Freeway Performance Measurement System: Mining Loop Detector Data

By Chao Chen, K. Petty, A. Skabardonis, Pravin Pratap Varaiya, Zhanfeng Jia. 2001

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zezhi Shao
2. Jeongwhan Choi
3. Shengna Guo
4. Zhao Zhang
5. Youfang Lin
6. Chao Song
7. Lei Bai
8. Zhanxing Zhu

Citations in the corpus

• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “(iv) PeMSD7(M) [6] - Data collected from Caltrain PeMS system, which contains data for 228 time-series, collected at 5 min interval.”
• Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, background): “• Traffic flow dataset in PeMSD4 This dataset, namely PeMSD4 [53], [54], covers the San Francisco Bay Area, and it is collected from January to February in 2018.”
• Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems (2003, background): “Loop data provide a powerful means to study and monitor traffic (2).”
• Unified Training of Universal Time Series Forecasting Transformers (2024, background): “LargeST LargeST (Liu et al., 2023a) (Table 12) collects the largest dataset from the California Department of Transportation Performance Measurement System (PeMS) (Chen et al., 2001) to date.”
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, methodology): “• PEMS-BAY This dataset comes from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS) [7].”
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (2021, methodology): “1 Datasets We verify the performance of STGODE on six real-world traffic datasets, PeMSD7(M), PeMSD7(L), PeMS03, PeMS04, PeMS07, and PeMS08, which are collected by the Caltrans Performance Measurement System(PeMS) in real time every 30 seconds[4].”
• LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (2020, unknown): “1 Dataset Description In the experiment, we use three real-world traffic datasets, namely PeMSD4, PeMSD7 and PeMSD8, which are collected by California Performance of Transportation(PeMS) [Chen et al., 2001] and widely used in the previous studies such as STGCN and ASTGCN [Yu et al.”
• Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting (2017, methodology): “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al. , 2001].”
• ADMSv2: A Modern Architecture for Transportation Data Management and Analysis (2019, methodology): “A pioneer system for managing transportation data is PeMS [2], short for Performance Evaluation Monitoring System, which collects and archives highway traffic data in a single Relational Database Management System (RDBMS).”
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020, methodology): “PeMS means Caltrans Performance Measure System (PeMS) [33], which measures the highway traffic of California in real-time every 30 seconds.”
• Bayesian Temporal Factorization for Multidimensional Time Series Prediction (2019, methodology): “For example, the highway traffic Performance Measurement System (PeMS) in California consists of more than 35,000 detectors, and it has been registering flow and speed information every 30 seconds since 1999 [4].”
• Graph Neural Controlled Differential Equations for Traffic Forecasting (2021, methodology): “For instance, the traffic forecasting task launched by California Performance of Transportation (PeMS) is one of the most popular problems in the area of spatio-temporal processing (Chen et al. 2001; Yu, Yin, and Zhu 2018; Guo et al. 2019).”
• Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (2022, background): “• PEMS-BAY is a traffic speed dataset collected from California Transportation Agencies (CalTrans) Performance Measurement System (PeMS) [5].”
• Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting (2020, methodology): “These data are collected from the Cal-trans Performance Measurement System (PeMS) (Chen et al. 2001).”
• Is Mamba Effective for Time Series Forecasting? (2024, unknown): “The best results are highlighted in bold red font, while the second best results are presented in underlined purple font.”
• Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, methodology): “The first kind of datasets are about highway traffic flow in California, which is collected by the Caltrans Performance Measurement System (PeMS) [57] in real time every 30 seconds.”
• PATH at 20—History and Major Milestones (2007, methodology): “The freeway Performance Measurement System now provides Web-based access to the loop detector data from freeways throughout California, including several years of archived data and a comprehensive set of tools for analyzing and displaying the data [16] .”
• Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting (2022, background): “• PEMS04 is a public traffic flow dataset collected from CalTrans PeMS [5].”