References

In press

Exhibitions

References

1.
Makoto Kasai and Hironobu Hasegawa: An application of chaotic time-series analysis to car-following data at capacity bottleneck, Proceedings of the 41st Conference of Japan Society of Traffic Engineers, Web conference, pp. 503-509, 2021. (in Japanese)
2.
Toru Seo, Yusuke Tago, Norihito Shinkai, Masakazu Nakanishi, Jun Tanabe, Daisuke Ushirogochi, Shota Kanamori, Atsushi Abe, Takashi Kodama, Satoshi Yoshimura, Masaaki Ishihara, and Wataru Nakanishi: Evaluation of large-scale complete vehicle trajectories dataset on two kilometers highway segment for one hour duration: Zen Traffic Data, International Symposium on Transportation Data & Modelling (ISTDM2021), Ann Arbor, Michigan, U.S.A., 2021.
3.
Kentaro Wada, Toru Seo, and Yasuhiro Shiomi: Flow breakdown, International Encyclopedia of Transportation (Vickerman, R. (Ed.)), Vol. 4, pp. 143-153, Elsevier, 2021. http://dx.doi.org/10.1016/B978-0-08-102671-7.10303-3
4.
Jaya Varshini Kala, Azusa Toriumi, Xiangdong Chen, Xi Lin, and Takashi Oguchi: Motorway gap distribution analysis for designing dedicated connected-and-automated-vehicle lanes, SEISAN KENKYU, Vol. 73, No. 2, pp. 113-118,2021.https://doi.org/10.11188/seisankenkyu.73.113
5.
Jaya Varshini Kala, Azusa Toriumi, Xiangdong Chen, Xi Lin, and Takashi Oguchi: Motorway gap distribution analysis for designing dedicated connected-and-automated-vehicle lanes, the 18th ITS Symposium 2020, Web conference, 2020.
6.
Garima Dahiya, Yasuo Asakura, and Wataru Nakanishi: A study of speed-density: Functional relations for varying spatiotemporal resolution using Zen Traffic Data, the 18th ITS Symposium 2020, Web conference, 2020.
7.
Toru Seo and Yoshiaki Sugimoto: Calibration-free traffic state estimation using probe vehicle data and detector data, Proceedings of Infrastructure Planning, Vol. 62, Web conference, 2020. (in Japanese)
8.
Kengo Sakai, Toru Seo, and Takashi Fuse: Traffic state estimation on mixed flow by lane with spacing information of autonomous car, Proceedings of Infrastructure Planning, Vol. 62, Web conference, 2020. (in Japanese)
9.
Garima Dahiya, Yasuo Asakura, and Wataru Nakanishi: A study of speed-density: Functional relations for varying spatiotemporal resolution using Zen Traffic Data,the IEEE 23rd International Conference on Intelligent Transportation Systems, Web conference, 2020. https://doi.org/10.1109/ITSC45102.2020.9294564
10.
Toru Seo: Calibration-free traffic state estimation method using single detector and connected vehicles with Kalman filtering and RTS smoothing, the IEEE 23rd International Conference on Intelligent Transportation Systems, Web conference, 2020. https://doi.org/10.1109/ITSC45102.2020.9294229
11.
Takahiro Igaki and Takashi Uchida: A modeling of micro traffic simulation by using vehicle trajectory data generated through image sensing, Proceedings of the Japan Society of Civil Engineers 2020 Annual Meeting, IV-68, Web conference, 2020. (in Japanese)
12.
Takahiro Igaki and Takashi Uchida: Consideration on applicability of vehicle trajectory data generated by image sensing, Proceedings of the 40th Conference of Japan Society of Traffic Engineers, pp. 229-234, Web conference, 2020. (in Japanese)
13.
Takashi Kodama, Yota Maehara, Masaaki Ishihara, Yoann Pencreach, Rei Yoshizaki, and Jun Tanabe: Improving the applicability of vehicle trajectory data observed from a certain position for risk assessment, Technical Paper, Transactions of Society of Automotive Engineers of Japan, Vol. 51, No. 5, pp. 812 – 817, 2020. https://doi.org/10.11351/jsaeronbun.51.812 (in Japanese)
14.
Kotaro Yoshida, Wataru Nakanishi, and Yasuo Asakura: Analysis of car-following behavior in different time zones by estimating the parameters of Newell’s model, Proceedings of Infrastructure Planning, Vol. 61, Web conference, 2020. (in Japanese)
15
