References

Patents

TAKASHIMA Yuta, YATAKA Ryoma, MORIMOTO Takuji, UNO Tomoki, UMEDA Taku; Mitsubishi Electric Corporation: Vehicle behavior prediction apparatus and vehicle behavior prediction method, JP, P2024-112373A, 2024/08/21. https://www.j-platpat.inpit.go.jp/c1801/PU/JP-2024-112373/11/en

In press

Masahiko Manabe, Takeshi Ando, and Mie Mikami: Big data for road traffic innovation: Masaaki Ishihara (Hanshin Expressway), Nikkei Construction, Vol. 745, pp. 44-45, 2020. (in Japanese) https://xtech.nikkei.com/atcl/nxt/mag/ncr/18/00108/100100006/

Exhibitions

PTV Group Japan Ltd.: Automotive Engineering Exposition 2023 Yokohama, Yokohama, May 24-26, 2023.
Automotive Engineering Exposition 2019 Yokohama, Yokohama, May 22-24, 2019.
Highway Technology Fair 2018, Tokyo, November 28-29, 2018.

1.: Temma Uchikoshi and Wataru Nakanishi: Time series clustering of lane change behavior by using complete vehicle trajectories, Research Paper, JSTE Journal of Traffic Engineering, Vol. 10, No. 1, pp. A_173-A_182, 2024. (in Japanese) https://doi.org/10.14954/jste.10.1_A_173
2.: Yoshiaki Irie, Masahiko Sano, Hiroaki Matsunaga, Daisuke Akasaka, and Mototsugu Miura: Study of dynamic traffic management based on automated driving/ADAS with connected system, International Journal of Automotive Engineering, Vol. 15, No. 2, pp. 82-89, 2024. https://doi.org/10.20485/jsaeijae.15.2_82
3.: Wataru Nakanishi: A consideration on sample size and model selection: An example of location dependent fundamental diagram, Proceedings of Infrastructure Planning, Tokyo, Vol. 68, 2023. (in Japanese)
4.: Toru Seo: The actual traffic flows, Macroscopic Traffic Simulation: Fundamental Mathematical Theory and Python Implementation, Chap. 2.6, Corona Publishing Co. Ltd., Tokyo, pp. 26-29, 2023. (in Japanese)
5.: Temma Uchikoshi and Wataru Nakanishi: Time series clustering of lane change behavior by using complete vehicle trajectories, Proceedings of the 43rd Conference of Japan Society of Traffic Engineers, Tokyo, pp. 587-594, 2023. (in Japanese) https://doi.org/10.14954/jsteproceeding.43.0_587
6.: Koki Oda, Toru Seo, and Wataru Nakanishi: Estimation of location-dependent fundamental diagram based on connected vehicle data and sparse modeling, Proceedings of the 43rd Conference of Japan Society of Traffic Engineers, Tokyo, pp. 513-520, 2023. (in Japanese)
7.: Masahiro Kinoshita and Yasuhiro Shiomi: Data-driven modeling of car-following behavior on freeways considering spatio-time effects: A comparison of different neural network structures, International Journal of Intelligent Transportation Systems Research, Vol. 21, pp. 86-98, 2023. https://doi.org/10.1007/s13177-022-00339-9
8.: Makoto Kasai, Hironobu Hasegawa, and Makoto Inoue: Discussion on spatial heterogeneity of reaction-delay in car-following behavior using Zen Traffic Data, Research Paper, JSTE Journal of Traffic Engineering, Vol. 9, No. 2, pp. A_357-A_366, 2023. (in Japanese) https://doi.org/10.14954/jste.9.2_A_357
9.: Yasuhiro Shiomi, Guopeng Li, and Victor L. Knoop: Analysis of stochasticity and heterogeneity of car-following behavior based on data-driven modeling, Transportation Research Board 102nd Annual Meeting, Washington D.C., U.S.A., 2023.
10.: Yuya Hattori, Yasuhiro Shiomi, Masahiko Sano, and Hiroaki Matsunaga: Evaluation of a dynamic lane assignment on urban freeways by using a microscopic traffic flow simulation, Transportation Research Board 102nd Annual Meeting, Washington D.C., U.S.A., 2023.
11.: Jia He, Jie Qu, Jian Zhan, and Zhengbing He: The impact of a single discretionary lane change on surrounding traffic: An analytic investigation, IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 1, pp. 554-563, 2022. https://doi.org/10.1109/TITS.2022.3209668
12.: Garima Dahiya： Fundamental Diagrams and Traffic State Estimation Methods: Analysis and Modeling Using Zen Traffic Data，Doctoral Thesis, Tokyo Institute of Technology, 2022. https://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?lv=en&q_publication_content_number=CTT100881193
13.: Makoto Kasai, Hironobu Hasegawa, and Makoto Inoue: Discussion on spatial heterogeneity of time-delay in car-following behavior, Research Paper, Proceedings of the 42nd Conference of Japan Society of Traffic Engineers, Tokyo, pp. 443-448, 2022. (in Japanese)
14.: Yoshiaki Irie and Daisuke Akasaka: The study of connected system specification for traffic flow control with effective lane utilization, Research Paper, International Journal of Automotive Engineering, Vol. 13, No. 3, pp. 132-138, 2022. https://doi.org/10.20485/jsaeijae.13.3_132
15.: Kaito Tamai, Nobuhiro Uno: Estimation of individual car-following model and analysis of vehicle behavior diversity using continuous vehicle trajectory data, Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol. 77, No. 5, pp. I_675-I_685, 2022. (in Japanese) https://doi.org/10.2208/jscejipm.77.5_I_675<
16.: Wataru Nakanishi: Sparse modelling for location dependent fundamental diagram with complete vehicle trajectory dataset, Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol. 78, No. 1, pp. 24-33, 2022. (in Japanese) https://doi.org/10.2208/jscejipm.78.1_24
