The diversified mobility on demand (MoD) systems integrate both traditional fuel vehicles and green transportation tools (e.g. shared bicycles and shared e-bikes), which can not only reduce the fleet size of traditional fuel vehicles but also address the demand for short-distance travel and alleviate environmental pollution. However, despite having a variety of travel tools, the existing MoD systems neglect the guidance on passengers according to their preferences and travel characteristics and thus lead to the failure of effective cooperation among multiple travel modes and additional waste of resources. This inspired us to design a novel order allocation mechanism for diversified MoD systems. Specifically, we construct a heterogeneous order graph based on the order sets, transform the minimum fleet problem into the minimum trajectory coverage problem on the heterogeneous order graph and propose a learning-based order allocation method LAMD$^{2}$containing three modules. i) The online breadth-first order search framework fully considers the characteristics of different travel modes and the interaction of multiple vehicles, and then leverages the competitive mechanism to well handle the heterogeneity of travel modes and improve the overall efficiency. ii) The multi-semantic travel mode selection module analyzes users' preferences for diversified travel modes based on multi-semantic historical travel data and then determines the service mode based on the similarity of order spatiotemporal characteristics. iii) The Reinforcement Learning (RL)-based order evaluation module evaluates the long-term benefits of expanding existing For-Hire Vehicle (FHV) trajectories with different orders and updates the behavioral strategies through interactive feedback with the environment. We implement and evaluate the proposed method with a real-world trajectory dataset, demonstrating that LAMD$^{2}$outperforms all the baselines and reduces the fleet size and energy consumption by the average of 2.93% and 8.01%, respectively, compared to the real-world systems.

Green Light Optimized Speed Advisory (GLOSA) systems have emerged to allow drivers to pass traffic lights during a green interval. However, various adaptive and intelligent traffic light control approaches have been adopted in many cities, resulting in the development of current GLOSA technologies lagging behind that of traffic light technologies. When taking diverse dynamic traffic lights into account, it is difficult to model the interactions between vehicles and traffic lights, which is further exacerbated by the hybrid control strategies of traffic lights. To this end, we design a new GLOSA systemSpeedAdvto provide optimal speed advisory for addressing diverse traffic lights. We formulate the problem as a Multi-Agent Markov Decision Process (MAMDP) with an implicit common goal and propose a heterogeneous-agent collaborative framework based on reinforcement learning. Three main modules are used in the system: i) a spatio-temporal relation reasoning module based on the phase-aware attention mechanism pays more attention to the traffic rules and traffic flow diversion of adjacent intersections to predict traffic conditions for a few seconds later; ii) a behavior approximating module based on imitation learning is introduced to approximate the phases of diverse traffic lights; iii) a speed advisory module provides the optimal speed advisory based on policy gradient reinforcement learning relying on the above two modules and other information collected by vehicles. We implement and evaluateSpeedAdvwith a real-world trajectory dataset, together with a field test based on a prototype system, demonstrating thatSpeedAdvimproves the overall performance by at least 24.1% in terms of travel time, energy consumption, safety, and comfort compared to the state-of-the-artGreenDrivemethod.

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we proposeLEVID, aLEarning-based cooperativeVehIcle-roaDscheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.

Energy and cost are two primary concerns when leveraging drones for urban sensing. With the advances of wireless charging technologies and the inspiration from the sparse crowdsensing paradigm, this paper proposes a novel drone-based collaborative sparse-sensing framework DroneSense, demonstrating its feasibility for sustainable urban-scale sensing. We focus on a typical use case, i.e., leveraging DroneSense to sense open parking spaces. DroneSense selects a minimum number of Points of Interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement. However, drone-based sensing is different from human-centric crowdsensing, resulting in a series of new problems, including which POIs are visited first, when and where to charge drones, which drones to charge first, how much to charge, and when to stop the scheduling. To this end, we design a holistic solution, including context-aware matrix factorization for parking occupancy data inference, progressive determination of task quantity, deep reinforcement learning (DRL) based task selection, energy-aware DRL-based task scheduling, and adaptive charger scheduling. Extensive experiments with a real-world on-street parking dataset from Shenzhen, China demonstrate the obvious advantages of DroneSense.

