Abdeltawab M. Hendawi

Panda: a predictive spatio-temporal query processor

This paper presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries that are widely used in several applications including traffic management, location-based advertising, and ride sharing. Unlike previous attempts in supporting predictive queries, Panda targets long-term query prediction as it relies on adapting a well-designed long-term prediction function to: (a) scale up to large number of moving objects, and (b) support large number of predictive queries. As a means of scalability, Panda smartly precomputes parts of the most frequent incoming predictive queries, which significantly reduces the query response time. Panda employs a tunable threshold that achieves a trade-off between query response time and the maintenance cost of precomptued answers. Experimental results, based on large data sets, show that Panda is scalable, efficient, and as accurate as its underlying prediction function.

Predictive spatio-temporal queries: a comprehensive survey and future directions

Predictive queries over spatio-temporal data proved to be vital in many location-based services including traffic management, ride sharing, and advertising. In the last few years, one of the most exciting work on spatio-temporal data management is about predictive queries. In this paper, we review the current research trends and present their related applications in the field of predictive spatio-temporal queries processing. Then, we discuss some basic challenges arising from new opportunities and open problems. The goal of this paper is to catch the interesting areas and future work under the umbrella of predictive queries over spatio-temporal data.

iRoad: a framework for scalable predictive query processing on road networks

This demo presents the iRoad framework for evaluating predictive queries on moving objects for road networks. The main promise of the iRoad system is to support a variety of common predictive queries including predictive point query, predictive range query, predictive KNN query, and predictive aggregate query. The iRoad framework is equipped with a novel data structure, named reachability tree, employed to determine the reachable nodes for a moving object within a specified future time Τ. In fact, the reachability tree prunes the space around each object in order to significantly reduce the computation time. So, iRoad is able to scale up to handle real road networks with millions of nodes, and it can process heavy workloads on large numbers of moving objects. During the demo, audience will be able to interact with iRoad through a well designed Graphical User Interface to issue different types of predictive queries on a real road network, to obtain the predictive heatmap of the area of interest, to follow the creation and the dynamic update of the reachability tree around a specific moving object, and finally to examine the system efficiency and scalability.

Routing service with real world severe weather

Traditional routing services aim to save driving time by recommending the shortest path, in terms of distance or time, to travel from a start location to a given destination. However, these methods are relatively static and to a certain extent rely on traffic patterns under relatively normal conditions to calculate and recommend an appropriate route. As such, they do not necessarily translate effectively during severe weather events such as tornadoes. In these scenarios, the guiding principal is not, optimize for travel time, but rather, optimize for survivability of the event, i.e., can we recommend an evacuation route to those users inside the hazardous areas. In this demo, we present a framework for routing services for evacuating and avoiding real world severe weather threats that is able to: (1) Identify the users inside the dangerous region of a severe weather event (2) Recommend an evacuation route to guide the users out to a safe destination or shelter (3) Assure the recommended route to be one of the shortest paths after excluding the risky area (4) Maintain the flow of traffic by normalizing the evacuation on the possible safe routes. During the demo, attendees will be able to use the system interactively through its graphical user interface within a number of different scenarios. They will be able to locate the severe weather events on real time basis in any area in USA and examine detailed information about each event, to issue an evacuation query from an existing dangerous area by identifying a destination location and receiving the routing direction on their mobile devices, to issue an avoidance routing query to ask for a shortest path that avoids the dangerous region, to have an inside look into the internal system components and finally, to evaluate the overall system performance.

CrowdPath: a framework for next generation routing services using volunteered geographic information

Our proposed system CrowdPath is based on the hypothesis that people know their commute area better than conventional routing services that use traditional digital roadmaps and shortest path algorithms. The knowledge and experiences of drivers reflected in volunteered commute routes may provide better routes. By leveraging such available volunteered geographic information (VGI), our goal is to investigate next-generation routing services to further reduce travel time, fuel consumption, and improve navigation. Previous related work summarizes GPS tracks into a landmark graph which is used for answering routing queries. In contrast, CrowdPath directly queries a collection of map-matched GPS tracks to recommend paths from a source location to a destination. Our evaluation using real GPS tracks illustrates the promise of CrowdPath in significantly reducing travel time compared to routes from common routing providers. In the future, CrowdPath may be extended to adapt route recommendations by start time and provide safe paths using volunteered crime and accident reports.

A Framework for Spatial Predictive Query Processing and Visualization

This demo presents the Panda system for efficient support of a wide variety of predictive spatio-temporal queries. These queries are widely used in several applications including traffic management, location-based advertising, and store finders. Panda targets long-term query prediction as it relies on adapting a long-term prediction function to: (a) scale up to large number of moving objects, and (b) support predictive queries. Panda does not only aim to predict the query answer, but, it also aims to predict the incoming queries such that parts of the query answer can be precomputed before the query arrival. Panda maintains a tunable threshold that achieves a trade-off between the predictive query response time and the system overhead in precomputing the query answer. Equipped with a Graphical User Interface (GUI), audience can explore the Panda demo through issuing predictive queries over a moving set of objects on a map. In addition, they are able to follow the execution of such queries through an eye on the Panda execution engine.

Data-Driven Robust Taxi Dispatch Under[-2pt] Demand Uncertainties

In modern taxi networks, large amounts of taxi occupancy status and location data are collected from networked in-vehicle sensors in realtime. They provide knowledge of system models on passenger demand and mobility patterns for efficient taxi dispatch and coordination strategies. Such approaches face new challenges: how to deal with uncertainties of predicted customer demand while fulfilling the system’s performance requirements, including minimizing taxis’ total idle mileage and maintaining service fairness across the whole city; how to formulate a computationally tractable problem. To address this problem, we develop a data-driven robust taxi dispatch framework to consider spatial-temporally correlated demand uncertainties. The robust vehicle dispatch problem we formulate is concave in the uncertain demand and convex in the decision variables. Uncertainty sets of random demand vectors are constructed from data based on theories in hypothesis testing, and provide a desired probabilistic guarantee level for the performance of robust taxi dispatch solutions. We prove equivalent computationally tractable forms of the robust dispatch problem using the minimax theorem and strong duality. Evaluations on four years of taxi trip data for New York City show that by selecting a probabilistic guarantee level at 75%, the average demand–supply ratio error is reduced by 31.7%, and the average total idle driving distance is reduced by 10.13% or about 20 million miles annually, compared with nonrobust dispatch solutions.

Smart Personalized Routing for Smart Cities

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.

Dynamic and Personalized Routing in PreGo

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 users 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.

RxSpatial: Reactive Spatial Library for Real-Time Location Tracking and Processing

Current commercial spatial libraries implemented strong support on functionalities like intersection, distance, and area for various stationary geospatial objects. The missing point is the support for moving object. Performing moving object real-time location tracking and computation on server side of GIS application is challenging because of high user volume of moving object to track, time complexity of analysis and computation, and requirement of real-timing. In this Demo, we present the RxSpatial, a real time reactive spatial library that consists of (1) a front-end, a programming interface for developers who are familiar with the Reactive framework and the Microsoft Spatial Library, and (2) a back-end for processing spatial operations in a streaming fashion. Then we provide the demonstration scenarios that show how RxSpatial is employed in real-world applications. The demonstration scenarios include criminal activity tracking, collaborative vehicle system, performance analysis and an interactive internal inspection.