This poster presents the PreGo system for smart touring services. PreGo is smart in the sense that it enables its users to consider many preferences, (e.g., distance, travel time, services, attractions, safety), in their routing queries. The recommended route is completely personalized based on each user's weights for each of the routing preferences.

In smart cities, commuters have the opportunities for smart routing that may enable selecting a route with less car accidents, or one that is more scenic, or perhaps a straight and flat route. Such smart personalization requires a data management framework that goes beyond a static road network graph. This paper introduces PreGo, a novel system developed to provide real time personalized routing. The recommended routes by PreGo are smart and personalized in the sense of being (1) adjustable to individual users preferences, (2) subjective to the trip start time, and (3) sensitive to changes of the road conditions. Extensive experimental evaluation using real and synthetic data demonstrates the efficiency of the PreGo system.

Existing routing services calculate the best route from source to destination over a road network graph. Most commercial routing services offer the best route in terms of either the shortest travel distance or the shortest travel time (with or without considering current traffic conditions). While travel distance and travel time are crucial route preferences for the commuter, other preferences are equally, or even more, important. Examples of other route preferences include fuel consumption, gas emissions, road safety, points of interest along the route, construction activities, open shops and restaurants. While some route preferences are static (e.g., travel distance and points of interests), other route preferences are dynamic and vary according to the time of the day (e.g., traffic-dependent travel time and the number of open shops/restaurants). Volunteered Geographic information (VGI) has been proposed as an approach to collect massive amounts of route information and, more specifically, the time varying parameters. This demo presents PreGo, a time-dependent multi-preference routing engine. During the demo, audience would interact with the PreGo routing engine to (1) find the optimal route w.r.t. The user's personal preferences for a given start time, (2) dynamically obtain the best start time for a trip given a set of preferences, (3) feed the system with VGI and examine their effect on the chosen route at real time, and (4) examine the correctness and efficiency of the PreGo selected routes compared to routes chosen by other commercial systems.

Road-network data compression reduces the size of the network to occupy lesser storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Compression (1) reduces the storage cost of memory and disks, and (2) reduces the I/O and communication overhead. There are several road network compression techniques proposed in literature. These techniques are evaluated by their compression ratios. However, none of these techniques takes into consideration the possibility that the generated compressed data can be used directly in map-matching. Map-matching is an essential component of routing services that matches a measured latitude and longitude of an object to an edge in the road network graph. In this paper, we propose a novel compression technique, named COMA, that significantly reduces the size of a given road network data. Another advantage of the proposed technique is that it enables the generated compressed road network graph to be used directly in map-matching without a need to decompress it beforehand. COMA smartly deletes those nodes and edges that will not affect neither the graph connectivity nor the accuracy of map-matching objects' location. COMA is equipped with an adjustable parameter, termed conflict factor C, by which location-based services can achieve a trade-off between the compression gain and map-matching accuracy. Extensive experimental evaluation on real road network data demonstrates competitive performance on compression-ratio and the high mapmatching accuracy achieved by the proposed technique.

We demonstrate a novel location aware services framework termed COMA for efficient compression and map-matching of road-network graph data. Key innovations include working demonstration of a new compression algorithm to eliminate nodes and edges that do not affect graph connectivity while ensuring object location map-matching accuracy. The demonstration features: (1) Algorithm to leverage compressed versions of road-network graphs for map-matching of objects locations to correct road edges without decompression, (2) Upwards of 75% compression ratio implying significant savings for road network data transmission costs, a key constraint for internet of things (IOT) devices, and (3) Use of a new controllable parameter, termed conflict factor C, whereby location aware services can trade the compression efficiency with map-matching accuracy at varying granularity. In addition to above features the demonstration features an extensible framework that enables experimentation and comparison between various compression and map-matching algorithms in a rich interactive interface. In this paper we outline data management challenges for location aware services using various scenarios for compression of a real road-network map of a large region of United States, along with both real and synthetic moving object trajectories distributed over this map. We describe the COMA framework through its map-based Graphical User Interface, ability to select the area of interest from the road-network map, and submit a compression request. COMA can export and save the compact versions of the selected area in different formats and plot the compressed graph over the original map for visual inspection to study the differences between compressed and uncompressed versions and various related statistics.