Fetching the paper…
Reading the bibliography…
Transportation has greatly benefited the cities' development in the modern civilization process.
Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978)
1978
Earlier work this paper cites.
Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21
1989
Earlier work this paper cites.
Yukawa, S., Kikuchi, M.: Coupled-map modeling of one-dimensional traffic flow. Journal of the Physical Society of Japan 64
1995
Earlier work this paper cites.
Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998)
1998
Earlier work this paper cites.
Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1
2001
Earlier work this paper cites.
Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32
2002
Earlier work this paper cites.
Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831
2003
Earlier work this paper cites.
Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005)
2005
Earlier work this paper cites.
Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7
2006
Earlier work this paper cites.
Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964
2006
Earlier work this paper cites.
Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010)
2010
Earlier work this paper cites.
Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44
2010
Earlier work this paper cites.
Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind
2011
Earlier work this paper cites.
Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013)
2013
Earlier work this paper cites.
Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013)
2013
Earlier work this paper cites.
Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16
2016
Earlier work this paper cites.
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR
2017
Earlier work this paper cites.
Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018)
2018
Cited alongside, same era.
Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019)
2019
Cited alongside, same era.
Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124
2019
Cited alongside, same era.
Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019)
2022
Later among the works it cites.
Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157
2022
Later among the works it cites.
Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5
2019
Cited alongside, same era.
de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151
2019
Cited alongside, same era.
Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6
2019
Cited alongside, same era.
Chao, Q., Bi, H., Li, W., Mao, T., Wang, Z., Lin, M.C., Deng, Z.: A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving. In: Computer Graphics Forum, vol. 39, pp. 287–308 (2020). Wiley Online Library
2020
Cited alongside, same era.
Dai, Z., Liu, X.C., Chen, X., Ma, X.: Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies 114
2020
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
2020
Cited alongside, same era.
Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22
2020
Cited alongside, same era.
Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182
2020
Cited alongside, same era.
2023
Closest in time.
de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23
2023
Closest in time.
Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al
2023
Closest in time.
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
NVIDIA: Simulation for self-driving vehicles (2023)
2023
Closest in time.
2023
Closest in time.
Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117
2023
Closest in time.
Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176
2023
Closest in time.
Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023)
2023
Closest in time.