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32
Efficient K-Nearest Neighbor Search in Time-Dependent Spatial Networks
, 2010
"... The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., travel-time) of each edge in the spatial network is constant. However, in real-world, edge-weights a ..."
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Cited by 11 (1 self)
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The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., travel-time) of each edge in the spatial network is constant. However, in real-world, edge-weights are timedependent and vary significantly in short durations, hence invalidating the existing solutions. In this paper, we study the problem of kNN search in timedependent spatial networks where the weight of each edge is a function of time. We propose two novel indexing schemes, namely Tight Network Index (T NI) and Loose Network Index (LNI) to minimize the number of candidate nearest neighbor objects and, hence, reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
Discovering and Quantifying Mean Streets: A Summary of Results
, 2007
"... Mean streets represent those connected subsets of a spatial network whose attribute values are significantly higher than expected. Discovering and quantifying mean streets is an important problem with many applications such as detecting high-crime-density streets and high crash roads (or areas) for ..."
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Cited by 8 (4 self)
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Mean streets represent those connected subsets of a spatial network whose attribute values are significantly higher than expected. Discovering and quantifying mean streets is an important problem with many applications such as detecting high-crime-density streets and high crash roads (or areas) for public safety, detecting urban cancer disease clusters for public health, detecting human activity patterns in asymmetric warfare scenarios, and detecting urban activity centers for consumer applications. However, discovering and quantifying mean streets in large spatial networks is computationally very expensive due to the difficulty of characterizing and enumerating the population of streets to define a norm or expected activity level. Previous work either focuses on statistical rigor at the cost of computational exorbitance, or
Online Computation of Fastest Path in Time-Dependent Spatial Networks
, 2011
"... The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. Howe ..."
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Cited by 8 (5 self)
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The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. In this paper, we study the online computation of fastest path in time-dependent spatial networks and present a technique which speeds-up the path computation. We show that our fastest path computation based on a bidirectional time-dependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real data-sets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.
The multi-rule partial sequenced route query
- GIS
"... Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorith ..."
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Cited by 8 (0 self)
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Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorithms. By formulating each restriction into rules, we propose a novel form of route query, namely the multi-rule partial sequenced route (MRPSR) query. Our work provides a unified framework that also subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. In this paper, we first prove that MRPSR is NP-hard and then present three heuristic algorithms to search for near-optimal solutions for the MRPSR query. Our extensive simulations show that all of the proposed algorithms can answer the MRPSR query effectively and efficiently. Using both real and synthetic datasets, we investigate the performance of our algorithms with the metrics of the route distance and the response time in terms of the percentage of the constrained points of interest (POI) categories. Compared to the LORD-based brute-force solution, the response times of our algorithms are remarkably reduced while the resulting route length is only slightly longer than the shortest route.
Towards Modeling the Traffic Data on Road Networks
"... A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding time-dependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for location-based services) require r ..."
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Cited by 6 (4 self)
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A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding time-dependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for location-based services) require realistic models that accurately represent the temporal behavior of such networks. In this paper, for the first time we propose a traffic modeling framework for road networks that enables 1) generating an accurate temporal model from archived temporal data collected from a spatiotemporal network (so as to be able to publish the temporal model of the spatiotemporal network without having to release the real data), and 2) augmenting any given spatial network model with a corresponding realistic temporal model custom-built for that specific spatial network (in order to be able to generate a spatiotemporal network model from a solely spatial network model). We validate the accuracy of our proposed modeling framework via experiments. We also used the proposed framework to generate the temporal model of the Los Angeles County freeway network and publish it for public use. 1.
The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations
, 2009
"... Multi Agent Simulation has increasingly been used for transportation simula-tion in recent years. With current techniques, it is possible to simulate systems consisting of several million agents. Such Multi Agent Simulations have been applied to whole cities and even large regions. In this paper it ..."
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Cited by 5 (1 self)
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Multi Agent Simulation has increasingly been used for transportation simula-tion in recent years. With current techniques, it is possible to simulate systems consisting of several million agents. Such Multi Agent Simulations have been applied to whole cities and even large regions. In this paper it is demonstrated how to adapt an existing multi agent transportation simulation framework to large-scale pedestrian evacuation simulation. The underlying flow model simu-lates the traffic based on a simple queue model where only free speed, bottleneck capacities, and space constraints are taken into account. The queue simulation, albeit simple, captures the most important aspects of evacuations such as the congestion effects of bottlenecks and the time needed to evacuate the endangered area. In the case of an evacuation simulation the network has time dependent attributes. For instance, large-scale inundations or conflagrations do not cover all the endangered area at once. These time dependent attributes are modeled as network change events. Network change events are modifying link parameters at predefined points in time. The simulation framework is demonstrated through a case study for the Indonesian city of Padang, which faces a high risk of being inundated by a tsunami.
Efficient Evaluation of k-NN Queries Using Spatial Mashups?
"... Abstract. K-nearest-neighbor (k-NN) queries have been widely studied in time-independent and time-dependent spatial networks. In this paper, we focus on k-NN queries in time-dependent spatial networks where the driving time between two locations may vary significantly at different time of the day. I ..."
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Cited by 5 (3 self)
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Abstract. K-nearest-neighbor (k-NN) queries have been widely studied in time-independent and time-dependent spatial networks. In this paper, we focus on k-NN queries in time-dependent spatial networks where the driving time between two locations may vary significantly at different time of the day. In practice, it is costly for a database server to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to an object of interest in terms of the driving time. Thus, we design a new spatial query processing paradigm that uses a spatial mashup to enable the database server to efficiently evaluate k-NN queries based on the route information accessed from an external Web mapping service, e.g., Google Maps, Yahoo! Maps and Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose a new spatial query processing algo-rithm that uses shared execution through grouping objects and users based on the road network topology and pruning techniques to reduce the number of external requests to the Web mapping service and pro-vides highly accurate query answers. We implement our algorithm using Google Maps and compare it with the basic algorithm. The results show that our algorithm effectively reduces the number of external requests by 90 % on average with high accuracy, i.e., the accuracy of estimated driving time and query answers is over 92 % and 87%, respectively. 1
A Lagrangian Approach for Storage of Spatio-Temporal Network Datasets: A Summary of Results ABSTRACT
"... Given a set of operators and a spatio-temporal network, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient data storage method that minimizes disk I/O access costs. Storing and accessing spatiotemporal networks is increasingly important in many societal applic ..."
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Cited by 4 (2 self)
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Given a set of operators and a spatio-temporal network, the goal of the Storing Spatio-Temporal Networks (SSTN) problem is to produce an efficient data storage method that minimizes disk I/O access costs. Storing and accessing spatiotemporal networks is increasingly important in many societal applications such as transportation management and emergency planning. This problem is challenging due to strains on traditional adjacency list representations when storing temporal attribute values from the sizable increase in length of the time-series. Current approaches for the SSTN problem focus on orthogonal partitioning (e.g., snapshot, longitudinal, etc.), which may produce excessive I/O costs when performing traversal-based spatio-temporal network queries (e.g., route evaluation, arrival time prediction, etc) due to the desired nodes not being allocated to a common page. We propose a Lagrangian-Connectivity Partitioning (LCP) technique to efficiently store and access spatio-temporal networks that utilizes the interaction between nodes and edges in a network. Experimental evaluation using the Minneapolis, MN road network showed that LCP outperforms traditional orthogonal approaches.
A Critical-Time-Point Approach to All-start-time Lagrangian Shortest Paths: A Summary of Results
"... Abstract. Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applic ..."
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Cited by 3 (0 self)
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Abstract. Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applications related to air travel, road travel, and other spatiotemporal networks. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths, meaning that a candidate path which is optimal for one start time may not be optimal for others. Determining a shortest path for each start-time leads to redundant computations across consecutive start times sharing a common solution. The proposed approach reduces this redundancy by determining the critical time points at which an optimal path may change. Theoretical analysis and experimental results show that this approach performs better than naive approaches particularly when there are few critical time points. 1
Evacuation Planning: A Spatial Network Database Approach
"... Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest ..."
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Cited by 3 (2 self)
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Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in face of natural disasters or terrorist attacks. Challenges arise due to violation of key assumptions (e.g. stationary ranking of alternative routes, Wardrop equilibrium) behind popular shortest path algorithms (e.g. Dijkstra’s, A*) and microscopic traffic simulators (e.g. DYNASMART). Time-expanded graphs (TEG) based mathematical programming paradigm does not scale up to large urban scenarios due to excessive duplication of transportation network across time-points. We present a new approach, namely Capacity Constrained Route Planner (CCRP), advancing the idea of Time-Aggregated Graph (TAG) to provide Earliest-Arrival-Time given any Start-Time. Laboratory experiments and field use in Twin-cities for Homeland Security scenarios show that CCRP is more efficient than previous methods. 1
