This study develops a methodology to consolidate transit stops.It develops a mathematical model and a program which takes stop consolidation decision(s) according to users generalized travel time savings and desired accessibility.The model iterates until the users generalized travel time savings are maximized.The study tests this mathematical model in different hypothetical scenarios.Six factors(distance between stops,passenger activity,average cruising speed,maximum walking distance,service frequency,and percentage of decreased passengers) with multiple levels were set to build the scenarios.Three responses(percentage of consolidated stops,percentages of travel time and operating time savings)were observed.The findings showed that the distance between the stops the passenger activity,and the probable demand change(or the percentage of decreased passengers) are the most influential factors.The frequency of service was found to be influential as well.The average cruising speed has very little influence on the response variables.Finally,the model is tested on two routes(route 900 and 930) of Al Ain City public bus service.It shows that 22 and 32 out of 98 and 126 stops can be consolidated in route 900 and 930 respectively.This can save considerable amounts of users travel and operating times.In monetary values,the savings are about $329,827 and $491,094 per year for routes 900 and 930,respectively. This study develops a methodology to consolidate transit stops.It develops a mathematical model and a program which takes stop consolidation decision (s) according to users generalized travel time savings and desired accessibility. Model the emulsions until the users generalized travel time savings are maximized. The study tests this mathematical model in different hypothetical scenarios. Six factors (distance between stops, passenger activity, average cruising speed, maximum walking distance, service frequency, and percentage of decreased passengers). (percentage of consolidated stops, percentages of travel time and operating time savings) were observed. The findings showed the distance between the stops the passenger activity, and the probable demand change (or the percentage of decreased passengers) were the most influential factors. The frequency of service was found to be influential as well. Average cruising speed has very little influence on the response variables .Finally, the model is demonstrated on two routes (route 900 and 930) of Al Ain City public bus service. It shows that 22 and 32 out of 98 and 126 stops can be consolidated in route 900 and 930 respectively.This can save an amount of users travel and operating times.In. monetary values, the savings are about $ 329,827 and $ 491,094 per year for routes 900 and 930, respectively.