K-Nearest Neighbor Search in Time-Dependent Spatial Networks
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Solution Overview
Problem
Existing k-nearest neighbor (k NN) query solutions for road networks fail to accurately and efficiently handle time-dependent travel times due to their reliance on constant edge weights and precomputation methods, which are inefficient and inaccurate in dynamic networks.
Innovation Solution
The development of a novel technique using Tight Network Index (TNI) and Loose Network Index (LNI) structures to efficiently find k NN in time-dependent road networks, allowing for real-time query processing and handling of database updates, by partitioning the network into sub-networks and filtering potential candidates based on time-dependent shortest path computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If precomputation methods with constant edge weights are used, then query processing is simplified, but accuracy deteriorates in time-dependent networks
Solution Approach 1:
The patent applies dynamics by making the edge weights time-dependent, allowing the network model to adapt to changing traffic conditions. The travel time on each edge is represented as a function of departure time, enabling accurate representation of dynamic road networks while maintaining efficient query processing through the proposed index structures.
Solution Approach 2:
The patent changes the parameter representation from constant edge weights to time-dependent functions. By modeling edge weights as functions of departure time and using precomputed travel time information, the system achieves both accuracy in dynamic conditions and efficiency in query processing.
2Measurement precision
If time-dependent travel times are accurately modeled, then query accuracy improves, but computation cost increases
Solution Approach 1:
The patent applies preliminary action by precomputing travel time information and storing it in index structures before query processing. The TNI and LNI structures are built in advance with precomputed travel times, allowing queries to be answered efficiently without performing expensive shortest path computations for each query.
Solution Approach 2:
The patent segments the network into regions covered by tight network indexes and loose network indexes. This segmentation allows the system to handle different parts of the network with appropriate index structures, reducing overall computation cost while maintaining accuracy.
3Reliability
If the entire network is searched for k NN queries, then completeness is ensured, but processing time increases
Solution Approach 1:
The patent applies local quality by creating tight network indexes that cover specific regions of the network. Each TNI covers a localized area with guaranteed correctness, allowing the system to answer queries by checking only relevant local regions rather than searching the entire network, thus ensuring completeness while reducing processing time.
Solution Approach 2:
The patent uses partial action by combining results from tight network indexes and loose network indexes. The TNI provides guaranteed correct results for covered regions, while LNI handles remaining cases, ensuring completeness without requiring exhaustive search of the entire network.
Data Source
AI summary
The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and 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.