Modelling of Driver and Pedestrian Behaviour – A Historical Review
Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.
Brackstone M, McDonald M. Car-following: A historical review. Transportation Research Part F: Traffic Psychology and Behaviour. 1999;2: 181-196. Available from: doi:10.1016/S1369-8478(00)00005-X [Accessed 9th March 2019].
Toledo T. Driving Behaviour: Models and Challenges. Transport Reviews. 2007;27:1: 65-84. Available from: doi:10.1080/01441640600823940 [Accessed 3rd March 2019].
Aghabayk K, Sarvi M, Young W. A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles. Transport Reviews: A Transnational Transdisciplinary Journal. 2015;35(1): 82-105. Available from: doi:10.1080/01441647.2014.997323 [Accessed 2nd March 2019].
Moridpour S, Sarvi M, Rose G. Lane changing models: A critical review. Transportation Letters: The International Journal of Transportation Research. 2010;2: 57-173.
Rahman M, Chowdhury M, Xie Y, He Y. Review of Microscopic Lane-Changing Models and Future Research Opportunities. IEEE Transactions on Intelligent Transportation Systems. 2013;14(4): 1942-1956. Available from: doi:10.1109/TITS.2013.2272074 [Accessed 21th March 2019].
Zheng Z. Recent developments and research needs in modelling lane changing. Transportation Research Part B: Methodological. 2014;60: 16-32. Available from: doi:10.1016/j.trb.2013.11.009 [Accessed 15th May 2020].
Pipes LA. An operational analysis of traffic dynamics. Journal of Applied Physics. 1953;24: 274-281.
Reuschel A. Fahrzeugbewegungen in der Kolonne. Österreichisches Ingenieur-Archiv. 1950;4: 193-215.
Chandler RE, Herman R, Montroll EW. Traffic dynamics: Studies in car following. Operations Research. 1959;6: 165-184.
Herman R, Montroll EW, Potts RB, Rothery RW. Traffic dynamics: Analysis of stability in car following. Operations Research. 1959;7: 86-106.
Gazis DC, Herman R, Potts RB. Car following theory of steady state traffic flow. Operations Research. 1959;7: 499-505.
Gazis DC, Herman R, Rothery RW. Nonlinear follow the leader models of traffic flow. Operations Research. 1961;9: 545-567.
Bevrani K, Chung E, Miska M. Evaluation of the GHR car following model for traffic safety studies. Proceedings of the 25th ARRB Conference, ARRB Group Ltd, Perth, W. A., Australia; 2012. p. 1-11.
Helly W. Simulation of Bottlenecks in Single Lane Traffic Flow. Proceedings of the Symposium on Theory of Traffic Flow. Research Laboratories, General Motors, New York: Elsevier; 1959. p. 207-238.
Xing J. A parameter identification of a car following model. Proceedings of the Second World Congress on ATT. 9-11 Nov. 1995, Yokohama, Japan; 1995. p. 1739-1745.
Newell GF. Instability in dense highway traffic: A review. Proceedings of the Second International Symposium on the Theory of Traffic Flow, London; 1963. p. 73-83.
Bando M, Hasebe K, Nakayama A, Shibata A, Sugiyama Y. Dynamical model of traffic congestion and numerical simulation. Physical Review E. 1995;51(2): 1035-1042.
Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Physical Review. 2000;62(2): 1805-1824. Available from: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.62.1805 [Accessed 15th March 2019].
Treiber M, Kesting M. Traffic Flow Dynamics: Data, Models and Simulation. 1st ed. Springer-Verlag Berlin Heidelberg; 2013.
Ciuffo B, Punzo V, Montanino M. Thirty Years of Gipps' Car-Following Model. Transportation Research Record Journal of the Transportation Research Board. 2012;2315: 89-99. Available from: doi:10.3141/2315-10 [Accessed 25th March 2019].
