Traditional traffic signal retiming workflows are severely bottlenecked, typically restricting public agencies to a rigid 3-to-5-year retiming cycle, during which corridor progression significantly decays due to evolving traffic demands. Signals becoming out of sync with reality with degrading performance over time – leading to congestion, inefficiencies, emissions across the network. By integrating AI-driven insights with HCM-compliant modeling, this blog details a new operational framework designed to eliminate data bottlenecks and enable efficient, multimodal corridor optimization.
1. Introduction: The Operational Realities of Signal Retiming
Modern traffic networks are complex systems that continue to grow to meet the demands of today. Supporting this growth is no easy task, and traffic engineers must balance multiple competing priorities such as tackling congestion while also designing networks that work for every type of road user. Managing these complex networks is very labour intensive. A standard retiming project requires multiple months of short-term data collection, tedious software network configuration, and manual data entry into siloed Central Management Systems (CMS) or Automated Traffic Management Systems (ATMS). Due to the highly manual and resource-intensive nature of this process, agencies often face severe project backlogs, allowing network performance to degrade as timing plans become out of sync with real-world conditions.
Within the United States, many agencies have reported challenges keeping their networks up to date, with many agencies citing staffing shortages and high workload demands as a significant barrier.1 Industry guidelines recommend that agencies review their signal timing plans every three to five years,2 but many agencies struggle to keep up with this cadence. In fact, within the United States, approximately 50% of agencies report that they review their signal timings with a frequency that meets this guideline, and up to 30% of agencies report that they do not review signal timings on a schedule.3
2. Overcoming the Network Configuration and Data Ingestion Bottleneck
One of the most significant challenges with signal retiming is that the re-timing process is still very manually driven and operates in traditional desktop-based environments. Building these models is very labour intensive, and technicians often manually define the model’s geometry, lane configurations, and transcribe turning movement data or timing plan data from static files.
While municipal agencies occasionally maintain historical base files, updating these networks with fresh counts and matching them to current controller parameters remains highly error-prone and inefficient. Manual inputs introduce substantial risk of data entry errors across phase and split tables. The architecture introduced by Miovision Traffic Optimization eliminates this data friction by establishing an automated data ingestion pipeline directly within the Miovision One platform. By leveraging pre-existing Global Location Configuration (GLC) topology data, continuous TMCs, and current timing plans the system automatically builds the intersection models. By unifying data streams, Miovision achieves a significant reduction in setup complexity. Once the underlying geometry is established, the manual alignment of disparate datasets is entirely automated. Combining the geometry with multimodal traffic volumes and the timing plan is seamless and allows for faster retiming cycles within Miovision One.
3. Methodological Rigor: Transparent, HCM-Compliant Analytics
One of the most common critiques levied against modern traffic engineering models is that they are often “black-boxes” with opaque methods that are difficult to trust and verify. To address this, the traffic model powering Miovision Signal Optimizer is built from the Highway Capacity Manual as its base. The Highway Capacity Manual’s methodologies, developed by the Transportation Research Board, provides a transparent and well-understood approach to evaluating the performance of traffic networks.4
The engine couples this with an AI-based Genetic Algorithm that uses the HCM-based traffic model to optimize and locate the best combination of splits, cycle lengths, and offsets. Genetic Algorithms are a class of advanced metaheuristic search algorithms modelled after the process of natural selection that allow the Optimizer to intelligently find the solution that best optimizes the traffic network.
By utilizing the HCM 6th Edition methodology as its analytical foundation, the platform ensures that the underlying math is completely transparent to the user. Implementing a well-known standard, helps users verify the methodologies and compare against industry benchmarks.
The platform generates comprehensive, engineer-ready impact forecasts. This allows technicians to conduct side-by-side analytical reviews of delay, Level of Service (LOS), volume-to-capacity (V/C) ratios, and 95th-percentile queue lengths prior to any field execution.
4. Expanding the Objective Function: Multi-Modal and Equitable Optimization
Traditional traffic engineering tools focus heavily on vehicular performance metrics, minimizing automobile delay sometimes at the direct expense of pedestrian and cyclist safety. While newer software versions may report pedestrian metrics, they rarely allow users to optimize for them directly within the core algorithm.
Miovision Signal Optimizer differentiates itself by allowing the user to explicitly factor vulnerable road users into the objective function, not commonly found among industry standard optimization tools. Our platform is inherently built to address multimodal needs, supporting all road users.
This mathematical expansion transitions corridor timing from strictly vehicle-centric throughput to an equitable transportation framework. For instance, if there is a high volume of pedestrians crossing a minor street and a lower volume of vehicle movement then pedestrians can be served more frequently. Users can select from predefined optimization goals—such as minimizing vehicle delay, maximizing corridor progression, or minimizing fuel consumption—or utilize a granular, sliding-scale interface to customize parameters based on local standard operating practices.
5. Closing the Operational Loop: Secure, Human-in-the-Loop Remote Deployment
Even the most methodologically sound timing plan can fail if the deployment workflow is fragmented. Traditionally, after an engineer generates an optimized plan, a technician must physically travel to the cabinet to manually input the new parameters, or transfer them through a complex, vendor-locked ATMS.
Miovision Controller Manager solves this “last mile” deployment challenge by integrating a spreadsheet-style Timing Plan Editor directly with field devices via open NTCIP protocols. This allows verified plans to be saved as drafts, cross-referenced, and pushed remotely after secure validation to compatible controllers within minutes.
Modifying intersection parameters introduces significant operational risk, the platform strictly enforces a Human-in-the-Loop control protocol:
- Engineering Oversight: The system acts strictly as a recommendation and execution engine; it does not autonomously alter active field parameters without explicit engineer sign-off.
- Pre-Deployment Validation: Active configurations are cross-referenced against MUTCD and ITE standards via a Mateo-driven intelligent reporting layer to flag critical faults or flash risks before deployment.
- Field Integrity Gating: The platform executes automated database comparisons to prevent overwriting critical field-level changes made by local maintenance technicians.
- Audit Trail Traceability: Every remote deployment is logged to a comprehensive, immutable audit trail, capturing the exact parameter modifications, deployment notes, timestamp, and user identity for full legal accountability.
6. User Benefits: Clearing the Operational Backlog
Ultimately, this integrated architecture serves as a critical force multiplier for resource-constrained traffic departments. By condensing a multi-month modeling and deployment cycle into a centralized, cloud-native workflow, signal technicians and engineers can shift from a reactive posture to proactive, continuous network management.
Reflecting on the realities of agency workloads, the tool provides a meaningful improvement to day-to-day operations. By providing account-wide cloud access, transparent HCM reporting, and real-time pre- and post-change performance validation via ATSPMs, this suite empowers agencies to perform advanced corridor retimings entirely in-house, optimizing municipal mobility without relying on expensive, slow, third-party consulting cycles.
Un avenir proactif pour la mobilité intelligente
By combining these tools with our existing Performance and Comprehensive Detection solutions, Miovision is providing a proactive tool for the modern traffic engineer. This informed, end-to-end traffic optimization solution allows agencies to Analyze network health, Optimize timing, Review and Deploy changes securely, and Validate with audit-ready reporting—all in one platform.
Il ne s'agit pas seulement d'améliorer la qualité des données, mais aussi d'obtenir de meilleurs résultats pour la communauté. Une circulation fluide, une sécurité renforcée et des villes plus intelligentes et mieux connectées sont désormais à portée de main, sans même avoir à quitter son bureau.
1 Traffic Signal Benchmarking and State of the Practice Report
2 FHWA Traffic Signal Timing Manual — Chapter 7, Section 7.1.2 “Frequency of Timing Updates”
3Traffic Signal Operation, Optimization, Maintenance and Management Practices in the Southeast US
4 Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis
