We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.

Improving the performance of transportation network is a crucial task in traffic management. In this paper, we start with a cooperative routing problem, which aims to minimize the chance of road network breakdown. To address this problem, we propose a subgradient method, which can be naturally implemented as a semi-centralized pricing approach. Particularly, each road link adopts the pricing scheme to calculate and adjust the local toll regularly, while the vehicles update their routes to minimize the toll costs by exploiting the global toll information. To prevent the potential oscillation brought by the subgradient method, we introduce a heavy-ball method to further improve the performance of the pricing approach. We then test both the basic and improved pricing approaches in a real road network, and simultaneously compare them with several baselines. The experimental results demonstrate that, our approaches significantly outperform others, by comprehensively evaluating them in terms of various metrics including average travel time and travel distance, winners and losers, potential congestion occurrence, last arrival time, toll costs and average traffic flows, with two different O-D profiles.

Non-recurring incidents such as accidents and vehicle breakdowns are the leading causes of severe traffic congestions in large cities. Consequently, anticipating the duration of such events in advance can be highly useful in mitigating the resultant congestion. However, availability of partial information or ever-changing ground conditions makes the task of forecasting the duration particularly challenging. In this paper, we propose an adaptive ensemble model that can provide reasonable forecasts even when a limited amount of information is available and further improves the prediction accuracy as more information becomes available during the course of the incidents. Furthermore, we consider the scenarios where the historical incident reports may not always contain accurate information about the duration of the incidents. To mitigate this issue, we first quantify the effective duration of the incidents by looking for the change points in traffic state and then utilize this information to predict the duration of the incidents. We compare the prediction performance of different traditional regression methods, and the experimental results show that the Treebagger outperforms other methods. For the incidents with duration in the range of 36 - 200 min, the mean absolute percentage error (MAPE) in predicting the duration is in the range of 25% - 55%. Moreover, for longer duration incidents (greater than 65 min), prediction improves significantly with time. For example, the MAPE value varies over time from 76% to 50% for incidents having a duration greater than 200 min. Finally, the overall MAPE value averaged over all incidents improves by 50% with elapsed time for prediction of reported as well as effective duration.

Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have the ability to learn and adapt the motion trajectories as well as controllers online. This paper presents a novel Hierarchical Interactive Learning (HIL) strategy which reduces the complexity of the exoskeleton sensory system and is able to handle varying interaction dynamics. The proposed HIL strategy is composed of two learning hierarchies, namely, high-level motion learning and low-level controller learning. The Dynamic Movement Primitives (DMPs) combined with Locally Weighted Regression (LWR) are employed to model and learn the motion trajectories, while reinforcement learning (RL) is used to learn the model-based controller. We demonstrate the efficiency of proposed HIL strategy on a single degree-of-freedom (DOF) platform as well as a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HIL strategy is able to handle the varying interaction dynamics with less interaction force between the pilot and the exoskeleton when compared to traditional model-based control algorithms.

Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical Human-Robot Interaction (pHRI). As a powerful representation tool for motion trajectories, Dynamic Movement Primitive (DMP) has been used extensively to model human movement trajectories. However, canonical DMPs only offers a general offline representation of human movement trajectory and neglects the real-time interaction term, therefore it cannot be directly applied to lower exoskeletons which need to model human motion trajectories online since different pilots have different trajectories and even one pilot might change his/her intended trajectory during walking. This paper presents a novel Coupled Cooperative Primitives (CCPs) scheme, which models the motion trajectories online. Besides maintaining canonical motion primitives, we also model the interaction term between the pilot and exoskeletons through impedance models and apply a reinforcement learning method based on Policy Improvement and Path Integrals (PI2) to learn the parameters online. Experimental results on both a single Degree-Of-Freedom (DOF) platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme.