Traffic Prediction using recurrent Temporal Graph Convolutional Network

Update time

In this tutorial, we will learn how to use a recurrent Temporal Graph Convolutional Network (TGCN) to predict traffic in a spatio-temporal setting. Traffic forecasting is the problem of predicting future traffic trends on a road network given historical traffic data, such as, in our case, traffic speed and time of day.

begin
    using Pkg
    Pkg.develop("GraphNeuralNetworks")
    Pkg.add("MLDatasets")
    Pkg.add("Plots")
end

Import

We start by importing the necessary libraries. We use GraphNeuralNetworks.jl, Flux.jl and MLDatasets.jl, among others.

begin
    using GraphNeuralNetworks
    using Flux
    using Flux.Losses: mae
    using MLDatasets: METRLA
    using Statistics
    using Plots
end

Dataset: METR-LA

We use the METR-LA dataset from the paper Diffusion Convolutional Recurrent Neural Network: Data-driven Traffic Forecasting, which contains traffic data from loop detectors in the highway of Los Angeles County. The dataset contains traffic speed data from March 1, 2012 to June 30, 2012. The data is collected every 5 minutes, resulting in 12 observations per hour, from 207 sensors. Each sensor is a node in the graph, and the edges represent the distances between the sensors.

dataset_metrla = METRLA(; num_timesteps = 3)

dataset METRLA:
  graphs  =>    1-element Vector{MLDatasets.Graph}

 g = dataset_metrla[1]

Graph:
  num_nodes   =>    207
  num_edges   =>    1722
  edge_index  =>    ("1722-element Vector{Int64}", "1722-element Vector{Int64}")
  node_data   =>    (features = "34269-element Vector{Any}", targets = "34269-element Vector{Any}")
  edge_data   =>    1722-element Vector{Float32}

edge_data contains the weights of the edges of the graph and node_data contains a node feature vector and a target vector. The latter vectors contain batches of dimension num_timesteps, which means that they contain vectors with the node features and targets of num_timesteps time steps. Two consecutive batches are shifted by one-time step. The node features are the traffic speed of the sensors and the time of the day, and the targets are the traffic speed of the sensors in the next time step. Let's see some examples:

size(g.node_data.features[1])

(2, 207, 3)

The first dimension correspond to the two features (first line the speed value and the second line the time of the day), the second to the nodes and the third to the number of timestep num_timesteps.

size(g.node_data.targets[1])

(1, 207, 3)

In the case of the targets the first dimension is 1 because they store just the speed value.

g.node_data.features[1][:,1,:]

2×3 Matrix{Float32}:
  1.17081    1.11647   1.15888
 -0.876741  -0.87663  -0.87652

g.node_data.features[2][:,1,:]

2×3 Matrix{Float32}:
  1.11647   1.15888  -0.876741
 -0.87663  -0.87652  -0.87641

g.node_data.targets[1][:,1,:]

1×3 Matrix{Float32}:
 1.11647  1.15888  -0.876741

function plot_data(data,sensor)
    p = plot(legend=false, xlabel="Time (h)", ylabel="Normalized speed")
    plotdata = []
    for i in 1:3:length(data)
        push!(plotdata,data[i][1,sensor,:])
    end
    plotdata = reduce(vcat,plotdata)
    plot!(p, collect(1:length(data)), plotdata, color = :green, xticks =([i for i in 0:50:250], ["$(i)" for i in 0:4:24]))
    return p
end

plot_data (generic function with 1 method)

plot_data(g.node_data.features[1:288],1)

Now let's construct the static graph, the temporal features and targets from the dataset.

begin
    graph = GNNGraph(g.edge_index; edata = g.edge_data, g.num_nodes)
    features = g.node_data.features
    targets = g.node_data.targets
end;

Now let's construct the train_loader and data_loader.

begin
    train_loader = zip(features[1:200], targets[1:200])
    test_loader = zip(features[2001:2288], targets[2001:2288])
end;

Model: T-GCN

We use the T-GCN model from the paper T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction, which consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN is used to capture spatial features from the graph, and the GRU is used to capture temporal features from the feature time series.

model = GNNChain(TGCN(2 => 100), Dense(100, 1))

GNNChain(Recur(TGCNCell(2 => 100)), Dense(100 => 1))

Training

We train the model for 100 epochs, using the Adam optimizer with a learning rate of 0.001. We use the mean absolute error (MAE) as the loss function.

function train(graph, train_loader, model)

    opt = Flux.setup(Adam(0.001), model)

    for epoch in 1:100
        for (x, y) in train_loader
            x, y = (x, y)
            grads = Flux.gradient(model) do model
                ŷ = model(graph, x)
                Flux.mae(ŷ, y) 
            end
            Flux.update!(opt, model, grads[1])
        end
        
        if epoch % 10 == 0
            loss = mean([Flux.mae(model(graph,x), y) for (x, y) in train_loader])
            @show epoch, loss
        end
    end
    return model
end

train (generic function with 1 method)

train(graph, train_loader, model)

GNNChain(Recur(TGCNCell(2 => 100)), Dense(100 => 1))

function plot_predicted_data(graph,features,targets, sensor)
    p = plot(xlabel="Time (h)", ylabel="Normalized speed")
    prediction = []
    grand_truth = []
    for i in 1:3:length(features)
        push!(grand_truth,targets[i][1,sensor,:])
        push!(prediction, model(graph, features[i])[1,sensor,:]) 
    end
    prediction = reduce(vcat,prediction)
    grand_truth = reduce(vcat, grand_truth)
    plot!(p, collect(1:length(features)), grand_truth, color = :blue, label = "Grand Truth", xticks =([i for i in 0:50:250], ["$(i)" for i in 0:4:24]))
    plot!(p, collect(1:length(features)), prediction, color = :red, label= "Prediction")
    return p
end

plot_predicted_data (generic function with 1 method)

plot_predicted_data(graph,features[301:588],targets[301:588], 1)

accuracy(ŷ, y) = 1 - Statistics.norm(y-ŷ)/Statistics.norm(y)

accuracy (generic function with 1 method)

mean([accuracy(model(graph,x), y) for (x, y) in test_loader])

0.42344558f0

The accuracy is not very good but can be improved by training using more data. We used a small subset of the dataset for this tutorial because of the computational cost of training the model. From the plot of the predictions, we can see that the model is able to capture the general trend of the traffic speed, but it is not able to capture the peaks of the traffic.

Conclusion

In this tutorial, we learned how to use a recurrent temporal graph convolutional network to predict traffic in a spatio-temporal setting. We used the TGCN model, which consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). We then trained the model for 100 epochs on a small subset of the METR-LA dataset. The accuracy of the model is not very good, but it can be improved by training on more data.

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