IoT: Swarm intelligence – How ants avoid gridlock

IoT: Swarm intelligence algorithms could provide a better way to allow city planners to create simulations and help them understand congestion challenges based on how vehicles and pedestrians navigate public spaces.

Traffic congestion is undoubtedly the bane of a driver’s existence. One fender bender or a little rainfall and traffic slows to a crawl – a highway closure creates nightmare backups.

Most cities use low-tech approaches to study traffic patterns – people counting vehicles passing through intersections at peak hours, over a fxed period. From that, the city executives decide whether or not to widen a road, add a stop sign, or install traffic lights. Swarm intelligence algorithms, such as Ant Colony Optimization (ACO) or Particle Swarm Optimization (PSO), could provide a better way to allow city planners to create simulations and help them understand congestion challenges based on how vehicles and pedestrians navigate public spaces.

Simulations using sensor-collected data would assist planners in identifying potential traffic challenges at granular levels – street, intersection, freeway ramp, and other locations. Congestion problems could be addressed more efficiently, cutting down the number of planning errors in the process. High-tech methods leveraging the Internet of Things can aid in cutting down traffic congestion. For example, existing technologies can detect smartphone Bluetooth signals (short range) and Wi-Fi signals (longer range) from vehicles as they pass through points where sensors detect and record their presence. By placing sensor detectors at key locations along roads, transportation managers can determine the general path of vehicles as they pass through these points. Having this greater insight into traffic flow and congestion points could help city planners to identify opportunities to smooth traffic flows and plan the infrastructure more accurately to support a city’s growing needs.

Swarm smarts: Particle Swarm Optimization (PSO) can point the way to lower congestion.

Most commuters have smartphones and many vehicles today have built-in electronics (Wi-Fi, Bluetooth, ZigBee) emitting signals that can be captured. Scanners in the streets, typically attached to street lights, can capture these signals. The number required will vary with the amount of traffic flowing through an area, distances between vehicles, and the level of reflection created by nearby signs, billboards, or any obstructions. These scanners need to be positioned in a pattern that increases the chance of detection. Once the data is captured, the raw information contained in Bluetooth, WiFi, and ZigBee data logs can be aggregated to provide greater insights into traffic patterns. Swarm intelligence holds a terrify potential to totally transform the way traffic patterns affect our daily lives and our daily commutes. It is now up to city planners throughout our major metropolises to recognize the benefits of these simulations and to create the necessary infrastructure.

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