Efficient automatic detection of incidents is a well-known problem in the field of transportation. Non-recurring incidents, such as traffic accidents, car breakdowns, and unusual congestion, can have a significant impact on journey times, safety, and the environment, leading to socio-economic consequences. To detect these traffic incidents, we propose a framework that leverages big data in transportation and data-driven Artificial Intelligence (AI)-based approaches. This paper presents the proposed methodology, conceptual and technical architecture in addition to the current implementation. Moreover, a comparison of data-driven approaches is presented, the findings from experiments to explore the task using real-world datasets are examined, while highlighting limitations of our work and identified challenges in the mobility sector and finally suggesting future directions.