Acoustic traffic sensors provide valuable information about road traffic at an affordable cost, gaining significant attention in recent years. However, the field of audio signal processing for Intelligent Transportation Systems (ITS) lacks real-world complex datasets. We introduce MELAUDIS, the first comprehensive real-world dataset designed for vehicle detection, traffic status monitoring, and vehicle type classification. In MELAUDIS audio recordings from multi-lane roads with two-way traffic are provided that encompass various traffic conditions, ambient noises, and weather settings, including urban environments and rainy weather. The dataset includes six vehicle types, bicycles, motorcycles, cars, buses, trucks, and trams, in both single-vehicle and multi-vehicle contexts. It is comprised of 5,792 background noise recordings, 7,345 vehicle sound samples, and 2,955 idling sound recordings, making it the largest urban acoustic dataset. Labeling and data cleansing required over 1,200 man-hours, improving classification accuracy from 65.1% to 82.84% using log-mel-spectrograms and CNNs. By offering a diverse range of labeled audio recordings, MELAUDIS serves as a benchmark to advance research in ITS.