A processor implemented method for providing an encoder-decoder model based Recurrent Neural Network, RNN, for Bus Arrival Time Prediction, BATP, the processor implemented method comprising: obtaining, via one or more hardware processors, a route information pertaining to one or more trips associated to one or more vehicles; segmenting, via the one or more hardware processors, the route information into a plurality of sections, wherein each of the plurality of sections comprises travel time of the one or more trips, and wherein the travel time comprises running time across a section of the plurality of sections and dwell time at one or more vehicle stops in the section; grouping, via the one or more hardware processors, for each trip (i) travel time of one or more previous sections with reference to a current section from the plurality of sections, and (ii) travel time of one or more previous sections of a historical trip from a previous week, to obtain a grouped trip data; characterized by generating, via an encoder comprised in the encoder-decoder model, a context vector using the grouped trip data, wherein the encoder-decoder model further comprises a decoder and one or more bidirectional layers at the decoder; predicting, by the decoder via the one or more hardware processors, a travel time for one or more subsequent sections, for one or more trips based on one or more exogenous inputs further comprising (i) travel time and entry time of the historical trip from the previous week, (ii) travel time and entry time of a closest previous trip of a closest previous vehicle corresponding to a current vehicle on a current trip, (iii) the context vector, (iv) a current position of one or more vehicles, and (v) the current time, wherein a final arrival time is obtained based on the predicted travel time of the one or more subsequent sections; and capturing, at the decoder of the encoder-decoder model, an upstream propagation of one or more congestions that originate downstream in the one or more subsequent sections, wherein the encoder-decoder model comprises the one or more bi-directional layers. obtaining, via one or more hardware processors, a route information pertaining to one or more trips associated to one or more vehicles; segmenting, via the one or more hardware processors, the route information into a plurality of sections, wherein each of the plurality of sections comprises travel time of the one or more trips, and wherein the travel time comprises running time across a section of the plurality of sections and dwell time at one or more vehicle stops in the section; grouping, via the one or more hardware processors, for each trip (i) travel time of one or more previous sections with reference to a current section from the plurality of sections, and (ii) travel time of one or more previous sections of a historical trip from a previous week, to obtain a grouped trip data; generating, via an encoder comprised in the encoder-decoder model, a context vector using the grouped trip data, wherein the encoder-decoder model further comprises a decoder and one or more bidirectional layers at the decoder; predicting, by the decoder via the one or more hardware processors, a travel time for one or more subsequent sections, for one or more trips based on one or more exogenous inputs further comprising (i) travel time and entry time of the historical trip from the previous week, (ii) travel time and entry time of a closest previous trip of a closest previous vehicle corresponding to a current vehicle on a current trip, (iii) the context vector, (iv) a current position of one or more vehicles, and (v) the current time, wherein a final arrival time is obtained based on the predicted travel time of the one or more subsequent sections; and capturing, at the decoder of the encoder-decoder model, an upstream propagation of one or more congestions that originate downstream in the one or more subsequent sections, wherein the encoder-decoder model comprises the one or more bi-directional layers.