Routing long trips of electric vehicles (EVs) is in growing demand and a non-trivial task as charging stops have to be planned along the way. Developing and testing realistic EV routing algorithms is challenging as multiple factors have to be considered such as traffic conditions, charging station availability, and car properties relevant to energy-consumption modeling. Moreover, testing and evaluating such algorithms requires realistic data and tools to simulate energy consumption and charging. This paper demonstrates a web-based testbed system for EV routing algorithms. Users can input start and end points on the map, set the car properties that influence the energy consumption, and adjust the charging station availability to see how the results change. The system visualizes a set of proposed routes as well as a number of alternative routes considered (but discarded) by the routing algorithm. Details of each leg of each route can be interactively explored. The highly configurable system allows the algorithm developers to ask what-if and why-not questions.

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.

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope.

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects. This scenario is characterized by large volumes of updates, for which reason location update technologies become important. A setting is assumed in which a central database stores a representation of each moving object's current position. This position is to be maintained so that it deviates from the user's real position by at most a given threshold. To do so, each moving object stores locally the central representation of its position. Then, an object updates the database whenever the deviation between its actual position (as obtained from a GPS device) and the database position exceeds the threshold. The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and, it proposes to use acceleration profiles together with the routes. Using real GPS-data and a corresponding real road network, the paper offers empirical evaluations and comparisons that include three existing approaches and all the proposed approaches.

Sustained advances in wireless communications, geo-positioning, and consumer electronics pave the way to a kind of location-based service that relies on the tracking of the continuously changing positions of an entire population of service users. This type of service is characterized by large volumes of updates, giving prominence to techniques for location representation and update. This paper presents several representations, along with associated update techniques, that predict the present and future positions of moving objects. An update occurs when the deviation between the predicted and the actual position of an object exceeds a given threshold. For the case where the road network, in which an object is moving, is known, we propose a so-called segment-based policy that predicts an object's movement according to the road's shape. Map matching is used for determining the road on which an object is moving. Empirical performance studies based on a real road network and GPS logs from cars are reported.