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.

Space–frequency modulation (SFM) signal is a potential waveform for multifunction radar with high degrees of freedom of space, time, and frequency. However, the introduction of the additional communication function will modulate the transmitting signal, which will severely deteriorate the autocorrelation function (ACF). Here, a modified CLEAN method is proposed to eliminate the influence of undesired sidelobes in ACF on the air target detection. In the proposed method, undesired sidelobes are treated as extra features of real target to obtain more precise estimation of its complex reflection coefficient. Moreover, by considering about the sparsity of air targets, the authors employ the sparse representation method to estimate the response of the current strongest target. Then the target occlusion is eliminated by iterative cancellation. Simulation results demonstrate that the proposed detector is reliable and effective for the SFM‐based integrated system.