Method and system for dynamically predicting vehicle arrival time using a temporal difference learning technique

A system (100), for dynamically predicting a vehicle arrival time comprising:

• a memory (102) storing instructions;
• one or more communication interfaces (106);
• and
• one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
• receive, a plurality of inputs comprising a current status of a vehicle, a target destination of the vehicle comprising a plurality of route segments associated with a trip, and an elapsed time of the vehicle from a source location of the trip to a current location of the vehicle, wherein the current status of the vehicle comprises the current location of the vehicle for a given time instance;
• fetch, a plurality of trip patterns for the plurality of inputs using a historical database, wherein each trip pattern from the plurality of trip patterns is a possible closest candidate trip that runs from the current location of the vehicle to the target destination;
• extract, a plurality of temporal events, from at least one trip pattern from the plurality of trip patterns,
• wherein the at least one trip pattern has similarly matches with the current location of the vehicle to the target destination,
• wherein the plurality of temporal events comprises a trip travel time (Tpv) for a day is based on the most recent vehicle travelled during the same day of the trip and a trip travel time (Tpw) for a week is based on the plurality of vehicles travelled during a particular time interval;
• construct, a markov reward process model using the current location of the vehicle for the given time instance and the elapsed time of the vehicle from the source location of the trip to the current location of the vehicle;
• obtain, a plurality of nonlinear features from the plurality of temporal events and the plurality of inputs using a 3-layer feed forward multilayer network which is based on a monte carlo process;
• and
• dynamically predict using a trained temporal difference predictor model (TTDPM) enabled by the markov reward process, the arrival time from the current location of the vehicle to the target destination based on the plurality of nonlinear features serving as an input to the trained temporal difference predictor model (TTDPM).

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:

• receiving, a plurality of inputs comprising a current status of a vehicle, a target destination of the vehicle comprising a plurality of route segments associated with a trip, and an elapsed time of the vehicle from a source location of the trip to a current location of the vehicle, wherein the current status of the vehicle comprises the current location of the vehicle for a given time instance;
• fetching, a plurality of trip patterns for the plurality of inputs using a historical database, wherein each trip pattern from the plurality of trip patterns is a possible closest candidate trip that runs from the current location of the vehicle to the target destination;
• extracting, a plurality of temporal events, from at least one trip pattern from the plurality of trip patterns,
• wherein the at least one trip pattern has similarly matches with the current location of the vehicle to the target destination,
• wherein the plurality of temporal events comprises a trip travel time (Tpv) for a day is based on the most recent vehicle travelled during the same day of the trip and a trip travel time (Tpw) for a week is based on the plurality of vehicles travelled during a particular time interval;
• constructing, a markov reward process model using the current location of the vehicle for the given time instance and the elapsed time of the vehicle from the source location of the trip to the current location of the vehicle;
• obtaining, a plurality of nonlinear features from the plurality of temporal events and the plurality of inputs using a 3-layer feed forward multilayer network which is based on a monte carlo process;
• and
• dynamically predicting, using a trained temporal difference predictor model (TTDPM) enabled by the markov reward process model the arrival time from the current location of the vehicle to the target destination based on the plurality of nonlinear features serving as an input to the trained temporal difference predictor model (TTDPM).