Abstract
The transmission and storage of global navigation satellite system (GNSS) data places very high demands on mobile networks and centralised data processing systems. GNSS applications including community based navigation and fleet management require GNSS data to be transmitted from a vehicle to a centralised system and then processed by a map-matching algorithm to determine the location of a vehicle within a road segment. Various data compression techniques have been developed to reduce the volume of data transmitted. There is also an independent literature relating to map-matching algorithms. However, no previous research has integrated data compression with a map-matching algorithm that accepts compressed data as an input without the need for decompression. This paper develops a novel GNSS data reduction algorithm with deterministic error bounds, which was seamless integrated with a specifically designed map-matching algorithm. The approach significantly reduces the volume of GNSS data communicated and improves the performance of the map-matching algorithm. The data compression extracts critical points in the trajectory and velocity–time curve of a vehicle. During the process of selecting critical points, the error of restoring vehicle trajectories and velocity–time curves are used as parameters to control the number of critical points selected. By setting different error bound values prior to the execution of the algorithm, the accuracy and volume of reduced data is controlled precisely. The compressed GNSS data, particularly the critical points selected from the vehicle’s trajectory is directly input to the map-matching algorithm without the need for decompression. An experiment indicated that the data reduction algorithm is very effective in reducing data volume. This research will be useful in many fields including community driven navigation and fleet management.
Highlights
Big data business analytics can improve the visibility, flexibility and integration of global supply chains and logistics processes, whilst effectively managing demand volatility and cost fluctuations (Genpact 2015; Wang et al 2016)
This paper addresses this research gap to provide an approach that: (1) reduces the volume of global navigation satellite system (GNSS) data that needs to be transmitted to a centralised system; (2) ensures that the data received at centralised system can be used as an input for a map-matching without the need of data decompression or restoration; (3) enhances the performance of map-matching in terms of running speed and accuracy, whilst simultaneously minimising computing time on the server
GNSS data is compressed by selecting critical points on velocity–time curve and spatial vehicle trajectory
Summary
Big data business analytics can improve the visibility, flexibility and integration of global supply chains and logistics processes, whilst effectively managing demand volatility and cost fluctuations (Genpact 2015; Wang et al 2016). The computation of individual WAZE user’s link travel time requires appropriate data to be transferred, stored and processed by map-matching algorithms in real time This poses significant challenges in terms of communication, storage and processing; (2) vehicle tracking for large fleets online e-retailing companies such as AMAZON or jd.com in China need to deliver a large volume of products from their warehouses to customers every day over a very large geographical areas. This paper addresses this research gap to provide an approach that: (1) reduces the volume of GNSS data that needs to be transmitted to a centralised system; (2) ensures that the data received at centralised system can be used as an input for a map-matching without the need of data decompression or restoration; (3) enhances the performance of map-matching in terms of running speed and accuracy, whilst simultaneously minimising computing time on the server. A field experiment is reported that illustrates the efficiency of the data compression method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
