Utilizing MapReduce to Improve Probe-Car Track Data Mining

Abstract

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

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Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

“…Given data volumes, cloud-computing technologies including Bigtable and MapReduce can be effectively adapted to handle FCD. Zheng et al [31] used the MapReduce technology to enhance floating car efficiency with map matching methods. Chen et al [11] presented a multi-criteria dynamic programming map matching algorithm that could be successfully used for matching FCD.…”

MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data

“…The loaded data are finally analyzed in a big data platform for decision-making. In this approach, an analysis operation is processed in a parallel/distributed way using MapReduce [21,22], which guarantees reasonable performance, but bottlenecks can occur during a transform operation. In fact, transform is the most time consuming phase in ETL because this operation includes filtering or aggregation of source data to fit the structure of the target database.…”