Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 9625-9636· 0 citations· 73 references
Abstract
Traffic prediction is a fundamental component of intelligent transportation systems, and recent research has explored a wide range of prediction tasks and modeling approaches. While several benchmarking frameworks have been proposed to support fair and reproducible evaluation, most existing benchmarks rely on manual maintenance, making it difficult to continuously integrate new models and evaluate them under a unified set of data processing and evaluation protocols as the literature grows. In this work, we present AgentCity, an AI-maintained framework for the continuous construction and evaluation of traffic prediction benchmarks. AgentCity formulates benchmark maintenance as an automated, agent-driven workflow that supports literature retrieval, model and data integration, and standardized evaluation under unified protocols. Built on top of AgentCity, we release a publicly accessible traffic prediction benchmark1 covering four representative tasks, including traffic state prediction, trajectory location prediction, estimated time of arrival prediction, and map matching, and aggregate 74 representative models evaluated across multiple datasets under consistent evaluation settings, together with task-wise leaderboards and detailed evaluation records. We further validate the reliability of AgentCity through benchmark validation studies on reproduction fidelity and consistency across different code-oriented agents. By automating key stages of benchmark maintenance, AgentCity supports the continuous integration and evaluation of traffic prediction models under unified protocols. 1Project website: https://www.agentcity.city/ Source code: https://github.com/Beihang-BIGSCity/AgentCity.
Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.
Zhi-Cheng Wang, Tao Zhang, Yi-Meng Zhu et al.· Applied Sciences· 0 citations
A generic framework is presented which exploits the zero-shot, few-shot and multi-modal capabilities of foundation models to forecast traffic flow, predict traffic incidents and improve public transit schedules.
W. T L· International Journal of Com...· 0 citations
Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration. Conventional monolithic machine learning models struggle to generalize across diverse operators, mobility modes, and traffic types, leaving a critical stochasticity gap between signal conditions and achievable throughput. To overcome these constraints in heterogeneous urban environments, we propose a Tiered Multi-Agent System (TMAS) that dynamically routes edge telemetry to context-aware Domain Micro-Agents, validated on a dataset of 48,618 samples collected in Sunway City, Malaysia, with Nemo Handy drive test software, spanning three Tier-1 mobile network operators, three mobility modes, namely (i) elevated pedestrian walkway, (ii) ground-level shuttle bus, and (iii) elevated bus rapid transit; and three traffic profiles, namely (i) persistent download, (ii) persistent upload, and (iii) adaptive video streaming. Our evaluations reveal that TMAS overcomes predictability bottlenecks, achieving a coefficient of determination (R2) of up to 0.931 and a Mean Absolute Error (MAE) as low as 0.53 Mbps. The system demonstrates high operational efficiency, with rapid micro-agent training times, low inference latencies, and agentic routing overhead of 0.004 to 0.126 ms. These latency characteristics indicate the architecture is a promising candidate for the response times required by next-generation wireless networks.
M. Kabeer, R. Nordin, Nadiva Nuriftitah et al.· arXiv.org· 0 citations
Overall, the proposed Improved Dolphin Swarm‐optimized Dynamic Recurrent Neural Network shows promising potential for supporting intelligent traffic management and reducing traffic congestion; however, further validation using larger and more diverse datasets is required to confirm its generalizability and reliability.
Mao-Sheng Yan, Yi-Han Wang, Qingfeng Dong et al.· Concurrency and Computation· 0 citations
Traffic congestion remains a prevalent issue in urban areas, contributing to environmental pollution, increased fuel consumption, and delays in emergency services. Addressing this challenge is paramount, with traffic flow prediction emerging as a pivotal technology within Intelligent Transportation Systems (ITS) to mitigate congestion and conserve time and energy. Extensive efforts have been directed towards developing predictive models, categorized into parametric, non-parametric, and hybrid approaches. While existing literature has extensively explored parametric and non-parametric models, this survey paper focuses on recent advancements in traffic flow prediction models, examining their merits and drawbacks. A comprehensive taxonomy of hybrid models utilized in traffic prediction is also provided. Additionally, the role of contextual features in enhancing traffic flow prediction is explored. Further, popular datasets employed for training traffic flow prediction models are discussed, accompanied by a comparative analysis of leading models’ performance using these datasets. The survey explores various performance metrics employed to evaluate prediction model efficacy. Furthermore, it identifies research gaps and challenges and outlines future research directions, aiming to contribute to the ongoing discourse on traffic flow prediction and inform future endeavors in this domain.
Unknown authors· ACM Transactions on Intellig...· 0 citations
Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.
N. Ahmadi, Yubo Jiao, J. Manzolli et al.· 0 citations
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