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This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problems of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

The International Staging System for multiple myeloma recently underwent a second revision (R2-ISS) to include gain/amplification of 1q21 and account for the additive prognostic significance of multiple high-risk features. The phase 3 ICARIA-MM (isatuximab–pomalidomide–dexamethasone vs. pomalidomide–dexamethasone) and IKEMA (isatuximab–carfilzomib–dexamethasone vs. carfilzomib–dexamethasone) studies provide large datasets for retrospectively validating the prognostic value of the R2-ISS in relapsed/refractory multiple myeloma. Of 609 pooled patients, 68 (11.2%) were reclassified as R2-ISS stage I, 136 (22.3%) as R2-ISS stage II, 204 (33.5%) as R2-ISS stage III, 55 (9.0%) as stage IV, and 146 (24.0%) “Not classified”. Median progression-free survival was shorter among those reclassified as R2-ISS stage II (HR 1.52, 95% CI 0.979–2.358), stage III (HR 2.59, 95% CI 1.709–3.923), and stage IV (HR 3.51, 95% CI 2.124–5.784) versus stage I. Adding isatuximab led to longer progression-free survival versus doublet therapy (adjusted HR 0.544 [95% CI 0.436–0.680]), with a consistent treatment effect observed across all R2-ISS stages. This is the first study to validate the R2-ISS with novel agents, including anti-CD38 monoclonal antibodies, and to show that R2-ISS, as a prognostic scoring system, can be applied to patients with relapsed/refractory multiple myeloma.

Urban mobility in the 21st century faces significant challenges, as the unsustainable trends of urban population growth, congestion, pollution, and low vehicle utilization worsen in large cities around the world. As autonomous vehicle technology draws closer to realization, a solution is beginning to emerge in the form of autonomous mobility-on-demand (AMoD), whereby fleets of self-driving vehicles transport customers within an urban environment. This dissertation introduces a systematic approach to the design, control, and evaluation of these systems. In the first part of the dissertation, a stochastic queueing-theoretical model of AMoD is developed, which allows both the analysis of quality-of-service metrics as well as the synthesis of control policies. This model is then extended to one-way car sharing systems, or human-driven mobility-on-demand (MoD) systems. Based on these models, closed-loop control algorithms are designed to efficiently route empty (rebalancing) vehicles in very large systems with thousands of vehicles. The performance of the algorithms and the potential societal benefits of AMoD and MoD are evaluated through case studies of New York City and Singapore using real-world data. In the second part of the dissertation, additional structural and operational constraints are considered for AMoD systems. First, the impact of AMoD on traffic congestion with respect to the underlying structural properties of the road network is analyzed using a network flow model. In particular, it is shown that empty rebalancing vehicles in AMoD systems will not increase congestion, in stark contrast to popular belief. Finally, the control of AMoD systems with additional operational constraints is studied under a model predictive control framework, with a focus on range and charging constraints of electric vehicles. The technical approach developed in this dissertation allows us to evaluate the societal benefits of AMoD systems as well as lays the foundation for the design and control of future urban transportation networks.

This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.

This paper presents a queueing network approach to the analysis and control of mobility-on-demand (MoD) systems for urban personal transportation. A MoD system consists of a fleet of vehicles providing one-way car sharing service and a team of drivers to rebalance such vehicles. The drivers then rebalance themselves by driving select customers similar to a taxi service. We model the MoD system as two coupled closed Jackson networks with passenger loss. We show that the system can be approximately balanced by solving two decoupled linear programs and exactly balanced through nonlinear optimization. The rebalancing techniques are applied to a system sizing example using taxi data in three neighborhoods of Manhattan, which suggests that the optimal vehicle-to-driver ratio in a MoD system is between 3 and 5. Lastly, we formulate a real-time closed-loop rebalancing policy for drivers and demonstrate its stability (in terms of customer wait times) for typical system loads.

In this paper we present and analyze a queueing-theoretical model for autonomous mobility-on-demand (MOD) systems where robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the entire network. We cast an autonomous MOD system within a closed Jackson network model with passenger loss. It is shown that an optimal rebalancing algorithm minimizing the number of (autonomously) rebalancing vehicles and keeping vehicles availabilities balanced throughout the network can be found by solving a linear program. The theoretical insights are used to design a robust, real-time rebalancing algorithm, which is applied to a case study of New York City. The case study shows that the current taxi demand in Manhattan can be met with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi fleet). Finally, we extend our queueing-theoretical setup to include congestion effects, and we study the impact of autonomously rebalancing vehicles on overall congestion. Collectively, this paper provides a rigorous approach to the problem of system-wide coordination of autonomously driving vehicles, and provides one of the first characterizations of the sustainability benefits of robotic transportation networks.

In this paper we study the routing and rebalancing problem for a fleet of autonomous vehicles providing on-demand transportation within a congested urban road network (that is, a road network where traffic speed depends on vehicle density). We show that the congestion-free routing and rebalancing problem is NP-hard and provide a randomized algorithm which finds a low-congestion solution to the routing and rebalancing problem that approximately minimizes the number of vehicles on the road in polynomial time. We provide theoretical bounds on the probability of violating the congestion constraints; we also characterize the expected number of vehicles required by the solution with a commonly-used empirical congestion model and provide a bound on the approximation factor of the algorithm. Numerical experiments on a realistic road network with real-world customer demands show that our algorithm introduces very small amounts of congestion. The performance of our algorithm in terms of travel times and required number of vehicles is very close to (and sometimes better than) the optimal congestion-free solution.

In this paper we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queueing network model. Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput. Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. Finally, we validate our theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and network flow models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.

The functions of the proto-oncoprotein c-Myc and the tumor suppressor p53 in controlling cell survival and proliferation are inextricably linked as “Yin and Yang” partners in normal cells to maintain tissue homeostasis: c-Myc induces the expression of ARF tumor suppressor (p14 ARF in human and p19 ARF in mouse) that binds to and inhibits mouse double minute 2 homolog (MDM2) leading to p53 activation, whereas p53 suppresses c-Myc through a combination of mechanisms involving transcriptional inactivation and microRNA-mediated repression. Nonetheless, the regulatory interactions between c-Myc and p53 are not retained by cancer cells as is evident from the often-imbalanced expression of c-Myc over wildtype p53. Although p53 repression in cancer cells is frequently associated with the loss of ARF, we disclose here an alternate mechanism whereby c-Myc inactivates p53 through the actions of the c-Myc-Inducible Long noncoding RNA Inactivating P53 (MILIP). MILIP functions to promote p53 polyubiquitination and turnover by reducing p53 SUMOylation through suppressing tripartite-motif family-like 2 (TRIML2). MILIP upregulation is observed amongst diverse cancer types and is shown to support cell survival, division and tumourigenicity. Thus our results uncover an inhibitory axis targeting p53 through a pan-cancer expressed RNA accomplice that links c-Myc to suppression of p53. c-Myc and p53 operate in a negative feedback manner to maintain cellular homeostasis. Here, the authors report a long noncoding RNA, MILIP as a downstream target of c-Myc and that MILIP represses p53 to support tumorigenicity.