The rapid advancements in sensor technologies has resulted in the increased use of Automatic Vehicle Location (AVL) systems for traffic data collection. Global Position System (GPS) sensors are the most commonly used AVL system, majorly because of it being a time-tested technology and being relatively cheap. Also, many of the transportation agencies have their vehicles equipped with GPS sensors. One of the interesting challenges in the field of Intelligent Transportation Systems (ITS) is to effectively mine useful information from such large-scale database accumulated over time. The current study analyses travel time data obtained from buses fitted with GPS devices in Chennai, India to understand its variation over time and space to find the spatial and temporal points of criticality. For this, Cumulative Frequency Distribution (CFD) curves, bar charts and boxplots were used. Inter-Quartile Range (IQR) was used as a measure to quantify the variations in travel time. Analysis showed that both travel time and its variation increased approximately 10% and 40%, respectively, from 2014 to 2016. This increase was observed to be primarily concentrated in six critical intersections during morning and evening peak hours. The findings from the study were further used in demonstrating possible user applications that can improve the efficiency of public transportation systems. As part of this, a real-time bus travel time prediction method was developed using a deep learning approach, Long and Short-Term Memory (LSTM) networks. Along with this, a robust fleet management system was also developed to check the adequacy of buses along the study corridor for different time of the day. © 2020 Taylor & Francis Group, LLC.