Intelligent Transportation Systems

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20 papers in the last four weeks, up 233% on the four weeks before. 0.2% of all new papers.

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Latest papers 200

Oct 7, 2026cs.LG

Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata

Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local interaction rules, while retaining the ability to reproduce complex macroscopic traffic phenomena. However, learning local transition rules from data while preserving physically meaningful constraints remains challenging, particularly for stochastic models. In this work, we propose a physics-informed neural cellular automaton (PI-NCA) for data-driven traffic flow modeling. Building on the standard neural cellular automaton (NCA), we design a neural architecture that is physically consistent with the road topology and guarantees conservation of the total number of vehicles, thereby constraining the learned transition rules to physically admissible dynamics. We further extend this framework to stochastic dynamics by parameterizing probabilistic transition rules while preserving the same physics-informed constraints. We evaluate the proposed models on multiple traffic scenarios generated by the well-established Nagel-Schreckenberg and Kerner-Klenov-Wolf cellular automata. The results demonstrate that the PI-NCA successfully learns the dynamics of both traffic models and consistently outperforms a standard NCA, while the stochastic extension captures probabilistic transition rules without compromising the imposed physical constraints.

Federica Bragone, Matthieu Barreau

Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden

Neural Cellular Automata Cellular Automata Intelligent Transportation Systems

Oct 4, 2026cs.MA

Synergizing Drone Delivery Order Pooling and Road Network Monitoring through Monitoring-Task Orderization

This paper investigates the real-time dispatch of a shared drone fleet for on-demand food delivery and urban road network monitoring. We consider a courier-drone collaborative setting in which couriers transport orders to launchpads and drones complete the final delivery leg to kiosks. Drones may consolidate multiple origin-destination orders within one flight and make monitoring-aware route adjustments to collect real-time traffic information subject to delivery-time constraints. This yields a joint decision problem coupling dynamic order-to-drone matching, multi-order pooling, routing, and time-varying monitoring under fleet-level competition and uncertainty. We propose monitoring-task orderization, which periodically converts road-network nodes with high congestion and stale information into virtual monitoring orders. Pooling these virtual tasks with food-delivery orders creates a unified heterogeneous task set and transforms the coupled matching-and-routing problem into an order-level decision process. Building on this abstraction, we formulate a decentralized graph-interdependent Multi-Agent Markov Decision Process and develop Graph Multi-Agent Q-Learning (Graph-MAQL), which captures localized inter-agent dependencies through bipartite match coordination graphs. Agent-task value estimates are then used as edge weights in a dynamic heterogeneous bipartite matching program for globally feasible execution. Experiments using real-world data reveal strong operational synergy between delivery and monitoring. Monitoring-task orderization improves monitoring performance by 25.1% with less than a 1% reduction in delivery performance, while Graph-MAQL improves the aggregate objective by up to 20.8%, reduces deadline violations by over 40%, and transfers zero-shot to higher demand intensity without retraining.

Yulong Hu, Meng Xu, Sen Li +1

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China · School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Aerial Robotics Intelligent Transportation Systems Multi-Robot Task Allocation Cooperative MARL

Oct 1, 2026cs.AI

Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

The rapid evolution of Intelligent Transportation Systems and Logistics (ITS&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review of Trustworthy Data and Machine Learning Operations (DataOps and MLOps) in the ITS&L domain, underscoring their importance in improving efficiency, reliability, and decision-making precision within transportation and logistics services. We begin by identifying gaps in current literature, offering clear context for our contribution. Subsequently, we explore the complexities of DataOps and MLOps, discussing their necessity, key components, available tools, practical insights, and case studies relevant to ITS&L. Additionally, we address the critical issue of Trustworthiness in AI applications, examining methods and tools designed to strengthen confidence in AI systems - especially in real-world ITS&L scenarios. The paper concludes with a discussion of persisting challenges and future prospects in this rapidly advancing field, aiming to serve as a vital resource for researchers, industry practitioners, and policy makers. Overall, this work not only establishes a foundational understanding of DataOps and MLOps in ITS&L but also charts a path for further research and innovation in developing more efficient, sustainable, and trustworthy intelligent transportation and logistics systems.

Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale +5

Intelligent Transportation Systems Trust in AI

Sep 29, 2026cs.AI

VLALight: A Vision-Language-Action Model for Traffic Signal Control

Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signalized intersections and provide rich visual observations of evolving traffic, existing TSC methods typically rely on manually engineered traffic states or separate perception modules, creating a gap between physical observations and control decisions. We present VLALight, the first vision-language-action (VLA) model for end-to-end traffic signal control from multi-view roadside videos. VLALight directly maps visual observations to coordinated signal actions through multi-target spatiotemporal traffic reasoning and topology-aware cooperative perception across intersections. To establish this capability, we develop a two-stage supervised cold-start training strategy for visual traffic understanding and signal decision-making, followed by cooperative agentic reinforcement learning that jointly optimizes local control and network-wide traffic efficiency. Furthermore, VLALight introduces adaptive fast and slow reasoning modes, enabling the policy to allocate deeper reasoning only when additional deliberation provides sufficient control benefits. Through balanced mode-aware rollouts and relative advantage optimization, VLALight learns to trade off decision quality and inference cost. Extensive experiments on seven real-world traffic-flow datasets across three urban networks demonstrate that VLALight consistently outperforms transportation-based, RL-based, and LLM/VLM-based baselines. Ablation studies validate the effectiveness of cooperative perception, network-level optimization, and adaptive reasoning. These results demonstrate the potential of VLA models for real-world physical traffic control. Our project is available at https://github.com/usail-hkust/VLALight.git.

