Traffic Management

What Is Traffic Management?

Traffic management, as part of a smart city model, enables private individuals and municipal officials to monitor and manage the flow of traffic. The machine learning-enabled IoT technology makes driving safer and helps law enforcement track crime—in real time.

Why Is It Important to Have a Good Traffic Management Model?

There are a range of uses for a traffic management model, all of which improve the safety of drivers and pedestrians. One example is traffic stop cameras which monitor the timing of traffic lights in order to reduce congestion—and, therefore, pollution— or t identify dangerous driving or criminal activity.

Other examples include V2X technologies that warn drivers of potential accidents and historical traffic data that inform decisions about public transportation.

What Internal Data Should I Have for a Good Traffic Management Model?

To build a good traffic management model, begin with internal data like city maps, road maintenance schedules, traffic plate issuance data, and traffic history.

Additionally, all traffic management systems need large data processing and data storage capabilities. Data storage, in particular, assists law enforcement services which may need to access past records of driving or parking violations.

What External Data Is Essential for a Good Model?

Essential external data for traffic management includes data streamed from cameras, radar, and other sensors. The system must also integrate with weather and air quality data.

What External Data May Prove Useful for a Good Model?

Additional external data includes supply chain management data, especially from major trucking or other supplier companies. It may also integrate with V2X technology and with traffic and routing data applications like Waze or WikiRoutes.

Many cities also use the TMDD (Traffic Management Data Dictionary) standards as guidelines for developing a proper traffic management system. Although not required, these guidelines provide a very useful traffic management framework for city planners to build upon.

What Are the Main Challenges of this Use Case?

The main challenge of this use case is cost. Even when cities do not invest in sensor technology themselves, there remain significant costs in integrating with open-source datasets. Subsequent operational costs, too, must be considered.

Another challenge is traffic forecasting. Historical and current traffic data provide information for cities to predict transportation and maintenance needs. However, they can never be certain of the outcomes.

Interesting Case Studies and Blogs to Look Into

ITE: Traffic Management Data Dictionary (TMDD) Standard for the Center-to-Center Communications
Intel: Improving traffic management with big data analytics

Tangible Examples of Impact

For years, mayors and smart city leaders have been moving the smart city conversation away from vendor-driven technology solutions — “toys” in the words of Cleveland Mayor Frank G. Jackson — toward more citizen-centric solutions that actually address residents’ priorities and needs.

Mayors from across the country say they are now more interested than before the pandemic in accelerating the adoption of digital city services. The vast majority of mayors also want to invest in technologies such as 5G wireless networks and universal Wi-Fi to meet residents’ need to be connected for remote work and learning.

StateTech: 8 Smart Cities to Watch in 2020