A reliability-based transit trip planning model under transit network uncertainty

Transit, although an important public transportation mode, is not thoroughly utilized in the United States. To encourage the public to take transit, agencies have developed systems and tools that assist travelers in accessing and using information. Transit data modeling and trip planner system architecture developments have helped advance these systems, and the recent emergence of transit trip planning algorithms promises further enhancement. Conventional transit trip planning algorithms are usually developed based on graph theory. In order to utilize these algorithms, certain assumptions must be made to support these algorithms (e.g. buses always run on time). However, these assumptions may not be realistic. To overcome these limitations, our study develops an innovative transit trip planning model using chance constrained programming. Unlike previous studies, which only minimized passenger-experienced travel time, our study also considers transit service reliability. Additionally, in-vehicle travel time, transfer time, and walking time are all included as elements of passenger-experienced travel time. Our transit trip planning model avoids the assumptions of previous studies by incorporating transit service reliability and is capable of finding reliable transit paths with minimized passenger-experienced travel time. The algorithm can also suggest a buffer time before departure to ensure on-time arrivals at a given confidence level. General Transit Feed Specification data, collected around Tucson, Arizona, was used to model the transit network using a “node-link” scheme and estimate link-level travel time and travel time reliability. Three groups of experiments were developed to test the performance of the proposed model. The experiment results suggested that the optimal anticipated travel time increased with increasing on-time arrival confidence level and walking was preferred over direct bus transfers that involved out of direction travel. The proposed model can also include additional travel modes and can easily be extended to include intercity trip planning.