Pan Zhang, Siqi Lai, Kemu Dong +1

The Hong Kong University of Science and Technology (Guangzhou) · Dalian University of Technology

Intelligent Transportation Systems Vision-Language-Action Models Traffic Signal Control

Sep 29, 2026cs.MA

Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps

Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, heterogeneous preferences, and imperfect information. We develop an agent-based model of a macroscopic weaving-ramp framework in which individual vehicles adapt their lane choices using an evolutionary game-theoretic update rule and altruism-based objectives providing a microscopic interpretation of the original Wardrop model. We prove convergence of the decentralized dynamics to the unique equilibrium predicted by the macroscopic theory. Beyond reproducing aggregate equilibrium behavior, the framework enables the study of deployment-level questions that cannot be addressed by static analysis. Simulation results demonstrate close agreement with the macroscopic predictions while revealing how convergence rates, adaptation to changing traffic conditions, heterogeneous altruism levels among CAVs, and imperfect state information influence system performance and the distribution of altruistic burden across vehicles. These results provide a bridge between equilibrium traffic theory and decentralized mixed-autonomy deployment.

Sheryl Paul, Kexin Wang, Ruolin Li +1

University of Southern California, Los Angeles, CA, USA.

Intelligent Transportation Systems Agent-Based Modeling Evolutionary Game Theory Autonomous Driving Simulation

Sep 28, 2026cs.LG

Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs

Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This assumption limits their applicability to practical vehicular environments, where vehicles may perform heterogeneous but related perception tasks with different output spaces. This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders. EN-HMTFL enables vehicles performing different tasks to collaboratively learn a transferable feature representation while preserving their task-specific models locally. Only the encoder is exchanged and aggregated through the hierarchy, whereas raw data and local decoder parameters remain at the vehicles. The proposed framework is evaluated on the MNIST and GTSRB datasets in different vehicular scenarios. Across the evaluated scenarios, EN-HMTFL improves accuracy by up to 24.0% relative to the compared representation-sharing benchmark. In scenarios where EN-HMTFL converges earlier, the reduction reaches up to 69 communication rounds (28.8%).

M. Saeid HaghighiFard, Sinem Coleri

Department of Electrical and Electronics Engineering, Koc¸ University, Istanbul, Türkiye

Hierarchical Federated Learning Intelligent Transportation Systems Federated Learning Heterogeneous Federated Learning

Sep 28, 2026math.OC

Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach

Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active traffic management (ATM). Existing approaches struggle to balance the adaptive performance of data-driven optimization with the resilience and transferability of model-based control. We propose a hybrid framework to coordinate lane-level variable speed limits (VSLs) and ramp metering across SWSs. First, we reconstruct L-METANET, a lane-level macroscopic traffic flow model that captures free and forced lane changes. Second, we combine XGBoost-SHAP with a random-parameters binary logit (RPBL) model to derive analytical equations for merging and diverging collision risks and formulate system cost and reward functions. Third, we develop MPC-STMAPPO, a hierarchical controller integrating model predictive control (MPC) and multi-agent reinforcement learning (MARL). Its upper MPC layer uses L-METANET for long-horizon rolling optimization and generates baseline commands; its lower spatiotemporal MAPPO (ST-MAPPO) layer, enhanced with Mamba cells and graph attention, produces residual actions for short-horizon adjustment. Real-world experiments on the 18-km Eastern Expressway in Changchun, China, show that L-METANET accurately reproduces lane-changing-induced flow redistribution and capacity drops, with state evolution aligned with ground truth. XGBoost-SHAP-RPBL achieves AUCs above 0.80 in most tasks, outperforming conventional logit models. MPC-STMAPPO converges faster and performs better across multiple metrics than MPC- and MARL-based baselines. Under randomly fluctuating demand, it also significantly outperforms pure MARL in generalization, demonstrating strong potential for industrial deployment.

Guodong Ma, Baofeng Sun, Wenyu Yang +1

School of Transportation, Jilin University, Changchun 130022, China · School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

Intelligent Transportation Systems Model Predictive Control Multi-Agent Reinforcement Learning

Sep 24, 2026cs.CV

Smartphone-Based Method for Automated Speed Enforcement

Smartphone cameras and computer vision (CV) hold significant promise in assisting public agencies with enforcing traffic laws and enhancing road safety. This work designs and tests a smartphone-based method for automated speed estimation and vehicle identification (license plate, make/model, and color recognition) via an automated pipeline to assist enforcement agencies in reliably identifying speeders. The CV code accurately recognizes nearly half (46%) of the license plates' text on 1,800 images from a Brazil open-source dataset, called UFPR-ALPR. Code tests on daytime recordings from hand-held smartphone videos (n = 73) and roadside cameras (n = 42) in Austin, Texas yield 60.8% accuracy for color detection (among all possible RGB color categories), 48.6% on vehicle make/manufacturer identification, and 16.89% on vehicle make and model identification. Prediction accuracy for speed estimation (within a 20% range), vehicle make (within the top 3 predictions), and license plate recognition (within the top 10 predictions) are 16.3%, 16.9%, and 29.7%, respectively. This paper also illuminates the legal, technological, and practical aspects of using smartphones for enforcement, including the potential use of recordings for enforcement purposes, emphasizing the need to transform the potential of smartphone-based CV technologies into practical tools for vital information on traffic violations.

