The public transport data sparsity from our previous article led to our attempt in finding more answers using heatmaps to zoom in to the limited data we have.
The gap between planned arrivals and actual arrivals seems to be a common issue in transport schedules. Log analysis might help us see that.
Why do multimodal public transport associations need to distribute ticket revenue to the multiple operators in charge of the multiple modes of transport?
Since big data can be well-implemented in public transport, what are the first, second and third party data for public transport? This article takes a look.