{"data":{"id":2,"backendId":"76a7db91-f81f-40ad-b5f2-63380f5ae308","title":"Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework","summary":"arXiv:2603.00010v1 Announce Type: new Abstract: Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed demand assumptions. Considering the limitations of these assumptions, this paper proposes a new framework, namely the Two-Level Rider Choice Transit Network Design (2LRC-TND), that leverages machine learning and contextual stochastic optimization (CSO) through constraint programming (CP) to incorporate two ","analysis":"This original research offers a high-value technical framework for urban planning, combining ML with optimization to solve complex infrastructure problems.","category":"technology","strategicTrack":"climate_tech","capitalRelevance":{"social":6,"cultural":3,"economic":7,"physical":6,"symbolic":4,"temporal":5,"informational":8,"psychological":2,"technological":9},"tags":["Machine Learning","Stochastic Optimization","Transit Network Design","Smart Cities","Demand Modeling"],"qualityScore":10,"valueScore":8,"interestScore":7,"potentialScore":8,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-04T10:13:18.217Z","createdAt":"2026-03-04 13:49:26"}}