Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Full frame distilled prediction

Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

Candidate categories

none

Consensus categories

none

Domain

Candidate signal: noneConsensus signal: none

Study design

Candidate signal: Simulation or modelingConsensus signal: none

Genre

Candidate signal: EmpiricalConsensus signal: Empirical

Teacher disagreement score

0.782

Threshold uncertainty score

0.284

Validation status

machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Teacher spread

0.175 · how far apart the two teachers sit on this one work

Validation status

score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue

Journal of Advanced Transportation

Topic

Traffic Prediction and Management Techniques

Field

Engineering

Canadian institutions

not available

Funders

National Natural Science Foundation of China

Keywords

Autoregressive integrated moving averageComputer scienceTraffic flow (computer networking)Support vector machineData miningAutoregressive modelWeightingArtificial intelligenceIntelligent transportation systemArtificial neural networkTime seriesMachine learningEngineeringStatisticsMathematics

Has abstract in OpenAlex

yes

API

/api/v1/works/W2916752133