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In recent years, deep learning models proved their ability to solve complex problems in the areas such as computer vision and natural language processing, and are receiving a lot of attention within the community of transportation systems as well. Though these are known as data-driven approaches, it is not yet reported whether providing a huge amount of data is sufficient or whether extra domain knowledge added as features will improve their performance. It is reasonable to expect that the performance of deep learning models will be improved by incorporating field-specific knowledge into the problem. This paper tries to address this question by taking Convolutional Neural Networks (CNNs) as a sample deep learning technique and comparing its performance with and without adding extra information about the data as feature input, for the application of bus travel time prediction. To extract extra information, the …