Modeling Motorcyclist–Pedestrian Near Misses: A Multiagent Adversarial Inverse Reinforcement Learning Approach
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 6
Abstract
Several studies have used surrogate safety measures obtained from microsimulation packages, such as VISSIM, for safety assessments. However, this approach has shortcomings: (1) microsimulation models are developed considering specific rules that tend to avoid collisions; and (2) existing models do not realistically model road users’ behavior and collision avoidance strategies. Moreover, the majority of these models rely on the single-agent modeling assumption (i.e., the remaining agents are considered components of a fixed and stationary environment). Nevertheless, this framework is not realistic, which can limit the models’ representation of the real world. This study used a Markov Game (MG) for modeling concurrent road users’ behavior and evasive actions in near misses. Unlike the conventional game-theoretic approach that considers single-time-step modeling, the MG framework models the sequences of road user decisions. In this framework, road users are modeled as rational agents that aim to maximize their own utility functions by taking rational actions. Road user utility functions are recovered from examples of conflict trajectories using a multiagent adversarial inverse reinforcement learning (MAAIRL) framework. In this study, trajectories from conflicts between motorcyclists and pedestrians in Shanghai, China, were used. Road user policies and collision avoidance strategies in near misses were determined with multiagent actor–critic deep reinforcement learning. A multiagent simulation platform was implemented to emulate pedestrian and motorcyclist trajectories. The results demonstrated that the multiagent model outperformed a Gaussian process inverse reinforcement learning single-agent model in predicting road user trajectories and their evasive actions. The MAAIRL model predicted the interactions’ postencroachment time with high accuracy. Moreover, unlike the single-agent framework, the recovered multiagent reward function captured the equilibrium concept in road user interactions. The multiagent model enables greater understanding of road users’ behavior in conflict interactions and captures the nonstationariness in the environment.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request. The videos used in training and testing are confidential as of data privacy agreement. Trajectory data are available from the corresponding author upon reasonable request. The intersection environment model and code created by the authors for the single and multiagent approaches in this study are available from the corresponding author upon reasonable request.
References
Abadi, M., et al. 2016. “TensorFlow: Large-scale machine learning on heterogeneous distributed systems.” Preprint, submitted November 2, 2016. https://arxiv.org/abs/1603.04467.
Abbeel, P., and A. Y. Ng. 2004. “Apprenticeship learning via inverse reinforcement learning.” In Proc., 21st Int. Conf. on Machine Learning, ICML 2004, 1–8. New York: Association for Computing Machinery.
Agarwal, A., M. Zilske, K. R. Rao, and K. Nagel. 2015. “An elegant and computationally efficient approach for heterogeneous traffic modelling using agent based simulation.” Procedia Comput. Sci. 52 (1): 962–967. https://doi.org/10.1016/j.procs.2015.05.173.
Albarran, S. E., and J. B. Clempner. 2019. “A Stackelberg security Markov game based on partial information for strategic decision making against unexpected attacks.” Eng. Appl. Artif. Intell. 81 (Dec): 408–419. https://doi.org/10.1016/j.engappai.2019.03.010.
Allen, B. L., B. T. Shin, and P. Cooper. 1978. “Analysis of traffic conflicts and collisions.” Transp. Res. Rec. 667 (1): 67–74.
Alsaleh, R., M. Hussein, and T. Sayed. 2020. “Microscopic behavioural analysis of cyclist and pedestrian interactions in shared spaces.” Can. J. Civ. Eng. 47 (1): 50–62. https://doi.org/10.1139/cjce-2018-0777.
Alsaleh, R., and T. Sayed. 2020. “Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning.” Transp. Res. Part F: Traffic Psychol. Behav. 70 (Apr): 37–57. https://doi.org/10.1016/j.trf.2020.02.007.
Alsaleh, R., and T. Sayed. 2021a. “Markov-game modeling of cyclist-pedestrian interactions in shared spaces: A multi-agent adversarial inverse reinforcement learning approach.” Transp. Res. Part C 128 (Jul): 103191. https://doi.org/10.1016/j.trc.2021.103191.
Alsaleh, R., and T. Sayed. 2021b. “Microscopic modeling of cyclists interactions with pedestrians in shared spaces: A Gaussian process inverse reinforcement learning approach.” Transportmetrica A: Transp. Sci. (Feb): 1–28. https://doi.org/10.1080/23249935.2021.1898487.
Alsaleh, R., and T. Sayed. 2022. “Do road users play Nash Equilibrium? A comparison between Nash and Logistic stochastic Equilibriums for multiagent modeling of road user interactions in shared spaces.” Expert Syst. Appl. 205 (Nov): 117710. https://doi.org/10.1016/j.eswa.2022.117710.
Alsaleh, R., T. Sayed, and M. H. Zaki. 2018. “Assessing the effect of pedestrians’ use of cell phones on their walking behavior: A study based on automated video analysis.” Transp. Res. Rec. 2672 (35): 46–57. https://doi.org/10.1177/0361198118780708.
Amundsen, F., and C. Hydén. 1977. “First workshop on traffic conflicts.” In Institute of Transportation Economics. Lund, Sweden: Lund Institute of Technology.
Autey, J., T. Sayed, and M. H. Zaki. 2012. “Safety evaluation of right-turn smart channels using automated traffic conflict analysis.” Accid. Anal. Prev. 45 (Mar): 120–130. https://doi.org/10.1016/j.aap.2011.11.015.
Bhattacharyya, R., B. Wulfe, D. Phillips, A. Kuefler, J. Morton, R. Senanayake, and M. Kochenderfer. 2020. “Modeling human driving behavior through generative adversarial imitation learning.” Preprint, submitted July 5, 2022. https://arxiv.org/abs/2006.06412.
Blue, V. J., and J. L. Adler. 2001. “Cellular automata microsimulation for modeling bi-directional pedestrian walkways.” Transp. Res. Part B: Methodol. 35 (3): 293–312. https://doi.org/10.1016/S0191-2615(99)00052-1.
Caliendo, C., and M. Guida. 2012. “Microsimulation approach for predicting crashes at unsignalized intersections using traffic conflicts.” J. Transp. Eng. 138 (12): 1453–1467. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000473.
Cervone, D., A. D’Amour, L. Bornn, and K. Goldsberry. 2014. “POINTWISE: Predicting points and valuing decisions in real time with NBA optical tracking data.” In Proc., SLOAN Sports Analytics Conf., 1–9. Cambridge, MA: MIT Press.
Chen, A. Y., Y. L. Chiu, M. H. Hsieh, P. W. Lin, and O. Angah. 2020. “Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences.” Transp. Res. Part C: Emerging Technol. 119 (Oct): 102744. https://doi.org/10.1016/j.trc.2020.102744.
Cunto, F., and F. F. Saccomanno. 2008. “Calibration and validation of simulated vehicle safety performance at signalized intersections.” Accid. Anal. Prev. 40 (3): 1171–1179. https://doi.org/10.1016/j.aap.2008.01.003.
Damsere-Derry, J., G. Palk, and M. King. 2017. “Road accident fatality risks for ‘vulnerable’ versus ‘protected’ road users in northern Ghana.” Traffic Inj. Prev. 18 (7): 736–743. https://doi.org/10.1080/15389588.2017.1302083.
Essa, M., and T. Sayed. 2015a. “Simulated traffic conflicts: Do they accurately represent field-measured conflicts?” Transp. Res. Rec. 2514 (1): 48–57. https://doi.org/10.3141/2514-06.
Essa, M., and T. Sayed. 2015b. “Transferability of calibrated microsimulation model parameters for safety assessment using simulated conflicts.” Accid. Anal. Prev. 84 (Nov): 41–53. https://doi.org/10.1016/j.aap.2015.08.005.
Essa, M., and T. Sayed. 2016. “A comparison between PARAMICS and VISSIM in estimating automated field-measured traffic conflicts at signalized intersections.” J. Adv. Transp. 50 (5): 897–917. https://doi.org/10.1002/atr.1381.
Fan, R., H. Yu, P. Liu, and W. Wang. 2013. “Using VISSIM simulation model and Surrogate Safety Assessment Model for estimating field measured traffic conflicts at freeway merge areas.” IET Intel. Transport Syst. 7 (1): 68–77. https://doi.org/10.1049/iet-its.2011.0232.
Finn, C., S. Levine, and P. Abbeel. 2016. “Guided cost learning: Deep inverse optimal control via policy optimization.” In Vol. 1 of Proc., 33rd Int. Conf. on Machine Learning, ICML 2016, 95–107. New York: CMT Association.
Fu, J., K. Luo, and S. Levine. 2017. “Learning robust rewards with adversarial inverse reinforcement learning.” Preprint, submitted July 4, 2022. https://arxiv.org/abs/1710.11248.
Georgila, K., C. Nelson, and D. Traum. 2014. “Single-agent vs. multi-agent techniques for concurrent reinforcement learning of negotiation dialogue policies.” In Proc., 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, 500–510. Stroudsburg, PA: Association for Computational Linguistics.
Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative adversarial networks.” Commun. ACM 63 (11): 139–144. https://doi.org/10.1145/3422622.
Guo, N., R. Jiang, S. C. Wong, Q. Y. Hao, S. Q. Xue, Y. Xiao, and C. Y. Wu. 2020. “Modeling the interactions of pedestrians and cyclists in mixed flow conditions in uni- and bidirectional flows on a shared pedestrian-cycle road.” Transp. Res. Part B: Methodol. 139 (Sep): 259–284. https://doi.org/10.1016/j.trb.2020.06.010.
Guo, Y., M. Essa, T. Sayed, M. M. Haque, and S. Washington. 2019. “A comparison between simulated and field-measured conflicts for safety assessment of signalized intersections in Australia.” Transp. Res. Part C: Emerging Technol. 101 (Apr): 96–110. https://doi.org/10.1016/j.trc.2019.02.009.
Helbing, D., and P. Molnár. 1995. “Social force model for pedestrian dynamics.” Phys. Rev. E 51 (5): 4282–4286. https://doi.org/10.1103/PhysRevE.51.4282.
Huang, F., P. Liu, H. Yu, and W. Wang. 2013. “Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections.” Accid. Anal. Prev. 50 (Jan): 1014–1024. https://doi.org/10.1016/j.aap.2012.08.018.
Hussein, M., and T. Sayed. 2015. “A unidirectional agent based pedestrian microscopic model.” Can. J. Civ. Eng. 42 (12): 1114–1124. https://doi.org/10.1139/cjce-2014-0363.
Hussein, M., and T. Sayed. 2017. “A bi-directional agent-based pedestrian microscopic model.” Transportmetrica A: Transp. Sci. 13 (4): 326–355. https://doi.org/10.1080/23249935.2016.1266531.
Huttenlocher, D. P., W. J. Rucklidge, and G. A. Klanderman. 1993. “Comparing images using the Hausdorff distance under translation.” IEEE Trans. Pattern Anal. Mach. Intell. 15 (9): 850–863. https://doi.org/10.1109/34.232073.
Iryo-Asano, M., and W. K. M. Alhajyaseen. 2017. “Modeling pedestrian crossing speed profiles considering speed change behavior for the safety assessment of signalized intersections.” Accid. Anal. Prev. 108 (Dec): 332–342. https://doi.org/10.1016/j.aap.2017.08.028.
Ismail, K., T. Sayed, and N. Saunier. 2011. “Methodologies for aggregating indicators of traffic conflict.” Transp. Res. Rec. 2237 (1): 10–19. https://doi.org/10.3141/2237-02.
Ismail, K., T. Sayed, and N. Saunier. 2013. “A methodology for precise camera calibration for data collection applications in urban traffic scenes.” Can. J. Civ. Eng. 40 (1): 57–67. https://doi.org/10.1139/cjce-2011-0456.
Ismail, K., T. Sayed, N. Saunier, and C. Lim. 2009. “Automated analysis of pedestrian-vehicle conflicts using video data.” Transp. Res. Rec. 2140 (1): 44–54. https://doi.org/10.3141/2140-05.
Lalropuia, K. C., and V. Gupta. 2019. “Modeling cyber-physical attacks based on stochastic game and Markov processes.” Reliab. Eng. Syst. Saf. 181 (Dec): 28–37. https://doi.org/10.1016/j.ress.2018.08.014.
Lan, L. W., and C. W. Chang. 2005. “Inhomogeneous cellular automata modeling for mixed traffic with cars and motorcycles.” J. Adv. Transp. 39 (3): 323–349. https://doi.org/10.1002/atr.5670390307.
