Lane Formation Beyond Intuition Towards an Automated Characterization of Lanes in Counter-flows

Luca Crociani, Giuseppe Vizzari, Andrea Gorrini, Stefania Bandini


Pedestrian behavioural dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between unidirectional and bidirectional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. The collective behaviour of lane formation emerges in condition of variable density and due to a self-organisation dynamic, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-) automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through an inter-rater agreement test between the results achieved by a human coder and by the algorithm.


pedestrian dynamics; lane formation; analysis; clustering

Full Text:



S. J. Older, “Movement of Pedestrians on Footways in Shopping Streets”, Traffic Eng. Control, 1968.

J. J. Fruin, Pedestrian Planning and Design. New York, Metropolitan Association of Urban Designers

M. Boltes and A. Seyfried, “Collecting pedestrian trajectories”, Neurocomputing, vol. 100, 2013, pp.


F. Solera, S. Calderara, and R. Cucchiara, “Socially Constrained Structural Learning for Groups

Detection in Crowd”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 5, 2016 pp. 995–1008.

S. D. Khan, S. Bandini, S. Basalamah, and G. Vizzari, “Analyzing crowd behavior in naturalistic

conditions: Identifying sources and sinks and characterizing main flows”, Neurocomputing, vol.

, pp. 543–563, 2016.

J. Zhang, W. Klingsch, A. Schadschneider, and A. Seyfried, “Ordering in bidirectional pedestrian

flows and its influence on the fundamental diagram”, J. Stat. Mech. Theory Exp., no. 2, 2012, p. 9.

A. Schadschneider, W. Klingsch, H. Klüpfel, T. Kretz, C. Rogsch, and A. Seyfried, “Evacuation

Dynamics: Empirical Results, Modeling and Applications”, in Encyclopedia of Complexity and

Systems Science, New York, NY: Springer New York, 2009, pp. 3142–3176.

J. Dzubiella, G. P. Hoffmann, and H. Löwen, “Lane formation in colloidal mixtures driven by an

external field”, Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., vol. 65, no.

, 2002.

C. Feliciani and K. Nishinari, “Empirical analysis of the lane formation process in bidirectional

pedestrian flow”, Phys. Rev. E, vol. 94, no. 3, 2016.

S. Hoogendoorn and W. Daamen, “Self-Organization in Pedestrian Flow”, in Fifth International

Conference on Traffic and Granular Flow, 2003, pp. 373–382.

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters

in Large Spatial Databases with Noise”, in Proceedings of the Second International Conference on

Knowledge Discovery and Data Mining, 1996, pp. 226–231.

A. Gorrini, L. Crociani, C. Feliciani, P. Zhao, K. Nishinari, and S. Bandini, “Social groups and

pedestrian crowds: experiment on dyads in a counter flow scenario”, in 8th International

Conference on Pedestrian and Evacuation Dynamics (PED2016), 2016, pp. 179-184. Also

available as arXiv Prepr. arXiv1610.08325.

J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data”,

Biometrics, 1977, pp. 159-174.

D. Helbing, P. Molnár, I. J. Farkas and K. Bolay, “Self-organizing pedestrian movement”,

Environment and planning B: planning and design, 28(3), 2001, 361-383.

A. R. Zamir, A. Dehghan and M. Sha, “GMCP-tracker: Global multi-object tracking using

generalized minimum clique graphs”, in Computer Vision–ECCV, 2012, pp. 343-356.

L. Crociani, A. Gorrini, C. Feliciani, G. Vizzari, K. Nishinari, S. Bandini, “Micro and macro

pedestrian dynamics in counterflow: the impact of social groups”, in 12th International

Conference on Traffic and Granular Flow - TGF 2017, in press. Also available as arXiv preprint



Copyright (c) 2020 Luca Crociani, Giuseppe Vizzari, Andrea Gorrini, Stefania Bandini

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.