Preprint
Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale On-Demand Multimodal Transit Systems
ArXiv.org
12/06/2022
DOI: 10.48550/arxiv.2212.03460
Abstract
Capturing latent demand has a pivotal role in designing public transit
services: omitting these riders can lead to poor quality of service and/or
additional costs. This paper explores this topic in the design of OnDemand
Multimodal Transit Systems with Rider Adoptions (ODMTS-DA). Prior work proposed
a bilevel optimization model between the transit agency and riders with choice
of adoption, and an exact algorithm to solve the resulting ODMTS-DA design
problem. However, due to the complexity and combinatorial nature of the
ODMTS-DA, the exact algorithm exhibits difficulties on large-scale instances.
This paper aims at addressing this challenge in order to find high-quality
ODMTS-DA designs in reasonable time. It proposes five heuristic algorithms
whose designs are driven by fundamental properties of optimal solutions. The
performance of the heuristic algorithms are demonstrated on two test cases
leveraging real data: a medium size case study for the Ann Arbor and Ypsilanti
region in the state of Michigan and a large-scale case study conducted in the
Atlanta metropolitan region in the state of Georgia. To evaluate the results,
besides directly comparing computational times and optimality gaps with the
exact algorithm, this paper introduces two additional metrics that leverage the
characteristics of optimal solutions with respect to customer adoption.
Computational results demonstrate that the heuristic algorithms find optimal
solutions for medium-size problem in short running times, and discover
high-quality solutions to the large-case study that improve upon the best
solution found by the exact algorithm in considerably less time. The ODMTS
designs obtained by these algorithms provide substantial benefits in terms of
convenience, operating cost, and carbon emissions.
Details
- Title: Subtitle
- Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale On-Demand Multimodal Transit Systems
- Creators
- Hongzhao GuanBeste BasciftciPascal Van Hentenryck
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2212.03460
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 12/06/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380603402771
Metrics
18 Record Views