Traffic Information Collection Research Articles

Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values

YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective

Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner keypoints, and offset vectors relative to the center of the 2D bounding boxes, to enhance the accuracy of 3D object bounding box detection. Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv7-3D network achieved significantly higher detection accuracy on the Rope3D dataset while reducing computational complexity by 60%.

Embedded Deep Learning System for Classification of Car Make and Model

Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.

A Study on the Development of CCTV Camera Autonomous Posture Calibration Algorithm for Simultaneous Operation of Traffic Information Collection and Monitoring

A Study on the Development of CCTV Camera Autonomous Posture Calibration Algorithm for Simultaneous Operation of Traffic Information Collection and Monitoring

자율주행자동차 사고의 책임귀속과 교통법제의 정비

Self-driving cars will free humans from driving. Starting with the Level 4 self-driving ADS, is designed to control itself and drive even in emergency circumstances. From now on, the existing concept of a driver in charge of the dynamic driving task(DDT) of driving will be disappeared. The problem of attribution of responsibility for traffic accidents still remains, but the legal situation in Korea has not yet adequately prepared for the subjects or the scope of liability. At this point, the UK's new self-driving car law recommended in 2022 is worth considering. The UK government is investing heavily with the goal of commercializing Level 4 autonomous vehicles by 2025. A number of topics related to autonomous vehicles are dealed with this recommendation. The most remarkable thing in relation to the liability for accidents is that the two subjects of Level 4 autonomous driving vehicles. Depending on whether the person could engage in DDT, it is divided into a user-in-charge and NUIC operator, respectively, and each qualification and responsibility is different. The driver is exempted from driving-related matters in the event of an accident, and the responsibility rests with the manufacturer and operator. For the manufacturer(the developer ASDE) to be indemnified, it would have to be demonstrated by them that there were obviously other causes. It should be sought to fundamental changes in the traffic law system related to the driver's license system, signal violations, and the collection and storage of various traffic information.

GhostCount: A lightweight convolution network based on high‐altitude video for vehicle instantaneous counting in dense traffic scenes

AbstractInstantaneous vehicle counting of traffic scenes based on high‐altitude video is an important way for real‐time traffic information collection in intelligent transportation systems (ITS). However, vehicle counts based on high‐altitude video are susceptible to problems such as denseness, occlusion and small size. The mainstream method is to use a Convolutional Neural Network (CNN) to output density maps and obtain vehicle count results. However, most CNNs are computationally expensive and have poor real‐time performance. Therefore, we propose a lightweight CNN named GhostCount, specially designed for high‐accuracy vehicle counts on edge devices. First, we combine ResNet‐18 and Lightweight RefineNet to build an encoder–decoder network architecture to effectively extract vehicle features in complex traffic scenes. Next, we replace the ordinary convolutional layers in ResNet‐18 with Ghost modules to lighten the network. Finally, a binary cross‐entropy loss function is introduced to suppress background noise. We demonstrate GhostCount on public datasets (TRANCOS, CARPK, PUCPR+) and our self‐built dataset (CSCAR). Results show that GhostCount can perform instantaneous vehicle counting with higher accuracy and faster inference speed than other representative lightweight CNNs. The method we propose would provide new solutions and ideas for ITS applications such as traffic information collection and smart parking management.

An Innovative and Cost-Effective Traffic Information Collection Scheme Using the Wireless Sniffing Technique

In recent years, the wireless sniffing technique (WST) has become an emerging technique for collecting real-time traffic information. The spatiotemporal variations in wireless signal collection from vehicles provide various types of traffic information, such as travel time, speed, traveling path, and vehicle turning proportion at an intersection, which can be widely used for traffic management applications. However, three problems challenge the applicability of the WST to traffic information collection: the transportation mode classification problem (TMP), lane identification problem (LIP), and multiple devices problem (MDP). In this paper, a WST-based intelligent traffic beacon (ITB) with machine learning methods, including SVM, KNN, and AP, is designed to solve these problems. Several field experiments are conducted to validate the proposed system: three sensor topologies (X-type, rectangle-type, and diamond-type topologies) with two wireless sniffing schemes (Bluetooth and Wi-Fi). Experiment results show that X-type has the best performance among all topologies. For sniffing schemes, Bluetooth outperforms Wi-Fi. With the proposed ITB solution, traffic information can be collected in a more cost-effective way.

Research on Optimization Model of Multisource Traffic Information Collection Combination Based on Genetic Algorithm.

