Cik Michael, Hebenstreit Cornelia, Horn Christopher, Schulze Gunnar, Traub Matthias, Schweighofer Erich, Hötzendorf Walter, Fellendorf Martin
2016
Guaranteeing safety during mega events has always played a role for organizers, their security guards and the action force. This work was realized to enhance safety at mega events and demonstrations without the necessity of fixed installations. Therefore a low cost monitoring system supporting the organization and safety personnel was developed using cell phone data and social media data in combination with safety concepts to monitor safety during the event in real time. To provide the achieved results in real time to the event and safety personnel an application for a Tablet-PC was established. Two representative events were applied as case studies to test and evaluate the results and to check response and executability of the app on site. Because data privacy is increasingly important, legal experts were closely involved and provided legal support.
Horn Christopher, Gursch Heimo, Kern Roman, Cik Michael
2016
Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.
Schulze Gunnar, Horn Christopher, Kern Roman
2015
This paper presents an approach for matching cell phone trajectories of low spatial and temporal accuracy to the underlying road network. In this setting, only the position of the base station involved in a signaling event and the timestamp are known, resulting in a possible error of several kilometers. No additional information, such as signal strength, is available. The proposed solution restricts the set of admissible routes to a corridor by estimating the area within which a user is allowed to travel. The size and shape of this corridor can be controlled by various parameters to suit different requirements. The computed area is then used to select road segments from an underlying road network, for instance OpenStreetMap. These segments are assembled into a search graph, which additionally takes the chronological order of observations into account. A modified Dijkstra algorithm is applied for finding admissible candidate routes, from which the best one is chosen. We performed a detailed evaluation of 2249 trajectories with an average sampling time of 260 seconds. Our results show that, in urban areas, on average more than 44% of each trajectory are matched correctly. In rural and mixed areas, this value increases to more than 55%. Moreover, an in-depth evaluation was carried out to determine the optimal values for the tunable parameters and their effects on the accuracy, matching ratio and execution time. The proposed matching algorithm facilitates the use of large volumes of cell phone data in Intelligent Transportation Systems, in which accurate trajectories are desirable.
Horn Christopher, Kern Roman
2015
In this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5 minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available.
Lindstaedt Stefanie , Reiter, T., Cik, M., Haberl, M., Breitwieser, C., Scherer, R., Kröll Mark, Horn Christopher, Müller-Putz, G., Fellendorf, M.
2013
Today, proper traffic incident management (IM) has to deal increasingly with problems such as traffic congestion and environmental sustainability. Therefore, IM intends to clear the road for traffic as quickly as possible after an incident has happened. Electronic data verifiably has great potential for supporting traffic incident management. As a consequence, this paper presents an innovative incident detection method using anonymized mobile communications data. The aim is to outline suitable methods for depicting the traffic situation of a designated test area. In order to be successful, the method needs to be able to calculate the traffic situation in-time and report anomalies back to the motorway operator. The resulting procedures are compared to data from real incidents and are thus validated. Special attention is turned to the question whether incidents can be detected quicker with the aid of mobile phone data than with conventional methods. Also, a focus is laid on the quicker deregistration of the incident, so that the traffic management can react superiorly.
Horn Christopher, Pimas Oliver, Granitzer Michael, Lex Elisabeth
2011
In this paper, we outline our experiments carried out at theTREC Microblog Track 2011. Our system is based on a plain text indexextracted from Tweets crawled from twitter.com. This index hasbeen used to retrieve candidate Tweets for the given topics. The resultingTweets were post-processed and then analyzed using three differentapproaches: (i) a burst detection approach, (ii) a hashtag analysis, and(iii) a Retweet analysis. Our experiments consisted of four runs: Firstly,a combination of the Lucene ranking with the burst detection, and secondly,a combination of the Lucene ranking, the burst detection, and thehashtag analysis. Thirdly, a combination of the Lucene ranking, the burstdetection, the hashtag analysis, and the Retweet analysis, and fourthly,again a combination of the Lucene ranking with the burst detection butin this case with more sophisticated query language and post-processing.We achieved the best MAP values overall in the fourth run.