Abstract Advanced route planning algorithms are one of the key enabling technologies for emerging electric and autonomous mobility. Large realistic data sets are needed to test such algorithms under conditions that capture natural time-varying traffic patterns and corresponding travel-time and energy-use predictions. Further, the time-varying availability of charging infrastructure and vehicle-specific charging-power curves may be necessary to support advanced planning. While some data sets and synthetic data generators capture some of the aspects mentioned above, no integrated testbeds include all of them. We contribute with a modular testbed architecture. First, it includes a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic volume distribution patterns, EV-specific data, and elevation data. These elements support the generation of time-dependent travel-time and energy-use weights in a road-network graph. The generator ensures that the data satisfies the FIFO property, which is essential for time-dependent routing. Next, the testbed provides a thin layer of services that can serve as building blocks for future advanced routing algorithms. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.

Rapidly increasing volumes of GPS data collected from vehicles provide new and increasingly comprehensive insight into the routes that drivers prefer. While routing services generally compute shortest or fastest routes, recent studies suggest that local drivers often prefer routes that are neither shortest nor fastest, indicating that drivers value route properties that are diverse and hard to quantify or even identify. We propose a routing service that uses an existing routing service while exploiting the availability of historical route usage data from local drivers. Given a source and destination, the service recommends a corresponding route that is most preferred by local drivers. It uses a route preference function that takes into account the number of distinct drivers and the number of trips associated with a route, as well as temporal aspects of the trips. The paper provides empirical studies with real route usage data and an existing online routing service.

Finding a location for a new facility s.t. the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. We propose two algorithms that adopt different approaches to computing the query. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings.

Mobile location-based services is a very successful class of services that are being used frequently by users with GPS-enabled mobile devices such as smartphones. This paper presents a study of how to exploit GPS trajectory data, which is available in increasing volumes, for the assessment of the quality of one kind of location-based service, namely routing services. Specifically, the paper presents a framework that enables the comparison of the routes provided by routing services with the actual driving behaviors of local drivers. Comparisons include route length, travel time, and also route popularity, which are enabled by common driving behaviors found in available trajectory data. The ability to evaluate the quality of routing services enables service providers to improve the quality of their services and enables users to identify the services that best serve their needs. The paper covers experiments with real vehicle trajectory data and an existing online navigation service. It is found that the availability of information about previous trips enables better prediction of route travel time and makes it possible to provide the users with more popular routes than does a conventional navigation service.

GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework.