IoT developments help ease urban traffic congestion

IoT developments help ease urban traffic congestion
While it is unlikely traffic jams will become a thing of the past, ICT and internet of things (IoT) solutions are easing congestion in significant ways.
 
Todd Kreter, SVP and GM of Roadway Sensors at Iteris explained that in the past five to 10 years the traffic industry had focused on “introducing the kind of sensors and connectivity that enables us to help public agencies with a rich set of data.”
 
Rapid development of ICT and IoT solutions offers new possibilities to increase the capacity of existing infrastructure, that's according to Sweco manager Bas van der Bijl, Manager, and ITS expert Stefan Hjort. “Communication between road users and traffic systems, and also more and cheaper IoT sensors, provide more traffic information for the systems to optimize the flows,” they said. “It becomes also possible to guide road users around congested areas, dividing the load over the network more equally.”
 
Stephen Smith, 
Co-Founder and Chief Scientist,
Rapid Flow Technologies

Some companies have developed solutions focused on integration of smart, real-time traffic signal control with emerging connected vehicle technology — the radios that will be going into vehicles to enable direct vehicle-to-infrastructure (V2I) communication. According to Stephen Smith, Co-Founder and Chief Scientist at Rapid Flow Technologies, this integration provides additional opportunities for mitigating/reducing congestion.
 
“In the longer term, V2I communication will provide much more accurate sensing of vehicles approaching a given intersection, and hence lead to better optimization of traffic flows. In the shorter term, there are also mobility enhancements that can be provided,” Smith said.
 
For the most part, the majority of traffic data comes from video and radar devices. Applying intelligent software algorithms to this collected data gives traffic controllers the tools to control congestion. For example, video outputs could help traffic controllers determine where cars are traveling, how fast they are going and what areas are most congested.
 
Using machine vision cameras to ease traffic congestion was one way to do this, explained Matthew Trushinski, Director of Marketing at Miovision. Machine vision can identify cars within video footage and count vehicle numbers. “Instead of a snapshot, traffic engineers can get a much bigger picture of how traffic is moving,” he said.
 
Including this technology in smart intersections can allow traffic engineers to measure what is happening 24/7. Insights from this data can allow cities to make changes and measure the results, iterating until congestion measurably improves.
 
Urban Traffic Management Experts from Kapsch TrafficCom noted that traffic solutions deployed to measure, detect and respond relied on several sources. These ranged from widely used traffic sensors (e.g., loops) to specialized video processing, as well as FCD (floating car data) and also crowdsourcing (e.g., Waze). “IoT is mostly centered on highly distributed sensing networks or mobile devices, such as vehicles themselves, that provide raw data to be processed for incident detection using time-series methods,” Kapsch TrafficCom said.
 
After detection, response plans can be selected from a pre-engineered library, or built more dynamically according to recent available resources in a congested area. In both cases, plans tend to reduce congestion by strategies such as information, rerouting and/or dynamic speed adjustment, according to Kapsch TrafficCom.
 
While daily traffic congestion cannot be truly prevented, it can be controlled for planned events (e.g., roadworks, sports events, etc.), recurring situations (e.g., rush hour) or short-term forecasts. This is achieved by designing mitigating actions such as action plans that can be launched on-demand and automatically according to predefined triggers, explained Kapsch TrafficCom. “The more proactive traffic operators and systems can be, the less impact we can achieve.”

LILIN Uses Smart Cameras for Urban Traffic Management

Steve Hu, Product Manager at Merit LILIN explains how his company’s intelligent video surveillance (IVS) solution and cameras are assisting in urban traffic management.
 
Steven Hu, Product Manager, Merit LILIN

Merit LILIN has developed traffic management solutions using its strengths and background as a video surveillance camera manufacturer. Such solutions include: an artificial intelligence (AI) solution to detect when a car is blocking a bus stop; intelligent video surveillance (IVS) solution on-the-edge for chevron-marked crossings on a freeway; and red-light signal detection to prevent running red lights.
 
Hu explained the bus-stop blocking detection is done with LILIN’s AI, which runs on a PC platform. “Blocking bus stops by cars can cause one- or two-lane congestion. The LILIN Navigator platform uses our AI engine and fires an alarm in the traffic managing center during blocking events,” he said.
 
The IVS solution for chevron-marked crossing detection on freeways utilizes one overview IVS camera for crossing events, two IVS cameras for license plate recognition, and one pan-tilt-zoom (PTZ) camera installed on site for traffic management. Hu noted that snapshots are used for ticketing and videos can be used for petition purposes.
 
The company has also designed cameras for 24-hour traffic signal detection — the LILIN S series IP cameras. With this, LILIN can provide AI vehicle recognition and counting with fisheye cameras. Fisheye cameras with AI can provide congestion control data for traffic signals. “Signal detection and congestion detection can be combined to become a smart traffic signal solution,” Hu said.
 
Additionally, LILIN provides its Navigator Control Center, which receives metadata events and recordings from its Navigator NVR throughout cities via fiber optics. The company also provides services for: license plate recognition; vehicle recognition for truck, bus and motorbike counting; parking violations; directional violation detection; and weigh bridge integration for traffic management. 

 
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