|
|
Pedestrian lane formation with following-overtaking model and measurement of system order |
Bi-Lu Li(李碧璐)1,2, Zheng Li(李政)1,2, Rui Zhou(周睿)1,2,†, and Shi-Fei Shen(申世飞)1,2 |
1 Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 2 School of Safety Science, Tsinghua University, Beijing 100084, China |
|
|
Abstract Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design. Lane formation, a typical self-organizing phenomenon, helps pedestrian system to become more orderly, the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world. In this study, a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed, and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed. Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation. A high tendency of following results in good lane formation. Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease. The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70% of his own. The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model. The presence of a small obstacle does not obstruct the walking of pedestrians; in contrast, it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.
|
Received: 15 September 2023
Revised: 19 October 2023
Accepted manuscript online: 06 November 2023
|
PACS:
|
05.45.Pq
|
(Numerical simulations of chaotic systems)
|
|
89.40.-a
|
(Transportation)
|
|
89.70.Cf
|
(Entropy and other measures of information)
|
|
05.65.+b
|
(Self-organized systems)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 71603146). |
Corresponding Authors:
Rui Zhou
E-mail: zhour@tsinghua.edu.cn
|
Cite this article:
Bi-Lu Li(李碧璐), Zheng Li(李政), Rui Zhou(周睿), and Shi-Fei Shen(申世飞) Pedestrian lane formation with following-overtaking model and measurement of system order 2024 Chin. Phys. B 33 020505
|
[1] Song W, Xu X, Wang B H and Ni S 2006 Physica A 363 492 [2] Kneidl A, Hartmann D and Borrmann A 2013 Transportation Research Part C: Emerging Technologies 37 223 [3] Shiwakoti N, Sarvi M, Rose G and Burd M 2010 Transportation Research Record 2196 176 [4] Lu L, Chan C Y, Wang J and Wang W 2017 Transportation Research Part C: Emerging Technologies 81 317 [5] Guo R Y 2018 Transportation Research Part C: Emerging Technologies 91 263 [6] Chen H, Zhang X, Yang W and Lin Y 2023 Transportmetrica B: Transport Dynamics 11 548 [7] Gupta A, Johnson J, Fei-Fei L, Savarese S and Alahi A 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, p. 2255 [8] Fang L, Jiang Q, Shi J and Zhou B 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA, p. 6797 [9] Dong B, Liu H, Bai Y, Lin J, Xu Z, Xu X and Kong Q 2021 arXiv: 2103.16273 [cs.CV] [10] Li J, Ma H and Tomizuka M 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 03-08, Macau, China, p. 6150 [11] Mohamed A, Qian K, Elhoseiny M and Claudel C 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA, p. 14424 [12] Gu T, Chen G, Li J, Lin C, Rao Y, Zhou J and Lu J 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-24, 2022, Orleans, LA, USA, p. 17113 [13] Makmul J 2016 "Microscopic and macroscopic models for pedestrian crowds", Ph. D. Dissertation (Universität Mannheim) [14] Duives D C, Daamen W and Hoogendoorn S P 2013 Transportation Research Part C: Emerging Technologies 37 193 [15] Kormanová A 2013 Acta Informatica Pragensia 2 39 [16] Yuan Y, Goñi-Ros B, Bui H H, Daamen W, Vu H L and Hoogendoorn S P 2020 Transportation Research Part C: Emerging Technologies 111 334 [17] Zeng W, Chen P, Yu G and Wang Y 2017 