Kometani E, Sasaki T. Dynamic behaviour of traffic with a nonlinear spacing-speed relationship. Proceedings of the Symposium on Theory of Traffic Flow. Research Laboratories, General Motors, New York: Elsevier; 1959. p. 105-119.
Panwai S, Dia H. Comparative Evaluation of Microscopic Car-Following Behaviour. IEEE Transactions on Intelligent Transportation Systems. 2005;6(3): 314-325. Available from: doi:10.1109/TITS.2005.853705 [Accessed 17th April 2019].
Gipps PG. A behavioural car following model for computer simulation. Transportation Research Part B: Methodological. 1981;15(2): 105-111.
Elefteriadou, L. An Introduction to Traffic Flow Theory. 1st ed. Springer-Verlag Berlin Heidelberg; 2014.
Michaels RM. Perceptual factors in car following. Proceedings of the 2nd International Symposium on the Theory of Road Traffic Flow, London, England. OECD; 1963.
Wiedemann R. Simulation des Strassenverkehrsflusses. Technical report, Institut für Verkehrswesen, Universität Karlsruhe, Karlsruhe, Germany; 1974. German.
Yousif S, Al-Obaedi J. Close following behaviour: Testing visual angle car following models using various sets of data. Transportation Research Part F: Traffic Psychology and Behaviour. 2011;14(2): 96-110.
Saifuzzaman M, Zheng Z. Incorporating human-factors in car-following models: A review of recent developments and research needs. Transportation Research Part C: Emerging Technologies. 2014;48: 379-403. Available from: doi:10.1016/j.trc.2014.09.008 [Accessed 9th April 2020].
Kikuchi C, Chakroborty P. Car following model based on a fuzzy inference system. Transportation Research Record: Journal of the Transportation Research Board. 1992;1365: 82-91.
Gao Q, Hu S, Dong C. The Modelling and Simulation of the Car-following Behaviour Based on Fuzzy Inference. International Workshop on Modelling, Simulation and Optimization, Hong Kong; 2008. p. 322-325. Available from: doi:10.1109/WMSO.2008.48 [Accessed 29th March 2019].
Won J, Lee S, Lee S, Kim T. Establishment of Car Following Theory Based on Fuzzy-Based Sensitivity Parameters. In: Cham T-J, Cai J, Dorai C, Rajan D, Chua T-S, Chia L-T. (eds.) Proceedings of the 13th International Multimedia Modelling Conference, MMM 2007, 9-12 January, 2007, Singapore. Lecture Notes in Computer Science, vol. 4352. Springer, Berlin, Heidelberg; 2007.
Zheng P, McDonald M. Application of fuzzy systems in the car-following behaviour analysis. Proceedings of the 2nd International Conference on Fuzzy Systems and Knowledge Discovery, Changsha, China. Vol. 3613; 2005. p. 782-791.
Hao H, Ma W, Xu H. A fuzzy logic-based multi-agent car-following model. Transportation Research Part C: Emerging Technologies. 2016;69: 477-496. Available from: doi:10.1016/j.trc.2015.09.014. [Accessed 5th May 2020].
Bennajeh A, Bechikh S, Ben Said L, Aknine S. A Fuzzy Logic-Based Anticipation Car-Following Model. In: Nguyen NT, Kowalczyk R. (eds.) Transactions on Computational Collective Intelligence XXX. Springer International Publishing; 2018. p. 200-222.
Cubranic-Dobrodolac M, Svadlenka L, Cicevic S, Dobrodolac M. Modelling driver propensity for traffic accidents: A comparison of multiple regression analysis and fuzzy approach. International Journal of Injury Control and Safety Promotion. 2020;27(2): 156-167. Available from: doi:10.1080/17457300.2019.1690002 [Accessed 17th April 2020].
Pomerleau DA. ALVINN: An autonomous land vehicle in a neural network. In: Touretzky DS. (ed.) Advances in Neural Information Processing Systems 1. San Mateo, CA: Morgan Kaufmann; 1989. p. 305-313.