In order to reduce the excessive use of multisource traffic information collection system, a multisource traffic information collection combination optimization mode is proposed based on genetic algorithm in this paper. This model is mainly used to analyze the traffic management data in the city. According to the collected data information, the characteristics of the traffic equipment can be effectively analyzed. Basing on the market demand and supply relationship, the multisource traffic information collection combination optimization model is used to complete the reorganization and optimization of the traffic information in this paper, to acquire the main convolution feature variables of the model. The data information combination processing is performed according to the acquired feature variables, and the genetic algorithm is used to adjust the multisource traffic information. During the process of information fusion data analysis, the multisource traffic information clustering and fuzzy constraint control can be performed effectively to realize the optimization of the team's traffic information collection combination. Finally, the simulation results show that the method proposed in this paper is more accurate in realizing the optimization process of multisource traffic information collection and combination and has a better degree of information fusion.

The influence of smart equipment Smart Hub over highway traffic in the Romanian border points

The pandemic event introduces some changes in the control procedures from Romanian highway network border points. The cargo flow is controlled on the output and input direction using a set of goods scales. This process generates a very long queue on Romanian Highway Network for cargo trucks with a negative impact for users and highway infrastructure. The use of data provided by road traffic information collection equipment, located on the highway and the implementation of smart equipment for their management and control of traffic management equipment can lead to the elimination of these negative aspects. To prove the need to implement this system a simulation model was developed, and a series of results were generated using real data collected from the highway for different operating scenarios.

Background Noise Filtering and Clustering With 3D LiDAR Deployed in Roadside of Urban Environments

Traffic information collection is an important foundation for intelligent transportation systems. In this paper, 3D Light Detection And Ranging (LiDAR) is deployed in the roadside of urban environments to collect vehicle and pedestrian information. A background filtering algorithm, including a mean background modeling to build a background map and a background difference method to filter static background noise points, is proposed for roadside fixed LiDAR facilities. Background points are filtered through the difference between data frames and a multi-level background map, and then there are still a small number of noise points. Aiming to reduce the noise points, a hierarchical maximum density clustering of applications with noise (HMDCAN) algorithm, utilizing both density clustering and hierarchical clustering, is proposed to effectively achieve both noise point filtering and target recognition. We verify our methods in a facility with a 16-channel LiDAR in which background filtering and target recognition are tested with different scenarios, and the accuracy rate is over 97%.

Traffic controlling and monitoring using IoT

The population is increasing the number of vehicles and the number of highways day after day; the time spent travelling, waste fuel, air pollution and related transport problems are also increasing. The greatest challenge for traffic administration authorities is therefore to monitor and monitor traffic. The related work has shown the Internet of Things and the integration of artificial intelligence to facilitate techniques for better urban and decision­making. This document creates a system model for IoT-based traffic information collection, processing and storing in real time. The aim is to ensure smooth mobility by providing roadside communications updates and rare incidents in real time. In particular, pre-alerting messages prevent and delay road congestion and peak hours in emergencies. The system also sends traffic updates from the database of administrative sensors. The system proposed evaluates the feature of the model and shows expected accurate results for vehicle detection and the minimum error in the estimates of occupancy.

BIM-based traffic analysis and simulation at road intersection design

Traffic analysis at road intersections is an important activity in the project design and planning phases because it allows selecting the most suitable traffic requirements and site characteristics. Therefore, traffic simulation models are indispensable and useful tools to support collaborative decision-making. The building information modeling (BIM) approach shows great potential to contribute to the traffic analysis and road design improvement; however, BIM has been adopted mainly in the building sector, rather than road infrastructure projects. Therefore, this paper presents a BIM-based methodological framework for traffic analysis and simulation of road intersection design. The proposed framework has five main steps: 1) BIM models and traffic information collection; 2) BIM model configuration; 3) BIM simulation, analysis, and calibration; 4) BIM cost analysis and documentation; and 5) alternatives comparison and recommendations. The application of the proposed framework to a case study shows several BIM implementation benefits in the traffic analysis at road intersections. Some of the most prominent BIM benefits observed in the case study are a better understanding of design, improved project quality, more efficient communications, scope clarification, and speed up the design process.

Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology

As a key element of ITS (intelligent traffic systems), traffic information collection facilities play a key role, with ITS being able to analyze the state of mixed traffic more appropriately and can provide effective technical support for the design, management, and the evaluation of constructions. Traffic Infrastructure. Focusing on image processing technology, this study takes pedestrians, electric motor, and vehicles in mixed traffic flow as the research object, and Gaussian mixed model, Kalman filtering, and Fisher linear discriminant are introduced in the recognition system. On this basis, the mixed motion flow data acquisition framework model is elaborated in detail, which includes attribute extraction, object recognition, and object tracking. Given the difficulty in capturing reliable images of objects in real traffic scenes, this study adopted a novel background and foreground classification method with region proposal network so as to decrease the number of regions proposal from 2000 to 300, which can detect objects fast and accurately. Experiments demonstrate that the designed programme can collect the flow data by detecting and tracking moving object in the surveillance video for mixed traffic. Further integration of various modules to achieve integrated collection is another important task for further research and development. In the future, research on dynamic calibration of monocular vision will be carried out for distance measurement and speed measurement of vehicles and pedestrians.

