Tracking vehicles during rush hour and managing congestion is at the forefront of virtually all transportation management centers. Having the ability to make timely and well-coordinated decisions regarding emergency response, public transportation, and traffic management starts with real-time data.
Recently, the West Midlands Combined Authority (WMCA) of the UK implemented drone tracking to assist transportation managers responsible for managing traffic. Harnessing the ability of drones to capture images of live traffic conditions helps commuters and traffic managers, but more importantly, empowers emergency vehicle personnel and transit vehicles to make more timely decisions.
Mike Bird, the WMCA Portfolio Lead for Transport, acknowledged the trial nature of leveraging drones to assist with more decisive traffic management decisions could only benefit the area, thus becoming a permanent fixture.
Smart cities worldwide are looking for more intelligent transportation systems when it comes to utilizing emerging technologies to further enhance emergency vehicle response times or public transit efficiency. Recognizing the importance of vehicle location data and traffic monitoring, LYT remains on the cutting edge in utilizing machine learning and AI technology to predict traffic conditions and give timely priority to those who need it.
Monitoring Traffic Congestion with Technology
Most transportation divisions and government agencies utilize a combination of transportation monitoring systems, from traffic cameras to radar and Lidar sensors. An integrated traffic management system combining data from various sources provides a comprehensive view of traffic conditions with many beneficial applications.
Unmanned aerial vehicles (drones) might be one solution, but harnessing AI and machine learning provides more intelligent transport and better predictability. Utilizing algorithms and machine learning can analyze the data collected to predict traffic patterns, identify congestion hotspots, and optimize traffic flow. AI allows for learning from historical data to make accurate predictions and assist emergency/transit vehicles with preemptive technologies.
See how both work below
Emergency Vehicle Preemption Technology:
Providing a bird’s eye view for emergency response teams can be done with a drone, or it can be done with intelligent transportation systems such as LYT.emergency. Harnessing existing CAD/AVL systems alongside networked traffic signals, LYT.emergency provides emergency preemption capabilities without all the clunky field equipment.
Emergency Vehicle Preemption (EVP) assists in providing a consistent green for first responders and allows for quicker emergency response times. LYT’s ability to provide integrations with dispatch data can help locate vehicles traveling to an incident, parking on the scene, or returning to the station providing real-time green lights as needed.
LYT.emergency customers have seen an average increase in emergency vehicle speed by 62%.
Transit Priority:
Utilizing machine learning and AI technology, LYT.transit can better predict when transit vehicles should be given greenlight priority without causing significant delays for the rest of the traffic. Considering when wheelchair access might be needed or times when heavy foot traffic is consistently present, all roll into the algorithm to reduce delays and keep buses on time.
LYT’s cloud-based transit prioritization systems take the entire picture of a system into account and use unbiased data-centric machine learning to predict the optimal time to grant the green light to transit vehicles at just the right time.
Customers who have implemented the LYT.transit solution have seen a 70% reduction in signal delay within the first six months after launch (TriMet). The upside to LYT.transit is that it requires no additional hardware or equipment setup. Unlike outdated transportation tracking methods that require months of hardware and equipment installation, LYT.transit can be fully operational in days providing real-time bus location data.