17.: Garima Dahiya, Yasuo Asakura, Wataru Nakanishi: Analysis of the single-regime speed-density fundamental relationships for varying spatiotemporal resolution using Zen Traffic Data, Asian Transport Studies, Vol. 8, 100066, 2022. https://doi.org/10.1016/j.eastsj.2022.100066
18.: Yuya Hattori, Yasuhiro Shiomi: On the relationship between lane flow ratio and travel time on the merging section of an urban freeway, JSTE Journal of Traffic Engineering, Vol. 8, No. 2, pp. A_159-A_168, 2022. (in Japanese) https://doi.org/10.14954/jste.8.2_A_159
19.: Hayato Nakagami, Nobuhiro Uno: Study on speed reduction on urban expressways by continuous trajectories, Research Paper, JSTE Journal of Traffic Engineering, Vol. 8, No. 2, pp. A_300-A_309, 2022. (in Japanese) https://doi.org/10.14954/jste.8.2_A_300
20.: Makoto Kasai and Hironobu Hasegawa: An application of chaotic time-series analysis to car-following data at capacity bottleneck, JSTE Journal of Traffic Engineering, Vol. 8, No. 2, pp. A_131-A_140, 2022. (in Japanese)
21.: Jaya Varshini Kala, Azusa Toriumi, and Takashi Oguchi: Modelling motorway gap distribution for evaluating merging opportunity of connected-and-automated-vehicles from dedicated lanes, the 19th ITS Symposium 2021, Web conference, 2021.
22.: Masahiko Sano, Hiroaki Matsunaga, Yoshiaki Irie, Satoshi Takamoto, Yohsuke Hashimoto, and Yasuhiro Shiomi: Study of the impact of ACC with considering stability of car-following behavior on urban freeway, Proceedings of Infrastructure Planning, Vol. 64, Web conference, 2021. (in Japanese)
23.: Makoto Kasai and Hironobu Hasegawa: Spatial-temporal distribution of Lyapunov exponent estimated with chaotic time-series analysis in car-following behavior at sag, Proceedings of Infrastructure Planning, Web conference, Vol. 64, 2021. (in Japanese)
24.: Takashi Kodama, Yoann Pencreach, Yuuto Tokunaga, and Jun Tanabe: Examination of mutual accident risk evaluation method using the vehicle trajectory data observed, Technical paper, Transactions of Society of Automotive Engineers of Japan, Vol. 52, No. 5, pp. 1149-1154, 2021. (in Japanese) https://doi.org/10.11351/jsaeronbun.52.1149
25.: Garima Dahiya and Yasuo Asakura: Exploring the performance of streaming-data-driven traffic state estimation method using complete trajectory data,International Journal of Intelligent Transportation Systems Research，Vol. 19, pp. 572-586, 2021.https://doi.org/10.1007/s13177-021-00263-4
26.: 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)
27.: Yuya Hattori and Yasuhiro Shiomi: On the relationship between lane flow ratio and travel time on the merging section of an urban freeway, Proceedings of the 41st Conference of Japan Society of Traffic Engineers, Web conference, pp. 487-494, 2021. (in Japanese)
28.: 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.
29.: 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
30.: Kotaro Yoshida, Wataru Nakanishi and Yasuo Asakura: Analysis of car-following behavior in different time zones by estimating the parameters of Newell's model, Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol. 76, No. 5, pp. I_1253-I_1261, 2021.(in Japanese) https://doi.org/10.2208/jscejipm.76.5_I_1253
31.: 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
32.: 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.
33.: 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.
34.: 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)
35.: 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)
36.: 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
37.: 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
38.: 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)
39.: 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)
40.: 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)
41.: 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)
42: Yasuhiro Shiomi: Deep learning based car following model, Proceedings of Infrastructure Planning, Vol. 61, Web conference, 2020. (in Japanese)
43.: 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
44.: 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)
45.: 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.(in Japanese) https://doi.org/10.11351/jsaeronbun.51.155
46.: 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)
47.: 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.
48.: 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）
49.: 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）
50.: 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 39^th Conference of Japan Society of Traffic Engineers, Tokyo, 2019. (in Japanese) https://doi.org/10.14954/jste.6.2_B_37
51.: Takashi Kodama, Masaaki 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)
52.: 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 16^th ITS Symposium, Kyoto, 2018. (in Japanese)
53.: 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 25^th ITS World Congress, Copenhagen, 2018.
54.: 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 38^th Conference of Japan Society of Traffic Engineers, Tokyo, 2018. (in Japanese)
55.: 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)