MARTA Reach: Piloting an On-Demand Multimodal Transit System in Atlanta

This paper reports on the results of the six-month pilot MARTA Reach, which aimed to demonstrate the potential value of On-Demand Multimodal Transit Systems (ODMTS) in the city of Atlanta, Georgia. ODMTS take a transit-centric view by integrating on-demand services and traditional fixed routes in order to address the first/last mile problem. ODMTS combine fixed routes and on-demand shuttle services by design (not as an after-thought) into a transit system that offers a door-to-door multimodal service with fully integrated operations and fare structure. The paper fills a knowledge gap, i.e., the understanding of the impact, benefits, and challenges of deploying ODMTS in a city as complex as Atlanta, Georgia. The pilot was deployed in four different zones with limited transit options, and used on-demand shuttles integrated with the overall transit system to address the first/last mile problem. The paper describes the design and operations of the pilot, and presents the results in terms of ridership, quality of service, trip purposes, alternative modes of transportation, multimodal nature of trips, challenges encountered, and cost estimates. The main findings of the pilot are that Reach offered a highly valued service that performed a large number of trips that would have otherwise been served by ride-hailing companies, taxis, or personal cars. Moreover, the wide majority of Reach trips were multimodal, with connections to rail being most prominent

Operating On-Demand Ride-Sharing Services

Public transit agencies are increasingly exploring mobility options to supplement their traditional rail, bus, and streetcar offerings. One such option is demand-responsive transport (DRT), “any non-fixed route system of transporting individuals that requires advanced scheduling by the customer”. DRT presents challenging design and operations problems, including fleet sizing, network design, and dispatching. In this thesis, we present optimization techniques to address operational challenges in demand responsive transit. In Chapter 2, we review the real-time dial-a-ride problem, a vehicle routing problem with pickups and deliveries, deviation, and capacity constraints, and present a dispatching algorithm, M-RTRS, which provides service guarantees, serving all customers with a small number of vehicles while minimizing wait times. In a computational study, we show that this algorithm scales to over 30,000 requests per hour, providing an effective way to support large-scale ride-sharing services in dense cities. In Chapter 3, we introduce an approach for vehicle dispatching, A-RTRS, that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. This is shown to decrease the average wait time of passengers in a computational study. In Chapter 4, we present a relocation algorithm designed to address two challenges faced when deploying a real-world real-time dial-a-ride service. The first, a lack of historic data, because in a real-world deployment, initial adoption may be slow, and thus accumulating the amount of data needed for the machine learning approach to demand prediction presented in Chapter 3 may be impractical. The second, that vehicles may be restricted in the locations that they may idle, which must be considered when relocating them. In a computational study, we show this approach yields similar average wait time decreases to A-RTRSPh.D

Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness

This paper reconsiders the ODMTS Design with Adoptions problem (ODMTS-DA) to capture the latent demand in on-demand multimodal transit systems. The ODMTS-DA is a bilevel optimization problem, for which Basciftci and Van Hentenryck (2022) proposed an exact combinatorial Benders decomposition. Unfortunately, their proposed algorithm only finds high-quality solutions for medium-sized cities and is not practical for large metropolitan areas. The main contribution of this paper is to propose a new path-based optimization model, called P-Path, to address these computational difficulties. The key idea underlying P-Path is to enumerate two specific sets of paths which capture the essence of the choice model associated with the adoption behavior of riders. With the help of these path sets, the ODMTS-DA can be formulated as a single-level mixed-integer programming model. In addition, the paper presents preprocessing techniques that can reduce the size of the model significantly. P-Path is evaluated on two comprehensive case studies: the mid-size transit system of the Ann Arbor Ypsilanti region in Michigan (which was studied by Basciftci and Van Hentenryck (2022)) and the large-scale transit system for the city of Atlanta. The experimental results show that P-Path solves the Michigan ODMTS-DA instances in a few minutes, bringing more than two orders of magnitude improvements compared to the existing approach. For Atlanta, the results show that P-Path can solve large-scale ODMTS-DA instances (about 17 millions of variables and 37 millions of constraints) optimally in a few hours or in a few days. These results show the tremendous computational benefits of P-Path which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand

Real-Time Idle Vehicle Relocation and Dynamic Pricing in Centralized Ride-Hailing Systems