Keya Li, Jahnavi Malagavalli, Lamha Goel +2

Department of Civil, Architectural and Environmental Engineering The University of Texas at Austin · Department of Computer Science The University of Texas at Austin · Chandra Department of Electrical and Computer Engineering The University of Texas at Austin

Intelligent Transportation Systems Automatic License Plate Recognition

Sep 24, 2026cs.LG

Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation

The estimation of Annual Average Daily Traffic (AADT) is vital for transportation planning and infrastructure maintenance, yet obtaining accurate values for an entire urban network across multiple years remains challenging due to the high cost and spatial sparsity of physical sensors. This research proposes a novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model. The methodology highlights a feature propagation algorithm on directed graphs, formulated as a Poisson energy minimization considering residues. The standard binary adjacency matrix is replaced with flow ratio matrices to capture real-world vehicle turn ratios at intersections. Validated in the city of Zurich, the algorithm demonstrates high computational efficiency, achieving convergence within minutes. Results indicate that the framework effectively reconciles theoretical models with empirical ground truths, yielding a normalized mean absolute error below

10\%

. This scalable approach provides a feasible solution for spatio-temporal network-wide AADT estimation through combining real-world limited sensor coverage and traffic models.

Linghang Sun, Qishen Zhou, Michail A. Makridis +1

Institute for Transport Planning and Systems, ETH Zurich, Zurich, 8093, Switzerland · School of Transportation, Jilin University, Changchun, 130012, China

Intelligent Transportation Systems Traffic Flow Estimation

Sep 23, 2026cs.LG

Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning

Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acoustic sensing (DAS), an innovative seismic observation tool, emerges as a promising solution for real-time urban traffic monitoring at high spatial and temporal scales. Distributed acoustic sensing repurposes existing underground fiber-optic cables as dense, continuous sensor arrays, enabling passive and privacy-preserving monitoring of roadway traffic activity at meter-level spatial and second-level temporal resolution. This study examines whether integrating DAS and deep learning models can serve as a continuous and efficient urban traffic observatory for revealing urban traffic dynamics (i.e. traffic volume and congestion, event-driven changes) at high spatiotemporal resolution. Using a DAS deployment along a roadway network in the City of College Station, Texas, USA, this study develops a deep learning-empowered analytical framework that converts raw ground vibration waveforms into spatiotemporal representations, detects vehicle trajectory, and infers traffic states from aggregated traffic volume and speed. A hybrid training strategy combining synthetic and manually annotated DAS images is used to improve vehicle detection under noisy and congested conditions, with model outputs further aggregated to characterize system-level traffic dynamics.

Hao Tian, Heng Cai, Xiaowei Chen +1

Department of Geography, Texas A&M University, College Station, TX 77840, USA · Department of Geology and Geophysics, Texas A&M University, College Station, TX 77840, USA

Intelligent Transportation Systems Traffic Flow Estimation Distributed Acoustic Sensing

Sep 23, 2026cs.LG

Learning Local Heterogeneity and Cross-Region Context for Large-Scale Traffic Forecasting

Traffic flow forecasting is essential to intelligent transportation systems. Large-scale traffic forecasting requires jointly modeling local spatial dependencies and cross-region context.Spatial dependencies between geographically neighboring nodes are heterogeneous due to differences in road identity and travel direction, while acquiring global information through allpairs node interactions incurs substantial computational costs. Therefore, capturing local heterogeneity while efficiently acquiring long-range context remains an important challenge in largescale traffic forecasting. To address these challenges, we propose LoReST, a Local-Region Spatial Temporal network that models spatial dependencies at two complementary granularities: node neighborhoods and road network regions. Specifically, relation-aware local aggregation captures heterogeneous dependencies within geographic neighborhoods through road and direction specific feature transformations. Cross-region interaction constructs region representations through mean pooling, exchanges long range context via inter-region attention, and broadcasts it back to nodes. By integrating local information aggregation with crossregion interaction, LoReST is able to effectively achieve spatial dependency learning in large-scale road networks. Experiments on four datasets of the LargeST benchmark show average relative reductions of 4.78%, 3.60%, and 5.75% in MAE, RMSE, and MAPE, respectively.

