Visit Places on YourWay: A Skyline Approach in Time-Dependent Networks

Many people take the same path every day, such as taking a specific autobahn to get home from work. However, one needs to frequently divert from this path, e.g., to visit a Point of Interest (POI) from a category like the category of restaurants or ATMs. Usually, people want to minimize not only their overall travel cost but also their detour cost, i.e., one wants to return to the known path as fast as possible. Finding such a POI minimizing both costs efficiently is highly challenging in case one considers time-dependent road networks which are the case in real-world scenarios. For such road networks time decency means the time a user needs to traverse a road, heavily depends on the user’s arrival time on that road. Prior works have several limitations, such as assuming that travel costs are coming from a metric space and do not change over time. Both assumptions hardly match real-world requirements: Just think of traffic jams at the rush hour. To overcome these limitations, we study how to solve this problem considering time-dependent road networks relying on linear skylines. Our main contribution is an efficient algorithm called STACY to find all non-dominated paths. A large-scale empirical evaluation on real-world data reveals that STACY is accurate, efficient and effective in real-world settings

Algorithms for advanced path optimization problems

© 2016 Dr. Saad AljubayrinWith the ever-increasing popularity of smart phones appended with a Global Positioning System (GPS), people tend to use GPS-based applications to assist them in reaching their destinations. Different people can have different optimization criteria in path finding. This thesis contributes into improving the current navigation systems by studying and solving three new path finding problems. Finding Lowest-Cost Paths in Settings with Safe and Preferred Zones Problem: Given a set of safe or preferred zones with zero or low cost, this problem finds paths that minimize the cost of travel from an origin to a destination. A life-critical application of this problem is navigating through scattered populated areas (safe zones) in hazardous environments such as deserts. In a more familiar scenario, a tourist who plans to walk to a given destination may prefer a path that visits interesting streets and blocks, e.g., with interesting houses, galleries, or other sights, (proffered zones) as much as possible. We solved this problem by proposing an algorithm that utilizes the properties of hyperbolas to elegantly describe a sparsely connected safe (preferred) zones graph. Skyline Trips of Multiple POIs Categories Problem: Given a road network with a set of Points of Interest (POIs) from different categories, a list of items the user is planning to purchase and a pricing function for items at each related POI, this problem finds the skyline trips in terms of both trip length and trip aggregated cost. This problem has important applications in everyday life. Specifically, it helps people choose the most suitable trips among the skyline trips based on two dimensions: trip total length and trip aggregated cost. We proposed a framework and two effective algorithms to efficiently solve the problem in real time which produce near optimal results when tested on real datasets. Finding Non-Dominated Paths in Uncertain Road Networks Problem: Given a source and a destination, this problem finds optimal and nondominated paths connecting the source and the destination, where optimality is defined in terms of the stochastic dominance among cost distributions of paths. This algorithm helps users choose the most suitable paths based on their personal timing preferences. We design an A based framework and propose a three-stage dominance examination method that employs extreme values in each candidate path’s cost distribution for early detection of dominated paths