Dev Oliver

Experiences with evacuation route planning algorithms

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in the event of natural disasters. Hurricane Rita and the recent tsunami revealed limitations of traditional approaches to provide emergency preparedness for evacuees and to predict the effects of evacuation route planning (ERP). Challenges arise during evacuations due to the spread of people over space and time and the multiple paths that can be taken to reach them; key assumptions such as stationary ranking of alternative routes and optimal substructure are violated in such situations. Algorithms for ERP were first developed by researchers in operations research and transportation science. However, these proved to have high computational complexity and did not scale well to large problems. Over the last decade, we developed a different approach, namely the Capacity Constrained Route Planner (CCRP), which generalizes shortest path algorithms by honoring capacity constraints and the spread of people over space and time. The CCRP uses time-aggregated graphs to reduce storage overhead and increase computational efficiency. Experimental evaluation and field use in Twin Cities Homeland Security scenarios demonstrated that CCRP is faster, more scalable, and easier to use than previous techniques. We also propose a novel scalable algorithm that exploits the spatial structure of transportation networks to accelerate routing algorithms for large network datasets. We evaluated our new approach for large-scale networks around downtown Minneapolis and riverside areas. This article summarizes experiences and lessons learned during the last decade in ERP and relates these to Professor Goodchilds contributions.

Summarizing trajectories into k -primary corridors: a summary of results

Given a set of GPS trajectories on a road network, the goal of the k-Primary Corridors (k-PC) problem is to summarize trajectories into k groups, each represented by its most central trajectory. This problem is important to a variety of domains, such as transportation services interested in finding primary corridors for public transportation or greener travel (e.g., bicycling) by leveraging emerging GPS trajectory datasets. Related trajectory mining approaches, e.g., density or frequency based hot-routes, focus on anomaly detection rather than summarization and may not be effective for the k-PC problem. The k-PC problem is challenging due to the computational cost of creating the track similarity matrix. A naïve graph-based approach to compute a single element of this track similarity matrix requires multiple invocations of common shortest-path algorithms (e.g., Dijkstra). To reduce the computational cost of creating this track similarity matrix, we propose a novel algorithm that switches from a graph-based view to a matrix-based view, computing each element in the matrix with a single invocation of a shortest-path algorithm. Experimental results show that these ideas substantially reduce computational cost without altering the results.

Ring-Shaped Hotspot Detection: A Summary of Results

Given a collection of geo-located activities (e.g., Crime reports), ring-shaped hotspot detection (RHD) finds rings, where concentration of activities inside the ring is much higher than outside. RHD is important for the applications such as crime analysis, where it may focus the search for crime sources location, e.g. The home of a serial criminal. RHD is challenging because of the large number of candidate rings and the high computational cost of the statistical significance test. Previous statistically significant hotspot detection techniques (e.g., Sat Scan) identify circular/rectangular areas, but can not discover rings. This paper proposes a dual grid based pruning (DGP) approach to detect ring-shaped hotspots. A case study on real crime data confirms that DGP detects novel ring-shaped regions, regions that go undetected by Sat Scan. Experiments show that DGP improves the computational cost of a naive approach substantially.

A K-Main Routes Approach to Spatial Network Activity Summarization: A Summary of Results

Data summarization is an important concept in data mining for finding a compact representation of a dataset. In spatial network activity summarization (SNAS), we are given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports) and the goal is to find k shortest paths that summarize the activities. SNAS is important for applications where observations occur along linear paths such as roadways, train tracks, etc. SNAS is computationally challenging because of the large number of k subsets of shortest paths in a spatial network. Previous work has focused on either geometry or subgraph-based approaches (e.g., only one path), and cannot summarize activities using multiple paths. This paper proposes a K-Main Routes (KMR) approach that discovers k shortest paths to summarize activities. KMR generalizes K-means for network space but uses shortest paths instead of ellipses to summarize activities. To improve performance, KMR uses network Voronoi, divide and conquer, and pruning strategies. We present a case study comparing KMRs network-based output (i.e., shortest paths) to geometry-based outputs (e.g., ellipses) on pedestrian fatality data. Experimental results on synthetic and real data show that KMR with our performance-tuning decisions yields substantial computational savings without reducing summary path coverage.

Significant Route Discovery: A Summary of Results

Given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports), Significant Route Discovery (SRD) finds all shortest paths in the spatial network where the concentration of activities is unusually high (i.e., statistically significant). SRD is important for societal applications in transportation safety, public safety, or public health such as finding routes with significant concentrations of accidents, crimes, or diseases. SRD is challenging because 1) there are a potentially large number of candidate routes (~1016) in a given dataset with millions of activities or road network nodes and 2) significance testing does not obey the monotonicity property. Previous work focused on finding circular areas of concentration, limiting its usefulness for finding significant linear routes on a network. SaTScan may miss many significant routes since a large fraction of the area bounded by circles for activities on a path will be empty. This paper proposes a novel algorithm for discovering statistically significant routes. To improve performance, the proposed algorithm features algorithmic refinements that prune unlikely paths and speeds up Monte Carlo simulation. We present a case study comparing the proposed statistically significant network-based analysis (i.e., shortest paths) to a statistically significant geometry-based analysis (e.g., circles) on pedestrian fatality data. Experimental results on real data show that the proposed algorithm, with our algorithmic refinements, yields substantial computational savings without reducing result quality.