Qi Feng, Zidong Wang, Bo Li +4

School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710129, China · Department of Computer Science, City University of Hong Kong, Hong Kong SAR, and also with Shenzhen Research Institute, City University of Hong Kong, China · School of Electronic Information (School of Artificial Intelligence), Northwest University, Xi’an, China

Intelligent Transportation Systems Traffic Forecasting Spatiotemporal GNNs

Sep 22, 2026cs.CV

A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality dataset of naturalistic vehicle trajectories collected at a highway merging segment in Fort Worth, Texas, to systematically assess radar sensing performance against drone-derived ground truth. The performance is evaluated at three levels: individual vehicle detection, trajectory tracking, and macroscopic traffic parameter estimation. For individual vehicle detection, the radar achieves an overall precision of 78% and a recall of 57%, with degraded performance under congested traffic conditions and at longer distances. At the trajectory level, the radar demonstrates reasonably strong tracking performance (IDF1 = 0.699), maintaining reliable vehicle identities when tracks are successfully established. For macroscopic traffic flow metrics, the radar accurately estimates space-mean speed (MAPE < 4%) but underestimates density and volume by approximately 23% due to missed detections. The paper also discusses practical deployment considerations and potential downstream applications of roadside radar sensing systems. To support reproducible research on infrastructure-based sensing systems, we have open-sourced the DRaT dataset on Zenodo: https://zenodo.org/records/20171110.

Tianheng Zhu, Woei-chyi Chang, Alamss Riaz +2

Lyles School of Civil and Construction Engineering Purdue University, West Lafayette, IN, USA

Intelligent Transportation Systems Traffic Flow Estimation Object Detection Multi-Object Tracking

Sep 22, 2026cs.CR

On the security and privacy of LLMs in Mobility

The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has the potential to impact more than 1.5 billion vehicles worldwide. As Large Language Models (LLMs) are increasingly adopted in mobility, concerns about cybersecurity, privacy, and reliability emerge. Accordingly, this paper surveys current applications and assesses these challenges. Since the European AI Act classifies transportation AI as high risk, we derive nine technical classes from its requirements to assess current research and future deployments. Our findings show that research mainly studies GPT and Llama models (over 50% of reviewed works) and traffic applications while largely neglecting security, privacy, and reliability. This gap extends to AI Act compliance: among 35 reviewed works, only one includes a partial vulnerability assessment and one a partial risk management system. We identify a clear gap between strong optimization performance and regulatory adherence, suggesting compliance is limited less by technology than by a focus on static performance over lifecycle safety, and underscoring an urgent need for security-by-design in safety-critical intelligent transportation systems.

Mauro Conti, Lorenzo Perinello, Umberto Salviati

University of Padova Italy · Fondazione Bruno Kessler Italy

Intelligent Transportation Systems LLM Security LLM Reliability AI Governance

Sep 21, 2026cs.CV

MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts

Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This allows comprehensive coverage by experts for highly specific, focused, and temporary situations.

Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel +3

ETH Zurich, Zurich, Switzerland. · Bosch Research, Hildesheim, Germany. · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria.

Intelligent Transportation Systems Mixture of Experts Domain-Incremental Learning Object Detection Incremental Learning

Sep 17, 2026cs.NI

NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment

Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, reporting unrealistically high message delivery in dense traffic. This study introduces resource-competition losses without requiring full protocol reimplementation. We labeled 10.5 million reception outcomes from ns-3 5G-LENA traces (calibrated on 3GPP scenarios and driven by SUMO trajectories) to fit NS3Learn - a closed-form model capturing half-duplex loss, scheduling collisions, receiver capture, and decoding. Evaluation spanned two signalized urban networks, six penetration levels (1-100%), and five random seeds per condition. NS3Learn achieved a mean absolute deviation of 0.06 in per-instant delivery compared to ns-3 5G-LENA, outperforming alternative models (0.44 and 0.55 deviation). Fitted parameters transferred to a distinct intersection with only 20% additional error. Crucially, using realistic communication models reversed simulated traffic speed trends and more than doubled predicted hard-braking events. The framework transfers reception realism between simulators via model distillation instead of full reimplementation. Every stage maps directly to an explicit physical mechanism. Researchers and transportation agencies can maintain existing simulation pipelines while accurately accounting for dense-traffic packet loss and denial-of-service impacts. Adapting to new radio configurations requires only offline refitting rather than code modification.

Rasheed Bello, Arthur Mukwaya, Gurcan Comert +5

South Carolina State University Orangeburg, SC 29117, USA · North Carolina A&T State University Greensboro, NC 27411, USA · Dakota State University Madison, SD 57042, USA +1

Surrogate Modeling Intelligent Transportation Systems Wireless Communications V2X Communication

Sep 15, 2026cs.LG

Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control

Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining their complementary capabilities. DRL learns control policies through interaction with the environment. MPC uses a system model to optimize control inputs while accounting for constraints. In DRL-MPC frameworks with shared control authority, both the DRL agent and the MPC controller each determine part of the control inputs. However, common learning formulations treat MPC as part of the environment and therefore do not explicitly account for MPC's contribution to control or its interaction with the DRL agent. This paper proposes a novel composite-gradient learning (CGL) method that integrates the MPC controller into the learning process by representing the DRL and MPC control inputs as a joint action and accounting for their interaction when updating the DRL agent during training. CGL is evaluated on two multi-class freeway traffic networks with different strengths of interaction between the DRL and MPC control inputs and it is compared with alternative methods that treat MPC as part of the environment or that only partially incorporate MPC into learning. The results show that CGL offers limited benefit under weak interaction, but learns higher-performing control policies than the alternative methods in a subset of training runs under strong interaction, although the average control-performance gains remain modest.

Giray Önür, Azita Dabiri, Bart De Schutter

Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands

RL Control Reinforcement Learning Intelligent Transportation Systems Model Predictive Control

Sep 15, 2026cs.RO

DriveMCP: An Agentic AI framework for Advanced Driver Assistance System

An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a modular and auditable pipeline. The architecture, referred to as DriveMCP, incorporates a sensor-like perception stack alongside DriveLM as the vision-language front end to generate a graph-structured scene understanding (Graph Visual Question Answering) and language-grounded driving information. Key compliance elements in world_state, including posted speed limits and jurisdiction cues, are derived from DriveLM outputs through a structured parsing layer rather than being injected as simulator ground truth. A stateful orchestration layer coordinates specialized experts exposed as Model Context Protocol (MCP) servers: (i) a Rules server that performs retrieval-augmented compliance reasoning over jurisdiction-specific traffic codes and sign conventions, (ii) a Weather server that estimates traction risk and contextual speed advisories, and (iii) an MCP-CAN server that surfaces Controller Area Network (CAN)/On-Board Diagnostics (OBD) telemetry and diagnostic context for health-aware risk shaping. These outputs are fused to generate a structured decision that prompts a recommended course of action. The outcome is then further filtered by a Responsibility-Sensitive Safety (RSS)-inspired guardrail that arbitrates speak versus act decisions under bounded online adaptation. In CARLA simulation across multilingual, cross-border, and dynamic speed-limit scenarios, DriveMCP reduces traffic infractions and overspeed relative to the VLM-Direct, VLM-Direct+RAG, and VLM-Tools-NoArbiter baselines, while improving hazard response time and maintaining sub-second advisory latency.

Farzad Nadiri, Mehdi Cina, Ahmad B. Rad

Department of Mechatronics Engineering, Simon Fraser University, Burnaby, BC, Canada

VLMs for Autonomous Driving Intelligent Transportation Systems Autonomous Driving Runtime Enforcement for AI Agents

Sep 14, 2026cs.LG

Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework

Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive and cover only a small share of intersections. We present a machine learning pipeline that predicts 2-hour PM peak pedestrian volume at 101 urban intersections in Portland, Oregon, from built-environment, land-use, and street-network features drawn from open GIS data. Starting from the Negative Binomial GLM used in practice, we add feature selection, count-aware gradient boosting, and repeated cross-validation, selecting one configuration by a combined rank over RMSE, MAPE, and SMAPE across four cross-validation strategies. The winner, a histogram-based gradient boosting model with Poisson loss and L1 Lasso feature selection, reduces cross-validated RMSE by 12% over the GLM baseline (89.8 to 78.7) and holdout RMSE by 19% (108.0 to 87.9). Code is released on GitHub.

Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri +1

Department of Computer Science, Portland State University Portland, Oregon, USA · Dept. of Civil and Environmental Engineering, Portland State University Portland, Oregon, USA · Transportation Research and Education Center (TREC), Portland State University Portland, Oregon, USA

Intelligent Transportation Systems

Sep 12, 2026cs.AI

Predicting Train Delays in Finland Using Machine Learning and Weather Data

Reliable railway operations depend increasingly on real-time environmental intelligence delivered through wireless sensor infrastructures, a capability that 6G networks will substantially enhance through integrated sensing and edge computing. Adverse weather, particularly in Arctic regions with extreme temperatures and heavy precipitation, remains a leading cause of train delays, yet most prediction approaches rely on raw meteorological inputs without exploiting domain-informed feature engineering. This paper investigates machine learning for train delay prediction using the Finland Integrated Train-Weather (FI-TW) dataset, which fuses railway operational records with observations from the Finnish Meteorological Institute's nationwide sensor network of approximately 200 stations communicating over wireless links. We evaluate three feature configurations using XGBoost at Oulu central station (101,146 observations): full weather features, instant weather observations only, and derived weather category scenarios. The category-based approach, employing hierarchical classifications such as Blizzard, Heavy Snow, and Extreme Cold, achieved an R^2 of 0.78, root mean squared error of 8.5 minutes, and mean absolute error of 3.7 minutes, representing an 11% R^2 improvement and 10% error reduction over alternative configurations. These results demonstrate that compact, domain-informed features derived from sensor streams outperform raw meteorological observations, offering bandwidth-efficient representations suitable for edge deployment over current and emerging wireless infrastructures.

Vinicius Pozzobon Borin, Jean Michel de Souza Sant'Ana, Nurul Huda Mahmood

Centre for Wireless Communications, University of Oulu, Oulu, Finland

Intelligent Transportation Systems Time Series Forecasting

Sep 11, 2026cs.LG

A Dynamic Fusion Large Language Model for Traffic Flow Prediction

Traffic flow prediction is a core supporting technology for intelligent transportation systems. It uses historical data to infer future traffic dynamics in specific areas, thereby helping to alleviate congestion and improve resource allocation efficiency. Traditional neural networks struggle to break through accuracy limits due to their reliance on singular feature modeling, while large language models (LLMs) suffer from insufficient capture of spatial topological information and mining spatiotemporal correlation. This study proposes a Dynamic Fusion Large Language Model (DF-LLM) for traffic flow prediction. The model incorporates three core components: spatiotemporal embedding module, spatiotemporal fusion module, and LLM backbone. The spatiotemporal embedding module enables synergistic representation of multi-scale spatiotemporal features. The spatiotemporal fusion module integrates spatial topology and dynamic dependencies via graph convolution. The LLM backbone adopts a differentiated parameter adaptation strategy to balance training efficiency and traffic data adaptability. Additionally, it introduces a context aggregation attention module to strengthens global dependencies. More importantly, the LLM backbone takes the residual connections to mitigate the gradient vanishing in deep networks. Experiments show that DF-LLM has achieved better performance by comparing the metrics on all the four datasets.

