This diploma thesis deals with the GPS navigations and their applications for portable phones and other portable devices. The thesis is divided into the theoretical and the practical part. In the theoretical part, the necessary terminology is defined. Further on, the different types of the GPS navigations are examined along with their applications and their particular advantages and disadvantages. A reader of this thesis will learn about the basic principles of how the GPS navigations work. In the practical part, the research part, the particular GPS devices undergo tests that had been based on particular hypotheses and that had been run according to predetermined conditions. The crucial part of this section is a comparison different types of GPS navigations with the GPS navigation Waze which has introduced an innovative approach to the creation of its content by the users themselves.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources. During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.

Modern traffic management systems often rely on static technologies, such as sensors and CCTV-cameras, in the gathering of data regarding the current traffic situation. Recent reports have shown that this method can result in a lack of coverage in Stockholm, Sweden. In addressing this issue, an alternative strategy to installing more sensors and CCTV-cameras could be to utilize crowdsourced traffic data from other sources, such as Waze. In order to examine the usage and potential utility of crowdsourced data in traffic management, the Swedish Transport Administration’s center in Stockholm, Trafik Stockholm, developed a web application which visualizes traffic data from both official sources and Waze. While the application was successful in doing so, it revealed the problem of integrating the traffic data from these two sources, as a significant portion of the data was redundant, and the reliability occasionally was questionable. This study aims at determining how issues regarding redundancy and reliability can be resolved in the integration of crowdsourced and official traffic data. Conducted using a design science research strategy, the study investigates these issues by designing and developing an artifact that implements integration methods to match alerts from the data sources based on temporal and spatial proximity constraints. The artifact was evaluated through test sessions in which real-time traffic data from all over Sweden was processed, and through acceptance testing with the stakeholders of the application. Analysis of the results from the evaluations shows that the artifact is effective in reducing the redundancy in the crowdsourced data and that it can provide a more solid ground for reliability assessment. Furthermore, the artifact met its expectations and requirements, demonstrating a proof-of-concept and a proof-of-acceptance. Based on these results, the study concludes that by analyzing temporal and spatial factors in crowdsourced data, redundancy issues in the integration of crowdsourced and official traffic can be resolved to a large extent. Furthermore, it is concluded that reliability issues in the same context can be resolved to a high degree by managing redundancy factors in combination with general traffic management factors. While the study is focused on traffic management, the issues of redundancy and reliability are not restricted to crowdsourced data in this context specifically. Thus, the results of the study are potentially of interest to researchers investigating other areas of application for crowdsourcing as well.

<div>With the increased evolution in technology over the past decade, there has been a gradual inclination towards utilizing advanced tools, like location-based applications which incorporate features such as constant route or traffic updates with Global Positioning System (GPS), among</div><div>others, which aid in smooth living. Such applications gain access to private information of users, among their other life hack qualities, thus producing a highly vulnerable ground for data exposure such as current location. With the increase in mobile application-based attacks, there exists a</div><div>constant threat scenario in terms of criminal activities which pose an ultimate challenge while tackling large amount of data. This research primarily focuses on the extent of user-specific data that can be obtained while forensically collecting and analysing data from Waze and HEREwego</div><div>applications on Android and iOS platforms. In order to address the lack of forensic research on the above mentioned applications, an in-depth forensic analysis is conducted in this study, utilizing Cellebrite, a professional tool to provide and verify the evidence acquired, that aid in any digital forensic investigations. On the Waze application, 12 artifacts were populated on the Android device and 17 artifacts on the iOS device, out of which 12 artifacts were recovered from the Android device (100% of the artifacts populated) and 12 artifacts from the iOS device (70.58% of the artifacts populated). Similarly on the HEREwego application, 14 artifacts were populated on the Android device and 13 artifacts on the iOS device, out of which 7 artifacts were recovered from the Android device (50% of the artifacts populated) and 7 artifacts from iOS device (53.84% of the artifacts populated).</div>