Deploying Smart Transportation Systems to Improve Traffic Efficiency in Saudi Cities
The client faced mounting congestion, unpredictable traffic flows, and rising emissions due to a lack of real-time monitoring and coordination between transport modes. Traffic lights operated on fixed schedules, and incident response times were delayed due to the absence of predictive analytics. There was limited integration between traffic control centers, transit operators, and city planning departments. Data was siloed, and decisions were often reactive rather than proactive.
We designed and implemented a comprehensive Smart Transportation Systems (STS) blueprint that incorporated adaptive traffic control, AI-driven congestion prediction, vehicle-to-infrastructure (V2I) communication, and centralized control dashboards. The system used IoT sensors, real-time data streams, and analytics platforms to optimize signal timing, route traffic proactively, and detect incidents early. An open data layer allowed seamless integration with mobility apps, public transit schedules, and logistics operators, enabling coordinated responses and better traveler information services.
- Assessed existing traffic management systems and sensor infrastructure
- Designed a phased ITS architecture integrating cameras, V2I sensors, and edge computing
- Installed adaptive signal control systems in high-congestion corridors
- Developed AI-based algorithms for traffic prediction and rerouting
- Connected public buses and fleet vehicles to control centers via IoT
- Created a centralized command dashboard with live system KPIs
- Trained municipal staff in traffic modeling and system maintenance
- Expand adaptive traffic signals across secondary corridors and school zones
- Integrate logistics operators into the V2I network for real-time delivery routing
- Launch a traveler information app with live updates and congestion alerts
- Use predictive maintenance for traffic cameras and signals to reduce downtime
- Introduce inter-agency coordination protocols during major events or disruptions
- Continuously calibrate AI algorithms based on local traffic behavior patterns
Average travel times during peak hours dropped by 21%, and intersection delays were reduced by 38%. Incident response times improved by 47%, resulting in fewer secondary collisions and faster traffic recovery. Fuel savings from smoother flows led to an annual reduction of 6,800 tons of CO₂. The system reduced traffic-related complaints by 52% and saved the municipality approximately SAR 11.5 million per year in congestion-related costs.