Jeppensen

Fast moving of vehicles and over congestion of vehicles on roads, highways leads to major accidents nowadays. Collision of vehicles due to unstable condition of drivers and violation of traffic rules are the other reasons of accidents. Generally, fixed sensors on motorized vehicles help the users to get alert notifications about the traffic and it is very useful for ambulance. But, unfortunately it does not prevents accident. To overcome this issue an alternative solution considering the halting of the vehicles engine automatically that violate the traffic signal. This solution gives a drastic change in reducing accidents. Here an IoT cloud system is used for identification of vehicles that violate the rules. Instant deduction of fine amount would make them not to breach the rules. Various demonstrations prove that our system avoids major risk accidents occurring on highways and urban roads.

In this paper a novel approach for aircraft trajectory generation is presented. Based on a pathfinding algorithm in conjunction with a cost function, a concept for a cost-optimized trajectory generator is introduced. The trajectory generator is able to incorporate time constraints along the aircraft’s route, as they will be an integral part of future airspace concepts to overcome the challenges of growing air-traffic. The target value, which is sought to be inimized by the pathfinding algorithm, is the result of the herein described cost function. Contrary to already existing approaches on aircraft trajectory optimization in this conceptual cost function an airline-wide, network centric, cost optimization can be performed, rather than focusing solely on a single aircraft. Evaluation of the cost-sensitive trajectory generator is performed in a fast-time simulation, results are compared to a non-optimizing trajectory generation. Results indicate average cost savings of 0.12% for arrival time variations of �30s at time constrained waypoints and proof general viability of the proposed concept. Further development of the herein presented method is recommended, especially in respect to Overall airline network optimization and disruption management use cases across the various entities of an airline.

Helicopters are widely used for operations close to terrain such as rescue missions; therefore all-weather capabilities are highly desired. To minimize or even avoid the risk of collision with terrain and obstacles, Synthetic Vision Systems (SVS) could be used to increase situational awareness. In order to demonstrate this, helicopter flights have been performed in the area of Zurich, Switzerland A major component of an SVS is the three-dimensional (3D) depiction of terrain data, usually presented on the primary flight display (PFD). The degree of usability in low level flight applications is a function of the terrain data quality. Today's most precise, large scale terrain data are derived from airborne laser scanning technologies such as LIDAR (light detection and ranging). A LIDAR dataset provided by Swissphoto AG, Zurich with a resolution of 1m was used. The depiction of high resolution terrain data consisting of 1 million elevation posts per square kilometer on a laptop in an appropriate area around the helicopter is challenging. To facilitate the depiction of the high resolution terrain data, it was triangulated applying a 1.5m error margin making it possible to depict an area of 5x5 square kilometer around the helicopter. To position the camera correctly in the virtual scene the SVS had to be supplied with accurate navigation data. Highly flexible and portable measurement equipment which easily could be used in most aircrafts was designed. Demonstration flights were successfully executed in September, October 2005 in the Swiss Alps departing from Zurich.