Exploring complicated dynamic spatiotemporal correlations has always been a challenging issue in traffic prediction. Besides, methods that make predictions directly from data with missing values, have received much attention due to the inevitable and pervasive nature of data incompleteness in real scenarios. In this paper, an end-to-end representation learning framework, named spatial–temporal periodical adaptive graph contrastive learning (ST-A-PGCL), is proposed to address such issues. ST-A-PGCL mainly consists of three independent branches to respectively model three long-term periodicities of traffic flow (recent, daily, and weekly periodicities). In each branch, the spatial and temporal correlations are extracted by improved adaptive graph convolution network (ImpAdapGCN) and fused seasonal-trend temporal convolution network (FST-TCN) in an encoder, respectively, to obtain hidden representation. Besides, each branch accepts one periodic segment which will be synthetically augmented with different missing patterns to simulate real scenarios (weak communication signal, detector malfunction, area-wide power failure, etc) and generate different views. These views will be fed into a periodical graph contrastive learning (PGCL) module to learn periodical similarity features based on Siamese network to defeat data incompleteness. Bidirectional gate recurrent unit (Bi-GRU) is selected to decode the hidden representations and generate final prediction results. Specifically, the overall framework is trained in end-to-end dual-task (traffic prediction and contrastive learning) process without requiring identifying the position of missing values. Our framework is evaluated across four real-world datasets and twenty baseline models. Experimental results show that the proposed ST-A-PGCL achieves superior prediction performance, especially in long-term prediction tasks with high missing rates. 

ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios

A comprehensive review of robot intelligent grasping based on tactile perception

Congestion in the world’s traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. Modern scholarly challenges arise alongside chances to greatly enhance traffic prediction made possible by the integration of modern technologies into transportation systems. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, deep neural network architecture based on long short term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers have been structured to build the deep neural network, in order to predict the performance of the traffic flow in four distinct junctions which has a great impact on the Internet of vehicles’ applications. The structure comprised of sixteen-layers, five of them are GRU-layers and one bi-directional LSTM-layer. The dataset employed in this work involved four congested junctions. The dataset extended from the first of November 2016 to 30th of June 2017. Cleaning and preprocessing operations were achieved on the dataset before feeding it to the designed deep neural network of this paper. Results show that the suggested method produced a comparable performance with respect to state-of-the art approaches. 

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10108955.pdf

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

The development of connected vehicle (CV) technology has created conditions for improving the traffic efficiency of intersections and provided support for more effective speed guidance at signalized intersections. First, this paper proposes a platoon speed guidance strategy to reduce the fuel consumption and delay of the platoon passing through the intersection and smooth traffic oscillation, which includes constant speed guidance, deceleration guidance, acceleration guidance, and stop guidance. Then, the optimal speed calculation method is designed, including the calculation of the platoon’s passable period and maximum number of passing vehicles, the platoon restructure method, the analysis of the trajectory of the vehicles, and the calculation of the optimal trajectory of the platoon based on the goal of minimum fuel consumption and delay. Finally, eight different intersection scenarios are designed to simulate the proposed platoon speed guidance strategy. The results show that the platoon speed guidance strategy can effectively reduce the fuel consumption and delay of the platoon passing through the intersection and smooth traffic oscillation. In addition, the influences of queue length and CV penetration rate on the platoon speed guidance strategy are also discussed. The results show that when the queue length affects the passable period, the improvement in fuel consumption, drive time, and delay will decrease as the queue length increases. And as the penetration rate increases, the strategy becomes increasingly effective in reducing the delay and fuel consumption of the platoon in general.

https://downloads.hindawi.com/journals/jat/2023/9984537.pdf

Research on the Platoon Speed Guidance Strategy at Signalized Intersections in the Connected Vehicle Environment

Traffic congestion is a pervasive issue that affects urban areas worldwide. This study investigates congestion on urban roads in an Indian smart city. Video graphic technique was employed to collect data across various traffic volumes. Travel time for different vehicle categories was estimated, and the travel time reliability congestion index was assessed. Multiple linear regression modelling was utilized to assess the attributes influencing congestion. Traffic signal synchronization and enforcement of traffic regulations, decentralization of important offices, and regulating roadside activities received strong recommendations from the majority of inhabitants. For improving the functionality and accessibility, related to the urban road network, these attributes should be enforced. The research intended to assess the functional effectiveness of urban roads using travel time reliability measures. 

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.

/pdf/visual-sorting-of-express-packages-based-on-the-multi-5eryj313.pdf

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Accelerated urbanization has increased regional traffic congestion. To alleviate traffic congestion in a homogeneous road network, combined with the advantage of real-time traffic information obtained by the Internet of Vehicles (IoVs), a boundary guidance strategy for traffic congestion region is proposed. The strategy considers the optimal operation state of traffic congestion region and is divided into two categories according to different destinations of traffic demands. Meanwhile, a method for the boundary guidance strategy is presented in which the macroscopic fundamental diagram (MFD) is used to determine the optimal accumulation, a traffic flow equilibrium model is established to calculate the real-time accumulation, and a fuzzy adaptive PID control algorithm is designed to calculate the optimal traffic inflow of the traffic congestion region. Furthermore, an example is selected for simulation. The results show that the boundary guidance strategy can effectively improve the operational state of the road network and alleviate traffic congestion. Finally, the influence of connected vehicle penetration rate on the strategy is discussed. The simulation results show that the strategy can improve the operation state of the road network under mixed traffic flow, and the higher the penetration rate, the more significant its effect on alleviating traffic congestion.

/pdf/boundary-guidance-strategy-and-method-for-urban-traffic-3w4oc8h5.pdf

Boundary Guidance Strategy and Method for Urban Traffic Congestion Region Management in Internet of Vehicles Environment

Bike-sharing systems with convenience and flexibility have been appearing more and more in cities and become a necessary tool of travel for people. However, the distribution of bikes is highly unbalanced due to the changes in user demand, which leads to the unfavorable situation of “no bikes available” or “too many bikes” at some bike stations. For this reason, this paper proposes a hybrid scheduling method, which combines truck-based scheduling (TBS) and user-based scheduling (UBS). Firstly, a hybrid scheduling model (HBS) combining TBS and UBS is established. Secondly, a method combining multilayer perceptron and genetic algorithm (MLP-GA) is proposed to solve the model. Thirdly, the HBS model is simulated and analyzed by the example. The results show that the MLP-GA method converges, has a faster running time than the genetic algorithm and can obtain solutions with lower total cost and shorter optimal truck path. Further analysis shows that HBS is more implementable in practice and can shorten the optimal truck path and reduce the scheduling total cost while allowing users to use the shared bike in an affordable way, thus realizing the efficient operation of the shared bike system. Finally, a sensitivity analysis of the reward coefficients is performed. This shows that as the reward coefficient increases, the cost of HBS generally shows an increasing trend when the reward coefficient is small, reaches a maximum value when the reward coefficient is 0.6, and decreases slightly thereafter.

Research on Hybrid Scheduling of Shared Bikes Based on MLP-GA Method

Short-term traffic flow prediction is an important component of intelligent transportation systems, which can support traffic trip planning and traffic management. Although existing predicting methods have been applied in the field of traffic flow prediction, they cannot capture the complex multifeatures of traffic flows resulting in unsatisfactory short-term traffic flow prediction results. In this paper, a multifeature fusion model based on deep learning methods is proposed, which consists of three modules, namely, a CNN-Bidirectional GRU module with an attention mechanism (CNN-BiGRU-attention) and two Bidirectional GRU modules with an attention mechanism (BiGRU-attention). The CNN-BiGRU-attention module is used to extract local trend features and long-term dependent features of the traffic flow, and the two BiGRU-attention modules are used to extract daily and weekly periodic features of the traffic flow. Moreover, a feature fusion layer in the model is used to fuse the features extracted by each module. And then, the number of neurons in the model, the loss function, and other parameters such as the optimization algorithm are discussed and set up through simulation experiments. Finally, the multifeature fusion model is trained and tested based on the training and test sets from the data collected from the field. And the results indicate that the proposed model can better achieve traffic flow prediction and has good robustness. Furthermore, the multifeature fusion model is compared and analyzed against the baseline models with the same dataset, and the experimental results show that the multifeature fusion model has superior predictive performance compared to the baseline models.

/pdf/a-multifeature-fusion-short-term-traffic-flow-prediction-202tabhc.pdf

A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings

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Papers

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Boundary Guidance Strategy and Method for Urban Traffic Congestion Region Management in Internet of Vehicles Environment

Research on Hybrid Scheduling of Shared Bikes Based on MLP-GA Method

A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings