Roadways in developing countries usually carry heterogeneous traffic where on-street bicyclists encounter a very complex interaction with various categories of vehicles. In order to quantify the operational conditions of bicyclists under such conditions, a reliable bicycle level of service (BLOS) model is yet to be devised. This study intends to partially fill this gap by proposing a BLOS model suitable for urban road segments in mid-sized cities carrying heterogeneous traffic. A recently introduced artificial intelligence technique namely, associativity functional network (FN) is implemented for the development of this model. FN is a problem-driven approach that overcomes several limitations of the artificial neural network (ANN) technique. The urban bicycling environments persisting on 74 street segments are analyzed, and 8 influencing variables (geometric, traffic and built environmental, etc. ) are identified. Of these variables, interruptions caused by frequent stoppages of intermittent public transits and frequency of driveways carrying high volume of traffic are newly introduced. In the modeling process, a strong relationship has been established between the identified variables and perceived BLOS scores collected through perception surveys. The resulting BLOS model has shown a high reliability for its applications in the mid-sized cities and has reported a high correlation coefficient (R) of 0. 94 with the average observations. Besides, a sensitivity analysis is also carried out to identify the relative importance of input variables based on their contribution in the BLOS estimation. As observed, effective width of the outside lane, traffic volume, and on-street parking activity are by far the most important variables, which contribute 38. 3％, 21. 8％, and 12. 7％ respectively in the prediction of facility BLOS. Thus, these three attributes should be largely prioritized while making any plan of actions for the betterment of bicyclists.