The performance of multimodal mobility systems relies on the seamlessintegration of conventional mass transit services and the advent ofMobility-on-Demand (MoD) services . A primal-dual approach, inspired by the market literature, yields a compact mixed integer linear programming (MILP)formulation . We provide atractable solution approach through a decomposition scheme and approximational algorithm that accelerates the computation and enables optimization of large-scale problem instances . We also show that our algorithmreduces the average runtime by 60\% compared to advanced MILP solvers . Thisresult seeks to establish a generic and simple-to-implement way of revampingand redesigning regional mobility systems in order to meet the increase intravel demand and integrate traditional fixed-line mass transit systems with new demand-responsive services . The authors also say that their algorithm reduces the average number of hybrid modes accessible to travelers in their proposed model of the proposed model is a low-cost solution . They say

Author(s) : Qi Luo, Samitha Samaranayake, Siddhartha Banerjee

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : systems - mobility - demand - transit - services -

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