Optimal Time-dependent Sequenced Route Queries in Road Networks

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM SIGSPATIA

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Nearest-Neighbor Queries in Customizable Contraction Hierarchies and Applications

Customizable contraction hierarchies are one of the most popular route planning frameworks in practice, due to their simplicity and versatility. In this work, we present a novel algorithm for finding k-nearest neighbors in customizable contraction hierarchies by systematically exploring the associated separator decomposition tree. Compared to previous bucket-based approaches, our algorithm requires much less target-dependent preprocessing effort. Moreover, we use our novel approach in two concrete applications. The first application are online k-closest point-of-interest queries, where the points of interest are only revealed at query time. We achieve query times of about 25 milliseconds on a continental road network, which is fast enough for interactive systems. The second application is travel demand generation. We show how to accelerate a recently introduced travel demand generator by a factor of more than 50 using our novel nearest-neighbor algorithm

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

Efficient query processing over uncertain road networks

One of the fundamental problems on spatial road networks has been the shortest traveling time query, with applications such as location-based services (LBS) and trip planning. Algorithms have been made for the shortest time queries in deterministic road networks, in which vertices and edges are known with certainty. Emerging technologies are available and make it easier to acquire information about the traffic. In this paper, we consider uncertain road networks, in which speeds of vehicles are imprecise and probabilistic. We will focus on one important query type, continuous probabilistic shortest traveling time query (CPSTTQ), which retrieves sets of objects that have the smallest traveling time to a moving query point q from point s to point e on road networks with high confidences. We propose effective pruning methods to prune the search space of our CPSTTQ query, and design an efficient query procedure to answer CPSTTQ via an index structure