Cities nationwide are facing growing pressure to modernize outdated traffic systems, especially as the potential of machine learning in traffic management becomes clearer. Yet many traffic cabinets and controllers date back decades, and are simply not equipped for handling today’s data-intensive tools. Western Systems bridges that gap by delivering modern, open-architecture cabinets, networked control platforms, and integration services, ensuring cities are well-prepared for advanced AI-driven solutions and ready for machine learning in their traffic management.
Modernized Hardware for Machine Learning in Traffic Management
Physical infrastructure is crucial to harnessing machine learning in traffic management effectively. Western Systems is a supplier of custom NEMA-compliant cabinets built with 5052-H32 aluminum, NEMA TS1/TS2 standards, and features like integrated battery backup (the “P-Plus” design). When combining traffic control and UPS in one enclosure, cities save on installation costs and minimize their downtime during power outages.
Within these cabinets, Western Systems installs advanced Yunex Traffic controllers And that enables straightforward integration with both current and future AI modules. The result is a future-ready control system that supports both adaptive and even predictive capabilities. This is an essential step toward fully embracing machine learning.
Unified Platforms: Paving the Way for Machine Learning in Traffic Management
Machine learning in traffic management takes more than just hardware alone—agencies need both a centralized platform for data collection and signal coordination. Western Systems often deploy solutions like Yunex TACTICS or Sitraffic Concert. These provide:
- Map-Based Dashboards: These dashboards allow real-time visualization of intersection status, traffic flow, and incidents.
- Adaptive Coordination: Automated algorithms to adjust green times and cycle lengths on the fly.
- Open Integration: Compatibility with legacy hardware via standard protocols, letting cities add new features gradually.
TACTICS and Concert continuously capture intersection data, which is invaluable for training or refining machine learning models later. Because these platforms embrace open architectures, they allow municipalities to implement specialized AI or advanced detection modules without having to re-engineer an entire system.
Smart Detection & Connectivity For Machine Learning in Traffic Management
High-quality data is crucial to making machine learning in traffic management thrive. Western Systems pairs modern controllers with advanced detection from providers like Iteris VantageNext or Currux Vision’s AI-based sensors. These systems identify pedestrians, cyclists, and vehicles with impressive accuracy, generating precise, real-time counts that shape timing decisions.
All that data moves to a central Traffic Management Center (TMC) over fiber-optic networks or wireless modems. This connectivity is also essential for future V2X (vehicle-to-everything) applications. By building a robust communication channel where cities can integrate connected vehicles and autonomous fleets, and strengthening data for AI-driven insights.
V2X Readiness: A Stepping Stone to Machine Learning
Western Systems has already rolled out CV2X for emergency vehicle preemption and transit priority in both Phoenix, AZ, and Roseville, CA. This allows first responders and buses to request green lights in real time. Though not strictly a machine learning feature, V2X implementations gather detailed traffic and vehicle performance data. This data can later feed machine learning in traffic management algorithms, enabling truly predictive control across multiple transportation modes.
Phased Deployment for Machine Learning in Traffic Management
A full replacement of citywide systems is often cost-prohibitive. The phased approach implemented by Western Systems prioritizes a city’s busiest intersections, making congestion relief immediate, but keeping compatibility with older devices. Cities then expand fiber connectivity over time, deploying updated controllers and adopting advanced detection. All the while, real time data streams are added to make machine learning more feasible and impactful.
Machine Learning in Coeur d’Alene, ID:
- Phase 1: Legacy controllers were swapped with Yunex M60 units and added Iteris cameras.
- Phase 2: Extended fiber for centralized oversight.
- Phase 3:Integrated advanced analytics focused on future machine learning enhancements.
This is a gradual method, but it delivers immediate efficiency gains and gets cities ready for machine learning in traffic management.
Real-World Impact
From Coeur d’Alene’s improved traffic near I-90 to Phoenix’s faster emergency response times, Western Systems proves that modernizing hardware and software yields tangible benefits now. More importantly, each upgrade—be it a new controller or a fiber-linked TMC—further prepares intersections for machine learning in traffic management down the road.
Paving the Way to Smarter Traffic Networks
Western Systems’ open-architecture cabinets, advanced ATMS software, and phased installation plans future-proof a city’s traffic networks. Instead of forcing a wholesale rip-and-replace, they help cities move toward AI-driven optimization and machine learning step by step, integrating the best-in-class controllers, analytics, and V2X modules. By laying a solid foundation now, municipalities ensure they can adopt—or scale up—machine learning capabilities whenever they’re ready, bridging legacy systems into a new era of adaptive, data-powered traffic management. Contact us today to learn more!