Mining topological dependencies of recurrent congestion in road networks

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often over-looked. This article proposes the ST-DISCOVERY algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-DISCOVERY can effectively reveal topological dependencies in urban road networks

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’

Automated Enrichment of Routing Instructions

Commonly used navigation instructions are based on metric turn descriptions (e.g. “turn left onto Nienburger Straße in 100 m”). For the user it is easy to follow the route, but later it is typically hard to remember how s/he got there. Orientation is based on remarkable objects or locations called landmarks. They are then linked and combined to so-called survey knowledge in the psychological model of a cognitive map. Some of today’s navigation systems also contain landmarks – they are, however, only used at decision points of the route. The goal of this research is to enhance the user's own sense of orientation by enriching common routing instructions with relational hints to landmarks. First, potential landmark objects are defined, extracted from OpenStreetMap and assigned an importance weight. The landmarks are then used to enrich the given routes: In the enrichment process, the influence of the landmarks is modeled as a decline of the weight by distance. Afterwards the most influential landmark is selected for each route segment. The 9-Intersection-Model and an adapted Direction-RelationMatrix are the core methods that are used to analyse and determine the relations between the route and the chosen landmarks. The automatic description of relevant landmarks along a route is implemented as an interactive web-map. The main goal of this paper is the development of the system. Still, a first evaluation was conducted, in order to test the users’ ability of orientation after using enriched instructions compared to users using the classic ones

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[no abstract available

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

Recognition of Repetitive Movement Patterns—The Case of Football Analysis

Analyzing sports like football is interesting not only for the sports team itself, but also for the public and the media. Both have recognized that using more detailed analyses of the teams’ behavior increases their attractiveness and also their performance. For this reason, the games and the individual players are recorded using specially developed tracking systems. The tracking solution usually comes with elementary analysis software allowing for basic statistical information extraction. Going beyond these simple statistics is a challenging task. However, it is worthwhile when it provides a better view into the tactics of team or the typical movements of an individual player. In this paper an approach for the recognition of movement patterns as an advanced analysis method is presented, which uses the players’ trajectories as input data. Besides individual movement patterns it is also able to detect patterns in relation to group movements. A detailed description is followed by a discussion of the approach, where different experiments on real trajectory datasets, even from other contexts than football, show the method’s benefits and features

GPS-Aided Video Tracking

Tracking moving objects is both challenging and important for a large variety of applications. Different technologies based on the global positioning system (GPS) and video or radio data are used to obtain the trajectories of the observed objects. However, in some use cases, they fail to provide sufficiently accurate, complete and correct data at the same time. In this work we present an approach for fusing GPS- and video-based tracking in order to exploit their individual advantages. In this way we aim to combine the reliability of GPS tracking with the high geometric accuracy of camera detection. For the fusion of the movement data provided by the different devices we use a hidden Markov model (HMM) formulation and the Viterbi algorithm to extract the most probable trajectories. In three experiments, we show that our approach is able to deal with challenging situations like occlusions or objects which are temporarily outside the monitored area. The results show the desired increase in terms of accuracy, completeness and correctness

GPS-Aided Video Tracking

Tracking moving objects is both challenging and important for a large variety of applications. Different technologies based on the global positioning system (GPS) and video or radio data are used to obtain the trajectories of the observed objects. However, in some use cases, they fail to provide sufficiently accurate, complete and correct data at the same time. In this work we present an approach for fusing GPS- and video-based tracking in order to exploit their individual advantages. In this way we aim to combine the reliability of GPS tracking with the high geometric accuracy of camera detection. For the fusion of the movement data provided by the different devices we use a hidden Markov model (HMM) formulation and the Viterbi algorithm to extract the most probable trajectories. In three experiments, we show that our approach is able to deal with challenging situations like occlusions or objects which are temporarily outside the monitored area. The results show the desired increase in terms of accuracy, completeness and correctness

Identification of similarities and prediction of unknown features in an urban street network

Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality