In the framework of 6G-driven Symbiotic Internet of Things (Symbiotic IoT), vehicular edge computing systems enhance the inference efficiency of large-scale AI models through collaboration between road side units (RSUs) and in-vehicle terminals. As a core component of symbiotic systems, RSUs face intermittent challenges posed by renewable energy supply when hosting offloaded vehicular deep learning tasks. Existing energy consumption prediction methods rely heavily on massive sampled data and lack a priori evaluation, leading to significant difficulties and risks in acquiring large-scale measured data: when the remaining power of RSUs is insufficient to sustain the sampling period, the prediction process is forced to interrupt, wasting both the operational energy already consumed and the additional scheduling computation energy. This article proposes a two-stage energy consumption prediction method for symbiotic Internet of Vehicles (IoV) edge nodes, addressing the above challenges through a collaborative mechanism of “a priori rough estimation—dynamic precise prediction.” In the first stage, a nonlinear regression model is established based on limited measured data, combined with analysis of model architecture and hardware instruction-level energy consumption characteristics, to enable a priori energy estimation without actual model execution. This supports lightweight decision-making for RSUs during task allocation, reducing computational overhead compared to traditional sampling methods. In the second stage, real-time energy consumption monitoring is used to dynamically update prediction curves, achieving prediction calibration within extremely short operation cycles to balance prediction accuracy with the energy constraints of edge nodes.Experimental results in typical scenarios of IOV applications demonstrate that the proposed a priori evaluation method achieves an average error of 10% under different data volumes and batch sizes, while the real-time prediction method yields short-term and long-term prediction errors below 3% and 6%, respectively—outperforming existing methods in accuracy. This approach effectively assists RSUs in energy management, reducing energy waste caused by the absence of a priori evaluation and prediction errors, and contributes to the efficient utilization of renewable energy in low-latency scenarios, such as connected vehicles.

Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city given the coarse-grained urban flow observations, is critically important to various smart city related applications such as urban planning and public safety. Previous works assume that the urban flow monitoring sensors are evenly distributed in space for data collection and thus the observed urban flows are complete. However, in real-world scenarios, sensors are usually unevenly deployed in space. For example, the traffic cameras are mostly deployed at the crossroads and central areas of a city, but less likely to be deployed in suburb. The data scarcity issue poses great challenges to existing methods for accurately inferring the fine-grained urban flows, because they require all urban flow observations to be available. In this paper, we make the first attempt to infer fine-grained urban flows based on the incomplete coarse-grained urban flow observations, and propose a Multi-Task urban flow Completion and Super-Resolution network (MT-CSR for short) to simultaneously complete the coarse-grained urban flows and infer the fine-grained flows. Specifically, MT-CSR consists of the data completion network (CMPNet for short) and data super-resolution network (SRNet for short). CmpNet is composed of a local spatial information based data completion module LocCmp and an auxiliary information based data completion module AuxCmp to consider both the local geographical and global semantic correlations for urban flow data completion. SRNet is designed to capture the complex associations between fine-and coarse-grained urban flows and upsample the coarse-grained data by stacking the designed super-resolution blocks. To gain an accurate inference, two parts are jointly conducted under a multi-task learning framework, and trained in an end-to-end manner using a two-stage training strategy. Extensive experiments on four large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.

Abnormal traffic incidents such as traffic accidents have become a significant health and development threat with the rapid urbanization of many countries. Thus it is critically important to accurately forecast the traffic accident risks of different areas in a city, which has attracted increasing research interest in the research area of urban computing. The challenges of accurate traffic risk forecasting are three-fold. First, traffic accident data in some areas of a city is sparse, especially for a fine-grained prediction, which may cause the zero inflation problem during model training. Second, the spatio-temporal correlations of the traffic accidents occurring in different areas are rather complex and non-linear, which is difficult to capture by existing shallow models like regression. Third, the occurrence of traffic accidents can be significantly affected by various context features including weather, POI and road network features. It is non-trivial to capture the complex associations between the diverse context features and traffic accident risks for building an accurate prediction model. To address the above challenges, this paper proposes a Multi-View Multi-Task Spatio-Temporal Networks (MVMT-STN) model to forecast fine- and coarse-grained traffic accident risks of a city simultaneously. Specifically, to address the data sparsity issue in a fine-grained prediction, we adopt a multi-task learning framework to jointly forecast both fine- and coarse-grained traffic accident risks by considering their spatial associations. For each granularity prediction, we design the channel-wise CNN and multi-view GCN to capture the local geographic dependency and global semantic dependency, respectively. In order to obtain the diverse impacts of the context features on traffic accidents, we also introduce a fusion learning module that integrates the channel-wise and multi-view features learned from different types of the external factors. We conduct extensive experiments over two large real traffic accident datasets. The results show that MVMT-STN improves the performance of traffic accident risk prediction in both fine- and coarse-grained prediction by a large margin compared with existing state-of-the-art methods.

Urban flow imputation, which aims to infer the missing flows of some locations based on the available flows of surrounding areas, is critically important to various smart city related applications such as urban planning and public safety. Although many methods are proposed to impute time series data, they may not be feasible to be directly applied on urban flow data due to the following reasons. First, urban flows have the complex spatial and temporal correlations which are much harder to be captured compared with time series data. Second, the urban flow data can be random missing (i.e., missing randomly in terms of times and locations) or block missing (i.e., missing for all locations in a particular time slot). Thus it is difficult for existing methods to work well on both scenarios. In this paper, we for the first time study the urban flow imputation problem and propose a generative-free Attention-based Spatial-Temporal Combine and Mix Completion Network model (AST-CMCN for short) to effectively address it. Specifically, AST-CMCN consists of a Spatial and Temporal Completion Network (SATCNet for short) and a Spatial-Temporal Mix Completion Network (STMCNet for short). SATCNet is composed of stacked GRUAtt modules to capture the geographical and temporal correlations of the urban flows, separately. STMCNet is designed to capture the complex spatial-temporal associations jointly between historical urban flows and current data. A Message Passing module is also proposed to capture new spatial-temporal patterns that never appear in the historical data. Extensive experiments on two large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.

Accurately predicting the urban spatio-temporal data is critically important to various urban computing tasks for smart city related applications such as crowd flow prediction and traffic congestion prediction. Existing models especially deep learning based approaches require a large volume of training data, whose performance may degrade remarkably when the data is scarce. Recent works try to transfer knowledge from the intra-city or cross-city multi-modal spatio-temporal data. However, the careful design of what to transfer and how between the multi-modal spatio-temporal data needs to be determined in advance. There still lacks an end-to-end solution that can automatically capture the common cross-domain knowledge. In this paper, we propose aDeepAttentiveAdaptationNetwork model named ST-DAAN to transfer cross-domainSpatio-Temporal knowledge for urban crowd flow prediction. ST-DAAN first maps the raw spatio-temporal data of source domain and target domain to a common embedding space. Then domain adaptation is adopted on several domain-specific layers through adding a domain discrepancy penalty to explicitly match the mean embeddings of the two domain distributions. Considering the complex spatial correlation in many urban spatio-temporal data, a global attention mechanism is also designed to enable the model to capture broader spatial dependencies. Using urban crowd flow prediction as a demonstration, we conduct experiments on five real-world large datasets over both intra- and cross-city transfer learning. The results demonstrate that ST-DAAN outperforms state-of-the-art methods by a large margin.