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A online boosting approach for traffic flow forecasting under abnormal conditions

https://doi.org/10.1109/fskd.2012.6234335
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-02605-8openalex-snapshot
Introduction to Modern Traffic Flow Theory and Control
inferredcites →https://doi.org/10.1007/s00170-007-1007-yopenalex-snapshot
A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction
inferredcites →https://doi.org/10.1016/0191-2615(84)90002-xopenalex-snapshot
Dynamic prediction of traffic volume through Kalman filtering theory
inferredcites →https://doi.org/10.1016/j.eswa.2008.07.069openalex-snapshot
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
inferredcites →https://doi.org/10.1016/s0968-090x(01)00004-3openalex-snapshot
Urban traffic flow prediction using a fuzzy-neural approach
inferredcites →https://doi.org/10.1016/s0968-090x(97)82903-8openalex-snapshot
Combining kohonen maps with arima time series models to forecast traffic flow
inferredcites →https://doi.org/10.1023/a:1024988512476openalex-snapshot
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
inferredcites →https://doi.org/10.1061/(asce)0733-947x(1991)117:2(178)openalex-snapshot
Nonparametric Regression and Short‐Term Freeway Traffic Forecasting
inferredcites →https://doi.org/10.1061/(asce)0733-947x(2003)129:2(161)openalex-snapshot
Traffic Prediction Using Multivariate Nonparametric Regression
inferredcites →https://doi.org/10.1061/(asce)0733-947x(2006)132:2(114)openalex-snapshot
Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach
inferredcites →https://doi.org/10.1061/(asce)0733-947x(2007)133:3(180)openalex-snapshot
Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting
inferredcites →https://doi.org/10.1080/00031305.1992.10475879openalex-snapshot
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
inferredcites →https://doi.org/10.1080/03081068808717359openalex-snapshot
Short term traffic forecasting using time series methods
inferredcites →https://doi.org/10.1109/glocom.1993.318226openalex-snapshot
Traffic prediction using neural networks
inferredcites →https://doi.org/10.1109/tits.2006.869623openalex-snapshot
A Bayesian Network Approach to Traffic Flow Forecasting
inferredcites →https://doi.org/10.1109/tnn.2006.880581openalex-snapshot
Parameter Incremental Learning Algorithm for Neural Networks
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4120320openalex-extracted
Improving Traffic Flow Prediction Via Learning the Periodic Features with Time Information
inferred← citeshttps://doi.org/10.1007/978-981-97-7007-6_31openalex-snapshot
ASTGCN for Traffic Flow Prediction Based on Weather Influence
inferred← citeshttps://doi.org/10.1007/s10489-021-02186-9openalex-snapshot
TL-FCM: A hierarchical prediction model based on two-level fuzzy c-means clustering for bike-sharing system
inferred← citeshttps://doi.org/10.1016/j.trc.2017.08.021openalex-snapshot
Forecasting journey time distribution with consideration to abnormal traffic conditions
inferred← citeshttps://doi.org/10.1016/j.trc.2018.12.004openalex-snapshot
Enhancing transportation systems via deep learning: A survey
inferred← citeshttps://doi.org/10.1016/j.tre.2014.10.005openalex-snapshot
Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem
inferred← citeshttps://doi.org/10.1016/j.trpro.2017.03.063openalex-snapshot
Identifying patterns under both normal and abnormal traffic conditions for short-term traffic prediction
inferred← citeshttps://doi.org/10.1049/iet-its.2018.5155openalex-snapshot
Multi‐model ensemble for short‐term traffic flow prediction under normal and abnormal conditions
inferred← citeshttps://doi.org/10.1049/iet-its.2020.0054openalex-snapshot
Hybrid model for predicting anomalous large passenger flow in urban metros
inferred← citeshttps://doi.org/10.1109/access.2019.2923663openalex-snapshot
A Special Event-Based K-Nearest Neighbor Model for Short-Term Traffic State Prediction
inferred← citeshttps://doi.org/10.1109/tits.2021.3083957openalex-snapshot
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
inferred← citeshttps://doi.org/10.1109/tits.2022.3216678openalex-snapshot
M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction
inferred← citeshttps://doi.org/10.1109/tmc.2020.3006713openalex-snapshot
Prediction of Traffic Flow via Connected Vehicles
inferred← citeshttps://doi.org/10.1109/tvt.2016.2585575openalex-snapshot
Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach
inferred← citeshttps://doi.org/10.2991/fmsmt-17.2017.87openalex-snapshot
Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree
inferred← citeshttps://doi.org/10.2991/icmmct-17.2017.200openalex-snapshot
An Abnormal Traffic Cleaning System
inferred← citeshttps://doi.org/10.3390/app14209455openalex-snapshot
Advancements in UAV-Enabled Intelligent Transportation Systems: A Three-Layered Framework and Future Directions
inferred← citeshttps://doi.org/10.3390/s21041121openalex-snapshot
From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability
inferred← citeshttps://doi.org/10.3390/s22218422openalex-snapshot
Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots
inferred← citeshttps://doi.org/10.3390/sym18091538openalex-snapshot
Event-Driven Topology Reconfiguration and Penetration-Aware Graph Attention for Mixed-Autonomy Traffic Forecasting
inferred← citeshttps://doi.org/10.5220/0006293400790089openalex-snapshot
Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model
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