Shared micromobility (e.g., shared bikes and electric scooters), as a kind of emerging urban transportation, has become more and more popular in the world. However, the blooming of shared micromobility vehicles brings some social problems to the city (e.g., overloaded vehicles on roads, and the inequity of vehicle deployment), which deviate from the city regulator's expectation of the service of the shared micromobility system. In addition, the multi-operator shared micromobility system in a city complicates the problem because of their non-cooperative self-interested pursuits. Existing regulatory frameworks of multi-operator vehicle rebalancing generally assume the intrusive control of vehicle rebalancing of all the operators, which is not practical in the real world. To address this limitation, we design REALISM, a regulatory framework for coordinated scheduling in multi-operator shared micromobility services that incorporates the city regulator's regulations in the form of assigning a score to each operator according to the city goal achievements and operators' individual contributions to achieving the city goal, measured by Shapley value. To realize the fairness-aware score assignment, we measure the fairness of assigned scores and use them as one of the components to optimize the score assignment model. To optimize the whole framework, we develop an alternating procedure to make operators and the city regulator interact with each other until convergence. We evaluate our framework based on real-world e-scooter usage data in Chicago. Our experiment results show that our method achieves a performance gain of at least 39.93% in the equity of vehicle usage and 1.82% in the average demand satisfaction of the whole city.

Shared electric micromobility has surged to a popular model of urban transportation due to its efficiency in short-distance trips and environmentally friendly characteristics compared to traditional automobiles. However, managing thousands of shared electric micromobility vehicles including rebalancing and charging to meet users’ travel demands still has been a challenge. Existing methods generally ignore human preferences in vehicle selection and assume all nearby vehicles have an equal chance of being selected, which is unrealistic based on our findings. To address this problem, we design PERCEIVE, a human preference-aware rebalancing and charging framework for shared electric micromobility vehicles. Specifically, we model human preferences in vehicle selection based on vehicle usage history and current status (e.g., energy level) and incorporate the vehicle selection model into a robust adversarial reinforcement learning framework. We further utilize conformal prediction to quantify human preference uncertainty and fuse it with the reinforcement learning framework. We evaluate our framework using two months of real-world electric micromobility operation data in a city. Experimental results show that our method achieves a performance gain of at least 4.02% in the net revenue and offers more robust performance in worst-case scenarios compared to state-of-the-art baselines.

Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.

Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.

A multilayer run-time reconfiguration architecture (MRRA) is developed for autonomous run-time partial reconfiguration of field-programmable gate-array (FPGA) devices. MRRA operations are partitioned into logic, translation, and reconfiguration layers along with a standardized set of application programming interfaces (APIs). At each level, resource details are encapsulated and managed for efficiency and portability during operation. In particular, FPGA configurations can be manipulated at runtime using on-chip resources. A corresponding logic control flow is developed for a prototype MRRA system on a Xilinx Virtex II Pro platform. The Virtex II Pro on-chip PowerPC core and block RAM are employed to manage control operations while multiple physical interfaces establish and supplement autonomous reconfiguration capabilities. Evaluations of these prototypes on a number of benchmark and hashing algorithm case studies indicate the enhanced resource utilization and run time performance of the developed approaches.

An integrated platform for fast genetic operators is presented to support intrinsic evolution on Xilinx Virtex II Pro Field Programmable Gate Arrays (FPGAs). Dynamic bitstream compilation is achieved by directly manipulating the bitstream using a layered design. Experimental results on a case study have shown that a full design as well as a full repair is achievable using this platform with an average time of 0.4 microseconds to perform the genetic mutation, 0.7 microseconds to perform the genetic crossover, and 5.6 milliseconds for one input pattern intrinsic evaluation. This represents a performance advantage of three orders of magnitude over JBITS and more than seven orders of magnitude over the Xilinx design tool driven flow for realizing intrinsic genetic operators on a Virtex II Pro device.