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International Journal of Machine Learning and Cybernetics

09.05.2024 | Original Article

Open-ti: open traffic intelligence with augmented language model

verfasst von: Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, Hua Wei

Erschienen in: International Journal of Machine Learning and Cybernetics

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Abstract

Transportation has greatly benefited the cities’ development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people’s daily commuting efficiency. However, intelligent transportation, as a cross-discipline, often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained large language models, especially the potential of being augmented with abilities to understand and execute intricate commands, we introduce Open-TI. Serving as a bridge to mitigate the industry-academic gap, Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence, it is augmented with the capability to harness external traffic analysis packages based on existing conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch—spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We eventually provide the formal implementation structure, and the open-ended design invites further community-driven enhancements. A demo video is provided at: https://​youtu.​be/​pZ4-5PXz9Xs.

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Literatur
1.
Yukawa S, Kikuchi M (1995) Coupled-map modeling of one-dimensional traffic flow. J Phys Soc Jpn 64(1):35–38CrossRef
2.
Chao Q, Bi H, Li W, Mao T, Wang Z, Lin MC, Deng Z (2020) A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving. In: Computer Graphics Forum, vol. 39, pp. 287–308. Wiley Online Library
3.
Dai Z, Liu XC, Chen X, Ma X (2020) 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 114:598–619CrossRef
4.
Zhou XS, Cheng Q, Wu X, Li P, Belezamo B, Lu J, Abbasi M (2022) A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transp 1(2):100017CrossRef
5.
Wei H, Xu N, Zhang H, Zheng G, Zang X, Chen C, Zhang W, Zhu Y, Xu K, Li Z (2019) 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
6.
Osorio C (2019) High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124:18–43CrossRef
7.
Lopez PA, Behrisch M, Bieker-Walz L, Erdmann J, Flötteröd Y-P, Hilbrich R, Lücken L, Rummel J, Wagner P, Wießner E (2018) Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582. IEEE
8.
Zhang H, Feng S, Liu C, Ding Y, Zhu Y, Zhou Z, Zhang W, Yu Y, Jin H, Li Z (2019) Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624
9.
Fellendorf M, Vortisch P (2010) Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93
10.
Lu J, Zhou XS (2023) 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 153:104223CrossRef
11.
Zhang S, Fu D, Zhang Z, Yu B, Cai P (2023) Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:​2309.​06719
12.
de Zarzà I, de Curtò J, Roig G, Calafate CT (2023) Llm multimodal traffic accident forecasting. Sensors 23(22):9225CrossRef
13.
Vaithilingam P, Zhang T, Glassman EL (2022) Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7
14.
Li Y, Gao C, Song X, Wang X, Xu Y, Han S (2023) Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06
15.
Tang J, Yang Y, Wei W, Shi L, Su L, Cheng S, Yin D, Huang C (2023) Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:​2310.​13023
16.
Mialon G, Dessì R, Lomeli M, Nalmpantis C, Pasunuru R, Raileanu R, Rozière B, Schick T, Dwivedi-Yu J, Celikyilmaz A, et al. (2023) Augmented language models: a survey. arXiv preprint arXiv:​2302.​07842
17.
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:​1810.​04805
18.
Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877–1901
19.
Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S et al (2023) Palm: Scaling language modeling with pathways. J Mach Learn Res 24(240):1–113
20.
Chen M, Tworek J, Jun H, Yuan Q, Pinto HPdO, Kaplan J, Edwards H, Burda Y, Joseph N, Brockman G et al. (2021) Evaluating large language models trained on code. arXiv preprint arXiv:​2107.​03374
21.
Liu Y, Han T, Ma S, Zhang J, Yang Y, Tian J, He H, Li A, He M, Liu Z, et al. (2023) Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017
22.
Tirumala K, Markosyan A, Zettlemoyer L, Aghajanyan A (2022) Memorization without overfitting: Analyzing the training dynamics of large language models. Adv Neural Inf Process Syst 35:38274–38290
23.
Zhou D, Schärli N, Hou L, Wei J, Scales N, Wang X, Schuurmans D, Cui C, Bousquet O, Le Q et al. (2022) Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:​2205.​10625
24.
25.
Li M, Song F, Yu B, Yu H, Li Z, Huang F, Li Y (2023) Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:​2304.​08244
26.
