Artificial intelligence (AI) based smart traffic management

A processor-executable method of optimizing traffic signals comprising:

• receiving, by at least one processor installed at a traffic signal and executing instructions stored in a memory installed at the traffic signal, image data streams including images of traffic entity streams captured by a plurality of image sensors installed in different directions at the traffic signal in an intersection wherein the traffic signal has a plurality of sets of traffic lights facing the different directions;
• analyzing, by the at least one processor using a plurality of machine learning (ML) models stored in the memory, the image data streams for vehicular identification;
• identifying based on the analysis, by the at least one processor, traffic entities representing vehicles of different types from the image data streams;
• generating, by the at least one processor, representations of different types of vehicles in the at least two traffic streams as multiples of the single entity based on a number of wheels on each of the different types of vehicles;
• obtaining, by the at least one processor, two aggregates of the representations corresponding to the at least two traffic streams;
• obtaining, by the at least one processor, corresponding relative densities of the at least two traffic streams from the two aggregates;
• calculating, by the at least one processor, a time for crossing the intersection by the traffic entity streams in the different directions based at least on the corresponding relative densities of the traffic entity streams;
• determining, by the at least one processor, one of the traffic entity streams having a maximum value for the time for crossing the intersection;
• transmitting, by the at least one processor, a first signal to turn green, to a set of traffic lights of the plurality of sets of traffic lights facing one of the traffic streams with the maximum value for the time for crossing the intersection;
• sensing, by the at least one processor, an end of a period of the maximum value of the time for crossing the intersection;
• and
• transmitting by the at least one processor, a second signal to change color to the set of traffic lights at the end of the period.

A non-transitory storage medium comprising machine-readable instructions that cause at least one processor to:

• receive image data streams including images of traffic streams captured by a plurality of image sensors installed in different directions at a traffic signal in an intersection wherein the traffic signal has a plurality of sets of traffic lights facing the different directions;
• analyze by a plurality of machine learning (ML) models, the image data streams for vehicular identification;
• identify, by the at least one processor, vehicles of different types from the image data streams;
• generate representations of different types of vehicles in the at least two traffic streams as multiples of the single entity based on a number of wheels on each of the different types of vehicles;
• obtain corresponding aggregates of the representations for each of the traffic streams;
• obtain respective relative densities of the traffic streams from the corresponding aggregates;
• calculate, a time for crossing the intersection by the traffic streams in the different directions from the respective relative densities;
• determine one of the traffic streams having a maximum value for the time for crossing the intersection;
• transmit, a first signal to turn green, to a set of traffic lights of the plurality of sets of traffic lights facing one of the traffic streams with the maximum value for the time for crossing the intersection;
• sense an end of a period of the maximum value for the time for crossing the intersection;
• and
• transmit a second signal to change color to the set of traffic lights at the end of the period.