Technology Trend of Traffic Density Prediction – A Systematic Literature Review

Road traffic becomes critical when it comes to the time consuming amount when moved from one to another location. The travel duration is affected directly to citizen activity during the day. Government tends to try solving the problem by developing new road for migrating the road capacity. Traffic management is the new needs to overcome congestion due to overcrowding and overcapacity. The current management system still utilizes information obtained from various entities on the road. Observation of conditions and situations on the road becomes very subjective. However, there are potential technologies that can be utilized to help the existing problems. With these problems and opportunities, there is in providing traffic density information that is more objective utilizing the latest technology. The development of various types of information system adaptation and the use of technology is able to provide information on a regular basis. Machine learning as a form of technology development that is being optimized, can solve the information needs typical of traffic control. Based on the needs, the technology application related to traffic management get higher overtime. This study aims to examine the trends of the technology application on traffic classification for traffic management using the Systematic Literature Review (SLR) method. The purpose of this study is to provide the background of relevant activities which are considered by researcher while developing traffic classification system. In addition, this research provides important insight into the need to make suitable and correlated methodologies which support the management of evidence in an appropriate manner.