Transportation Research Part C: Emerging Technologies 80 37 [18] Helbing D 1992 Complex Syst. 6 391 [19] Okazaki S 1993 Journal of Architecture Planning and Environmental Engineering 432 [20] Guo R Y and Huang H J 2008 Physica A 387 580 [21] Blue V J and Adler J L 2001 Transportation Research Part B: Methodological 35 293 [22] Asano M, Iryo T and Kuwahara M 2010 Transportation Research Part C: Emerging Technologies 18 842 [23] Bazzani A, Giorgini B, Rambaldi S, Gallotti R and Giovannini L 2010 J. Stat. Mech. 2010 823 [24] Guo W, Wang X and Zheng X 2015 Physica A 432 87 [25] Zhang D, Zhu H, Hostikka S and Qiu S 2019 Physica A: 525 72 [26] Rio K and Warren W 2014 Pedestrian and Evacuation Dynamics 2012, June 6-8, 2012, Germany, p. 561 [27] Gazis D C, Herman R and Rothery R W 1961 Operations Research 9 545 [28] Zeng W, Chen P, Nakamura H and Iryo-Asano M 2014 Transportation Research Part C: Emerging Technologies 40 143 [29] Helbing D, Farkas I and Vicsek T 2000 Nature 407 487 [30] Yuan Z, Jia H, Liao M, Zhang L, Feng Y and Tian G 2017 Frontiers of Information Technology & Electronic Engineering 18 1142 [31] Lee J, Kim T, Chung J H and Kim J 2016 KSCE Journal of Civil Engineering 20 1099 [32] Luo L, Liu X, Fu Z, Ma J and Liu F 2020 Physica A 550 124149 [33] Yuen J K K and Lee E W M 2012 Safety Science 50 1704 [34] Zhang D, Zhu H, Du L and Hostikka S 2018 Phys. Lett. A 382 3172 [35] Chen X, Treiber M, Kanagaraj V and Li H 2017 Transport Reviews 38 625 [36] Saberi M, Aghabayk K and Sobhani A 2015 Physica A 434 120 [37] Zhang J, Klingsch W, Schadschneider A and Seyfried A 2012 J. Stat. Mech. 2012 P02002 [38] Helbing D and Vicsek T 1999 New J. Phys. 1 13 [39] Cirillo E N and Muntean A 2020 arXiv: 2002.06548 [nlin.CG] [40] Nowak S and Schadschneider A 2012 Phys. Rev. E 85 066128 [41] Rex M and Löwen H 2007 Phys. Rev. E 75 051402 [42] Sethna J 2006 Statistical mechanics: entropy, order parameters, and complexity (New York: Oxford University Press, USA) p. 87 [43] Zeng Y, Ye R, Song W, Luo S, Meng F and Vizzari G 2021 Physica A 566 125655 [44] Sharif M H and Djeraba C 2012 Pattern Recognition 45 2543 [45] Helbing D and Molnár P 1995 Phys. Rev. E 51 4282 [46] Yang L, Li J and Liu S 2008 Physica A 387 3281 [47] Chu J C, Chen A Y and Lin Y F 2017 Transportation Research Part C-emerging Technologies 85 664 [48] Bandini S, Mondini M and Vizzari G 2014 Transportation Research Part C: Emerging Technologies 40 251 [49] Müller M, Charypar D and Gross M 2003 Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation July 26-27, 2003, San Diego, California, p. 154 [50] Seyfried A, Steffen B, Klingsch W and Boltes M 2005 J. Stat. Mech. 2005 10002 [51] Miao Z H and Zhihui L 2016 Journal of System Simulation 28 9 [52] Wang W L, Lo S M, Liu S B and Kuang H 2014 Transportation Research Part C: Emerging Technologies 44 21 [53] Jin C J, Jiang R, Wong S C, Xie S, Li D, Guo N and Wang W 2019 Transportation Research Part C: Emerging Technologies 109 137 [54] Wang J, Zhang L, Shi Q, Yang P and Hu X 2015 Physica A 428 396 [55] Von Krüchten C and Schadschneider A 2017 Physica A 475 129 [56] Weidmann U 1992 Transporttechnik der Fussgänger (Institut für Verkehrsplanung, Transporttechnik, Strassen- und Eisenbahnbau (IVT), ETH Zürich) p. 45 [57] Cao S, Seyfried A, Zhang J, Holl S and Song W 2017 J. Stat. Mech. 2017 033404 [58] Haghani M and Sarvi M 2019 Phys. Lett. A 383 110 |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|