Time-Dependent Link Travel Time Approximation for Large-Scale Dynamic Traffic Simulations

Genaro Peque, Hiro Harada, Takamasa Iryo

研究成果: Conference contribution

抄録

Large-scale dynamic traffic simulations generate a sizeable amount of raw data that needs to be managed for analysis. Typically, big data reduction techniques are used to decrease redundant, inconsistent and noisy data as these are perceived to be more useful than the raw data itself. However, these methods are normally performed independently so it wouldn’t compete with the simulation’s computational and memory resources. In this paper, we propose a data reduction technique that will be integrated into a simulation process and executed numerous times. Our interest is in reducing the size of each link’s time-dependent travel time data in a large-scale dynamic traffic simulation. The objective is to approximate the time-dependent link travel times along the y - axis to reduce memory consumption while insignificantly affecting the simulation results. An important aspect of the algorithm is its capability to restrict the maximum absolute error bound which avoids theoretically inconsistent results which may not have been accounted for by the dynamic traffic simulation model. One major advantage of the algorithm is its efficiency’s independence from the input data complexity such as the number of sampled data points, sampled data’s shape and irregularity of sampling intervals. Using a 10 × 10 grid network with variable time-dependent link travel time data complexities and absolute error bounds, the dynamic traffic simulation results show that the algorithm achieves around 80%–90% of link travel time data reduction using a small amount of computational resource.

本文言語English
ホスト出版物のタイトルComputational Science – ICCS 2019 - 19th International Conference, Proceedings
編集者João M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
出版社Springer Verlag
ページ562-576
ページ数15
ISBN(印刷版)9783030227432
DOI
出版ステータスPublished - 2019
外部発表はい
イベント19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
継続期間: 2019 6 122019 6 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11538 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference19th International Conference on Computational Science, ICCS 2019
国/地域Portugal
CityFaro
Period19/6/1219/6/14

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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