54,830 results on '"TRAFFIC engineering"'
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2. Enhancing transportation: connecting TSMO and planning.
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Transportation -- Planning. -- United States ,Transportation engineering -- United States. ,Traffic engineering -- United States. ,Traffic engineering ,Transportation engineering ,Transportation -- Planning - Published
- 2018
3. Enhancing transportation: connecting TSMO and construction.
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Roads -- Maintenance and repair -- Safety measures. -- United States ,Road work zones -- United States. ,Traffic engineering -- United States. ,Traffic flow -- United States. ,Road work zones ,Roads -- Maintenance and repair -- Safety measures ,Traffic engineering ,Traffic flow - Published
- 2018
4. A modified-YOLO based vehicle-pedestrian detection model for improved urban mobility.
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Desai, Karnavi, Sahatiya, Prashant, and Singh, Dheeraj Kumar
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DEEP learning , *EMERGENCY vehicles , *TRAFFIC signs & signals , *TRAFFIC engineering , *TRAFFIC congestion , *TRAFFIC lanes , *PEDESTRIANS - Abstract
Congestion on traffic lanes is a key issue impeding the growth of a metropolitan metropolis. The reason for this is the growing number of cars on the road, which causes significant time delays at traffic lights. Several strategies and procedures have been developed over the years to address this issue and make traffic control systems more dynamic. The static traffic control systems operated on predetermined timings that were assigned to each traffic lane and could not be changed. There was also no mechanism for counting and detecting pedestrians at zebra crossings, nor for detecting emergency vehicles in traffic. Here in this research, we will investigate multiple machine learning and deep learning models for car and pedestrian identification in traffic-prone lanes. Furthermore, we will propose a system that will contain a deep learning model to recognize various types of cars, pedestrians, and overlapping and intersecting vehicles on traffic lanes. Finally, a choice will be taken to free up the crowded lanes depending on the traffic count on each lane. [ABSTRACT FROM AUTHOR]
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- 2024
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5. IOT based fire and traffic density detection using AI based drone.
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Narahari, Sujatha Canavoy, Polaboina, Uday Raj, Rishika, K., and Gudipalli, Abhishek
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ARTIFICIAL intelligence , *TRAFFIC density , *MACHINE learning , *COMMERCIAL drones , *TRAFFIC monitoring , *CONVOLUTIONAL neural networks , *TRAFFIC engineering - Abstract
The reality is that the population and number of automobiles on the street are growing day by day. The efficient monitoring of continuously increasing traffic is a critical challenge that traffic operators are facing. Traffic control is generally achieved using stationary video recorders, observers or manual counters. Hence, Unmanned Aerial Vehicles or drones are the high-quality answers to this trouble as they lessen the price of the set-up of numerous cameras and sensors. This paper's principal ideology is split into phases. The first one is the monitoring of traffic using a drone which helps in analyzing the traffic in a particular area and making interpretations. The second is fire detection and extinguisher. Here the drone is used to detect fire with the aid of a fire sensor as it helps the fire-fighters to analyze the affected area and prioritize their work. This project also up-skills in designing a quad-copter. Our motivation for this project lays both personal interests in a better understanding of object detection and academic research. A drone consists of a camera to cover a wider area which helps in traffic analysis. Here, the number of vehicles are estimated. Drones have recently emerged as an outstanding technology in the business market and are geared up with a camera, geo-positioning sensors, and communications hardware that may relay statistics. This project is implemented by using artificial intelligence and the Internet of Things for Fire Detection. To be more specific traffic detection is achieved using Deep Learning algorithm which is the Convolutional Neural Networks Algorithm using python. Fire sensors are mounted on the drone, and it flies on a flight path within the building and sends fire alert data to the system controller upon sensing a fire condition via fire sensor and also puts off the fire using the water pump. For fire detection the sensor used in the drone basically detects IR (Infra-Red) light wavelength that is emitted from the fire flame which helps fire-fighters to quickly assess and monitor dangerous areas which results in more safety and limits the time it takes to scan the whole area. [ABSTRACT FROM AUTHOR]
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- 2024
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6. High performance packet classification algorithm for network security systems using modified grid-of-tries.
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Muthumanikandan, V., Sannasi, Ganapathy, Perumal, T. Sudarson Rama, and Sushmitha, J.
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CLASSIFICATION algorithms , *COMPUTER network security , *SECURITY systems , *INTRUSION detection systems (Computer security) , *MULTICORE processors , *TRAFFIC engineering - Abstract
In order to enhance the functionality of network applications including traffic engineering and intrusion detection, the packet classification problem has received extensive research during the past decade. Software-based packet classification algorithms are gaining significant attention due to their extremely high flexibility in satisfying various industrial requirements for security and network systems, which has coincided with the general improvement of hardware architectures and the rising popularity of multi-core multi-threaded processors over the past few years. These methods require extremely big tables internally to achieve fast classification, and the size of the tables could possibly grow along with the size of the rule set. They cannot be utilised with a big rule set as a result. To solve this issue, we present a novel software-based packet classification algorithm dubbed the grid-of-tries approach that combines the partition decision trees in a search table to support both high scalability and quick classification performance. Our proposed algorithm exhibits a very high categorization speed, regardless of the quantity of rules, with smaller tables and shorter table building time, while the majority of generic partitioning-based packet classification algorithms demonstrate acceptable scalability at the expense of reduced classification speed. In this study, we suggest the Grid-of-tries approach, a novel packet classification algorithm to bridge the theoretical and practical gap. In terms of classification speed, memory utilisation, and preprocessing time, our technique outperforms other well-known algorithms. The results of our tests demonstrate how the proposed method facilitates network systems to handle significant traffic in the most efficient way. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Cost effective portable traffic light system using Esp32.
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Prasad, Ch. Rajendra, Rao, P. Ramchandar, Bhavani, Ch., Sriya, K., Vyshnavi, P., and Samala, Srinivas
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TRAFFIC cameras , *TRAFFIC signs & signals , *TRAFFIC regulations , *STORAGE battery charging , *TRAFFIC engineering , *TRAFFIC congestion , *CLOUD computing - Abstract
This paper presents a cost-effective portable traffic light system, which is most suitable, especially in densely populated cities where the population and number of running vehicles are much more than its capacity. Firstly, we need to properly analyse the locations at which traffic signals are not installed and traffic jam is observed for a particular instant of time such as office/work opening and closing hours, festival times etc. So that portable feature will be advantageous. Secondly, the Traffic light system works without the power supply, it is also capable of working in any weather conditions with built-in storage of charge through batteries. This traffic light is adjustable and two-faced for easy installation and the current location of the traffic light can be detected. Finally, to generate fine on vehicle which does not follow traffic regulations camera and cloud application is used. In some areas, traffic management tries to control traffic manually which is not ideal. So, we need portable machines as an instant solution to control traffic in many of the places where the crowd is seen on festivals and occasional days near temples. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Evaluation and classification of Al-Najaf road urban network.
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Rwadhah, Dhuha Kareem, Al-Jameel, Hamid Athab, and Abd Abas, Ahmed Yahya
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STREETS , *HIGHWAY capacity , *TRAFFIC surveys , *TRAFFIC engineering , *TRAFFIC flow , *TRAFFIC congestion - Abstract
One of the basic parts that cities depend on for movement and mobility is a road network. Therefore, improving and developing is network directly or indirectly affects various social, cultural, and economic activities. Al-Najaf is a city in Iraq that is currently undergoing development, but is facing challenges with traffic congestion in multiple areas of its street network. Regular diagnosis of the causes, locations, and severity of traffic congestions is crucial for two primary reasons. Firstly, it enables the classification of roads based on the gradual increase in vehicular traffic over time. Secondly, it facilitates the proposal of traffic engineering solutions that are tailored to the existing road networks. This study aims to assess the overall performance of the primary urban streets within the street network of Najaf city during morning and evening peak hours. The collection of field data involved the use of a video camera for the purpose of determining traffic volume, as well as conducting traffic surveys that entailed measurements of lane count and length. Additionally, a speed gun was utilized to ascertain the speed of free flow. The evaluation procedures from the Highway Capacity Manual (HCM) are utilized to evaluate the operational efficiency of designated urban roads. As per the research findings, certain roads that exhibit free flow speed characteristics akin to highways during the early morning hours have been categorized as Classes I and II instead. The findings suggest that the percentages of urban street categories, including principal and minor arterials, collectors, and local roads, do not conform to the established sustainable standards outlined in the sustainable road hierarchy. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Bias-Resilient Elephant Flow Detection in Distributed SDNs Through Federated Learning
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Boussaoud, Kaoutar, Bellouch, Mohamed, Ayache, Meryeme, En-Nouaary, Abdeslam, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Kartik, S., editor
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- 2024
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10. Comprehensive Evaluation of the Performance of a New High-Speed Train Based on TOPSIS-Entropy Weight Method
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Chen, Shuangyang, Zhang, Xin, Zhang, Tingfeng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor
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- 2024
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11. Optimization design of F-type Traffic Signage based on ANSYS Finite Element Analysis
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Yan, Lixia, Fang, Zhiyang, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Xiang, Ping, editor, Yang, Haifeng, editor, Yan, Jianwei, editor, and Ding, Faxing, editor
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- 2024
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12. Efficient Multi-tunnel Flow Scheduling for Traffic Engineering
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Xu, Renhai, Li, Wenxin, Li, Keqiu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tari, Zahir, editor, Li, Keqiu, editor, and Wu, Hongyi, editor
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- 2024
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13. Traffic signal control model for the intersection with a work zone.
