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Related Experiment Video

Updated: Dec 5, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Probabilistic Regularized Extreme Learning for Robust Modeling of Traffic Flow Forecasting.

Jungang Lou, Yunliang Jiang, Qing Shen

IEEE Transactions on Neural Networks and Learning Systems

|October 16, 2020

Summary

A new probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), improves traffic flow forecasting accuracy. This method effectively handles noisy data, outperforming conventional systems in real-world tests.

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Artificial Intelligence
Machine Learning
Traffic Engineering

Background:

Adaptive neurofuzzy inference system (ANFIS) excels in noise-free learning but struggles with noisy data.
Accurate real-time traffic flow forecasting is challenging due to noise and outliers in complex traffic conditions.

Purpose of the Study:

To propose a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS).
To enhance traffic flow forecasting accuracy by capturing complex correlations in traffic data, even with noise and outliers.

Main Methods:

Development of the probabilistic R-ELANFIS system, integrating probabilistic regularized extreme learning machine with ANFIS.
Utilizing a novel objective function that minimizes both the mean and variance of model bias.
Experimental validation using real-world traffic flow data.

Main Results:

The proposed probabilistic R-ELANFIS demonstrated competitive performance in forecasting ability and generalizability.
Outperformed conventional ANFIS, kernel-based approaches, and neural network approaches in experiments.
Effectively captured correlations among traffic flow data, improving prediction accuracy.

Conclusions:

Probabilistic R-ELANFIS offers a robust solution for accurate traffic flow forecasting, particularly in the presence of noise and outliers.
The novel objective function contributes to improved model bias management and predictive performance.
The system shows significant potential for real-world traffic management applications.