1. A system comprising: a plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is associated with a vehicle; a data collection engine (DCE) communicating with the first RFID chip, wherein the DCE comprises: a power transmission subsystem including a power source and arranged to transmit power from the power source to the first RFID chip; a transceiver configured to receive first data including identification information from the first RFID chip; a controller operatively coupled to the transceiver; and one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate one or more messages indicative of the identification information and location information to be sent by the transceiver to a server device via a network connection, wherein the first RFID chip includes control logic for generating the identification information; wherein the server device comprises: a transceiver configured to, via the network connection, receive the one or more messages from the DCE, and a pick-up request from a client device; a plurality of trained models for generating output values corresponding to a present event associated with the pick-up request based upon at least the identification information; a controller operatively coupled to the transceiver; one or more memory sources operatively coupled to the controller, the one or more memory sources configuring the controller to: perform pre-processing on the location and identification information as a plurality of input attributes to generate an input data set; and generate the output value from the trained model based upon the input data set, wherein the output value is one or more of: a travel path, a pick-up location for the pick-up request and a price for the pick-up request; wherein the trained models include: a trained Self-Organizing Map (SOM) including a plurality of network nodes arranged in a grid or lattice and in fixed topological positions, an input layer with a plurality of input nodes representing input attributes of past events, wherein each of the plurality of input nodes is connected to all of the plurality of network nodes by a plurality of synaptic weights; and a trained neural network model (NNM) including an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons, wherein each of the plurality of hidden neurons includes an activation function, the activation function is one of: (1) the sigmoid function f(x)=1/(1+e −x ); (2) the hyperbolic tangent function f(x)=(e 2x −1)/(e 2x +1); and (3) a linear function f(x)=x, wherein x is a summation of input neurons biased by synoptic weights.