Agent bases systems
Detecting collision probabilities and alerting pilots/drivers is a very important aspect of safety in places like airports. GPS based systems are useful in assessing post incident scenarios. Augmenting them with artificial intelligence helps in preemptively avoiding incidents. In one of our deployments we developed one such solution where a GPS+LoRa based system was enhanced with a collision detection simulator. Post incident play back system was also developed. This solution was listening to web sockets to fetch the vehicle locations and measure collision probabilities on the fly. In case of any detection it would send alert to stakeholders.
In this case study the vehicle driver and pilot are the simulated agents and they have finite number of states resulting in finite probable scenarios of incidents.
Kaiinos anD ai
Agent based models track each agent in time and space. We start our development with defining agents (human or physical) and implementing their behavior. Simulators which can capture and represent such behavior help planners to ideate and execute context specific plans. Later these data models can easily be converted into information pipelines to monitor the actual implementation of the plans.
Agent based systems which help in predicting user behaviors save precious financial resources for planners who launch taxi services. Estimating demand in given routes and assessing human behavior in case of delays helps in planning exact number of trips and frequencies before launching the taxi services.
In one such case study, Kaiinos developed an analytical system where a planner can choose parameters like number of rides, frequencies, pickup buffer etc. Based on these parameters demand estimation is done every second and vehicular movement is shown to the planner. At the end of the rides the metrics which help in finalizing the routes/rides will be presented to the planner.