Editing Ai Urban Planning (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Traffic flow prediction with graph neural networks:'''
<syntaxhighlight lang="python">
import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv
from torch_geometric.data import Data
import numpy as np

class TrafficGNN(torch.nn.Module):
    """Predict traffic flow using road network graph structure."""
    def __init__(self, in_channels, hidden, out_channels):
        super().__init__()
        self.gat1 = GATConv(in_channels, hidden, heads=4, concat=True)
        self.gat2 = GATConv(hidden * 4, hidden, heads=1, concat=False)
        self.predictor = torch.nn.Linear(hidden, out_channels)

    def forward(self, x, edge_index, edge_attr=None):
        x = F.elu(self.gat1(x, edge_index))
        x = F.dropout(x, p=0.3, training=self.training)
        x = F.elu(self.gat2(x, edge_index))
        return self.predictor(x)  # Predicted traffic flow per road segment

# Road network as graph: nodes=intersections, edges=road segments
# Node features: historical traffic counts, time features, weather
# Edge features: road type, length, capacity
# Target: next 15/30/60 min traffic flow per segment

# Construct PyG Data object from road network
def build_road_graph(adjacency_matrix, node_features):
    edge_index = torch.tensor(np.argwhere(adjacency_matrix > 0).T, dtype=torch.long)
    x = torch.tensor(node_features, dtype=torch.float)
    return Data(x=x, edge_index=edge_index)
</syntaxhighlight>

; Urban AI application landscape
: '''Traffic signal control''' → SURTRAC (RL), SCATS, Siemens SCOOT + ML
: '''Public transit''' → Google Maps ETA, Transit App, MTA real-time prediction
: '''Parking''' → ParkMobile AI, smart parking sensors + occupancy prediction
: '''Crime/safety''' → ShotSpotter (acoustic gunshot detection), PredPol (controversial)
: '''Infrastructure''' → NYC fire inspection ML, pipe failure prediction (Fracta)
: '''City digital twins''' → Sidewalk Labs Mesa, NVIDIA Omniverse City Simulation
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