Wang Y, Ma X, Chen W (2023) Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:​2309.​02233
27.
Liang Y, Wu C, Song T, Wu W, Xia Y, Liu Y, Ou Y, Lu S, Ji L, Mao S et al. (2023) Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:​2303.​16434
28.
Liu C, Yang S, Xu Q, Li Z, Long C, Li Z, Zhao R (2024) Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:​2401.​10134
29.
Tong L, Pan Y, Shang P, Guo J, Xian K, Zhou X (2019) 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 5:1–16CrossRef
30.
Dosovitskiy A, Ros G, Codevilla F, Lopez A, Koltun V (2017) Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16. PMLR
31.
Mullakkal-Babu FA, Wang M, van Arem B, Shyrokau B, Happee R (2020) A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Trans Intell Transp Syst 22(6):3430–3443CrossRef
32.
de Souza F, Verbas O, Auld J (2019) Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151:858–863CrossRef
33.
Oppe S (1989) Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3):225–232MathSciNetCrossRef
34.
Boukerche A, Tao Y, Sun P (2020) Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Comput Netw 182:107484CrossRef
35.
Masek P, Masek J, Frantik P, Fujdiak R, Ometov A, Hosek J, Andreev S, Mlynek P, Misurec J (2016) A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11):1872CrossRef
36.
Maroto J, Delso E, Felez J, Cabanellas JM (2006) Real-time traffic simulation with a microscopic model. IEEE Trans Intell Transp Syst 7(4):513–527CrossRef
37.
NVIDIA: Simulation for self-driving vehicles (2023)
38.
Gulino C, Fu J, Luo W, Tucker G, Bronstein E, Lu Y, Harb J, Pan X, Wang Y, Chen X et al. (2023) Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:​2310.​08710
39.
Behrisch M, Bieker L, Erdmann J, Krajzewicz D (2011) Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation . ThinkMind
40.
Qadri SSSM, Gökçe MA, Öner E (2020) State-of-art review of traffic signal control methods: challenges and opportunities. Eur Transp Res Rev 12:1–23CrossRef
41.
Wei H, Zheng G, Yao H, Li Z (2018) 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
42.
Willumsen LG (1978) Estimation of an od matrix from traffic counts-a review
43.
Abrahamsson T (1998) Estimation of origin-destination matrices using traffic counts-a literature survey
44.
Medina A, Taft N, Salamatian K, Bhattacharyya S, Diot C (2002) Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4):161–174CrossRef
45.
Mahmassani HS (2001) Dynamic network traffic assignment and simulation methodology for advanced system management applications. Netw Spat Econ 1:267–292CrossRef
46.
Mahmassani HS, Zhou X (2005) 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
47.
Zhou X, Qin X, Mahmassani HS (2003) Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transp Res Rec 1831(1):30–38CrossRef
48.
Zhou X, Erdoğan S, Mahmassani HS (2006) Dynamic origin-destination trip demand estimation for subarea analysis. Transp Res Rec 1964(1):176–184CrossRef
49.
Zhou X, List GF (2010) An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transp Sci 44(2):254–273CrossRef
50.
Zhou X, Lu C, Zhang K (2013) Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions
51.
Krishnakumari P, Van Lint H, Djukic T, Cats O (2020) A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113:38–56CrossRef
52.
Fedorov A, Nikolskaia K, Ivanov S, Shepelev V, Minbaleev A (2019) Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6:1–15CrossRef
53.
Pamuła T, Żochowska R (2023) Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Eng Appl Artif Intell 117:105550CrossRef
54.
Fu H, Lam WH, Shao H, Kattan L, Salari M (2022) 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 157:102555CrossRef
55.
Kumarage S, Yildirimoglu M, Zheng Z (2023) A hybrid modelling framework for the estimation of dynamic origin-destination flows. Transportation Research Part B: Methodological 176:102804CrossRef
56.
Mei H, Lei X, Da L, Shi B, Wei H (2023) Libsignal: an open library for traffic signal control. Machine Learning, 1–37
57.
Cools S-B, Gershenson C, D’Hooghe B (2013) Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55
58.
Wei H, Zheng G, Gayah V, Li Z (2021) Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsl 22(2):12–18CrossRef
59.
Zheng G, Zang X, Xu N, Wei H, Yu Z, Gayah V, Xu K, Li Z (2019) Diagnosing reinforcement learning for traffic signal control. arXiv preprint arXiv:​1905.​04716
60.
Wei H, Chen C, Zheng G, Wu K, Gayah V, Xu K, Li Z (2019) Presslight: Learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1290–1298
Metadaten
Titel
Open-ti: open traffic intelligence with augmented language model
verfasst von
Longchao Da
Kuanru Liou
Tiejin Chen
Xuesong Zhou
Xiangyong Luo
Yezhou Yang
Hua Wei
Publikationsdatum
09.05.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02190-8