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.

Capacity-Constrained network-voronoi diagram: a summary of results

Given a graph and a set of service centers, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the graph into a set of contiguous service areas that meet service center capacities and minimize the sum of the distances (min-sum) from graph-nodes to allotted service centers. The CCNVD problem is important for critical societal applications such as assigning evacuees to shelters and assigning patients to hospitals. This problem is NP-hard; it is computationally challenging because of the large size of the transportation network and the constraint that Service Areas (SAs) must be contiguous in the graph to simplify communication of allotments. Previous work has focused on honoring either service center capacity constraints (e.g., min-cost flow) or service area contiguity (e.g., Network Voronoi Diagrams), but not both. We propose a novel Pressure Equalizer (PE) approach for CCNVD to meet the capacity constraints of service centers while maintaining the contiguity of service areas. Experiments and a case study using post-hurricane Sandy scenarios demonstrate that the proposed algorithm has comparable solution quality to min-cost flow in terms of min-sum; furthermore it creates contiguous service areas, and significantly reduces computational cost.

Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning

Given a set of trajectories on a road network, the goal of the All-Pair Network Trajectory Similarity (APNTS) problem is to calculate the similarity between all trajectories using the Network Hausdorff Distance. This problem is important for a variety of societal applications, such as facilitating greener travel via bicycle corridor identification. The APNTS problem is challenging due to the high cost of computing the exact Network Hausdorff Distance between trajectories in spatial big datasets. Previous work on the APNTS problem takes over 16 hours of computation time on a real-world dataset of bicycle GPS trajectories in Minneapolis, MN. In contrast, this paper focuses on a scalable method for the APNTS problem using the idea of row-wise computation, resulting in a computation time of less than 6 minutes on the same datasets. We provide a case study for transportation services using a data-driven approach to identify primary bicycle corridors for public transportation by leveraging emerging GPS trajectory datasets.

Identifying K Primary Corridors from urban bicycle GPS trajectories on a road network

Given a set of GPS tracks on a road network and a number k, the K-Primary-Corridor (KPC) problem aims to identify k tracks as primary corridors such that the overall distance from all tracks to their closest primary corridors is minimized. The KPC problem is important to domains such as transportation services interested in finding primary corridors for public transportation or greener travel (e.g., bicycling) by leveraging emerging GPS trajectory datasets. However, the problem is challenging due to the large amount of shortest path distance computations across tracks. Related trajectory mining approaches, e.g., density or frequency based hot-routes, focus on anomaly detection rather than identifying representative corridors minimizing total distances from other tracks, and thus may not be effective for the KPC problem. Our recent work proposed a k-Primary Corridor algorithm that precomputes a column-wise lookup table of network Hausdorff distances. This paper extends our recent work with a new computational algorithm based on lower bound filtering. We design lower bounds of network Hausdorff distances based on the concept of track envelopes and propose three different track envelope formation strategies based on random selection, overlap, and Jaccard coefficient respectively. Theoretical analysis on proof of correctness as well as computational cost models are provided. Extensive experiments and case studies show that our new algorithm with lower bound filtering significantly reduces the computational time of our previous algorithm, and can help effectively determine primary bicycle corridors.

Discovering persistent change windows in spatiotemporal datasets: a summary of results

Given a region S comprised of locations that each have a time series of length |T|, the Persistent Change Windows (PCW) discovery problem aims to find all spatial window and temporal interval pairs <Si, Ti> that exhibit persistent change of attribute values over time. PCW discovery is important for critical societal applications such as detecting desertification, deforestation, and monitoring urban sprawl. The PCW discovery problem is challenging due to the large number of candidate patterns, the lack of monotonicity where sub-regions of a PCW may not show persistent change, the lack of predefined window sizes for the ST windows, and large datasets of detailed resolution and high volume, i.e., spatial big data. Previous approaches in ST change footprint discovery have focused on local spatial footprints for persistent change discovery and may not guarantee completeness. In contrast, we propose a space-time window enumeration and pruning (SWEP) approach that considers zonal spatial footprints when finding persistent change patterns. We provide theoretical analysis of SWEPs correctness, completeness, and space-time complexity. We also present a case study on vegetation data that demonstrates the usefulness of the proposed approach. Experimental evaluation on synthetic data show that the SWEP approach is orders of magnitude faster than the naive approach.