COMPLEX DYNAMIC ROUTE SEQUENCING FOR MULTI-VEHICLE FLEETS USING TRAFFIC AND REAL-WORLD CONSTRAINTS

Methods and systems to dynamically and optimally sequence routes based on historical and real-time traffic conditions, and to predict anticipated traffic conditions along the dynamically generated route are disclosed. Route sequencing may be based on a set of predefined constraints, e.g., distance, time, time with traffic, or any objective cost function. The system of the present invention may be implemented in a vehicle fleet comprising one or more vehicles with one or more depots, or with no depots. An optimization server obtains real-time, historical and/or predicted future traffic, weather, hazard, and avoidance-zone data on road segments to generate a route, while staying within parameters and constraints set by an automatic machine learning process, an artificial intelligence program, or a human administrator. The platform may be coupled to sensors positioned on roads, e.g., speed radar or camera, and sensors positioned in vehicles, e.g., GPS system or on-board diagnostic sensor.