The NTSA is a body charged with the mandate of managing the transport sector in Kenya while at the same time ensuring a reduction in the number of lives lost due to road carnage. The body has been keen on inculcating technology in its activities in a bid to improve efficiency. In 2014, NTSA announced plans to launch high-tech number plates as well as digital driving licenses in the country.
The launch was slated to take place this month but a court case delayed the launch which is expected to take place in July, following an out of court settlement of the matter. The smart number plates will store among other information the car’s chassis number, the vehicle’s make, type, colour, engine, transmission, date and place of manufacture; insurance validity, owner’s.
In the digital driving licenses front, Chinese firm Huawei and local firm Copy Cat collaborated in the project under the Transport Integrated Management System (TIMS). TIMS is a web portal that incorporates all functions of registration, licencing, inspection and enforcement of all motor vehicles and trailers online. The NTSA plans to use TIMS to centralize road transport data and make this data available for public use. In addition, the portal will offer intelligent traffic management by allowing tracking and monitoring of PSVs.
As part of its continued plans to reply on technology in fighting road carnage and reducing graft, the NTSA has announced it will buy 200 body cams for its officers. The devices will be issues to traffic policemen as well as NTSA officers manning various highways. NTSA says it hopes the body cams will improve transparency and help them handle complaints and issues by motorists against them. Tenders for the same open in 7 days, with delivery set in the next 60 days.
The body cams have been widely used across the world by law enforcement officials especially when dealing with traffic management. The devices are usually worn on top of an officers clothing and record interactions, while feed of the same is sent to a command center or even locally within the device. Here, the footage can later be analyzed for any issues that may emerge.
