| Summary: | Transit stop spacing is one of the tools used to improve service along an existing transit route. The cost components that are essential for a proper evaluation of service on a transit route are walk time, ride time and operational time. These three can further be quantified in $/hr using appropriate cost factors to compare results. This research focused on the evaluation and optimization of an existing transit line with parcel level disaggregation of demand, and using the
real street network to model walk access. The use of parcels and the street network with the transit stops is a new contribution to research in transit operations planning. Once the evaluation data model is ready, different alternatives may be explored in the evaluation mode. Comparison is made from a base case scenario of historic stops to an alternative where stops may be inserted or removed within the physical boundary of the historic stop set. The important consideration in the
process of stop consolidation is the isolation of the impact that a stop will have in the stop set. What is the effect that a stop would have on the transit route's performance when it is either inserted in as in the case of a new stop being added or it is removed as in the case of an existing stop? Our modeling paradigm allows in-depth exploration of impacts of inclusion and exclusion of stops with due regard to the interaction of all the stops interacting with it. The optimization
procedure employs network theory to link a scenario with a path, and an arc with immediate costs of a triplet or quintuplet or septuplet, etc. The maximum-flow / minimum-cut theorem is used to find the minimum number of paths (scenarios) needed to cover all the arcs (immediate costs) in the flow network. And with the multi-dimensional state space optimizer we present the optimal number of stops that minimize the impact of the addition of removal stop in the stop consolidation process. A
case study of both evaluation and optimization of two transit routes is presented. The first case study of a light rail transit service in Boston, the Green Line "B" trains study shows that an optimization using a walk to ride unit cost ratio of 1 yields a reduction of stops from 23 stops to 12 stops, while with walk to ride unit cost ratio of 2, the same route should have 16 stops. Optimization on Albany metro area Route 55 bus transit yields a reduction from 59 stops to 28 stops for a
walk to ride unit cost ratio of 1, while for a ratio of 2 it yields 37 stops.
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