Mobile crowdsensing (MCS) and unmanned vehicle sensing (UVS) provide two complementary paradigms for large-scale urban sensing. Generally, MCS has a lower cost but often confronts sensing imbalance and even blind areas due to the limitation of human mobility, whereas UVS is often capable of completing more demanding tasks at the expense of limited energy supply and hardware cost. Thus, it is significant to investigate whether we could integrate the two paradigms for high-quality urban sensing in a cost-effective collaborative way. However, it is nontrivial due to complex and long-term optimization objectives, uncontrolled dynamics, and a large number of heterogeneous agents. To address the collaborative sensing problem, we propose an actor-critic-based heterogeneous collaborative reinforcement learning (HCRL) algorithm, which leverages several key ideas: local observation to handle expanded state space and extract the states of neighbor nodes, generalized model to avoid environment nonstationarity and ensure the scalability and stability of network, and proximal policy optimization to prevent the destructively large policy updates. Extensive simulations based on a mobility model and a realistic trace data set are conducted to confirm that HCRL outperforms the state-of-the-art baselines.

Nowadays we are witnessing an explosive growth of mobile data traffic, but users still often experience insufficient or unstable network bandwidth in many realistic scenarios. Unmanned aerial vehicle mounted base stations (UAV-BSs) provide a novel and promising solution for serving regions with bandwidth shortfall. It is significant to investigate how to deploy UAVs for maximizing the sum throughput of a set of clients scattered in various locations in an effective and efficient way. A basic idea is to use RF ray tracing simulations as a hint to narrow down the search space of UAVs for conducting measurements. Furthermore, we study two key sub-problems, chunk selection, which finds an optimal subset of chunks in the region as the search space of UAVs, and chunk search, which plans the scanning path to cover the search space with the min-max time consumption required for all UAVs. They are both proved to be NP-hard, and heuristic algorithms are proposed to solve them efficiently. A prototype system, UAV-Net, is implemented to conduct measurements by a UAV mounted WiFi AP communicating with 7 clients scattered in a campus, and extensive simulations are combined, reporting an obvious throughput gain with a small measurement overhead and time consumption.

With the explosive growth of mobile devices such as smartphones, it is convenient for participants to perform mobile crowd sensing (MCS) tasks. It is a useful way to recruit participants to perform location-dependent tasks. We first propose Min-Max Task (MMT) planning problem in MCS systems, considering time-sensitivity and heterogeneity of sensing tasks. In other words, how to design a cooperation scheme, in which the participants spend as little time as possible. Then, to address MMT problem, we propose a Memetic based Bidirectional General Variable Neighborhood (MBGVN) algorithm, in which all tasks are separated into groups and traveling path is designed for each participant. Finally, extensive experiments are conducted to demonstrate the benefits of our scheme, outperforming other similar state-of-the-art algorithms.

Unmanned Aerial Vehicles (UAVs) have been widely used in various applications such as inspection, security surveillance, and aerial photography, in which the cooperation of multiple UAVs is significantly important for better accomplishing complex tasks due to the limited capability for individual UAV s. Task planning is the primary issue for the cooperation of multiple UAV s, and has attracted extensive research interests. However, most research fails to account adequately for limited energy on each UAV, which involves in many factors such as different operations for performing a task and various movement patterns besides the distance and turns that have been commonly considered. By contrast, we conduct a series of experiments to obtain the energy model of UAV s. Furthermore, we focus on the energy-efficient min-max task planning (E2M2TP) problem by considering the heterogeneity of tasks and integrating various energy factors, which is beneficial for balancing the workload and energy consumption among UAV s and thus reducing the number of required UAVs. We show that E2M2TP is NP-hard, and propose an energy-aware variable neighbor search (EVNS) algorithm to iteratively optimize both task allocation and path planning. Extensive simulations are conducted to validate that EVNS outperforms the other state-of-the-art algorithms.