Using AI + IoT technology to improve road safety and traffic barrier monitoring.
Safety barriers have greatly improved the survivability of freeway accidents but they require monitoring and maintenance, and roads must often be cleared following an incident.
Without monitoring, delays can occur while waiting for crews to clear the road and replace damaged infrastructure. If not replaced in a timely manner, this can cause further problems and reduce the effectiveness of the barrier.
Additionally, routine visual inspection of barriers leads to inefficiencies and, unfortunately, causes unnecessary truck rolls as a result of inspecting intact infrastructure that didn’t need it.
Get notifications of barrier collisions and improve responsiveness and maintenance.
The objective of this solution was to improve accident response times and improve barrier maintenance, while reducing unnecessary truck rolls.
IoT sensors were installed to monitor the freeway safety barrier and provide notification of impacts so as to improve the department’s responsiveness to incidents, and reduce further consequences onsite in the event of an accident.
Defining what data to collect is critical
We started by identifying the key data, which in this solution this included:
- Daily weather data
- Traffic data
- Location of crash barriers
- Crash barriers movement
- Accelerometer sensor battery life
- Accelerometer sensor connectivity status
Initial goals of the solution
The first goal of the traffic barrier monitoring solution was to connect with LoRa-based accelerometer sensors so that data would be collected consistently and reliably, ensuring data quality and integrity by providing the following abilities to:
- Monitor real-time data received from the Accelerometer via a web-based and mobile system
- Ensure device management monitoring for the Accelerometer sensors was set in place
- Define business logic to the application in-order to meet the highway maintenance engineers business objectives
- Provide alarm and alert notifications via email or SMS messages when there was communication downtime, or when a crash happened
- Start collecting enough historical data to enable the ability to test different logic to predict crash hotspots
- Provide real-time SMS notification with the GPS location of the crash to the highway maintenance crew
- Test the application, making sure all of the above goals are met, based on the below solution architecture
Before setting the solution live, we tested four critical aspects of the solution:
The Rayven AI + IoT platform is built with security as a top priority and our proprietary security architecture ensures that data is secure at all points of the environment.
The solution includes data encryption in transit from device-to-cloud, as well as device authentication; security (Bearer) tokens; SSL, AES and RSA encryption; as well as additional device security checks done via automated polling.
As the devices are expected to work in highly-remote areas, the solutions were designed to use low-band Lora devices, which transmit the data directly to our servers using a secure MQTT connection. To maximize battery life, data is transmitted only when a crash happens, and to ensure the system is always working even when data isn’t sent, a heartbeat pattern was implemented. In the heartbeat pattern, the device sends device-to-cloud messages at least once in a fixed amount of time, in this case every 24 hours.
Once the devices started sending data, quite a lot of testing was done in-order to ensure that we received reliable streaming connectivity data from all devices and that we would be aware if devices weren’t transmitting data as expected. In addition, significant testing and calibration was done to define the right Accelerometer threshold that would define a crash versus, for example, human tampering or movement because of rocks or animals hitting the crash barriers, minimizing the possibility of false positives.
Industrial data science
The objective of exploratory data analysis was to observe trends in the data and compare them between different locations:
- Compare traffic between different crash incidents
- Compare weather data between different crash incidents
- Compare time of day and day of week between different crash incidents
- Forecast chances of a crash at a specific location based on historical data
We are continuing to collect data and are working on improving our forecasting capability. In addition, we are planning to roll-out hundreds of additional sensors on the highway to increase coverage and help improve responsiveness and maintenance even further.
Data sources used to deliver the AI + IoT Road Safety & Traffic Barrier Monitoring solution.
New and existing data was used to create the custom applications and business insights for the IoT Road Safety & Traffic Barrier Monitoring solution.
Key features of our IoT Road Safety & Traffic Barrier Monitoring AI + IoT solution
All of these features were customized to fit the customers specific business objectives.
Real-time traffic barrier monitoring
Identify when accidents occur in real-time at any location where a traffic barrier is located and send notifications to improve incident response times.
Crash alerts and notifications
Provide real-time alerts and notifications of the time and location of accidents, with a map link to the location and reduce incident repose time.
Device management monitoring
Easily and securely onboard, configure, monitor, and remotely manage your IoT devices at scale. Make sure they are always performing as expected, and get alerts and notifications if not, to ensure your devices are always transmitting and that your data does not get lost on the way.
Knowing when an accident occurs is critical. Knowing where and when the next one might happen can help you ensure that you always have the right resources at hand.
Incident and issue management
Ensure that there is a process in place to have the right people respond based on agreed protocols.
Traffic barriers are assets that need to be maintained. Knowing which barrier to prioritize for maintenance can make the difference between life and death. Access to historical data, like weather data, past accidents, etc. can help identify which barriers to prioritize.
Rayven's AI + IoT Road Safety & Traffic Barrier Monitoring solution deliver real-time business outcomes.
Lower cost of maintenance
Sending maintenance crew to carry out visual inspections or to respond to false alarms is time consuming and expensive. Using real-rime data to send maintenance crew to areas which need maintenance reduces the cost of maintenance and increases the level of service performance.
Fewer maintenance calls
Sending maintenance crew where accidents actually happen at the time they happen, and alerting the closest team, means maintenance is done faster and more efficiently. Digital processes ensure maintenance has taken place, reducing errors and additional call-outs.
Service level compliance
Monitoring your barriers and their maintenance digitally, means you always have a clear view of compliance service levels, providing you with the confidence you need.
Working barriers save lives
Traffic barriers save lives. Ensuring that they are repaired immediately after damage occurs reduces the chances of lives lost due to poorly managed and maintained barriers.
Having real-time data on the performance of all your traffic barriers, means you have more control of the level of services you provide, and are less reliant on reports from 3rd party contractors, enabling you to provide a higher level of service then ever before.
Renewal of maintenance contract
Providing better service and Lower cost of maintenance, provides you with a new level of service that gives you the edge you need to renew your next maintenance contract.
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