Intelligent Transportation Systems (ITS) are gaining popularity in developing countries like India. Two major components of ITS are Advanced Traveler Information System (ATIS) and Advanced Public Transportation Systems (APTS). A combined application of these two components is the estimation of bus arrival times. Accurate bus arrival time information will enhance the credibility of the transit system, thereby leading to higher patronage. Significant research has already been conducted using Automatic Vehicle Location (AVL) system for predicting bus arrival times. However, there is limited literature available on the application of probe vehicle speed data and passenger data for predicting bus transit travel time. It is believed that passenger data influences bus travel time in cities of developing countries such as India. Thus, this paper focuses on the application of probe vehicle speed data and passenger data for predicting bus transit travel time. The improvement brought about by the probe vehicle speed data and passenger data in prediction of travel time over the use of only GPS data is also studied. Along with passenger data, GPS data was collected using probe buses on one of the busiest bus routes on weekday evening peak hours in Chennai city, India. Preliminary data analysis revealed that similar traffic conditions prevail over the route during the evening peak hours on all weekdays. Thus, Multiple Linear Regression (MLR) models which do well in such recurrent traffic conditions were developed. Results conclude that: use of passenger data and speed data from probe buses helped improve the performance of the model. Copyright ASCE 2008.