Nature-Inspired Cloud–Crowd Computing for Intelligent Transportation System

Abstract

Nowadays, it is crucial to have effective road traffic signal timing, especially in an ideal traffic light cycle. This problem can be resolved with modern technologies such as artificial intelligence, cloud and crowd computing. We hereby present a functional model named Cloud–Crowd Computing-based Intelligent Transportation System (CCCITS). This model aims to organize traffic by changing the phase of traffic lights in real-time based on road conditions and incidental crowdsourcing sentiment. Crowd computing is responsible for fine-tuning the system with feedback. In contrast, the cloud is responsible for the computation, which can use AI to secure efficient and effective paths for users. As a result of its installation, traffic management becomes more efficient, and traffic lights change dynamically depending on the traffic volume at the junction. The cloud medium collects updates about mishaps through the crowd computing system and incorporates updates to refine the model. It is observed that nature-inspired algorithms are very useful in solving complex transportation problems and can deal with NP-hard situations efficiently. To establish the feasibility of CCCITS, the SUMO simulation environment was used with nature-inspired algorithms (NIA), namely, Particle Swarm Optimization (PSO), Ant Colony Optimization and Genetic Algorithm (GA), and found satisfactory results.