In this study, we are developing next-generation traffic signal control system, SMART SIGNAL, which is operated using a traffic big-data. To improve urban’s chronic recurrent congestion, SMART SIGNAL conducts real-time traffic signal control based on travel time data of traffic information systems. This research project started in 2015 and is scheduled to end in 2019. This research project consists of three sub-tasks, which are traffic big-data bank system, signal operation algorithm, and field test for SMART SIGNAL. The traffic big-data bank system includes the travel time and traffic volume data from public and private sector’s traffic information systems. Additionally, this system contains taxi trajectory data, CCTV image and smartphone based traffic data. This big-data system predicts the travel time and traffic volume by intersection movement for real-time signal control. The smart signal operation algorithm of SMART SIGNAL consists three sub-algorithm of PRE-CON, CAERUS, and NIMOS. PRE-CON makes today’s signal timing plan using historical traffic data. CAERUS is traffic responsive signal control algorithm based on predicted travel time. NIMOS is spillback control algorithm for oversaturated condition. In this project, field experiment is planned in 2019 in Seoul.

Adverse weather is a big challenge not only for the safety of drivers but the safety of Autonomous Vehicles (AV). The gap between human-driving and AV-driving in terms of adverse-weather-perception can be a new challenge for highway engineers. Solutions minimizing the gap need to be defined. By this, the smart road technologies can be specified and developed. The way how to define and quantify the gap is introduced in this presentation.