Dynamic pricing and idle vehicle relocation are important tools for addressing demand-supply imbalance that frequently arises in the ride-hailing markets. Although interrelated, pricing and relocation have largely been studied independently in the literature. Moreover, the current mainstream methodologies, optimization and reinforcement learning (RL), suffer from significant computational limitations. The optimization needs to be solved in real-time and often trades off model fidelity (hence solution quality) for computational efficiency. Reinforcement learning requires a large number of samples to be trained offline and often struggles to achieve full coordination among the fleet. This thesis expands the research horizon and addresses the limitations of existing approaches. Chapter 1 designs an optimization model for computing both pricing and relocation decisions. The model ensures reasonable waiting time for the riders by reducing or postponing the demand that is beyond the service capacity. The postponement is by giving out discounts to riders who are willing to wait longer in the system, thus leveling off the peak without pricing out riders. Experiments show that the model ensures short waiting time for the riders without compromising the benefits (revenue and total rides served) of the platform. The postponement helps serve more riders during mild imbalances when there are enough vehicles to serve postponed riders after the peak. Chapter 2 presents a machine learning framework to tackle the computational complexity of optimization-based approaches. Specifically, it replaces the optimization with an optimization-proxy: a machine learning model which predicts its optimal solutions. To tackle sparsity and high-dimensionality, the proxy first predicts the optimal solutions on the aggregated level and disaggregates the predictions via a polynomial-time transportation optimization. As a consequence, the typical NP-Hard optimization is reduced to a polynomial-time procedure of prediction and disaggregation. This allows the optimization model to be considered at higher fidelity since it can be solved and learned offline. Experiments show that the learning + optimization approach is computationally efficient and outperforms the original optimization due to its higher fidelity. Chapter 3 extends one step further from Chapter 2, refining the optimization-proxy by reinforcement learning (RL). Specifically, RL starts from the optimization-proxy and improves its performance by interacting with the system dynamics and capturing long-term effects that are beyond the capabilities of the optimization approach. In addition, RL becomes far easier to train starting from a good initial policy. This hybrid approach is computationally efficient in both online deployment and offline training stages, and outperforms optimization and RL by combining the strengths of both approaches. It is the first Reinforcement Learning from Expert Demonstration (RLED) framework applied to the pricing and relocation problems and one of the few RL models with a fully-centralized policy.Ph.D

Vehicle dispatch in high-capacity shared autonomous mobility-on-demand systems

Ride-sharing is a promising solution for transportation issues such as traffic congestion and parking land use, which are brought about by the extensive usage of private vehicles. In the near future, large-scale Shared Autonomous Mobility-on-Demand (SAMoD) systems are expected to be deployed with the realization of self-driving vehicles. It has the potential to encourage a car-free lifestyle and create a new urban mobility mode where ride-sharing is widely adopted among people. This thesis addresses the problem of improving the efficiency and quality of vehicle dispatch in high-capacity SAMoD systems. The first part of the thesis develops a dispatcher which can efficiently explore the complete candidate match space and produce the optimal assignment policy when only deterministic information is concerned. It uses an incremental search method that can quickly prune out infeasible candidates to reduce the search space. It also has an iterative re-optimization strategy to dynamically alter the assignment policy to take into account both previous and newly revealed requests. Case studies of New York City using real-world data shows that it outperforms the state-of-the-art in terms of service rate and system scalability. The dispatcher developed in this part can serve as a foundation for the next two parts, which consider two kinds of uncertain information, stochastic travel times and the dynamic distribution of requests in the long-term future, respectively. The second part of the thesis describes a framework which makes use of stochastic travel time models to optimize the reliability of vehicle dispatch. It employs a candidate match search method to generate a candidate pool, uses a set of preprocessed shortest path tables to score the candidates and provides an assignment policy that maximizes the overall score. Two different dispatch objectives are discussed: the on-time arrival probabilities of requests and the profit of the platform. Experimental studies show that higher service rates, reliability and profits can be achieved by considering travel time uncertainty. The third part of the thesis presents a deep reinforcement learning based approach to optimize assignment polices in a more far-sighted way. It models the vehicle dispatch problem as a Markov Decision Process (MDP) and uses a policy evaluation method to learn a value function from the historic movements of drivers. The learned value function is employed to score candidate matches to guide a dispatcher optimizing long-term objective, and will be continually updated online to capture the real-time dynamics of the system. It is shown by experiments that the value function helps the dispatcher to yield higher service rates