Peak-hour traffic adds 30–40 minutes to a commute — and that's not fatalism, but a challenge for AI. We develop congestion prediction systems that reduce delays by 15–25%. At the core is a hybrid of graph neural networks and event-aware transformers. The model considers road topology, data flow from 300+ sensors, and events (accidents, weather, concerts). Our clients are cities and transport operators who need not just to predict a jam but to redistribute traffic in real time. Below is how we do it: from data collection to integration with navigators and traffic lights.
The problem with classical methods is that they ignore road topology and event dynamics. For example, after a concert at a stadium, traffic redistributes non-linearly — 40% of drivers choose alternative routes. Our AI model predicts such scenarios with up to 92% accuracy on a one-hour horizon. For comparison, traditional LSTM models show MAPE of 14–18%, while our architectures based on GCN+WaveNet achieve 8–11%.
We use PyTorch and PyTorch Geometric for building graph models, and for event encoding — Temporal Fusion Transformer (TFT). Accuracy evaluation is performed on historical data broken down by day type. The result is flow speed prediction with MAPE <10% on a 30-minute horizon.
How AI improves traffic prediction accuracy
Prediction relies on four data groups:
- Sensors: induction loops (count, speed), video detectors (vehicle classification), Wavetronix/RTMS radars.
- Floating car data: aggregated GPS tracks from navigation services and taxi fleets.
- Infrastructure: road graph (OpenStreetMap), traffic light phases, pedestrian crossings.
- Events: planned (matches, concerts) and anomalous (accidents, construction, snowfall).
We combine them into a spatial-temporal model where each sensor is a graph node and roads are edges.
Why graph neural networks are more effective than LSTM
Traditional LSTMs ignore road topology. Graph convolutions (GCN) account for the fact that speed at a neighboring intersection affects the current one. Comparison of approaches:
| Model | Spatial dependency | Temporal dependency | MAPE (30 min) | Latency (inference) |
|---|---|---|---|---|
| LSTM | No | Yes | 14–18% | <1 ms per node |
| GCN + LSTM | Yes (static edges) | Yes | 10–13% | 2–5 ms per graph |
| Graph WaveNet | Yes (adaptive matrix) | Yes (dilated conv) | 8–11% | 3–8 ms per graph |
Comparative analysis on city sensor data over 12 months We use the architecture:
# TrafficGCN — hybrid of GCN and LSTM
import torch
from torch_geometric.nn import GCNConv
class TrafficGCN(nn.Module):
def __init__(self, n_nodes, in_features, hidden, out_features):
super().__init__()
self.gcn1 = GCNConv(in_features, hidden)
self.gcn2 = GCNConv(hidden, hidden)
self.lstm = nn.LSTM(hidden, hidden, batch_first=True)
self.fc = nn.Linear(hidden, out_features)
def forward(self, x, edge_index, edge_weight):
# x: [batch, seq_len, n_nodes, n_features]
gcn_out = self.gcn1(x, edge_index, edge_weight).relu()
gcn_out = self.gcn2(gcn_out, edge_index, edge_weight)
lstm_out, _ = self.lstm(gcn_out)
return self.fc(lstm_out[:, -1, :])TrafficGCN architecture details
The model uses two graph convolutions with residual connections and an LSTM layer for temporal dynamics. Training: AdamW, lr=0.001, batch_size=32, 100 epochs. Graph size — up to 5000 nodes, 15000 edges. Accuracy evaluation is performed on a held-out set (20% of data).Key architectures — DCRNN, Graph WaveNet, ASTGCN — differ in how they handle temporal dependencies. The choice depends on graph size and forecast horizon.
How events are taken into account
Traffic is non-linear: after a football match, peak occurs in 30–60 min; an accident reduces capacity by 40–80%; rain decreases speed by 10–20%. We add event flags as input features:
event_features = {
'stadium_match_flag': upcoming_match_within_3h,
'weather_rain_intensity': precipitation_forecast,
'roadwork_active': roadwork_on_segment,
'incident_nearby': incident_within_1km_duration,
'holiday_flag': is_holiday,
'school_day': not is_school_holiday
}These features are fed into the model as future covariates (Temporal Fusion Transformer). Without them, accuracy on anomalous days drops by 20%.
What does AI traffic light optimization provide?
Traditional SCOOT/SCATS react to current traffic without prediction. We replace them with an RL agent: action — phase plans, state — current and predicted speeds, reward — total network delay. On a corridor of 10–20 intersections, coordination creates a 'green wave'. Result: reduction in average travel time by 10–20%. Such smart traffic lights based on RL are an example of AI transportation systems.
Informing drivers
Predictions are sent to:
- variable message signs (travel time, detours),
- push notifications in apps (navigation services),
- API for navigation services.
System metrics:
| Metric | Value |
|---|---|
| MAPE of flow speed (15 min) | <10% |
| MAPE of travel time (30 min) | <12% |
| Incident detection latency | <5 min |
| Reduction in average travel time | 10–25% |
A 10–25% reduction in average travel time is tangible for every driver. For a city of a million people, this yields significant savings in public costs.
Case study: deployment in a city of 2 million residents (from our practice)
For one of our clients — City N with 2 million residents — we deployed Graph WaveNet with event-aware layers. After calibration on historical data, MAPE of speed on a 30-minute horizon was 7.8% (normal days) and 10.2% (event days). The system is integrated with the local traffic management center via the NTCIP 1211 protocol. This enabled real-time congestion analysis and coordination of 500 intersections.
What is included in the development?
- Data audit: sensor availability, FCD quality, road network layout.
- Prototype model: LSTM baseline in 2–3 weeks.
- GNN + event-aware model calibrated to the city.
- Integration with traffic light controllers and navigation services.
- Documentation, operator training, warranty support.
Timeline: basic prediction — from 5–6 weeks; full system — 4–5 months. Accurate estimate after data analysis.
We guarantee accuracy: MAPE not exceeding target values on the test set. Team experience — over 5 years in ITS (Intelligent Transport Systems) and 20+ projects in traffic forecasting. Our solutions fall under AI transportation and ML transportation.
Get a consultation on your system architecture. Contact us to evaluate your project — we'll select an architecture that fits your budget and city infrastructure. Order a preliminary data audit: we'll analyze available sensors and provide an initial accuracy estimate within two weeks.