Pomerleau DA. Progress in neural network-based vision for autonomous robot driving. Proceedings of the Intelligent Vehicle Symposium, Detroit, MI. 1992. p. 391-396.
Fix E, Armstrong HG. Modelling human performance with neural networks. Proceedings of the International Joint Conference on Neural Networks, San Diego, CA; 1990. p. 247-252.
Dougherty MS, Kirby HR, Boyle RD. The use of neural networks to recognise and predict traffic congestion. Traffic Engineering and Control. 1993;34(6): 311-314.
Dougherty M. A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies.1995;3(4): 247-260.
Panwai S, Dia H. Neural agent car-following models. IEEE Transactions on Intelligent Transportation Systems. 2007;8(1): 60-70.
Khodayari A, Ghaffari A, Kazemi R, Braunstingl R. A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans. 2012;42(6): 1440-1449. Available from: doi:10.1109/TSMCA.2012.2192262 [Accessed 1st June 2020].
Zhou M, Qu X, Li X. A recurrent neural network based microscopic car following model to predict traffic oscillation. Transportation Research Part C: Emerging Technologies. 2017;84: 245-264. Available from: doi:10.1016/j.trc.2017.08.027 [Accessed 9st June 2020].
Hua C. A new car-following model considering recurrent neural network. International Journal of Modern Physics B. 2019:33(26). Available from: doi:10.1142/S0217979219503041 [Accessed 4th May 2020].
Khodayari A, Ghaffari A, Kazemi R, Braunstingl R. Modify Car Following Model by Human Effects Based on Locally Linear Neuro Fuzzy. IEEE Intelligent Vehicles Symposium (IV), 5-9 June 2011, Baden-Baden, Germany. 2011. p. 661-666. Available from: doi:10.1109/IVS.2011.5940465
Zarringhalam R, Ghaffari A. Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems. SAE International Journal of Transportation Safety. 2013:1(1): 58-63. Available from: doi:10.4271/2013-01-0623 [Accessed 7th June 2020].
Wang J, Zhang L, Lu S, Wang Z. Developing a car-following model with consideration of driver’s behaviour based on an Adaptive Neuro-Fuzzy Inference System. Journal of Intelligent & Fuzzy Systems. 2015:30(1): 461-466. Available from: doi:10.3233/IFS-151770 [Accessed 10th June 2020].
Zheng F, Liu C, Liu X, Jabari SE, Lu L. Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow. Transportation Research Part C: Emerging Technologies. 2020;112: 203-219. Available from: doi:10.1016/j.trc.2020.01.017 [Accessed 18th June 2020] .
Li T, Guo F, Krishnan R, Sivakumar A, Polak J. Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles. Transportation Research Part C: Emerging Technologies. 2020;115: 102630. Available from: doi:10.1016/j.trc.2020.102630 [Accessed 13th June 2020].
Zeidler V, Buck HS, Kautzsch L, Vortisch P, Weyland CM. Simulation of autonomous vehicles based on Wiedemann’s car following model in PTV Vissim. Transportation Research Board 98th Annual Meeting, 13-17 January 2019, Washington DC, United States; 2019.
Mar J, Lin HT. The Car-following model and Lane-changing Collision Prevention System Based on the Cascaded Fuzzy Inference System. IEEE Transactions on Intelligent Transportation Systems. 2005;54(3): 910-924. Available from: doi:10.1109/TVT.2005.844655 [Accessed 21th March 2019].
Michon AJ. A critical view of driver behaviour models: What do we know, what should we do?. In: Evans L, Schwing RC. (eds) Human Behaviour and Traffic Safety. Springer, Boston, MA; 1985. p. 485-520.
Schlenoff C, Madhavan R, Kootbally Z. PRIDE: A hierarchical, integrated prediction framework for autonomous on-road driving. Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, Orlando, FL; 2006, p. 2348-2353. Available from: doi:10.1109/ROBOT.2006.1642053 [Accessed 28th March 2019].
Webster NA, Suzuki T, Chung E, Kuwahara M. Tactical Driver Lane Change Model Using Forward Search. Proceedings of the 86th Transportation Research Board Annual Meeting, Washington, DC; 2007. p. 1-22.
Gipps PG. A Model for the Structure of Lane changing Decisions. Transportation Research Part B: Methodological. 1986;20(5): 403-414.
Wiedemann R, Reiter U. Microscopic Traffic Simulation: the Simulation System Mission, Background and Actual State. CEC Project ICARUS (V1052), Final Report, vol. 2, Appendix A. Brussels: CEC; 1992.
Hidas P. Modelling Lane Changing and Merging in Microscopic Traffic Simulation. Transportation Research Part C: Emerging Technologies. 2002;10(5-6): 351-371.
Ahmed KI, Ben-Akiva M, Koutsopoulos HN, Mishalani RG. Models of Freeway Lane Changing and Gap Acceptance Behaviour. Proceedings of the 13th International Symposium on the Theory of Traffic Flow and Transportation, 24-26 July1996, Lyon, France; 1996. p. 501-515.
Toledo T. Integrated driving behaviour modelling. PhD thesis. Massachusetts Institute of Technology; 2002.
Das S, Bowles BA, Houghland CR, Hunn SJ, Zhang Y. Microscopic simulations of freeway traffic flow. Proceedings of the 32nd Annual Simulation Symposium, San Diego, CA, USA; 1999. p. 79-84. Available from: doi:10.1109/SIMSYM.1999.766457 [Accessed 3rd April 2019].
Moridpour S, Sarvi M, Rose G, Mazloumi E. Lane-changing decision model for heavy vehicle drivers. Journal of Intelligent Transportation Systems. 2012;16(1): 24-35. Available from: doi:10.1080/15472450.2012.639640 [Accessed 13th June 2020].
Hou Y, Edara P, Sun C. A genetic fuzzy system for modelling mandatory lane changing. 2012 15th International IEEE Conference on Intelligent Transportation Systems, 16-19 September 2012, Anchorage, AK, United States. 2012.
Balal E, Cheu RL, Sarkodie-Gyan T. A binary decision model for discretionary lane changing move based on fuzzy inference system. Transportation Research Part C: Emerging Technologies. 2016;67: 47-61. Available from: doi:10.1016/j.trc.2016.02.009 [Accessed 21th June 2020].
Hunt JG, Lyons GD. Modelling dual carriageway lane-changing using neural networks. Transportation Research Part C: Emerging Technologies. 1994;2(4): 231-245.
Dumbuya A, Booth A, Reed N, Kirkham A, Philpott T, Zhao J, Wood R. Complexity of traffic interactions: Improving behavioural intelligence in driving simulation scenarios. In: Bertelle C, Duchamp GH, Kadri-Dahmani H. (eds) Complex Systems and Selforganization Modelling. Understanding Complex Systems. New York, NY, USA: Springer-Verlag; 2009. p. 201-209.
Ding C, Wang W, Wang X, Baumann M. A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow. Mathematical Problems in Engineering; 2013. Available from: doi:10.1155/2013/967358 [Accessed 11th June 2020].
Gao J, Murphey YL, Zhu H. Multivariate time series prediction of lane changing behaviour using deep neural network. Applied Intelligence. 2018;48: 3523-3537. Available from: doi:10.1007/s10489-018-1163-9 [Accessed 16th June 2020].
Tang J, Yu S, Liu F, Chen X, Huang H. A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network. Expert Systems with Applications. 2019;130: 265-275. Available from: doi:10.1016/j.eswa.2019.04.032 [Accessed 17th June 2020].
Zhang X, Sun J, Qi X, Sun J. Simultaneous modelling of car-following and lane-changing behaviours using deep learning. Transportation Research Part C: Emerging Technologies. 2019;104: 287-304. Available from: doi:10.1016/j.trc.2019.05.021 [Accessed 21th June 2020].
Kita H. A merging-giveway interaction model of cars in a merging section: A game theoretic analysis. Transportation Research Part A: Policy and Practice. 1999;33(3-4): 305-312.
Wang M, Hoogendoorn SP, Daamen W, van Arem B, Happee R. Game theoretic approach for predictive lane-changing and car-following control. Transportation Research Part C: Emerging Technologies. 2015;58(part A): 73-92. Available from: doi:10.1016/j.trc.2015.07.009 [Accessed 23th May 2020].
Ali Y, Zheng Z, Haque M, Wang M. A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment. Transportation Research Part C: Emerging Technologies. 2019;106: 220-242. Available from: doi:10.1016/j.trc.2019.07.011 [Accessed 26th April 2020].
Ji A, Levinson D. A review of game theory models of lane changing. Transportmetrica A: Transport Science. 2020;16(3): 1628-1647. Available from: doi:10.1080/23249935.2020.1770368 [Accessed 28th May 2020].
Liu M, Shi J. A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model. Journal of Intelligent Transportation Systems. 2018;23(4): 309-318.
Choi S, Yeo H. Framework for simulation-based lane change control for autonomous vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV) 11-14 June 2017, Los Angeles, CA, USA.
Cao P, Hu Y, Miwa T, Wakita Y, Morikawa T, Liu X. An optimal mandatory lane change decision model for autonomous vehicles in urban arterials. Journal of Intelligent Transportation Systems. 2017;21(4): 271-284. Available from: doi:10.1080/15472450.2017.1315805 [Accessed 22th May 2020].
Rahman S, Abdel-Aty M, Lee J, Rahman H. Safety benefits of arterials’ crash risk under connected and automated vehicles. Transportation Research Part C: Emerging Technologies. 2019;100: 354-371. Available from: doi:10.1016/j.trc.2019.01.029 [Accessed 11th April 2020].
Hu X, Sun J. Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area. Transportation Research Part C: Emerging Technologies. 2019;101: 111-125 Available from: doi:10.1016/j.trc.2019.01.029 [Accessed 16th May 2020].
Vechione M, Balal E, Long Cheu R. Comparisons of mandatory and discretionary lane changing behaviour on freeways. International Journal of Transportation Science and Technology. 2018;7(2): 124-136. Available from: doi:10.1016/j.ijtst.2018.02.002 [Accessed 17th April 2020].
Teknomo K. Microscopic Pedestrian Flow Characteristics: Development of an Image Processing Dana Collection and Simulation Model. PhD Thesis. Tohoku University; 2002.
Blue V, Adler J. Emergent Fundamental Pedestrian Flows From Cellular Automata Microsimulation. Transportation Research Record. 1998;1644: 29-36. Available from: doi:10.3141/1644-04. [Accessed 6th May 2019].
Gipps P, Marksjö B. A micro-simulation model for pedestrian flows. Mathematics and Computers in Simulation. 1985;27: 95-105. Available from: doi:10.1016/0378-4754(85)90027-8. [Accessed 8th April 2019].
Xu W, Liu L, Zlatanova S, Pernard W, Xiong Q. A pedestrian tracking algorithm using grid-based indoor model. Automation in Construction. 2018;92: 173-187. Available from: doi:10.1016/j.autcon.2018.03.031 [Accessed 23th April 2020].
Okazaki S, Matsushita S. A Study of Simulation Model for Pedestrian Movement with Evacuation and Queuing. Proceedings of the International Conference on Engineering for Crowd Safety, 1993, London, England. p. 271-280.
Helbing D, Molnar P. Social Force Model for pedestrian dynamics. Physical Review E. 1995;51: 4282-4286.
