Traffic management using real-time data

By incorporating real-time data into traffic management solutions, transportation authorities are building more sustainable and responsive systems that alleviate congestion, reduce the likelihood of accidents, improve emergency response times and optimize public resources. In this article, we’ll share how real-time traffic data is being used to improve the safety and reliability of road networks and public transportation and explore the different types of data that are valuable in traffic management. We’ll also explain how Snowpipe Streaming enables integrated real-time data for traffic management.

How does real-time traffic data enhance traffic management?

With congestion on roadways increasing, real-time data is becoming crucial to creating smart, efficient traffic management solutions. Real-time traffic data can be applied to a range of use cases.

Dynamic management

When cities and municipalities have access to real-time traffic information from cameras, sensors, and other data sources, they can make immediate adjustments to ease congestion. One example is the smart traffic light. Equipped with sensors and video detection, smart traffic systems use AI and ML algorithms to process traffic data to dynamically change traffic lights in response to real-time weather and traffic patterns. Smart traffic lights can even be configured to give priority to public transit vehicles as they receive signals from transponders embedded into the vehicles. 

Smart parking solutions 

When commuters make multiple circles around the block looking for a parking space, they contribute to traffic congestion. Smart parking solutions can reduce this congestion. Using a combination of AI, edge computing and interconnectivity, smart parking solutions leverage real-time data gathered from a network of cameras and sensors to detect open parking spaces. The systems can alert commuters to open parking spaces via a mobile app or via visual cues such as multidirectional LED signage mounted on lampposts. 

More efficient public transit

Public transportation plays an important role in reducing traffic congestion and providing eco-friendly and affordable alternatives to car travel. Real-time data gathered from traffic sensors and GPS transponders mounted on public transit trains, buses and vans are fed into algorithms that calculate estimated arrival times at each stop. Commuters can access this information via a mobile app. This up-to-the-minute information helps commuters make better decisions on when to head to the bus stop or train station and adjust travel plans to work around unexpected delays. 

Real-time hazard alerts

Accidents, stalled vehicles and stranded commuters all require a timely response. Cameras and thermal sensors equipped with advanced video analytics can detect a range of road hazards, sending alerts to relevant authorities or emergency services. Additionally, hazard alerts that an accident or other road incident is ahead can be sent to drivers. One application is the use of thermal sensors that can operate in total darkness and cover large distances. These sensors have a range of uses, from picking up the heat signature of a stranded motorist to detecting fires in traffic tunnels. 

Improved emergency response

Real-time traffic data can expedite travel for ambulances and other emergency vehicles, allowing them to arrive on the scene faster. Real-time traffic management systems dynamically process an emergency vehicle’s entire route from the station to the site of the incident, communicating wirelessly with traffic signals along the way to clear traffic congestion for the first responder’s vehicle. The system dynamically tracks the emergency response vehicle, automatically adjusting traffic signal preemption requests as needed. 

Real-time traffic data sources

Powering every modern traffic management system are vast quantities of data gathered from multiple sources. Modern data platforms enable municipalities to seamlessly integrate real-time data sources into their traffic management solutions. Here are the most valuable sources of real-time traffic data.

Live video feeds

Cameras placed at strategic locations on roadways and atop traffic signals provide visual data that allows traffic operators to monitor traffic flow, detect incidents and assess road conditions in real time. Live video feeds are an essential component of traffic management, allowing them to detect and respond to congestion, accidents, objects on the road, fires, vulnerable pedestrians and a host of other issues that require immediate attention.

Remote sensors

Two primary types of traffic sensors provide real-time traffic data: intrusive and nonintrusive. Intrusive sensors are embedded into the roadway, while nonintrusive ones are typically mounted beside or above the roadway. An example of an intrusive sensor would be an inductive loop detector (ILD), frequently used to detect vehicle movement, presence, count and occupancy. Nonintrusive sensors include radar, infrared and ultrasonic, which are used to measure vehicle speed, detect lane occupancy and vehicle direction, and gauge traffic volume. 

Mobile phone data

Mobile phone data, or floating cellular data, can be a valuable resource in traffic management, allowing authorities to gain real-time insights into traffic patterns and congestion. By aggregating anonymized data from mobile phone users with location tracking enabled, traffic management systems can track the movement of people and vehicles in real time. This data helps identify traffic bottlenecks, optimize traffic light timing and plan long-term road infrastructure improvements.

Connected vehicle data

Modern cars are outfitted with a network of onboard sensors that generate valuable data, including vehicle speed, road conditions, airbag deployments, hard braking events and more. AI-enabled traffic management systems combine connected vehicle data with other sources of real-time traffic data, such as live video feeds, to automatically reduce speed limits, dispatch emergency services or send hazard alerts to approaching vehicles. Connected vehicle data can also be used for predictive analysis, helping authorities anticipate traffic issues and optimize traffic management strategies proactively. 

Accelerate your real-time traffic management with Snowflake Snowpipe

Snowpipe Streaming offers a suite of advanced features for building more efficient and effective real-time data applications. Snowflake Streaming can help government entities build and support the streaming data ingestion and analytics infrastructure required to leverage real-time data use cases such as traffic data.

Ingest real-time and historical data directly into Snowflake

Real-time data is valuable, but pairing it with historical data provides additional context and empowers traffic managers to make more informed decisions. Snowpipe Streaming resolves infrastructure management complexity, serving as a native streaming data ingestion offering to the Snowflake Data Cloud. With Snowpipe Streaming, data engineers and developers no longer need to stitch Snowflake Cloud Data Platform together a patchwork of different systems and tools to work with real-time streaming and batch data in one single system. 

Prepare and transform data using the language of your choice with Snowpark

Snowflake enables developers to write code using the language of their choice, including Python, Java and Scala, and run that code directly in Snowflake. Snowpark allows developers the option to expand beyond Snowflake’s original SQL development interface, allowing them to prepare and transform their data directly in Snowflake with results securely shareable.

Elastic storage and compute resources

Snowflake is a fully managed service that automatically grows or shrinks capacity based on current Snowpipe load. With access to Snowflake’s unique multi-cluster shared data architecture, municipalities can tap into the performance, scale, elasticity and concurrency required for powering even the most complex, resource-intensive real-time traffic management use cases.

Snowflake Streaming + Snowflake Connector for Kafka

Using Snowpipe Streaming in your data loading chain from Kafka results in lower load latencies, with corresponding lower costs for loading similar volumes of data. Whether you opt to use Snowpipe Streaming as a standalone client or as part of a Kafka architecture, you can create scalable and reliable data pipelines through a fully managed underlying infrastructure with built-in observability. 

Go from point A to point B faster with Snowflake

Real-time data sources keep traffic flowing, improving the quality of commuting in an increasingly interconnected world. The Snowflake Data Cloud has a wide range of deeply integrated capabilities for building a real-time traffic management system. With Snowflake, cities and other municipalities can leverage superior performance, functionality and cost-efficiency to provide more responsive, reliable citizen services.