Public buses are an important part of the urban transportation mix. However, a considerable disadvantage of buses is their slow speed, which is in part due to frequent stops, but also due to the lack of segregation from other vehicles in traffic. As such, assessing bus routes and the respective sections that are prone to congestion is an important aspect of route planning, scheduling, and the creation of dedicated bus lanes. In this work we use bus tracking data from the Washington Metropolitan Area Transit Authority, to discover speed patterns of specific bus routes in relation to the road network throughout the day. Specifically, we focus on using these patterns to identify free flow segments, bus stop locations, traffic light locations and road segments prone to congestion.

Fixed-route bus systems are an important part of the urban transportation mix. A considerable disadvantage of buses is their slow speed, which is in part due to frequent stops, but also due to the lack of segregation from other vehicles in traffic. As such, assessing bus routes is an important aspect of route planning, scheduling, and the creation of dedicated bus lanes. In this work, we use bus tracking data from the Washington Metropolitan Area Transit Authority to discover speed patterns in relation to bus stops throughout the day. This gives us an insight on whether the routes are affected by traffic congestion or more random events such as traffic lights. We first employ a macro-level qualitative analysis to identify patterns across different trips. A micro-level quantitative analysis further refines this approach by analyzing the speed patterns around bus stops. Our analysis is based on bus odometer data, which is a onedimensional representation of trips that has considerable accuracy when looking at speed patterns. Exploiting route metadata in relation to stops, we use Dynamic Time Warping to cluster different stops based on their speed profiles throughout the day. The clustering can be used to generate a spatiotemporal route profile and we show how such a profile provides actionable intelligence for route planning purposes.

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