A ride-hailing service, where passengers choose the origin and destination of their ride, preserves location privacy if the origin and destination of the ride are not revealed to anyone other than the driver. Pseudonymity means that the driver and the passenger use a pseudonym and do not reveal their true identity to each other. The service is trusted if a dishonest passenger cannot use the service without paying, and a driver must provide the service to get paid. In this paper, we introduce cryptotrasnport---a ride-hailing service powered by cryptocurrency and blockchain---and we show that it can preserve privacy, pseudonymity, and trust, simultaneously. Furthermore, by using blockchain technology, cryptotransport matches riders with vehicles in a decentralized fashion, without relying on any company or organization to manage the system.

This article investigates the problem of geosocial similarity among users of online social networks, based on the locations of their activities (e.g., posting messages or photographs). Finding pairs of geosocially similar users or detecting that two sets of locations (of activities) belong to the same user has important applications in privacy protection, recommendation systems, urban planning, and public health, among others. It is explained and shown empirically that common distance measures between sets of locations are inadequate for determining geosocial similarity. Two novel distance measures between sets of locations are introduced. One is the mutually nearest distance that is based on computing a matching between two sets. The second measure uses a quad-tree index. It is highly scalable but incurs the overhead of creating and maintaining the index. Algorithms with optimization techniques are developed for computing the two distance measures and also for finding the k -most-similar users of a given one. Extensive experiments, using geotagged messages from Twitter, show that the new distance measures are both more accurate and more efficient than existing ones.

In integration of road maps modeled as road vector data, the main task is matching pairs of objects that represent, in different maps, the same segment of a real-world road. In an ad hoc integration, the matching is done for a specific need and, thus, is performed in real time, where only a limited preprocessing is possible. Usually, ad hoc integration is performed as part of some interaction with a user and, hence, the matching algorithm is required to complete its task in time that is short enough for human users to provide feedback to the application, that is, in no more than a few seconds. Such interaction is typical of services on the World Wide Web and to applications in car-navigation systems or in handheld devices. Several algorithms were proposed in the past for matching road vector data; however, these algorithms are not efficient enough for ad hoc integration. This article presents algorithms for ad hoc integration of maps in which roads are represented as polylines. The main novelty of these algorithms is in using only the locations of the endpoints of the polylines rather than trying to match whole lines. The efficiency of the algorithms is shown both analytically and experimentally. In particular, these algorithms do not require the existence of a spatial index, and they are more efficient than an alternative approach based on using a grid index. Extensive experiments using various maps of three different cities show that our approach to matching road networks is efficient and accurate (i.e., it provides high recall and precision). General Terms:Algorithms, Experimentation

When integrating geo‐spatial data sets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real‐world entity). This article investigates location‐based join algorithms for integration of several data sets. First, algorithms for integration of two data sets are presented and their performances, in terms of recall and precision, are compared. Then, two approaches for integration of more than two data sets are described. In one approach, all the integrated data sets are processed simultaneously. In the second approach, a join algorithm for two data sets is applied sequentially, either in a serial manner, where in each join at least one of the joined data sets is a single source, or in a hierarchical manner, where two join results can be joined. For the two approaches, join algorithms are given. The algorithms are designed to perform well even when location of objects are imprecise and each data set represents only some of the real‐world entities. Results of extensive experiments with the different approaches are provided and analyzed. The experiments show the differences, in accuracy and efficiency, between the approaches, under different circumstances. The results also show that all our algorithms have much better accuracy than applying the commonly used one‐sided nearest‐neighbor join.

A route search is an enhancement of an ordinary geographic search. Instead of merely returning a set of entities, the result is a route that goes via entities that are relevant to the search. The input to the problem consists of several search queries, and each query defines a type of geographical entities. When visited, some of the entities succeed in satisfying the user while others fail to do so; however, only the probability of success is known prior to arrival. The main task is to find a route that visits at least one satisfying entity of each type. In an interactive search , the route is computed in steps. In each step, only the next entity of the route is given to the user, and after visiting that entity, the user provides a feedback specifying whether the entity satisfies her. This paper investigates interactive route search in the presence of order constraints that specify that some types of entities should be visited before others. We present heuristic algorithms for interactive route search for two cases, depending on whether the constraints define a complete order or a partial one. The main challenge is to utilize the feedback in order to compute a route that is shorter and has a higher degree of success, compared to routes that are computed non-interactively. We also discuss how to compare the results of the algorithms and introduce suitable measures for doing so. Experiments on real-world data illustrate the efficiency and effectiveness of our algorithms.

In a probabilistic route search, there is a start location, a target location, and search queries Q1, ..., Qn. Each Qi has an answer set Ai consisting of geo-spatial objects and their probabilities. The probability of an object o ∈ Ai specifies the likelihood that o satisfies Qi. The goal is to compute a route that is short and yet has a high probability of satisfying all the Qi. This paper investigates interactive route search. Upon arrival at each object, the user provides feedback specifying whether the object satisfies its corresponding query. The goal is to compute the next object to be visited, based on the feedback. Several heuristic algorithms are given and compared experimentally.

In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces route-search queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering route-search queries is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and real-world data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a route-search query. Categories and Subject Descriptors

Integration of two road maps is finding a matching between pairs of objects that represent, in the maps, the same real-world road. Several algorithms were proposed in the past for road-map integration; however, these algorithms are not efficient and some of them even require human feedback. Thus, they are not suitable for many important applications (e.g., Web services) where efficiency, in terms of both time and space, is crucial. This paper presents two efficient algorithms for integrating maps in which roads are represented as polylines. The main novelty of these algorithms is in using only the locations of the endpoints of the polylines rather than trying to match whole lines. Experiments on real-world data are given, showing that our approach of integration based on matching merely endpoints is efficient and accurate (that is, it provides high recall and precision).