Table 1 Spatiotemporal FSE methods

 All

All spatial locations within the research road segment

26

–

 All upstream

All upstream spatial locations within the research road segment

13

–

 Upstream

Only direct upstream neighbour(s)

44

–

 Upstream + downstream

Only direct upstream and downstream neighbours

34

–

 Downstream

Only direct downstream neighbour(s)

4

–

 Higher order

Higher order neighbours (neighbours of neighbours), starting from the second order

7

–

 Window

Several upstream and downstream neighbours

8

–

 Predefined maximum lag

A set of lags {1, 2,  … , T}, where T is a predefined maximum time lag

–

95

 Travel time

With one of the dimensions (spatial or temporal) fixed, the other can be limited by travel time between spatial locations

6

6

 Micro-simulation

Estimate spatiotemporal relationships using individual cars’ routes

2

–

 Network characteristics

Use network characteristic (i.e. betweenness centrality and vulnerability) to discover complementary links

3

–

 CCF

Cross-correlation function between traffic at different spatial locations

32

26

 Graphical LASSO

Graphical least absolute shrinkage and selection operator

4

1

 Granger causality

Granger causality tests, incl. Vector autoregressive model

2

2

 LARS

Least-angle regression

3

2

 MARS

Multivariate adaptive regression splines

3

3

 Custom

Authors’ custom formulas (e.g. a combination of physical distance and correlation between spatial locations)

9

2

 Empirical

Empirical feature selection based on the forecasting model characteristics (information criterion, RMSE, permutation feature importance, etc.)

12

50

 GA

Genetic algorithm with spatiotemporal links in a chromosome and the model performance is based on a fitness function

5

3

 PSO

Particle swarm optimisation with spatiotemporal links in a candidate solution

2

2

 PSO-GA

Combination of genetic algorithm and particle swarm optimisation

1

1

 LASSO

Least absolute shrinkage and selection operator (L1-norm loss function) regularisation

7

3

 MCP, SCAD

Maximum concave penalty regularisation

Smoothly clipped absolute deviation regularisation

1

1

 SRM

Structural Risk Minimisation

1

1

 Regularised kernel

Regularised kernel function (i.e. Laplacian)

1

1

 RBM

Restricted Boltzmann machine, usually as part of a deep learning network

2

2

 Sparse AE

Sparse autoencoders, usually as part of a deep learning network

1

1

 LSTM

Long short-term memory unit stores temporal information for either long or short time periods

–

5

 Internal

Other methodology-specific regularisation

–

2

 Spatial clustering/ Temporal aggregation

Clustering of spatial locations using different methods (self-organising maps, empirical grouping, etc.)

For temporal dimension – selection of an appropriate temporal aggregation level

12

1

 PCA-EVD

Principal component analysis, based on eigenvalue decomposition

9

8

 PCA-SVD

Principal component analysis, based on singular-value decomposition

4

2

 LSDA

Local shrunk discriminant analysis

1

1

 NMF

Non-negative matrix factorisation

2

1

 SSA

Singular spectrum analysis

3

3