Method and system to predict cost of transportation of goods

A computer system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, cause the one or more processors to perform a method for predicting cost of transportation for one or more trips on real-time dynamic basis, the method comprising:
• receiving, at a cost prediction system, a first set of data associated with one or more trips for transporting goods from a source place to a destination place, wherein the first set of data is received from a user through one or more communication devices;
• fetching, at the cost prediction system, a historical data associated with transportation, wherein the historical data is obtained from a plurality of sources, wherein the historical data comprises a set of data associated with a plurality of vehicles and a plurality of lanes;
• creating, at the cost prediction system, a profile of each type of vehicle of the plurality of vehicles by utilizing the historical data and real-time data associated with the plurality of vehicles, wherein the profile of each type of vehicle is created based on a plurality of factors;
• creating, at the cost prediction system, a profile of the plurality of lanes by utilizing the historical data and the real-time data associated with the plurality of lanes, wherein the profile of the plurality of lanes is based on a plurality of historical lanes parameters;
• analyzing, at the cost prediction system, the first set of data, the historical data and the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes by utilizing machine learning and regression techniques, wherein the first set of data, the historical data, the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes are analyzed to identify a pattern similar to the first set of data, wherein the analyzing is done in real time;
• calculating, at the cost prediction system, the estimated cost of transportation for the one or more trips based on the analysis of the first set of data, the historical data and the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes;
• displaying, at the cost prediction system, the estimated cost of transporting goods from the source place to the destination place of the one or more trips, wherein the estimated cost is displayed on the one or more communication device associated with the user;
• and
• updating, at the cost prediction system, the estimated cost of transporting goods for the one or more trips on the real-time dynamic basis, wherein the estimated cost is updated based on a plurality of parameters.

A non-transitory computer-readable storage medium encoding computer executable instructions that, when executed by at least one processor, performs for predicting cost of transportation for one or more trips on real-time dynamic basis, the method comprising:

• receiving, at a computing device, a first set of data associated with one or more trips for transporting goods from a source place to a destination place, wherein the first set of data is received from a user through one or more communication devices;
• fetching, at the computing device, a historical data associated with transportation, wherein the historical data is obtained from a plurality of sources, wherein the historical data comprises a set of data associated with a plurality of vehicles and a plurality of lanes;
• creating, at the computing device, a profile of each type of vehicle of the plurality of vehicles by utilizing the historical data and real-time data associated with the plurality of vehicles, wherein the profile of each type of vehicle is created based on a plurality of factors;
• creating, at the computing device, a profile of the plurality of lanes by utilizing the historical data and the real-time data associated with the plurality of lanes, wherein the profile of the plurality of lanes is based on a plurality of historical lanes parameters;
• analyzing, at the computing device, the first set of data, the historical data, the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes by utilizing machine learning and regression techniques, wherein the first set of data, the historical data, the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes are analyzed to identify a pattern similar to the first set of data, wherein the analyzing is done in real time;
• calculating, at the computing device, the estimated cost of transportation for the one or more trips based on the analysis of the first set of data, the historical data, the data associated with the profile of each type of vehicle and the data associated with the profile of the plurality of lanes;
• displaying, at the computing device, the estimated cost of transporting goods from the source place to the destination place of the one or more trips, wherein the estimated cost is displayed on the one or more communication device associated with the user;
• and
• updating, at the computing device, the estimated cost of transporting goods for the one or more trips on real-time dynamic basis, wherein the estimated cost is updated based on a plurality of parameters, wherein the plurality of parameters comprises occurrence of entropy in the one or more trips or one or more similar trips, one or more offers or discounts in the parameters associated with the one or more trips, peak seasons.