The paper introduces a quantum-inspired representation of driver behavior that models drivers as evolving density matrices, capturing continuous, probabilistic, context-dependent, and history-dependent interactions among behavioral variables. Trained unsupervised on the I‑24 MOTION dataset, the framework identifies three interpretable driving regimes—free flow, transition, and congestion—and reproduces macroscopic traffic phenomena such as the fundamental diagram and hysteresis loops. The representation also enhances practical applications by providing context-dependent parameters for classical car‑following models and enabling autonomous vehicles to forecast nearby drivers’ motion in real time.
By Mohammad Elayan, Omid Armantalab, Wissam Kontar
arXiv:2609.21945v1 Announce Type: new
Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...
By Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin
arXiv:2412.02520v4 Announce Type: replace-cross
Abstract: Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. T...
By Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.
By Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter
arXiv:2606. 07556v1 Announce Type: cross Abstract: Accurate measurement of traffic volumes and flows is vital for modern intelligent transportation.
By Masaaki Inoue, Akifumi Okuno, Shintaro Fukushima
RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.
By Guangyu Wang, Zhidan Liu
REARL is a closed‑loop simulation enhancement framework that combines real traffic data with large language models (LLMs) to improve autonomous driving simulations. It clusters real traffic, uses cluster centers as representative scenarios for the LLM, and employs a sliding‑window detector to monitor vehicle speed and spacing discrepancies. When thresholds are exceeded, the LLM adjusts vehicle decision‑making or selects matching real vehicle actions, resulting in lower Hellinger distance and MAPE compared to baselines in a HighD highway setting.
By Xiaojun Bi (Minzu University of China, Beijing, China), Jun Jiang (Minzu University of China, Beijing, China), Yiwen Sun (Peking University, Beijing, China, BIGAI, Beijing, China), Quanyi Ou (Minzu University of China, Beijing, China), Ke Cheng (Beihang University, Beijing, China), Mingjie Bi (BIGAI, Beijing, China), Yexin Li (BIGAI, Beijing, China)
The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
By Baran Can G\"ul, Mert Nak{\i}p, Nasser Jazdi, Michael Weyrich
arXiv:2608. 13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.
By Davide Andrea Guastella, Eladio Montero Porras, Evangelos Pournaras, Gianluca Bontempi
arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.
By Donggyu Min, Seongjin Choi, Dong-Kyu Kim
ResLearn-XR is a residual learning framework designed to predict extended reality (XR) network traffic and estimate Quality-of-Experience (QoE) risk. It uses a two‑stage temporal learning structure: a base sequence prediction model followed by task‑specific residual components that operate in value space for traffic forecasting and in logit space for probabilistic QoE risk estimation. The framework introduces a Data Descriptor Algorithm (DDA) to convert packet‑level observables into frame‑timing‑aware descriptors and is evaluated on a newly constructed XR Traffic‑QoE dataset, achieving significant reductions in SMAPE for both traffic prediction and QoE‑risk estimation compared to single‑stage baselines.
By Yoga Suhas Kuruba Manjunath, Jie Gao, Lian Zhao
arXiv:2608.29658v1 Announce Type: cross
Abstract: Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smar...
By Limiao Zhang, Yuhui Lu, Jie Gao, Hao Jiang, Haiping Ma, Xingyi Zhang