Prototype Design of Cement/Emulsified Asphalt Based Piezoelectric Composites and its Potential Application in Vehicle Speed Sensing

Piezoelectric composites (PC) embedded in pavement have shown great potential in traffic information sensing. As the main form of transportation, the road is the source of much traffic information including vehicle load information. The research into PC can supplement the collection of traffic information used in the development of intelligent technologies and provide effective solutions to problems existing in the process of information gathering. In this study, a 2-2 cement/emulsified asphalt-based PC was prepared with the cutting-filling method. To optimize the PC preparation, the effects of the volume fraction of the piezoelectric phase and the matrix phase composition on the piezoelectric properties of the PC were investigated by employing the finite element method. The results indicated that the smaller the volume fraction of the piezoelectric phase, the higher the voltage output of the PC, and the higher the sensitivity to external load, but the greater the stress concentration at the interface between the piezoelectric phase and the matrix phase. In addition, the greater the amount of emulsified asphalt in the matrix phase, the higher the voltage output of the PC. However, a higher content of emulsified asphalt will undermine the fluidity of the matrix phase. Based on the simulation analysis, performance optimization of the cement/emulsified asphalt PC was achieved. According to the voltage output characteristic of PC under a moving load, a placement scheme of PCs in the asphalt pavement was also proposed, which enables vehicle speeds to be be sensed with high precision.

Reliable Traffic Monitoring Mechanisms Based on Blockchain in Vehicular Networks

Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.

Hybrid game approach‐based channel congestion control for the Internet of Vehicles

Communications between the Internet of Vehicles in smart cities helps increase the awareness and safety among drivers. However, the channel congestion problem is considered as a key challenge for the communication networks due to continuing collection and exchange of traffic information in dense environments. The channel congestion problem degrades the efficiency and reliability of the ad hoc network. Therefore, the adaptation of the data rate and power control is considered as one of the effective solutions to mitigate channel congestion. This paper develops a new hybrid game transmission rate and power channel congestion control approach on the Internet of Vehicle networks where the nodes play as greedy opponents demanding high information rates with the maximum power level. Furthermore, the existence of a Nash equilibrium, which is the optimal information rate and power transmission for every vehicle, is established. Simulation results demonstrate that the proposed approach enhances the network performance by an overall percentage of 42.27%, 43.94% and 47.66% regarding of channel busy time, messages loss and data collision as compared to others. This increases the awareness and performance of the vehicular communication network.

Decentralized Autonomous Navigation of a UAV Network for Road Traffic Monitoring

With the increase of population and the fast growth of private vehicle ownership in urban areas, many roads have become more congested than ever before. The road traffic information plays a crucial role in traffic management. The currently available traffic information collection systems mainly rely on static road-side units, which passively record the traffic information. This article considers an alternative option, i.e., the usage of unmanned aerial vehicles (UAVs) for aerial surveillance, which is more efficient than the conventional ground-based strategies. We focus on deploying a UAV network to monitor the traffic on a road. We propose a decentralized navigation scheme that enables the UAV network to detect blockage and then gather to the blocked area to have better views of the ground vehicles. This scheme only requires the UAVs to share some light measurement information and positions, and it can be implemented in real time.

Travel mode recognition of urban residents using mobile phone data and MapAPI

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

Optimization Model of Traffic Sensor Layout considering Traffic Big Data

In order to improve the accuracy, reliability, and economy of urban traffic information collection, an optimization model of traffic sensor layout is proposed in this paper. Considering the impact of traffic big data, a set of impact factors for traffic sensor layout is established, including system cost, multisource data sharing, data demand, sensor failures, road infrastructure, and sensor type. The impacts of these influential factors are taken into account in the traffic sensor layout optimization problem, which is formulated in the form of multiobjective programming model that includes minimum system cost, maximum truncation flow, minimum path coverage, and an origin-destination (OD) coverage constraint. The model is solved by the tolerant lexicographic method based on a genetic algorithm. A case study shows that the model reflects the influence of multisource data sharing and fault conditions and satisfies the origin-destination coverage constraint to achieve the multiobjective optimization of traffic sensor layout.

Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network

Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.