Lanzaro, G., T. Sayed, and R. Alsaleh. 2022. “Can motorcyclist behavior in traffic conflicts be modeled? A deep reinforcement learning approach for motorcycle-pedestrian interactions.” Transportmetrica B 10 (1): 396–420. https://doi.org/10.1080/21680566.2021.2004954.
Lee, T. C., J. W. Polak, and M. G. H. Bell. 2009. “New approach to modeling mixed traffic containing motorcycles in urban areas.” Transp. Res. Rec. 2140 (1): 195–205. https://doi.org/10.3141/2140-22.
Levine, S., and V. Koltun. 2012. “Continuous inverse optimal control with locally optimal examples.” In Proc., 29th Int. Conf. on Machine Learning. Madison, WI: Omnipress.
Levine, S., Z. Popovic, and V. Koltun. 2011. “Nonlinear inverse reinforcement learning with Gaussian processes.” In Vol. 24 of Proc., Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
Littman, M. L. 1994. “Markov games as a framework for multi-agent reinforcement learning.” In Proc., Machine Learning. Burlington, MA: Morgan Kaufmann Publishers.
Liu, Y., R. Alsaleh, and T. Sayed. 2021a. “Modeling lateral interactions between motorized vehicles and non-motorized vehicles in mixed traffic using accelerated failure duration model.” Transportmetrica A: Transp. Sci. (Apr): 1–24. https://doi.org/10.1080/23249935.2021.1908443.
Liu, Y., R. Alsaleh, and T. Sayed. 2021b. “Modeling the influence of mobile phone use distraction on pedestrian reaction times to green signals: A multilevel mixed-effects parametric survival model.” Transp. Res. Part F: Traffic Psychol. Behav. 81 (Aug): 115–129. https://doi.org/10.1016/j.trf.2021.05.020.
Mahmud, S. M. S., L. Ferreira, M. S. Hoque, and A. Tavassoli. 2017. “Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs.” IATSS Res. 41 (4): 153–163. https://doi.org/10.1016/j.iatssr.2017.02.001.
Mnih, V., A. P. Badia, L. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver, and K. Kavukcuoglu. 2016. “Asynchronous methods for deep reinforcement learning.” In Proc., 33rd Int. Conf. on Machine Learning, ICML 2016, 2850–2869. New York: CMT Association.
Naci, H., D. Chisholm, and T. D. Baker. 2009. “Distribution of road traffic deaths by road user group: A global comparison.” Inj. Prev. 15 (1): 55–59. https://doi.org/10.1136/ip.2008.018721.
Nasernejad, P., T. Sayed, and R. Alsaleh. 2021. “Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian process inverse reinforcement learning (GP-IRL) approach.” Accid. Anal. Prev. 161 (May): 106355. https://doi.org/10.1016/j.aap.2021.106355.
Nasernejad, P., T. Sayed, and R. Alsaleh. 2022. “Multiagent modeling of pedestrian-vehicle conflicts using Adversarial Inverse Reinforcement Learning.” Transportmetrica A: Transp. Sci. (Apr): 1–35. https://doi.org/10.1080/23249935.2022.2061081.
Ng, A. Y., and S. Russel. 2000. “Algorithms for inverse reinforcement learning.” In Proc., Int. Conf. on Machine Learning. New York: Association for Computing Machinery.
Nguyen, L. X., and S. Hanaoka. 2011. “An application of social force approach for motorcycle dynamics.” In Proc., Eastern Asia Society for Transportation Studies. Bangkok, Thailand: Eastern Asia Society for Transportation Studies.
Nguyen, L. X., S. Hanaoka, and T. Kawasaki. 2012. “Describing non-lane-based motorcycle movements in motorcycle-only traffic flow.” Transp. Res. Rec. 2281 (1): 76–82. https://doi.org/10.3141/2281-10.
Nguyen, L. X., S. Hanaoka, and T. Kawasaki. 2014. “Traffic conflict assessment for non-lane-based movements of motorcycles under congested conditions.” IATSS Res. 37 (2): 137–147. https://doi.org/10.1016/j.iatssr.2013.10.002.
Parker, M., and C. Zegeer. 1989. Traffic conflict techniques for safety and operations: Engineers guide. Washington, DC: FHWA.
Paul, M., and I. Ghosh. 2020. “Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic.” Int. J. Inj. Control Saf. Promot. 27 (2): 121–135. https://doi.org/10.1080/17457300.2019.1669666.
Perkins, R. S., and J. I. Harris. 1968. “Traffic conflict characteristics—Accident potential at intersections.” Highway Res. Rec. 224 (Jun): 35–43.
Qiao, Q., and P. A. Beling. 2013. “Inverse reinforcement learning with Gaussian process.” In Proc., American Control Conf., 113–118. New York: IEEE.
Robin, T., G. Antonini, M. Bierlaire, and J. Cruz. 2009. “Specification, estimation and validation of a pedestrian walking behavior model.” Transp. Res. Part B: Methodol. 43 (1): 36–56. https://doi.org/10.1016/j.trb.2008.06.010.
Saunier, N., and T. Sayed. 2006. “A feature-based tracking algorithm for vehicles in intersections.” In Proc., Third Canadian Conf. on Computer and Robot Vision, CRV 2006. New York: IEEE.
Saunier, N., T. Sayed, and C. Lim. 2007. “Probabilistic collision prediction for vision-based automated road safety analysis.” In Proc., IEEE Conf. on Intelligent Transportation Systems, ITSC, 872–878. New York: IEEE.
Savitzky, A., and M. J. E. Golay. 1964. “Smoothing and differentiation of data by simplified least squares procedures.” Anal. Chem. 36 (8): 1627–1639. https://doi.org/10.1021/ac60214a047.
Sayed, T., and S. Zein. 1999. “Traffic conflict standards for intersections.” Transp. Plann. Technol. 22 (4): 309–323. https://doi.org/10.1080/03081069908717634.
Schulte, O., M. Khademi, S. Gholami, Z. Zhao, M. Javan, and P. Desaulniers. 2017. “A Markov Game model for valuing actions, locations, and team performance in ice hockey.” Data Min. Knowl. Discovery 31 (6): 1735–1757. https://doi.org/10.1007/s10618-017-0496-z.
Shimosaka, M., T. Kaneko, and K. Nishi. 2014. “Modeling risk anticipation and defensive driving on residential roads with inverse reinforcement learning.” In Proc., 17th IEEE Int. Conf. on Intelligent Transportation Systems, ITSC 2014, 1694–1700. New York: IEEE.
Shiomi, Y., T. Hanamori, N. Uno, and H. Shimamoto. 2012. “Modeling traffic flow dominated by motorcycles based on discrete choice approach.” In Proc., hEART: European association for research in transportation. Washington, DC: Transportation Research Board.
So, J., B. Park, S. M. Wolfe, and G. Dedes. 2015. “Development and validation of a vehicle dynamics integrated traffic simulation environment assessing surrogate safety.” J. Comput. Civ. Eng. 29 (5): 04014080. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000403.
Song, J., H. Ren, S. Ermon, and D. Sadigh. 2018. “Multi-agent generative adversarial imitation learning.” In Vol. 32 of Proc., Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
Sutton, R. S., and A. G. Barto. 2018. “Reinforcement learning: An introduction.” In IEEE transactions on neural networks. Cambridge, MA: MIT Press.
Tageldin, A., and T. Sayed. 2019. “Models to evaluate the severity of pedestrian-vehicle conflicts in five cities.” Transportmetrica A: Transp. Sci. 15 (2): 354–375. https://doi.org/10.1080/23249935.2018.1477853.
Tageldin, A., T. Sayed, and X. Wang. 2015. “Can time proximity measures be used as safety indicators in all driving cultures? Case study of motorcycle safety in China.” Transp. Res. Rec. 2520 (1): 165–174. https://doi.org/10.3141/2520-19.
Tarko, A. P., and P. Songchitruksa. 2005. “Estimating the frequency of crashes as extreme traffic events.” In Proc., 84th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Tomasi, C., and T. Kanade. 1991. “Detection and tracking of point features.” Int J Comput Vision 9: 137–154.
Trinadha Rao, V., and V. R. Rengaraju. 1998. “Modeling conflicts of heterogeneous traffic at urban uncontrolled intersections.” J. Transp. Eng. 124 (1): 23–34. https://doi.org/10.1061/(ASCE)0733-947X(1998)124:1(23).
Vlahogianni, E. I., G. Yannis, and J. C. Golias. 2012. “Overview of critical risk factors in Power-Two-Wheeler safety.” Accid. Anal. Prev. 49 (Nov): 12–22. https://doi.org/10.1016/j.aap.2012.04.009.
Waizman, G., S. Shoval, and I. Benenson. 2015. “Micro-simulation model for assessing the risk of vehicle-pedestrian road accidents.” J. Intell. Transp. Syst. Technol. Plann. Oper. 19 (1): 63–77. https://doi.org/10.1080/15472450.2013.856721.
Waizman, G., S. Shoval, and I. Benenson. 2018. “Traffic accident risk assessment with dynamic microsimulation model using range-range rate graphs.” Accid. Anal. Prev. 119 (Oct): 248–262. https://doi.org/10.1016/j.aap.2018.07.027.
Wang, L., et al. 2019. “Road traffic mortality in China: Analysis of national surveillance data from 2006 to 2016.” Lancet Public Health 4 (5): e245–e255. https://doi.org/10.1016/S2468-2667(19)30057-X.
Wu, G., G. Tan, J. Deng, and D. Jiang. 2021. “Distributed reinforcement learning algorithm of operator service slice competition prediction based on zero-sum markov game.” Neurocomputing 439 (Jun): 212–222. https://doi.org/10.1016/j.neucom.2021.01.061.
Wu, Y., E. Mansimov, S. Liao, R. Grosse, and J. Ba. 2017. “Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation.” In Vol. 30 of Proc., Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
Yang, J., W. Deng, J. Wang, Q. Li, and Z. Wang. 2006. “Modeling pedestrians’ road crossing behavior in traffic system micro-simulation in China.” Transp. Res. Part A: Policy Pract. 40 (3): 280–290. https://doi.org/10.1016/j.tra.2005.08.001.
Yu, L., J. Song, and S. Ermon. 2019. “Multi-agent adversarial inverse reinforcement learning.” In Proc., Int. Conf. on Machine Learning, 7194–7201. New York: Association for Computing Machinery.
Zaki, M. H., and T. Sayed. 2013. “A framework for automated road-users classification using movement trajectories.” Transp. Res. Part C: Emerging Technol. 33 (Aug): 50–73. https://doi.org/10.1016/j.trc.2013.04.007.
Zaki, M. H., T. Sayed, and G. Mori. 2013. “Classifying road users in urban scenes using movement patterns.” J. Comput. Civ. Eng. 27 (4): 395–406. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000237.
Zaki, M. H., T. Sayed, and X. Wang. 2016. “Computer vision approach for the classification of bike type (motorized versus non-motorized) during busy traffic in the city of Shanghai.” J. Adv. Transp. 50 (3): 348–362. https://doi.org/10.1002/atr.1327.
Zhang, C., and A. Hammad. 2012. “Multiagent approach for real-time collision avoidance and path replanning for cranes.” J. Comput. Civ. Eng. 26 (6): 782–794. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000181.
Zheng, L., T. Sayed, and F. Mannering. 2021. “Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions.” Anal. Methods Accid. Res. 29 (Mar) 100142.
Ziebart, B. D., A. L. Maas, J. A. Bagnell, and A. K. Dey. 2008. “Maximum entropy inverse reinforcement learning.” In Proc., 23rd AAAI Conf. on Artificial Intelligence, 1433–1438. Menlo Park, CA: Association for the Advancement of Artificial Intelligence.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 6 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 16, 2022
Accepted: Jul 5, 2022
Published online: Sep 8, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 8, 2023
ASCE Technical Topics:
- Business management
- Decision making
- Engineering fundamentals
- Game theory
- Gaussian process
- Highway and road management
- Highway transportation
- Highways and roads
- Human and behavioral factors
- Infrastructure
- Lifeline systems
- Mathematics
- Pedestrians and cyclists
- Practice and Profession
- Probability
- Public administration
- Public health and safety
- Safety
- Stochastic processes
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Utilities
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Maged Shoman, Gabriel Lanzaro, Tarek Sayed, Suliman Gargoum, Autonomous Vehicle–Pedestrian Interaction Modeling Platform: A Case Study in Four Major Cities, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8097, 150, 9, (2024).