Traffic State Sequence

Traffic state sequence analysis focuses on predicting future traffic conditions by modeling the temporal and spatial evolution of traffic patterns. Current research emphasizes the development of sophisticated deep learning models, including convolutional neural networks, recurrent neural networks (like GRUs), graph convolutional networks, and attention mechanisms, often integrated within a multi-granularity framework to capture complex spatio-temporal dependencies. These advancements aim to improve the accuracy and efficiency of traffic forecasting, particularly in addressing challenges like irregular data and long-range dependencies. The ultimate goal is to enhance intelligent transportation systems through better traffic management, optimized resource allocation, and improved urban planning.

Papers

November 12, 2024

Bayesian Deep Learning Approach for Real-time Lane-based Arrival Curve Reconstruction at Intersection using License Plate Recognition Data
Yang He, Chengchuan An, Jiawei Lu, Yao-Jan Wu, Zhenbo Lu, Jingxin Xia
Full State Reconstruction Intersection Scenario Bayesian Deep Learning Arrival Estimation Traffic State Sequence

October 16, 2024

Incorporating Long-term Data in Training Short-term Traffic Prediction Model
Xiannan Huang, Shuhan Qiu, Yan Cheng, Quan Yuan, Chao Yang
Training Data Full Model Long Term Traffic Prediction Short Term Traffic Traffic State Sequence

May 31, 2024

An Attention-Based Multi-Context Convolutional Encoder-Decoder Neural Network for Work Zone Traffic Impact Prediction
Qinhua Jiang, Xishun Liao, Yaofa Gong, Jiaqi Ma
Encoder Decoder Traffic Data Traffic Congestion Context Attention Traffic State Sequence

May 3, 2024

Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks
Zijian Zhang, Yujie Sun, Zepu Wang, Yuqi Nie, Xiaobo Ma, Peng Sun, Ruolin Li
Timely Survey Transportation System Mobility Management Mobility Data Forecasting Task Travel Behavior Human Mobility Forecasting Traffic State Sequence Forecasting Application

April 8, 2024

STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Zhengyang Zhao, Haitao Yuan, Nan Jiang, Minxiao Chen, Ning Liu, Zengxiang Li
Traffic Forecasting Traffic Data Road Network Accurate Traffic Prediction Traffic State Sequence

August 31, 2023

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network
Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Yanjie Fu, Jingbo Zhou, Yu Mei, Hui Xiong
Traffic Forecasting Accurate Traffic Forecasting Traffic State Sequence

February 17, 2023

Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Min Liu, Yuwei Wang, Bo Gao
Traffic Data Traffic Flow Forecasting Traffic State Sequence

Traffic State Sequence

Papers

Bayesian Deep Learning Approach for Real-time Lane-based Arrival Curve Reconstruction at Intersection using License Plate Recognition Data

Incorporating Long-term Data in Training Short-term Traffic Prediction Model

An Attention-Based Multi-Context Convolutional Encoder-Decoder Neural Network for Work Zone Traffic Impact Prediction

Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks

STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting