Special Issue on Recent Advances in Urban Transport and Logistics Through Optimization and Analytics

2021

Preprint

This paper considers the design of On-Demand Multimodal Transit Systems (ODMTS) that combine fixed bus/rail routes between transit hubs with on-demand shuttles to serve the first/last miles to/from the hubs. The design problem aims at finding a network design for the fixed routes to allow a set of riders to travel from their origins to their destinations, while minimizing the sum of the travel costs, the bus operating costs, and rider travel times. The paper addresses two gaps in existing tools for designing ODMTS. First, it generalizes prior work by including ridesharing in the shuttle rides. Second, it proposes novel fleet-sizing algorithms for determining the number of shuttles needed to meet the performance metrics of the ODMTS design. Both contributions are based on Mixed-Integer Programs (MIP). For the ODMTS design, the MIP reasons about pickup and dropoff routes in order to capture ridesharing, grouping riders who travel to/from the same hub. The fleet-sizing optimization is modeled as a minimum flow problem with covering constraints. The natural formulation leads to a dense graph and computational issues, and is refined in an optimized model working on a sparse graph. The methodological contributions are evaluated on a real case study: the public transit system of the broader Ann Arbor and Ypsilanti region in Michigan. The results demonstrate the substantial potential of ridesharing for ODMTS, as costs are reduced by about 26% with minimal increase in transit times. Compared to the existing system, the designed ODMTS also cuts down costs by 35% and reduces transit times by 38%.

Section: Review Of Related Literaturesupporting

confidence: 72%

Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systems

Auad¹,

2021

Preprint

“…More precisely, for simplicity in the notations, the paper assumes that the transit price is half of the shuttle cost of a trip. 1 With this pricing model, the weighted cost and convenience for an on-demand shuttle between i and j for the ODMTS and riders is given by (1 − θ) g 2 d ij + θt ij , where g is the cost of using a shuttle per mile. Moreover, the shuttles act as feeders to bus system or serve the local demand.…”

Section: Problem Statementmentioning

confidence: 99%

“…By ignoring the latent demand, the ODMTS may be designed suboptimally, providing a lower convenience or higher costs. This concern was raised in [1] who articulated the potential of leveraging data analytics within the planning process and proposing transit systems that encourage riders to switch transportation modes.…”

Section: Introductionmentioning

confidence: 99%

Bilevel Optimization for On-Demand Multimodal Transit Systems

Basciftci¹,

2019

Preprint

This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders.

“…This paper is motivated by a significant worry of transit agencies: the need to capture latent demand in the design of ODMTS. This concern, which recognizes the complex interplay between transit agencies and riders (Cancela et al 2015), was also raised by Campbell and Van Woensel (2019): they articulated the potential of (1) leveraging data analytics within the planning process and (2) proposing transit systems that encourage riders to switch transportation modes. As a consequence, rider preferences and the induced mode choices should be significant factors in the design of transit systems (Laporte et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems

Basciftci¹,

2021

Preprint

This paper studies how to integrate rider mode preferences into the design of On-Demand Multimodal Transit Systems (ODMTS). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, i.e., new riders adopting the system, is not captured. The paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and nogood cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper bounds on trip durations for the follower problems and valid inequalities that strenghten the nogood cuts.The proposed method is validated using an extensive computational study on a real data set from AAATA, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and accessibility criteria. The designed ODMTS feature high adoption rates and significantly shorter trip durations compared to the existing transit system and highlight the benefits in accessibility for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude.