Traffic Prediction and Management Techniques

Latest papers

• Spatio-Temporal Partial Sensing Forecast for Long-term Traffic

  Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu zhang, Haibo Wang, Shigang Chen · 1 October 2026

  Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper studies partial sensing forecast of long-term traffic, assumin…

• Travel Time Prediction in Supply Chain Management Using Machine Learning

  Balaji Venkateswaran · 1 October 2026

  The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and heavily relies on the transportation and logistic…

• AdaST: Adaptive Coupling for Spatial-Temporal Forecasting

  Zhenyu Lei, Chenghao Liu, Yushun Dong, Qi R. Wang, Jundong Li · 30 September 2026

  Spatial-temporal (ST) forecasting underpins many real-world systems such as traffic, climate, and energy networks. While existing methods implicitly assume strong spatiotemporal coupling, we observe that real-world ST data exhibits distinct coupling regimes, ranging from temporal-dominated and spati…

• More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting

  Lewei Xie, Haoyu Zhang, Jiajun Zhou, Yulong Chen, Guanxing Chen, Yu-An Huang, Hau-San Wong, Yifan Zhang, Zhi-An Huang · 28 September 2026

  Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. However, conventional graph-based continual learning methods tie forecasting representations to the current sensor layout, so sensor expansion can alt…

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

  Linghang Sun, Qishen Zhou, Michail A. Makridis, Anastasios Kouvelas · 25 September 2026

  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 …

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

  Hao Tian, Heng Cai, Xiaowei Chen, Yifan Yang · 25 September 2026

  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 …

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

  Qi Feng, Zidong Wang, Bo Li, Xiaoguang Gao, Jiayu Zhang, Chenfeng Wang, Kaifang Wan · 24 September 2026

  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 identi…

• Disentangling Heterogeneous Traffic Dynamics for Multi-Step Traffic Forecasting via Adaptive Spectral Decomposition

  Zijun Huang, Chenrui Fu, Wenhao Wang, Xiaochuan Gou, Chih-Chieh Hung, Guanyao Li · 23 September 2026

  Accurate multi-step traffic forecasting remains challenging because observed traffic signals contain heterogeneous temporal dynamics with different characteristics and levels of predictability. Existing approaches typically model these dynamics within a unified representation or rely on predefined d…

• JEPA Guided Diffusion: Predictive Vision-Language Conditioning for Generative Traffic Forecasting

  Trinh Tra Giang Nguyen, Thanh Nguyen Vo, Nguyen Hoai Thuong Bui, Ha Duc Bui · 21 September 2026

  Accurate traffic forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent diffusion-based video generation models produce visually plausible predictions but require expensive end-to-end training and often entangle scene understanding with image sy…

• Explaining spatial information flow in short-term traffic forecasting models using a gated graph attention network

  Yue Li, Shujuan Chen, Ying Jin · 18 September 2026

  Short-term traffic forecasting supports real-time monitoring and control of road networks, and graph attention networks (GAT) are the standard means of representing spatial dependence in these models. GAT layers are widely described as capturing the influence of neighbouring locations, but this is s…

• Scene-Conditioned Relation Routing for urban cellular activity forecasting

  Qingzhong Li, Jingye Lin, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing · 18 September 2026

  Urban cellular activity forecasting requires jointly modeling heterogeneous spatiotemporal signals, including SMS usage, mobile network traffic, and call activity. Existing methods often separate temporal modeling, spatial relation learning, and multi-signal prediction, relying on fixed graph struct…

• FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

  Fermin Orozco, Man Luo, Johan Wahlstr\"om · 18 September 2026

  Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial constraints, motivating federated spatial-temporal approaches. In such federated settings, each client observes traffic over a distinct sensor subgra…

• AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting

  Zhixiang Wu, Yining Liu, Bo Zhao, Szu-Yu Chen, Huiran Duan, Chu Lin, Chuanguang Yang · 16 September 2026

  Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous sources such as physical fields, satellite imagery, and in-situ sensors. Three obstacles persist: (i) modalities have different spatio-temporal sampl…

• How Good Are Time-Series Foundation Models for Pedestrian Crowd Count Forecasting? A Cross-Dataset Comparative Study

  Theivaprakasham Hari, Ziteng Li, Yanan Xin, Winnie Daamen, Serge Hoogendoorn · 16 September 2026

  Pedestrian-count forecasting supports pedestrian-oriented Intelligent Transportation Systems (ITS), including crowd monitoring, pedestrian-traffic staffing and routing, and proactive risk mitigation during surges. Recent time-series foundation models (FMs) report strong zero-shot accuracy on heterog…

• STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

  Jiawen Chen, Qi Shao, Yongjian Chang, Mingtong Zhou, Duxin Chen, Wenwu Yu · 15 September 2026

  Spatio-temporal traffic forecasting is a fundamental big data analytics task for intelligent transportation systems, where massive urban sensor streams exhibit heterogeneous, non-stationary, and structurally dynamic patterns. Although recent deep learning and large language model (LLM)-based methods…

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

  Bahareh Golchin, Banafsheh Rekabdar, Sirisha Kothuri, Joseph Broach · 14 September 2026

  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 int…

• A Dynamic Fusion Large Language Model for Traffic Flow Prediction

  Xue Qiu, Jianli Xiao · 11 September 2026

  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 brea…

• TD-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting

  Mohamad Alkadamani, Halim Yanikomeroglu · 9 September 2026

  Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation. This paper proposes the Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT), a graph neur…

• SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons

  Yuhang Wang, Kailang Ma, Zirui Li, Mingfeng Fan, Kitae Jang, Changju Lee, Heye Huang · 9 September 2026

  Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle platoon. This limitation may lead to accurate but string-unstable predictions. We propose SSP-DMGTimeNet, a physics-constrain…

• CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects

  Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi, Hisashi Kashima · 31 August 2026

  Predicting non-periodic traffic congestion caused by sudden incidents (e.g., accidents and road damage) is crucial for advanced intelligent transportation systems. However, incident-driven congestion is difficult to forecast because incidents are extremely sparse, occur at specific times and locatio…

• A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems

  Varvara Mama, Eleni Veroni, Nikolaos Kapsalis, Christos D. Nikolopoulos, Anargyros T. Baklezos · 28 August 2026

  In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are based on mostly static data, the proposed work takes into account dynamic traffic conditions, thus enabling optimal performance, even in cases of u…

• A Multi-View Coupled Tensor Decomposition for Lightweight Online Adaptive Traffic Prediction

  Quan Yu, Jie Ni, Yu-Hong Dai, Xiongjun Zhang · 27 August 2026

  Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing observations and anomalous disturbances make this task challenging, particularly when prediction relies on a single tra…

• Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026

  Maciej Adamiak, Sascha Fendrich, Julian Psotta, Alexander Zipf · 25 August 2026

  The dataset provides individual vehicle speed observations on European E-roads: motorways, trunk roads, primary and secondary roads, as tagged in OpenStreetMap as e-road, for the years 2022-2026. Speeds are derived from Copernicus Sentinel-2 Level-2A satellite optical imagery using a processing pipe…

• HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance

  Xiao Wang, Shun Ren Yang, Hui Nien Hung · 19 August 2026

  Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path rerouting can be highly effective in simulation, but assumes that essentially every on-road vehicle is replanned every decision period. We propose HLSR, a selective hybr…

• General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting

  Mattis thor Straten, Yannick Wolker, Steffen Strohm, Prathvish Mithare, Ralf Krestel, Matthias Renz · 19 August 2026

  Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge…