Abstract
Traffic jams have recently become a significant problem in provincial cities that tend to have poor railway services in Japan. Therefore, the main means of transportation are public buses, taxies, and private vehicles. Moreover, traffic accidents and road construction sites frequently block traffic. It is therefore difficult to estimate the travelling time from the origin to destination in real-time. To estimate the travelling time, we must predict the behaviors of many vehicles that depend on an “origin to destination” (OD) traffic volume. In our previous study, we proposed an estimation method for OD traffic volume using two types of big data, a road traffic census and mobile spatial statistics. In this study, we evaluated our proposed method on various situations through a traffic simulation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
MILT: International visitor survey. http://www.mlit.go.jp/kankocho/en/siryou/toukei/syouhityousa.html
Uesaka, K., Monma, T., Matsumoto, S., Hashimoto, H., Mizuki, T.: The FY2010 Road Traffic Census Results of the General Traffic Volume Surveys (Overview). www.nilim.go.jp/english/annual/annual2012/84.pdf
Murase, A.: How mobile spatial statistics Began. NTT Docomo J. 14(3), 1
Kim, J., Kurauchi, F., Uno, N., Hagihara, T., Daito, T.: Using electronic toll collection data to understand traffic demand. J. Intell. Transp. Syst. 18(2), 190–203 (2014)
Wolf, J., Randall, G., Bachman, W.: Elimination of the travel diary: experiment to derive trip purpose from global positioning system travel data. Transp. Res. Rec. J. Transp. Res. Board 1768(1), 125–134 (2001)
Akagi, Y., Nishimura, T., Kurashima, T., Toda, H.: A fast and accurate method for estimating people flow from spatiotemporal population data. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) (2018)
NTT DoCoMo: AI Bus. https://www.itsap-fukuoka.jp/demo/AI_Bus.html
Nakashima, H., Sano, S., Hirata, K., Shiraishi, Y., Matsubara, H., Kanamori, R., Koshiba, H., Noda, I.: One cycle of smart access vehicle service development
Takai, M., Martin, J., Kaneda, S., Maeno, T.: Scenargie as a network simulator and beyond. J. Inf. Process. 27(1), 2–9
Open Street Map Japan. https://openstreetmap.jp
Acknowledgment
This work was supported by JSPS KAKENHI Grant No. 16K00433. Data on the Mobile Spatial Statistics in Kanazawa and Nonoichi were provided by NTT DoCoMo.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Someya, K., Kiyohara, R., Saito, M. (2020). Estimation Method of Traffic Volume Using Big-Data. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-29029-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29028-3
Online ISBN: 978-3-030-29029-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)