Abstract
Although transit stop location problem has been extensively studied, the two main categories of modeling methodologies, i.e., discrete models and continuum approximation (CA) ones, seem have little intersection. Both have strengths and weaknesses, respectively. This study intends to integrate them by taking the advantage of CA models’ parsimonious property and discrete models’ fine consideration of practical conditions. In doing so, we first employ the state-of-the-art CA models to yield the optimal design, which serves as the input to the next discrete model. Then, the stop location problem is formulated into a multivariable nonlinear minimization problem with a given number of stop location variables and location constraint. The interior-point algorithm is presented to find the optimal design that is ready for implementation. In numerical studies, the proposed model is applied to a variety of scenarios with respect to demand levels, spatial heterogeneity, and route length. The results demonstrate the consistent advantage of the proposed model in all scenarios as against its counterparts, i.e., two existing recipes that convert CA model-based solution into real design of stop locations. Lastly, a case study is presented using real data and practical constraints for the adjustment of a bus route in Chengdu (China). System cost saving of 15.79% is observed by before-and-after comparison.
Highlights
Transit route design problem can be divided into two categories: transit network design and single transit route design [1,2,3,4,5]
Well-designed transit routes constitute as the basic bricks to the big transit network in many cities for defending the wide spread of roadway traffic congestion. e design of a single transit route mainly concerns the locations of stops/stations and the service headways/frequencies during the operation periods
E transit stop location problem has been extensively studied in the literature
Summary
Transit route design problem can be divided into two categories: transit network design and single transit route design [1,2,3,4,5]. Wirasinghe and Ghoneim [15] proposed a more general CA-based model for determining bus stop spacing (expressed as a function of location). In Wirasinghe and Ghoneim and Medina et al [15, 18], the continuous stop density/spacing function was discretized into specific locations via the integral method. Their models still lack the consideration of realistic street layout and practical location restrictions, e.g., intersections, bridges, and natural obstacles, where no bus stops should be placed. We propose an optimization framework that integrates CA models with discrete ones for locating bus stops with respect to location constraint.
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