Soft computing in big data intelligent transportation systems

•We presentNeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems.•We integrate the fuzzy control module into the NeverStop system design. The fuzzy control architecture completes the integration and modeling of the traffic control systems.•We present a genetic algorithm illustrating how the average waiting time is derived. The involvement amplifies the NeverStop system and facilitates the fuzzy control module.•NeverStop utilizes fuzzy control method and genetic algorithm to adjust the waiting time for the traffic lights, consequently the average waiting time can be significantly reduced.

The academic and industry have entered big data era in many computer software and embedded system related fields. Intelligent transportation system problem is one of the important areas in the real big data application scenarios. However, it is posing significant challenge to manage the traffic lights efficiently due to the accumulated dynamic car flow data scale. In this paper, we present NeverStop, which utilizes genetic algorithms and fuzzy control methods in big data intelligent transportation systems. NeverStop is constructed with sensors to control the traffic lights at intersection automatically. It utilizes fuzzy control method and genetic algorithm to adjust the waiting time for the traffic lights, consequently the average waiting time can be significantly reduced. A prototype system has been implemented at an EBox-II terminal device, running the fuzzy control and genetic algorithms. Experimental results on the prototype system demonstrate NeverStop can efficiently facilitate researchers to reduce the average waiting time for vehicles.