierungen beitragen und somit Entwicklern von Fahrradnavigationssystemen bei Designentscheidungen unterstützen.With the increasing promotion of sustainable forms of mobility in the context of the traffic policies, bicycles represent an environmentally friendly alternative to motorized private transport This especially accounts for coping with inner-city routes. However, in contrast to car drivers, the perceived riding comfort and safety of cyclists strongly depends on route characteristics, such as surface conditions or traffic infrastructure. However, currently available bicycle navigation systems hardly consider these factors relevant for the route choice of cyclists, and the visualization is often limited to a simple highlighting of the recommended route. Therefore, this article investigates the appropriateness of different cartographic representations of bicycle routes for the visual communication of route characteristics, such as type of terrain, terrain roughness, terrain gradient, and interruptions. A user survey is conducted to assess the effectiveness, attractiveness, appropriateness, and dispensability of a legend of the various display options for the different route features. The results of the survey indicate that many of the proposed visualization variants are appropriate for the visual communication of bicycle routes. This concerns in particular color representations as well as representations using symbols. However, with respect to the bicycle route features tested, the most appropriate representation heavily depends on the information being communicated. The findings of this study should contribute to the development of route visualizations that are specifically tailored to the needs of cyclists and thus support developers of bicycle navigation systems in making design decisions