AI-based traffic and road management systems are enabling transport authorities comprehensive planning, monitoring, and maintenance control over all their streets.

Much more than smart apps for trip planning, ride-hailing, and accessing transport authority services, AI-based traffic, and road management systems offer a strategic level of control and improvement in transport planning. Two key areas are in focus – firstly, the strategic planning realm of demand-based public transport and micro-transit optimization.

AI enables optimizing city-level public transport modes and neighborhood-level micro-transit according to established user patterns as well as real-time dynamic user needs. A 2-way communication between transit systems and users entails a network of sensors and user apps informing public transport systems of the real-time ‘demand’ or user requirements and informing users of the ‘supply’ of dynamically optimized need-based transport facilities. Progressive data analysis of user patterns and experience is used to adapt and optimize transport modes, routes, frequencies, and capacities.

Secondly – the Internet of Roads, an adaptation of the Internet of Things to all things transport. AI enables monitoring, optimizing, and maintaining of road networks, traffic lights, parking facilities, and traffic. City-wide transport data collection from sensors, cameras, and user apps facilitates real-time tracking of traffic, different transport modes - public and private, pedestrians, and cyclists.

This information is used not only to control and optimize traffic flow, re-route users away from congestion areas, and emergency services but also in addressing longer-term transport planning strategies. Deep-learning algorithms can also help in predicting maintenance requirements of road and transport facilities preventing expensive repairs, failures, and accidents.

(Source: www.dataversity.net; www.here.com; ww.sciencedirect.com)