Wang P. Understanding social-force model in psychological principles of collective behaviours. To be published in Physics and Society. [Preprint] 2016. Available from: https://arxiv.org/abs/1605.05146. [Accessed: 12th April 2019].
Antonini G, Bierlaire M, Weber M. Discrete choice models of pedestrian walking behaviour. Transportation Research Part B: Methodological. 2006;40(8): 667-687. Available from: doi:10.1016/j.trb.2005.09.006 [Accessed 20th April 2020].
Lovas, GG. Modelling And Simulation Of Pedestrian Traffic Flow. Transportation Research Part B: Methodological. 1994;28(6): 429-443.
Ossen S, Hoogendoorn SP. Heterogeneity in car-following behaviour: Theory and empirics. Transportation Research Part C: Emerging Technologies. 2011;19(2): 182-195.
Sarvi M. Heavy commercial vehicles following behaviour and interactions with different vehicle classes. Journal of Advanced Transportation. 2013;47: 572-580. Available from: doi:10.1002/atr.182 [Accessed 25th April 2019].
Aghabayk K, Sarvi M, Young W. Attribute selection for modelling driver’s car-following behaviour in heterogeneous congested traffic conditions. Transportmetrica A: Transport Science. 2014;10(5): 457-468.
Lu Z, Fu T, Fu L, Shiravi S, Jiang C. A video-based approach to calibrating car-following parameters in VISSIM for urban traffic. International Journal of Transportation Science and Technology. 2016:5(1): 1-9. Available from: doi:10.1016/j.ijtst.2016.06.001 [Accessed 16th April 2019].
Brockfeld E, Kühne RD, Wagner P. Calibration and validation of microscopic traffic flow models. Transportation Research Record Journal of the Transportation Research Board. 2004;1876: 62-70.
St-Aubin P, Saunier N, Miranda-Moreno L. Large-scale automated proactive road safety analysis using video data. Transportation Research Part C: Emerging Technologies. 2015;58: 363-379.
Durrani U, Lee C. Calibration and Validation of Psychophysical Car-Following Model Using Driver’s Action Points and Perception Thresholds. Journal of Transportation Engineering, Part A: Systems. 2019;145(9). Available from: doi:10.1061/JTEPBS.0000264 [Accessed 23th April 2020].
Menneni S, Sun C, Vortisch P. Microsimulation Calibration Using Speed-Flow Relationship. Transportation Research Record: Journal of the Transportation Research Board. 2008;2088: 1-9. Available from: doi:10.3141/2088-01
Sharma H, Swami B. MOE-analysis for oversaturated flow with interrupted facility and heterogeneous traffic for urban roads. International Journal of Transportation Science and Technology. 2012;1(3): 287-296.
Ištoka Otković I, Tollazzi T, Šraml M. Calibration of microsimulation traffic model using neural network approach. Expert Systems with Applications. 2013;40: 5965-5974. Available from: doi:10.1016/j.eswa.2013.05.003 [Accessed 20th April 2019].
Hollander Y, Liu R. The principles of calibrating traffic microsimulation models. Transportation. 2008;35(3): 347-362. Available from: doi:10.1007/s11116-007-9156-2 [Accessed 20th December 2019].
Feliciani C, Gorrini A, Crociani L, Vizzari G, Nishinari K, Bandini S. Calibration and validation of a simulation model for predicting pedestrian fatalities at unsignalized crosswalks by means of statistical traffic data. Journal of Traffic and Transportation Engineering (English Edition). 2020;7(1): 1-18. Available from: doi:10.1016/j.jtte.2019.01.004 [Accessed 29th April 2020].
United States Department of Transportation Federal Highway Administration. Surrogate Safety Assessment Model and Validation: Final Report. Available from: https://www.fhwa.dot.gov/publications/research/safety/08051/index.cfm [Accessed 18th May 2020].
Copyright (c) 2020 Karlo Babojelić, Luka Novacko
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).