On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures

The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing services significantly reduce the meeting frictions between drivers and riders and provide the platform with unprecedented flexibility and challenges in system management. A big issue that arises with service expansion is the empty miles produced by ride-sourcing vehicles. To overcome the physical and temporal frictions that separate drivers from customers and effectively reposition themselves towards desired destinations, ride-sourcing vehicles generate a significant number of vacant trips. These empty miles traveled result in inefficient use of the available fleet and increase traffic demand, posing substantial impacts on system operations. To tackle the issues, my dissertation is dedicated to deepening our understanding of the formation and the externalities of empty miles, and then proposing countermeasures to bolster system performance. There are two essential and interdependent contributors to empty miles generated by ride-sourcing vehicles: cruising in search of customers and deadheading to pick them up, which are markedly dictated by forces from riders, drivers, the platform, and policies imposed by regulators. In this dissertation, we structure our study of this complex process along three primary axes, respectively centered on the strategies of a platform, the behaviors of drivers, and the concerns of government agencies. In each axis, theoretical models are established to help understand the underlying physics and identify the trade-offs and potential issues that drive behind the empty miles. Massive data from Didi Chuxing, a dominant ride-sourcing company in China, are leveraged to evidence the presence of matters discussed in reality. Countermeasures are then investigated to strengthen management upon the empty miles, balance the interests of different stakeholders, and improve the system performance. Although this dissertation scopes out ride-sourcing services, the models, analyses, and solutions can be readily adapted to address related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163209/1/xzt_1.pd

On the partial Π \Pi -property of some subgroups of prime power order of finite groups

Let $H$ be a subgroup of a finite group $G$. We say that $H$ satisfies the partial $\Pi$-property in $G$ if if there exists a chief series $\varGamma_{G}: 1 =G_{0} < G_{1} < \cdot\cdot\cdot < G_{n}= G$ of $G$ such that for every $G$-chief factor $G_{i}/G_{i-1} (1\leq i\leq n)$ of $\varGamma_{G}$, $| G / G_{i-1} : N_{G/G_{i-1}} (HG_{i-1}/G_{i-1}\cap G_{i}/G_{i-1})|$ is a $\pi (HG_{i-1}/G_{i-1}\cap G_{i}/G_{i-1})$-number. In this paper, we study the influence of some subgroups of prime power order satisfying the partial $\Pi$-property on the structure of a finite group.Comment: arXiv admin note: substantial text overlap with arXiv:2304.11451. text overlap with arXiv:1301.6361 by other author

An Integer L-shaped Method for Dynamic Order Dispatching in Autonomous Last-Mile Delivery with Demand Uncertainty

Given the potential to significantly reduce the cost and time in last-mile delivery, autonomous delivery solutions via delivery robots or unmanned aerial vehicles have received increasing attention. This paper studies the dynamic order dispatching problem in an autonomous last-mile delivery system with intrinsic demand uncertainty. We consider a rolling order-fulfilment context and formulate a two-stage stochastic programming to take into account both existing unfulfilled orders and future incoming requests to minimize the total expected delays in package delivery. The considered uncertainty includes the stochastic arrival of delivery requests, their types, locations/delivery distances, and associated penalties for late delivery. Due to the constrained service capacity, the size of the problem grows exponentially as the number of simulated scenarios increases. In this study, we propose a modified integer L-shaped method, which (i) significantly reduces the number of nodes in the branching tree, and (ii) simplifies the computation of optimality cuts. The computational results show that these two modifications improve the average running time by roughly 10 times and 1,000 times compared to the non-customized L-shaped method and the classic branch-and-bound method, respectively. The linearly growing computational speed in response to the number of scenarios enables it as a viable solution for large-sized problems in reality

Neural Moving Horizon Estimation for Robust Flight Control

Estimating and reacting to disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into neural networks for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a portable neural network that has only 7.7% of the learnable parameters of the latter. The proposed method is general and can be applied to robust adaptive control of other robotic systems

Reinforcement Causal Structure Learning on Order Graph

Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence(AAAI2023

Stabilization computation for a kind of uncertain switched systems using non-fragile sliding mode observer method

A non-fragile sliding mode control problem will be investigated in this article. The problem focuses on a kind of uncertain switched singular time-delay systems in which the state is not available. First, according to the designed non-fragile observer, we will construct an integral-type sliding surface, in which the estimated unmeasured state is used. Second, we synthesize a sliding mode controller. The reachability of the specified sliding surface could be proved by this sliding mode controller in a finite time. Moreover, linear matrix inequality conditions will be developed to check the exponential admissibility of the sliding mode dynamics. After that, the gain matrices designed will be given along with it. Finally, some numerical result will be provided, and the result can be used to prove the effectiveness of the method

Characteristics of Plasmon Coupling Mode in SPR Based LPFG

Abstract-Based on mode coupled theory, this paper investigates the coupled characteristics and the excitation conditions of the surface plasmon mode in the structure of SPR based LPFG coated with metal film, and analyzes the strain sensing properties of this LPFG. Firstly, according to the properties of surface plasmon wave (SPW) in a cylindrical waveguide, SPW electromagnetic field distribution in the LPFG coated with metal film can be obtained by solving the electromagnetic wave equation. Using the coupled-mode theory, the coupled-mode equations of SPR based LPFG is derived and set up, and the coupled conditions between the core mode and SPW are given. Further, transmission spectra of LPFG coated with metal film can be obtained by solving coupled-mode equation, the results show that the SPR peak is generated by the coupling of core mode and SPW. By analyzing the influence of grating period on the SPR peak, it can be known the tiny change of the grating period will cause obvious deviation of the SPR peak. So this structure of SPR based LPFG is very suitable for strain sensor. Further calculation shows the strain sensitivity can reach 2.04 pm/με, superior to the traditional LPFG sensors

Search for spin-dependent gravitational interactions at the Earth range

Among the four fundamental forces, only gravity does not couple to particle spins according to the general theory of relativity. We test this principle by searching for an anomalous scalar coupling between the neutron spin and the Earth gravity on the ground. We develop an atomic gas comagnetometer to measure the ratio of nuclear spin-precession frequencies between $^{129}$Xe and $^{131}$Xe, and search for a change of this ratio to the precision of 10$^{-9}$ as the sensor is flipped in the Earth gravitational field. The null results of this search set an upper limit on the coupling energy between the neutron spin and the gravity on the ground at 5.3$\times$10$^{-22}$~eV (95\% confidence level), resulting in a 17-fold improvement over the previous limit. The results can also be used to constrain several other anomalous interactions. In particular, the limit on the coupling strength of axion-mediated monopole-dipole interactions at the range of the Earth radius is improved by a factor of 17.Comment: Accepted by Physical Review Letter