Fetching the paper…
Reading the bibliography…
In the research of Intelligent Transportation Systems (ITS), traffic simulation is a key procedure for the evaluation of new methods and optimization of strategies.
H. Wei, N. Xu, H. Zhang, G. Zheng, X. Zang, C. Chen, W. Zhang, Y. Zhu, K. Xu, and Z. Li, “Colight: Learning network-level cooperation for traffic signal control,” in Proceedings of the 28th ACM international conference on information and knowledge management , 2019, pp. 1913–1922
1922
Earlier work this paper cites.
S. A. Stouffer, “Intervening opportunities: a theory relating mobility and distance,” American sociological review , vol. 5, no. 6, pp. 845–867, 1940
1940
Earlier work this paper cites.
G. K. Zipf, “The p 1 p 2/d hypothesis: on the intercity movement of persons,” American sociological review , vol. 11, no. 6, pp. 677–686, 1946
1946
Earlier work this paper cites.
H. Mahmassani, “Dynamic traffic assignment and simulation for advanced network informatics (dynasmart),” in the 2nd International Seminar on Urban Traffic Networks, 1992 , 1992
1992
Earlier work this paper cites.
C. Gawron, “An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model,” International Journal of Modern Physics C , vol. 9, no. 03, pp. 393–407, 1998
1998
Earlier work this paper cites.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical review E , vol. 62, no. 2, p. 1805, 2000
2000
Earlier work this paper cites.
A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model mobil for car-following models,” Transportation Research Record , vol. 1999, no. 1, pp. 86–94, 2007
2007
Earlier work this paper cites.
M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, “Sumo–simulation of urban mobility: an overview,” in Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation . ThinkMind, 2011
2011
Earlier work this paper cites.
F. Simini, M. C. González, A. Maritan, and A.-L. Barabási, “A universal model for mobility and migration patterns,” Nature , vol. 484, no. 7392, pp. 96–100, 2012
2012
Earlier work this paper cites.
P. Varaiya, “Max pressure control of a network of signalized intersections,” Transportation Research Part C: Emerging Technologies , vol. 36, pp. 177–195, 2013
2013
Earlier work this paper cites.
M. S. Iqbal, C. F. Choudhury, P. Wang, and M. C. González, “Development of origin–destination matrices using mobile phone call data,” Transportation Research Part C: Emerging Technologies , vol. 40, pp. 63–74, 2014
2014
Earlier work this paper cites.
E. Van der Pol and F. A. Oliehoek, “Coordinated deep reinforcement learners for traffic light control,” Proceedings of learning, inference and control of multi-agent systems (at NIPS 2016) , vol. 8, pp. 21–38, 2016
2016
Earlier work this paper cites.
K. W Axhausen, A. Horni, and K. Nagel, The multi-agent transport simulation MATSim . Ubiquity Press, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
A. Bojchevski, O. Shchur, D. Zügner, and S. Günnemann, “Netgan: Generating graphs via random walks,” in International conference on machine learning . PMLR, 2018, pp. 610–619
2018
Cited alongside, same era.
Z. He, S. Yang, W. Zhang, and J. Zhang, “Perceiving commerial activeness over satellite images,” in Companion Proceedings of the The Web Conference 2018 , 2018, pp. 387–394
2018
Cited alongside, same era.
H. Zhang, S. Feng, C. Liu, Y. Ding, Y. Zhu, Z. Zhou, W. Zhang, Y. Yu, H. Jin, and Z. Li, “Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario,” in The world wide web conference , 2019, pp. 3620–3624
X. Cong, B. Yang, F. Gao, C. Chen, and Y. Tang, “Virtual platoon based cavs cooperative driving at unsignalized intersection,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 53–58
2022
Later among the works it cites.
J. Zhang, C. Chang, H. Pei, X. Peng, Y. Guo, R. Lian, Z. Chen, and L. Li, “Cavsim: A microscope traffic simulator for connected and automated vehicles environment,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 3719–3724
2022
Later among the works it cites.
M. Yazdani, M. Sarvi, S. A. Bagloee, N. Nassir, J. Price, and H. Parineh, “Intelligent vehicle pedestrian light (ivpl): A deep reinforcement learning approach for traffic signal control,” Transportation research part C: emerging technologies , vol. 149, p. 103991, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
X. Geng, Y. Li, L. Wang, L. Zhang, Q. Yang, J. Ye, and Y. Liu, “Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 3656–3663
2019
Cited alongside, same era.
N. Pourebrahim, S. Sultana, A. Niakanlahiji, and J.-C. Thill, “Trip distribution modeling with twitter data,” Computers, Environment and Urban Systems , vol. 77, p. 101354, 2019
2019
Cited alongside, same era.
H. Shi, Q. Yao, Q. Guo, Y. Li, L. Zhang, J. Ye, Y. Li, and Y. Liu, “Predicting origin-destination flow via multi-perspective graph convolutional network,” in 2020 IEEE 36th International conference on data engineering (ICDE) . IEEE, 2020, pp. 1818–1821
2020
Cited alongside, same era.
Z. Liu, F. Miranda, W. Xiong, J. Yang, Q. Wang, and C. Silva, “Learning geo-contextual embeddings for commuting flow prediction,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 01, 2020, pp. 808–816
2020
Cited alongside, same era.
F. Simini, G. Barlacchi, M. Luca, and L. Pappalardo, “A deep gravity model for mobility flows generation,” Nature communications , vol. 12, no. 1, p. 6576, 2021
2021
Cited alongside, same era.
S. Feng, X. Yan, H. Sun, Y. Feng, and H. X. Liu, “Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment,” Nature communications , vol. 12, no. 1, p. 748, 2021
2021
Cited alongside, same era.
A. Kusari, P. Li, H. Yang, N. Punshi, M. Rasulis, S. Bogard, and D. J. LeBlanc, “Enhancing sumo simulator for simulation based testing and validation of autonomous vehicles,” in 2022 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2022, pp. 829–835
2022
Cited alongside, same era.
2023
Later among the works it cites.
F. Yu, H. Yan, R. Chen, G. Zhang, Y. Liu, M. Chen, and Y. Li, “City-scale vehicle trajectory data from traffic camera videos,” Scientific data , vol. 10, no. 1, p. 711, 2023
2023
Later among the works it cites.
C. Liang, Z. Huang, Y. Liu, Z. Liu, G. Zheng, H. Shi, K. Wu, Y. Du, F. Li, and Z. J. Li, “Cblab: Supporting the training of large-scale traffic control policies with scalable traffic simulation,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 4449–4460
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Zhang, W. Ao, D. Jin, L. Liu, and Y. Li, “A city-level high-performance spatio-temporal mobility simulation system,” in Proceedings of the 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility , 2023, pp. 23–32
2023
Later among the works it cites.
C. Gulino, J. Fu, W. Luo, G. Tucker, E. Bronstein, Y. Lu, J. Harb, X. Pan, Y. Wang, X. Chen, et al. , “Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
X. Zhang, Y. Liu, Y. Lin, Q. Liao, and Y. Li, “Uv-sam: Adapting segment anything model for urban village identification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 20, 2024, pp. 22 520–22 528
2024
Closest in time.
U. C. Bureau, “Lehd origin-destination employment statistics data (2002-2021),” 2024
2024
Closest in time.