Xue Qiu, Jianli Xiao

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

Intelligent Transportation Systems Traffic Flow Estimation Multi-Scale Feature Fusion LLM-Based Time Series Forecasting Spatiotemporal GNNs

Sep 9, 2026cs.LG

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

We introduce MetroLLM-Bench, a 955-case benchmark for testing language models as the policy layer of a transit kiosk. It covers six real metro systems, ranging from 37 to 414 stations, and eleven categories that include routing, fare calculation, disruptions, accessibility, and adversarial input. In each case, the model must call structured tools and submit a machine-renderable terminal state containing an outcome, a per-ticket fare quote when applicable, and a kiosk action. Fourteen deterministic scoring components form Tier 1; eight semantic-quality components form Tier 2, six of which use a language-model judge. We report Tier 1 and the combined score of both tiers. A stratified 75/25 split reserves 717 cases for training-data generation and 238 for held-out evaluation. We evaluate twenty-six models from six vendors, of which twenty-three are ranked. On the held-out partition, a 4B Qwen 3.5 student trained through parameter-efficient fine-tuning (PEFT) exceeds both GPT-5.6 tiers on Tier 1 (91.3 against 90.6 and 90.0) and matches GPT-5.4 full at maximum reasoning effort (91.4), with a 2.6 GB Q4_K_M footprint. Larger 9B and 27B students provide no further Tier 1 improvement over the 4B student at this training scale. Across the four Qwen sizes, the PEFT gain over the corresponding base model decreases from +7.03 points at 2B (three training seeds) to -0.91 at 27B; every seed shows the same direction at every size. A deterministic rule-based baseline reaches 84.6 on Tier 1, with the remaining language-model advantage concentrated in policy adaptation, compound scenarios, accessibility, and temporal reasoning. Muse Glimmer 30B leads the composite ranking, and serving configuration alone moves the Qwen 3.5-to-3.8 comparison by 2.7 Tier 1 points. The benchmark, harness, reproduction guide, and fine-tuned students are released at https://github.com/continker/metrollm-bench.

Remco Hendriks

Continker

LLM Evaluation Intelligent Transportation Systems LLM Tool Use

Sep 2, 2026cs.LG

TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosis

Diagnosing collective anomalies from urban trajectories is increasingly important for traffic governance, as it reveals what happened, who was involved, and where and when the event occurred. Existing detectors efficiently produce scores or labels, whereas vision--language pipelines provide richer semantics; neither couples verifiable diagnosis with low-latency monitoring. The central challenge is to recognize collective patterns and recover exact event details from the source trajectories without running the full diagnostic pipeline for every monitored window. We therefore separate always-on screening from on-demand diagnosis: screening raises alerts, while diagnosis releases only source-verified what--who--where--when records. We present TrajMind, a fast-and-slow framework that switches three role-specialized LoRA adapters over one frozen vision--language backbone. Its slow path, \textit{TrajMind

_{\text{slow}}

}, chains canvas-based typing, type-conditioned localization over serialized trajectories, and executable verification, yielding structured, evidence-backed diagnoses. Additionally, the fast path, \textit{TrajMind

_{\text{fast}}

}, screens each window in a single text-only pass, delivering efficient structured alerts. Extensive experiments show that, TrajMind

_{\mathrm{slow}}

outperforms the strongest baselines by at least

15.3

percentage points in anomaly typing and

13.8

percentage points in localization. These gains persist under cross-city transfer, and TrajMind

_{\mathrm{fast}}

reduces latency by

41.1\%

and maintains binary balanced accuracy of at least

93.5\%

. Together, TrajMind delivers accurate, evidence-backed diagnoses across cities and efficient front-line monitoring.

Jiahao Wu, Zhenqun Yang, Chen Jason Zhang +1

The Hong Kong Polytechnic University · Hong Kong, China

Anomaly Detection Agent Trajectory Analysis Intelligent Transportation Systems Vision-Language Models

Sep 2, 2026cs.CV

VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation

End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Severe occlusions and dense traffic at intersections limit the perception capability of single-agent systems, motivating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation for perception and planning. However, existing evaluation protocols face a fundamental trade-off: open-loop evaluation fails to capture error accumulation and recovery from deviations, while closed-loop evaluation is costly, difficult to scale, and often relies on simulated environments that may suffer from domain gaps. To bridge this gap, we propose VIPS, a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation. VIPS extends pseudo-simulation by integrating vehicle and infrastructure observations. This enables scalable yet realistic evaluation of robustness and error propagation without full simulation. We further present CoS-V2X, a cooperative planning framework based on sparse representations. CoS-V2X models vehicle-infrastructure interactions using compact features for efficient communication and robust decision-making under heterogeneous observations. Code and dataset are available at https://vips2026.github.io.

Hoonhee Cho, Jae-Young Kang, Giwon Lee +3

KAIST, Republic of Korea

Autonomous Driving Autonomous Driving Benchmarks Intelligent Transportation Systems

Sep 1, 2026cs.RO

Context-Aware Intelligent Vehicles

Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet management, all under tight requirements on accuracy, latency, cost, and reliability. Meeting these requirements is challenging because vehicles operate in complex, uncertain, and rapidly changing environments while running on resource-constrained computing platforms. This paper argues that context-situational factors that give meaning to sensor signals and constrain decisions-should be treated as a first-class principle for next-generation vehicle systems, and operationalized as a unified, shared state for learning, risk assessment, and closed-loop control across the software stack. We systematically review state-of-the- art (SOTA) context-aware methods spanning (i) environment understanding, (ii) planning and control, (iii) safety and security, and (iv) connected vehicles. Based on a trend analysis of context-aware design, we identify four key technical challenges in building a general contextual engine for future intelligent vehicles: multi-modal context fusion, temporal context modeling, handling rare events, and collaborative context sharing. We hope this survey will motivate the development of robust and efficient context-aware vehicle applications.

Liangkai Liu, Shuyao Shi, Mingke Wang +4

Department of Computer Science and Engineering, University of Michigan, USA · General Motors, Warren, Michigan, USA

Intelligent Transportation Systems Autonomous Driving

Aug 30, 2026physics.soc-ph

ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction

Accurate traffic flow prediction is fundamental to intelligent transportation systems, playing a pivotal role in urban mobility optimization and smart city development. While Graph Neural Networks (GNNs) integrated with time series forecasting have emerged as promising solutions, two critical limitations persist: (1) the quadratic complexity of attention-based architectures hinders real-time deployment in large-scale networks, and (2) high-frequency noise in sensor data significantly degrades prediction reliability. These challenges are particularly acute in metropolitan scenarios where both computational efficiency and noise robustness are paramount. To address these limitations, we introduce \textbf{ButterMamba}, a novel and efficient framework based on State Space Models (SSMs). ButterMamba consists of two key components: (1) a Butterworth Spectral Filtering module that preprocesses the data by removing high-frequency noise, allowing the model to focus on significant underlying trends, and (2) a Spatial-Temporal State Mixer that uses a parallel Mamba architecture to efficiently capture both long-range temporal dependencies and complex spatial correlations across the road network. By decoupling noise filtering from spatial-temporal modeling, ButterMamba achieves superior predictive accuracy with linear computational complexity. Extensive experiments on three public datasets demonstrate that ButterMamba not only outperforms existing state-of-the-art models in terms of prediction accuracy but also considerably reduces training time and memory usage.

Limiao Zhang, Yuhui Lu, Jie Gao +3

State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University · Institute of Physical Science and Information Technology, Anhui University · School of Innovation and Entrepreneurship, Shandong University +1

Mamba Intelligent Transportation Systems Traffic Flow Estimation Spatiotemporal Forecasting State Space Models

Aug 13, 2026cs.CV

UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations

Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions between vehicles and vulnerable road users. A useful system should explain how a traffic event develops, why it happens, and when the relevant interaction occurs, yet this remains difficult for multimodal large language models (MLLMs) because traffic videos contain sparse events and varied viewpoints. We introduce UniTraffic-Agent, the MR-CAS solution for Track~3 of the 10th AI City Challenge, which includes Traffic Anomaly Reasoning (TAR) and two out-of-domain evaluations: FETV for fisheye traffic events and PSI-VQA for pedestrian intention reasoning. UniTraffic-Agent follows an observe--reason--act--verify workflow that samples timestamped visual evidence, reasons over all questions from the same clip in one request, and converts responses through task-specific action adapters. On the official Public leaderboards, MR-CAS ranks 16th on TAR with a score of 0.5780, 2nd on FETV with 0.4884, and 4th on PSI-VQA with 64.4161. The code is available at https://github.com/Roclp/UniTraffic-Agent.

Peng Li, Qianqian Xu, Shilong Bao +2

School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China · Beijing Academy of Artificial Intelligence (BAAI), Beijing, China +1

Pedestrian Intention Prediction Intelligent Transportation Systems Video Reasoning VLM Reasoning Multimodal Agents Anomaly Detection

Aug 11, 2026cs.AI

RTSKG: Building a Rail Transit Station Knowledge Graph Dataset

Rail transit systems play a vital role in urban mobility and economic development. As key components of such systems, rail transit stations function as critical transport hubs that enhance urban accessibility and stimulate development in surrounding areas. City-level rail transit station related tasks (e.g., ridership prediction) require large-scale urban data, but current studies often neglect complex interactions among various urban entities in terms of data organization. In this paper, to address the above issue, we build a Rail Transit Station Knowledge Graph (RTSKG) dataset which explicitly models the spatial and semantic interactions among different kinds of urban entities, to benefit city-level rail transit station related tasks. RTSKG integrates heterogeneous urban entities, such as rail transit stations, road segments, and points of interest, with a specially designed unified schema, and is accessible as Linked Data at https://w3id.org/rtskg/. Evaluations on station-area store recommendation and knowledge-enhanced ridership prediction demonstrate the effectiveness of RTSKG, highlighting its potential to support city-level rail transit station analysis.

Shutong Zhu, Tianxing Wu, Runfeng Liu +3

School of Computer Science and Engineering, Southeast University, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · School of Architecture, Southeast University, China

KG Construction Intelligent Transportation Systems Knowledge Graphs

Aug 9, 2026cs.AI

A QUBO-Inspired Computational Framework for Airport Landside Bottleneck Diagnosis and Dynamic Dispatch Optimization

Airport landside traffic centers connect terminal arrivals with taxis, ride-hailing vehicles, private cars, buses, metro services, parking facilities, and terminal-area roadways. Peak arrivals can create coupled congestion across passenger queues, vehicle queues, pickup berths, storage areas, and access roads. This study proposes a QUBO-inspired computational framework for bottleneck diagnosis and dynamic dispatch in this setting. Shanghai Pudong International Airport and Hangzhou Xiaoshan International Airport serve as case airports. A five-minute state model links passenger arrivals, vehicle supply, pickup berth service, vehicle storage, and road capacity. Bottleneck diagnosis uses service intensity, road demand saturation, bottleneck frequency, queue severity, shadow-price leverage, and a composite congestion severity index. Two dispatch schemes are tested under consistent demand inputs: finite-action model predictive control and quadratic-unconstrained-binary-optimization-inspired simulated annealing. In the strong-peak baseline scenario, the QUBO-inspired method reduces the final passenger queue from 3445 to 2477 passengers at Shanghai Pudong and from 2053 to 1482 passengers at Hangzhou Xiaoshan. Case results indicate different dominant bottlenecks. Shanghai Pudong is more affected by road saturation, whereas Hangzhou Xiaoshan is more affected by pickup berth service. Robustness tests under demand, supply, service, road-capacity, modal-share, and random-noise perturbations show retained queue-reduction benefits under the tested uncertainty levels.

Wuming Lei, Xiaobin Li, Mingyan Sun +3

East China Jiaotong University, Nanchang, China

Intelligent Transportation Systems

Aug 8, 2026cs.AI

Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challenges

Large multimodal agents (LMAs) are increasingly proposed for intelligent transportation systems (ITS), but existing studies often conflate multimodality, agency, empirical performance, and deployment readiness. This review provides an auditable evidence map of 42 primary study families released between January 2023 and 3 August 2026 within a corpus of 91 mapped sources. It distinguishes model-level, system-level, and hybrid multimodality and classifies each family by system architecture and action authority. Evidence is assessed independently through functional capability (C0-C3), validation setting (E0-E4), three evidence propositions (P1-P3), and eight methodological-concern domains (Q1-Q8). Transportation semantics (P1) are directly evaluated in 23 families and multidimensional integration (P3) in 24; 19 families directly evaluate both. Evidence reconciliation (P2) remains unresolved because no family demonstrates the complete provenance-challenge-handling-comparison-outcome chain. Fourteen families reach C3, but 13 remain at E2; only one reaches E3 and none reaches E4. Across ITS domains, LMAs are best supported for semantic interpretation, intent translation, evidence organisation, scenario authoring, explanation, and specialist-tool coordination. Numerical forecasting, optimisation, simulation fidelity, hard constraints, low-level control, safety fallback, and final authority should remain with independently verifiable specialist systems or accountable humans. The review therefore supports bounded orchestration rather than replacement and provides a matched comparative evaluation protocol and staged roadmap for accountable deployment. The living evidence repository is available at https://github.com/pangjunbiao/ITS-LMA-Review.

Muhammad Ayub Sabir, Shaohong Zheng, Zhiyu Qu +2

Intelligent Transportation Systems Multimodal Agents Multimodal Model Evaluation

Aug 8, 2026eess.SY

Hierarchical Multi-Task Federated Learning in VANETs

Vehicular Ad hoc Networks (VANETs) increasingly rely on federated learning (FL) to enable collaborative intelligence without sharing raw sensory data. However, most existing vehicular FL frameworks assume that all vehicles train a single global model for a common task, which limits their applicability in practical vehicular environments where vehicles may perform heterogeneous learning tasks under non-independent and identically distributed (non-IID) data, intermittent connectivity, and high mobility. To address these challenges, this paper proposes an AutoEncoder-based Reliability-Optimized Hierarchical Multi-Task Federated Learning (AERO-HMTFL) framework for dynamic multi-hop clustered VANETs. The proposed framework introduces a tri-weighted clustering metric that jointly considers vehicular mobility, shared-model similarity, and task affinity to produce mobility-stable, semantically aligned clusters. Each vehicle employs a split-model architecture comprising a shared autoencoder-based representation module and multiple task-specific heads, with only the shared autoencoder parameters exchanged while the task heads remain local. To improve robustness, cluster heads perform reliability-aware aggregation based on historical validation performance and participation frequency, while the Evolved Packet Core (EPC) conducts global shared-autoencoder fusion across clusters. Extensive simulations demonstrate that, compared with the multi-task federated learning benchmarks, AERO-HMTFL achieves up to 13% higher sustained EPC-level accuracy, exhibits more stable learning dynamics, and reduces EPC-level packet transmissions by approximately 87-97%. Under short-range connectivity, it also requires approximately 13-29% fewer communication rounds to converge.

M. Saeid HaghighiFard, Sinem Coleri

Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey

Hierarchical Federated Learning Communication-Efficient Distributed Training Intelligent Transportation Systems Clustered Federated Learning Federated Learning