With the rapid development of Intelligent Transportation Systems (ITS), how to provide users with a natural and efficient human-machine interface is now becoming a crucial issue for driver safety. It is no doubt that speech will be one of the best mediators of human-machine interaction; however, the performance of automatic speech recognition (ASR) always radically degrades when the input speech is corrupted by varying noises. In this paper, we consider the use of histogram equalization (HEQ) for robust ASR. A novel data fitting scheme was presented to efficiently approximate the inverse of the cumulative density function of training speech for HEQ, which has the merits of lower storage and time consumption compared to the conventional table-lookup or quantile based HEQ approaches. Moreover, a more elaborate attempt of using multiple inverse functions for different noise conditions was investigated as well. All experiments were carried out on the Aurora-2 standard database and task. Very encouraging results were obtained. The proposed robustness technique has also been properly integrated into our prototype system for in-vehicle traffic information retrieval using spoken queries.