Yasuhiro Shiomi: Deep learning based car following model, Proceedings of Infrastructure Planning, Vol. 61, Web conference, 2020. (in Japanese)
16.
Elnara Abdullaeva, Takashi Oguchi, Azusa Toriumi, and Masahiro Kato: Modeling two-vehicle interaction at freeway-on ramp merging section with game theory, SEISAN KENKYU, Vol. 72, No. 2, pp. 153-158, 2020.https://doi.org/10.11188/seisankenkyu.72.153
17.
Takashi Kodama, Masaaki Ishihara, Yota Maehara , Norifumi Shinkai, Masakazu Nakanishi, and Jun Tanabe: A study of the method of driving behavior evaluation necessary for the statistical grasp of the occurrence mechanism of traffic events, JSTE Journal of Traffic Engineering, Vol. 6, No. 2 (Special Edition B), pp. B_37 - B_45, 2020. https://doi.org/10.14954/jste.6.2_B_37 (in Japanese)
18.
Takashi Kodama, Masaaki Ishihara, Yota Maehara , Norifumi Shinkai, Masakazu Nakanishi, and Jun Tanabe: Examination of safety assessment method of scenario extracted from the trajectory data observed, Technical Paper, Transactions of Society of Automotive Engineers of Japan, Vol. 51, No. 1, pp. 155 – 160, 2020. https://doi.org/10.11351/jsaeronbun.51.155 (in Japanese)
19.
Elnara Abdullaeva, Takashi Oguchi, Azusa Toriumi, and Masahiro Kato: Modeling two-vehicle interaction at freeway-on ramp merging section with game theory,the 17th ITS Symposium 2019, 1-B-12, Kanazawa, 2019. (in Japanese)
20.
Takashi Kodama, Masaaki Ishihara, Norihito Shinkai, Jun Tanabe, and Satoru Nakajo: Evaluation of the impact of a vehicle trajectory on traffic by utilizing all vehicle trajectory data observed on expressway, Proceedings of the 26th ITS World Congress, Singapore, 2019.
21.
Takashi Kodama, Masaaki Ishihara, Yota Maehara, Norifumi Shinkai, Masakazu Nakanishi, and Jun Tanabe: Examination of safety assessment method of scenario extracted from the trajectory data observed, JSAE Congress (Autumn), Sendai, 2019. (in Japanese)
22.
Masaaki Ishihara, Takashi Kodama, Satoshi Yoshimura, Yota Maehara, Kentaro Suzuki, and Jun Tanabe: The utility of actual vehicle trajectory data on expressway, JSAE Congress (Autumn), Sendai, 2019. (in Japanese)
23.
Takashi Kodama, Masaaki Ishihara, Yota Maehara, Norifumi Shinkai, Masakazu Nakanishi, and Jun Tanabe: A study of the method of driving behavior evaluation necessary for the statistical grasp of the occurrence mechanism of traffic events, Proceedings of the 39th Conference of Japan Society of Traffic Engineers, Tokyo, 2019. (in Japanese) https://doi.org/10.14954/jste.6.2_B_37
24.
Takashi Kodama, Masaoki Ishihara, Yota Maehara, Norifumi Shinkai, Masakazu Nakanishi, and Jun Tanabe: Evaluation of trends and evaluation methods on safety associated with lane change on expressway, Proceedings of Infrastructure Planning, Vol. 59, Nagoya, 2019. (in Japanese)
25.
Takashi Kodama, Jun Tanabe, Masaaki Ishihara, Kentaro Suzuki, Norihito Shinkai, Masakazu Nakanishi, Junko Nitta, Toshihisa Takada, Satoru Nakajo, and Masahiro Koibuchi: Examination for application to actual verification of all vehicle trajectory data for virtual verification, Proceedings of the 16th ITS Symposium, Kyoto, 2018. (in Japanese)
26.
Takashi Kodama, Kentaro Suzuki, Jun Tanabe, Satoru Nakajo, and Nobuhiro Uno: Data conversion of actual traffic situation by learning type image-sensing and its application, Proceedings of the 25th ITS World Congress, Copenhagen, 2018.
27.
Takashi Kodama, Hideyuki Suzuki, Hiroyuki Masumoto, Kentaro Suzuki, Masakazu Nakanishi, Yusuke Tago, and Jun Tanabe: Consideration of issues in vehicle trajectory data generation by image sensing using asynchronous cameras, Proceedings of the 38th Conference of Japan Society of Traffic Engineers, Tokyo, 2018. (in Japanese)
28.
Takashi Kodama, Hiroyuki Masumoto, Kentaro Suzuki, Jun Tanabe, Masakazu Nakanishi, Yusuke Tago, Satoshi Nakajo, and Masahiro Koibuchi: Utility study on traffic flow data generated by image sensing, Proceedings of Infrastructure Planning, Vol. 57, Tokyo, 2018. (in Japanese)