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Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.

Multi-step service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial-temporal dependencies such as inter-station semantic dependencies, inter-line spatial dependencies and inter-service temporal dependencies, which are not effectively modeled by existing methods. To address these challenges, we propose a spatio-temporal hashing multigraph convolution network, called ST-HMGCN. ST-HMGCN constructs two types of subgraphs from perspectives of physical adjacency and semantic similarity to explicitly capture spatialtemporal dependencies among bus stations/lines, and integrates the inter-service temporal correlations to achieve the servicelevel bus passenger flow forecasting. Moreover, it utilizes the hashing graph convolution to extract the dynamic spatial correlations among graph nodes. Furthermore, a temporal-attention block with residual connections is used to model the non-linear temporal correlations between different time intervals of each station, which significantly reduces the error propagation among prediction time steps. Finally, we use a large-scale real bus operation data set to conduct an extensive evaluation of STHMGCN and 11 state-of-the-art baselines, and further leverage the passenger prediction results of our model to provide crowdedness-aware route recommendation. Experimental results verify the effectiveness of the proposed modeling method and its application value in intelligent transportation.

This note considers the design and analysis of a distributed sensor fault accommodation scheme for a class of nonlinear dynamical systems, modelled as interconnected subsystems. Each subsystem is subject to possible local sensor faults or may be impacted by a sensor fault in a neighboring subsystem due to the exchange of information between neighboring controllers. The proposed adaptive approximation-based distributed fault accommodation scheme is triggered in the event that a sensor fault is detected. The stability of the closed-loop system in the presence of sensor faults is rigorously analyzed both for the period prior to the detection of the fault, as well as after the detection of the fault when the fault accommodation scheme is activated. The effectiveness of the proposed scheme is illustrated by a simulation example.

This paper develops a decentralized actuator fault compensation scheme for hypersonic vehicles, using a model transform based backstepping control design. Different from the actuator dynamics represented by second-order ordinary differential equations (ODE) studied in the existing literature, a new actuator dynamics is studied in this paper, modeled by a fourth-order partial differential equation (PDE), i.e., Euler-Bernoulli beam (EBB) equation. An invertible model transformation is introduced to transform the EBB PDE-ODE cascade system into a heat-like PDE-ODE cascade system, and a new target system is constructed from a two-step backstepping procedure. A novel decentralized fault-tolerant control scheme is proposed for the heat-like PDE-ODE cascade system and the well-posedness of the system solutions is analyzed. The closed-loop system stability and the well-posedness property of the original EBB PDE-ODE cascade system are ensured. Simulation results illustrate the effectiveness of the proposed fault compensation scheme.

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this paper. The control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering thresholds. Considering the event-induced control input error and system fault threat, a novel event-triggered active fault accommodation scheme is designed, which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of faults. The closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously analyzed. Special cases for the fault accommodation design under constant triggering threshold are also derived. An example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.

Reliable traffic flow prediction is of great value in the field of transportation, which, for example, contributes to traffic control and public safety. The key of achieving better performance is to well capture the non-linear spatial-temporal dependency. The state-of-the-art works consider both aspects, but they ignore the effect of the global trend on local dynamics and fail to capture long-term dynamic dependencies. In this article, we propose a novel Global-Local Temporal Convolutional Network (GL-TCN) to break through these limitations. Specifically, a novel local temporal convolutional mechanism is proposed to capture the long-term local dynamics effectively. Meanwhile, the global and local flow patterns are integrated to handle the effect of the global flow trend on local dynamics. To the best of our knowledge, this is the first work to utilize the temporal convolutional network for traffic flow prediction. Experiments on two real-world datasets demonstrate the superior performance of our method over several state-of-the-art baselines.

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 propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling 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., Comment: 13 pages, 10 figures

In this article, two novel adaptive fault-tolerant control schemes for a class of nonlinear strict-feedback cyber-physical systems (CPSs) with deception attacks are presented. Deception attacks, such as false data-injection attacks, which destroy sensor networks, make the outputs and states of the CPSs unavailable. It is very difficult and challenging for a designer to achieve the tracking control under the circumstance of cyberattacks. To realize the tracking control for the studied CPSs, we propose a new coordinate transformation technology without precedent, where it takes the attack gains into account and uses the compromised states to design the corresponding controllers. In the backstepping design process, Nussbaum functions are presented to alleviate the influence of the unknown attack gains. Furthermore, we consider the actuator faults problem, which includes the loss of effectiveness and the bias fault. By skillfully designing the adaptive laws, the effect of actuator faults is completely eliminated. It is theoretically proved that the first proposed tracking control scheme can guarantee all signals in the closed-loop system are bounded and the output can track the desired reference signal. In addition, the second adaptive control scheme is also developed for the CPSs under the actuator faults and a more general assumption on the deception attacks is proposed simultaneously. Finally, the feasibility of the new proposed methods is verified by MATLAB simulation analysis.

Bus services play a crucial role in urban transit. It is significant to achieve the fine-grained service-level passenger flow prediction (SPFP), namely to predict the total number of passengers for each service of each bus line passing through each station during the next short-term interval. However, it faces great challenges due to complex factors including inter-station and inter-line spatial dependencies, intra-station and inter-service temporal dependencies, and internal/external influences. To address these challenges, we propose a multitask deep-learning (MDL) approach, called MDL-SPFP , to jointly predict the arriving bus service flow, line-level on-board passenger flow and line-level boarding/alighting passenger flow by leveraging well-designed deep neural networks called ARM . The MDL framework can mutually reinforce the prediction of each type of flow, and finally integrate the outputs to achieve the fine-grained service-level prediction. The ARM network combines three modules, Attention mechanism, Residual block and Multi-scale convolution, to well capture various complex non-linear spatio-temporal dependencies and influence factors. Extensive experiments based on a large-scale realistic bus operation dataset are conducted to confirm that our MDL-SPFP approach outperforms 10 state-of-the-art baselines, and improves 22.39% accuracy than the best baseline.

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.

Incentive mechanism design has aroused extensive attention for crowdsourcing applications in recent years. Most research assumes that participants are already in the system and aware of the existence of crowdsourcing tasks. Whereas in real-life scenarios without this assumption, it is more effective to leverage incentive tree mechanisms that incentivize both users’ direct contributions and solicitations to other users. Although such mechanisms have been investigated, we are the first to propose budget-balanced incentive tree mechanisms, called generalized lottrees , which require the total payout to be equal to the announced budget, while guaranteeing several desirable properties including continuing contribution incentive , continuing solicitation incentive , value proportional to contribution , unprofitable solicitor bypassing , and unprofitable Sybil attack . Moreover, three types of generalized lottree mechanisms, 1-Pachira , $K$ K -Pachira and Sharing-Pachira , are presented for supporting diversified requirements. A solid theoretical guideline on the mechanism selection is provided based on the Cumulative Prospect Theory. Both extensive simulations and realistic experiments with 82 users are conducted to confirm our theoretical analysis.

Oil spill incidents endanger the marine environment. A spaceborne multispectral imager can be used to observe the floating oil slicks on a large scale. However, traditional methods are only suitable for small-scale detection situations. It is difficult to distinguish oil slicks from the heterogeneous marine environment in large area images because the spectral information on oil slicks and seawater are analogous and mutative. We suppose that finding out the sensitivities of spectral indices to seawater environment contributes to avoiding identifying seawater as oil slick look-alikes. Based on this assumption, this research conducted experiments to test several proposed oil slick detection indices: fluorescence index (FI), rotation-absorption index (RAI), shortwave infrared (SWIR) reflectance, chlorophyll (CHL) spectral characteristic, and colored dissolved organic matter (CDOM) spectral characteristic. The experimental results indicated that the indices were sensitive to different marine environments and combining the complementary indices could extract oil slicks from the heterogeneous marine environment accurately.

Mobility models, which reproduce traces with basic crowd mobility patterns, are crucial for realistic mobile network simulation and performance evaluation of the planning strategies used for urban networks (such as transit network, communication network, and crowdsensing network). However, trajectories generated by traditional models are often perceived as not realistic for urban context or lack of scalability and universality. This article presents a data-driven trajectory generating technique, named as TraG, that produces synthetic trajectories with the help of some real-world trajectories. Our technique can automatically extract the context features and statistical mobility features, which characterize the mobility of a specific urban crowd from the input empirical traces and, then, regenerate more trajectories based on demand. Moreover, we also summarize the power-law scale correlation of crowd mobility based on four real-world open datasets, including public bicycle traces in New York City and Washington, D.C., taxicab traces in San Francisco and Shenzhen. Finally, we validate the proposed TraG model via the continuous San Francisco taxicab traces, and the result demonstrates that the trajectories simulated by TraG not only inherit the fundamental statistical features of crowd mobility from real traces, but also reflect the features of urban context.

In this article, a systematic fault diagnosability evaluation, including fault detectability and isolability, is established in a quantitative manner for two-dimensional systems. With ingenious data formulation, a parity relation of two-dimensional systems is first established, then the Kullback-Leibler divergence is employed as the key measure for the diagnosability analysis based on the established parity relation. The basic idea is to quantify the distribution differences among each fault scenario-related system dynamic behavior. Explicit necessary and sufficient condition for fault diagnosability is further derived based on the appropriately introduced definitions corresponding to the two directions evolving system properties. Finally, the effectiveness of the proposed method is verified by two examples.