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Yang, Da, Chen, Yuting, Feng, Tingwei, Zheng, Bin, and Su, Gang
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TRAFFIC signs & signals , *TRAFFIC engineering , *TRAFFIC signal control systems , *TRAFFIC flow - Abstract
When a work zone is located at an intersection, it greatly reduces the capacity and change the traffic flow characteristics. However, the impacts of work zones have not attracted much attention, and the signal control method of the intersection with a work zone has not been investigated yet. This paper focuses on a specific type of work zone, which is located within the area of an intersection. The saturation flow rate model, traffic wave model, traffic delay model, and emptying time model are proposed to capture the traffic flow characteristics of the intersection with an island work zone, and a signal control model is further put forward. The real data is collected to calibrate, validate, and evaluate the proposed models. The results indicate that the proposed signal control model can reduce about 15.6% of queue length and 17.2% of traffic delay for the intersection with an island work zone. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Field‐tested signal controller to mitigate spillover using trajectory data.
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Han, Yu, Han, Zhe, Ding, Fan, Li, Fuliang, Wang, Hao, and Wang, Xingmin
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TRAFFIC signal control systems , *TRAFFIC signs & signals , *TRAFFIC engineering , *SIGNALS & signaling , *CITY traffic - Abstract
Trajectory data from connected vehicles (CVs) provide a continuous and reliable means of obtaining information that can be leveraged to optimize traffic signals. This paper proposes a real‐time traffic signal control method using CV trajectory data as the sole input. The primary goal of the proposed signal control method is to prevent queue spillover, which may significantly decrease the traffic efficiency on urban networks and induce high delays to the travelers. The proposed method formulates the signal control problem via a linear quadratic optimization model, considering the constraints related to the duration and variability of green lights in practical traffic signal control systems. Compared to conventional max‐pressure‐based methods, the optimization model offers enhanced efficiency in handling these constraints, making it highly suitable for real‐life implementation. The proposed method has undergone testing in both simulated environments and real‐world applications. In the simulation experiments, the proposed method has been demonstrated to effectively reduce spillover risks and outperform a conventional max‐pressure‐based approach even when the CV penetration rate is as low as 5%. In the real‐world experiment, the proposed method had been tested in a traffic network around the Beijing Capital International Airport for several months. The severity of spillovers, which was represented by two performance indicators, had been significantly reduced after implementing the proposed method. [ABSTRACT FROM AUTHOR]
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- 2024
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15. TrafficGPT: Viewing, processing and interacting with traffic foundation models.
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Zhang, Siyao, Fu, Daocheng, Liang, Wenzhe, Zhang, Zhao, Yu, Bin, Cai, Pinlong, and Yao, Baozhen
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LANGUAGE models , *URBAN transportation , *CHATGPT , *CITY traffic , *TRAFFIC engineering - Abstract
With the promotion of ChatGPT to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, large language models (LLMs) struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT—a fusion of multiple LLMs and traffic foundation models. This integration yields the following key enhancements: 1) empowering LLMs with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git. • We propose the TrafficGPT framework, which combines large language models with expertise in traffic management. • TrafficGPT empowers LLMs to observe, analyze, process traffic data, and offer insightful decisions for traffic management. • We compare the proposed model with ChatGPT-4 Data Analyst to highlight its response accuracy and efficiency. • The adaptability and stability of TrafficGPT across various tasks, utilizing different foundational LLMs, are validated. [ABSTRACT FROM AUTHOR]
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- 2024
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16. A Graphical Approach to Automated Congestion Ranking for Signalized Intersections Using High-Resolution Traffic Signal Event Data.
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Wang, Peirong (Slade), Khadka, Swastik, and Li, Pengfei (Taylor)
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SIGNALIZED intersections , *TRAFFIC signs & signals , *TRAFFIC congestion , *PARETO analysis , *TRAFFIC monitoring , *TRAFFIC engineering , *MATHEMATICAL optimization - Abstract
In recent years, high-resolution traffic signal event data has provided valuable insights into understanding and managing congestion at signalized intersections. While existing applications primarily employ automated traffic signal performance monitoring (ATSPM) systems as postanalysis tools for identifying everyday congestion causes, traffic engineers are increasingly overwhelmed by the number of ATSPM-capable intersections. The workload increases extensively as the number of ATSPM-capable intersections rises mainly due to the necessity of manually checking and generating performance figures. Nonetheless, an advanced ATSPM system capable of automatically detecting time-of-day congestion bottlenecks among multiple intersections and suggesting "top intersections of interest" would significantly aid traffic managers in monitoring historical congestion and preventing future congestion occurrences. This paper introduces an efficient graphical automated congestion ranking method for capable intersections, leveraging high-resolution traffic signal event data as the basis for automated congestion ranking. To accomplish these objectives, we build upon ATSPM concepts by continuously generating ATSPM measures of effectiveness (MOEs). Utilizing continuously generated ATSPM performance measures in Frisco, Texas, over several months, we devise an efficient graphical method for ranking hourly congestion levels among the studied ATSPM-capable intersections. All intersections are assessed and ranked using a multiobjective optimization technique, the Pareto front method. The points on the Pareto front represent dominating intersections with at least one inferior performance measurement, warranting prioritized improvement. The dominating points identified from the test dataset were validated and further explained using Purdue coordination diagrams (PCD), along with another individual dataset--Wejo-connected vehicle data. The outcomes of this approach have proven the validity of the method. [ABSTRACT FROM AUTHOR]
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- 2024
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17. A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions.
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Dai, Rongjian, Ding, Chuan, Yu, Bin, and Hu, Jia
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TRAVEL time (Traffic engineering) , *TRAFFIC signal control systems , *TRAFFIC signs & signals , *TRAFFIC engineering , *ADAPTIVE control systems , *ASSIGNMENT problems (Programming) - Abstract
With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Identification and quantification of both isomers of hexahydrocannabinol, (9R)-hexahydrocannabinol and (9S)- hexahydrocannabinol, in three different matrices by mass spectrometry.
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Bottinelli, Charline, Baradian, Pauline, Poly, Amélie, Hoizey, Guillaume, and Chatenay, Camille
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ISOMERS , *MASS spectrometry , *SALIVA , *TRAFFIC engineering , *CANNABINOID receptors , *CANNABINOIDS , *CANNABIDIOL - Abstract
Context: Hexahydrocannabinol (HHC), a compound derived from synthetic production using cannabidiol (CBD) or delta-9-tetrahydrocannabinol (Δ9 -THC), has gained recent attention due to its presence in seized materials across Europe. Sold legally in various forms, HHC poses potential health risks, particularly as a legal alternative to THC in some countries. Despite its historical description in the 1940s, limited toxicology data, pharmacological understanding, and analytical methods for HHC exist. Method: This study proposes analytical techniques using mass spectrometry to detect, identify, and quantify (9R)-HHC and (9S)-HHC, concurrently with THC and CBD in various matrices, including oral fluid, whole blood, and seized material. Three distinct methods were employed for different matrices: GC/MS for seized material, GC/MS/MS for whole blood, and UHPLC/MS/MS for oral fluid. Methods were validated qualitatively for oral fluid with a FLOQSwab® device and quantitatively in whole blood and seized material according to Peters et al's recommendations and ICH guidelines. Results: Validated methods were considered reliable in detecting and quantifying HHC isomers in terms of repeatability, reproducibility, and linearity with r ² systematically >0.992. These methods were applied to authentic cases, including seized materials and biological samples from traffic control (whole blood and oral fluid). In seized materials, (9R)-HHC levels ranged from 2.09% to 8.85% and (9R)- HHC/(9S)-HHC ratios varied from 1.36 to 2.68. In whole blood sample, (9R)-HHC and (9S)-HHC concentrations were, respectively, 2.38 and 1.39 ng/mL. For all analyzed samples, cannabinoids such as THC and CBD were also detected. Conclusion: This research contributes analytical insights into differentiating and simultaneously analyzing (9R)-HHC and (9S)-HHC, using widely applicable mass spectrometric methods. The study emphasizes the need for vigilance among toxicologists, as new semisynthetic cannabinoids continue to emerge in Europe, with potential health implications. The findings underscore the importance of reliable analytical methods for monitoring these compounds in forensic and clinical settings. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Study on Driver Behavior Pattern in Merging Area under Naturalistic Driving Conditions.
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Li, Yan, Zhang, Han, Wang, Qi, Wang, Zijian, and Yao, Xinpeng
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TRAFFIC conflicts , *ACCELERATION (Mechanics) , *LANE changing , *MOTOR vehicle driving , *TRAFFIC engineering , *TRAFFIC safety , *TRAFFIC flow - Abstract
To reduce the risk of traffic conflicts in merging area, driver's behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings. [ABSTRACT FROM AUTHOR]
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- 2024
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20. Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization.
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Kumarasamy, Vijayalakshmi K., Saroj, Abhilasha Jairam, Liang, Yu, Wu, Dalei, Hunter, Michael P., Guin, Angshuman, and Sartipi, Mina
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TRAFFIC signs & signals , *DIGITAL twins , *REINFORCEMENT learning , *TRAFFIC signal control systems , *TRAFFIC congestion , *DIGITAL learning , *INTELLIGENT transportation systems , *TRAFFIC engineering - Abstract
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. [ABSTRACT FROM AUTHOR]
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- 2024
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21. Resilient strain and stiffness degradation of Yellow River silt under cyclic loads.
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Wang, Yuke, Jiang, Rui, Gao, Yufeng, and Shao, Jinggan
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CYCLIC loads , *SILT , *HIGHWAY engineering , *SOIL classification , *TRAFFIC engineering - Abstract
The use of natural river silt treated as a 'waste material' has become the focus of research in the Yellow River flooded area, especially in the field of highway engineering. It is therefore of great significance to study the deformation and stiffness degradation of Yellow River silt (YRS) under long-term cyclic loads. To this end, a series of undrained cyclic triaxial tests on YRS for a large number of cycles (10 000) was carried out using a triaxial apparatus and the effects of confining pressure and cyclic stress ratio on the resilient strain of the YRS were explored. The critical cyclic stress and the critical cyclic stress ratio between the plastic shakedown state and incremental collapse state of the YRS were determined, and the modulus-softening behaviour of the YRS was assessed. The results of this study not only provide theoretical support for the long-term engineering performance of YRS, but also provide theoretical guidance for the cyclic response of all similar types of subgrade soils. [ABSTRACT FROM AUTHOR]
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- 2024
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22. Improving superelevation in spiral transitions based on lateral acceleration rate.
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Pourkhani, Hossein and Kordani, Ali Abdi
- Abstract
High collision rates on horizontal curves compared with other roadway elements make them one of the most critical elements in a transportation network and thus good candidates for new studies, especially of the stability of a vehicle driving on a horizontal curve. In this regard, spiral transition curves as facilities to enter horizontal alignments are very important. While the superelevation changes in these curves have always been linear, the aim of this study was to evaluate non-linearity in the superelevation attainment function (SAF). Various scenarios were used in order to cover different situations. As a simulation tool, the CarSim software package was used to model three kinds of passenger cars (sport utility vehicle, utility truck and sedan). In addition, 16 types of spiral–curve–spiral road plans for three different conditions of road slope, six types of warping in the SAF and two types of functional speed were examined (a total of 1728 scenarios). The results showed that non-linearity might produce better results than other proposed methods; for example, it was found that a parabolic function reduced the lateral acceleration rate by over 50% for high superelevation and design speed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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23. Forecasting traffic speed using spatio-temporal hybrid dilated graph convolutional network.
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Zhang, Lei, Guo, Quansheng, Li, Dong, Pan, Jiaxing, Wei, Chuyuan, and Lin, Jianxin
- Abstract
Due to the complex routes and the dynamic changing factors in transportation, precise traffic speed prediction is very difficult. Traditional prediction methods only focus on a single monitoring site, without establishing a relationship between different sites, so the precision is poor. The deep learning method can model traffic networks well, but suffers from information loss and the disadvantage of single input data. A multisource spatio-temporal hybrid dilated graph convolutional network (GCN) for forecasting traffic speed is proposed in this paper. A GCN based on hybrid dilated convolution can extract the influence of adjacent information and capture dynamic spatial and non-linear temporal correlations. Considering multisource data will increase the forecasting precision and improve the generalisation ability. Using a real-world data set, the performance of the proposed model was validated against other baselines (a fully connected neural network, convolutional neural network and spatio-temporal GCN). The proposed model was found to be superior to other models as it considers proximity information, which is often overlooked, and multifactorial influence. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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24. Environmentally friendly electric vehicles: a silent menace to vulnerable road users?
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Siamidoudaran, Meisam, Siamidodaran, Mehdi, and Konuralp, Hilmiye
- Abstract
Prediction models have been extensively used in the field of road safety. However, none of these models have yet been particularly applied to injuries related to zero-emission electric vehicles (EVs), which may lead to different outcomes due to their inaudible engines. Using an optimisable classification tree, the aim of this first-ever study was to predict the likelihood of personal injury severities stemming from EV-related crashes on Britain's roads. The prediction model was found to be capable of detecting significant and insignificant factors. These factors provide important insights into how the severity of injuries could be reduced in the future deployment of EVs. Although there was an increased risk for injuries classified as 'slight severity', particularly at lower urban speed limits, several predictors are suggesting that EVs do not pose more of a risk to a certain group. Contrary to popular belief, no convincing evidence was found to suggest that eco-friendly EVs are 'silent killers' for vulnerable road users. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
25. Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks.
- Author
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Żochowska, Renata and Pamuła, Teresa
- Subjects
- *
URBAN transportation , *TRAFFIC estimation , *TRAFFIC flow , *REMOTE sensing devices , *DEEP learning , *TRAFFIC engineering , *TRAFFIC congestion - Abstract
Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Influence of adaptive signal control technology (ASCT) on severity of intersection-related crashes.
- Author
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Kodi, John H., Ali, MD Sultan, Kitali, Angela E., Alluri, Priyanka, and Sando, Thobias
- Subjects
- *
ADAPTIVE control systems , *SPEED limits , *DRUGGED driving , *SIGNALIZED intersections , *RANDOM effects model , *TRAFFIC engineering - Abstract
Adaptive signal control technology (ASCT) is an advanced traffic control system that optimizes signal timing based on real-time traffic demand. ASCT can potentially improve the operation and safety of intersections by establishing dynamic coordination among signalized intersections in real-time. This study used a binary Bayesian logit model with random effects, which accounts for unobserved heterogeneity, to explore the impacts of ASCT on the severity of intersection-related crashes in Florida. Two distinct ASCT types (Type I and II) were analyzed to assess their impacts on crash severity. The analysis revealed that ASCT reduced the likelihood of a fatal plus injury (FI) crash by 14.6%. This reduction was significant at a 90% Bayesian credible interval (BCI). Also, each ASCT type (Type I and II) showed a potential reduction in the likelihood of a FI crash, although the decrease was not significant at a 90% BCI. Other factors such as driving under the influence, angle crashes, dark lighting conditions, posted speed limit, and median along a minor approach, were associated with a higher risk of a FI crash. Transportation agencies could use the study results to justify the deployment and expansion of ASCT at signalized intersections with a high frequency of severe crashes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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27. A Framework for Evaluating the Safety and Homogenizing Effect of Freeway Traffic Controllers on Mixed Traffic Conditions.
- Author
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Silgu, Mehmet Ali
- Subjects
- *
TRAFFIC safety , *TRAFFIC conflicts , *TRAFFIC flow , *TRAFFIC engineering , *MARKET penetration , *EXPRESS highways - Abstract
Due to recent advancements in connected and autonomous vehicles (CAVs), freeway traffic control (FTC) has become a trendy area of research. Combining CAVs and control measures has opened more efficient traffic flow possibilities. However, while there is extensive research on how traffic control and CAVs can enhance traffic flow performance, their safety benefits should be evaluated more comprehensively. It is challenging to distinguish their contributions to traffic flow safety. Studies on the safety of CAVs in traffic flow indicate that traffic flow safety improves with higher market penetration rates (MPR) of CAVs. However, this finding only sometimes aligns with the traffic flow performance effects of CAVs on freeways. This paper introduces a framework for assessing the safety effects of FTC strategies in mixed traffic scenarios involving human-driven vehicles and CAVs. The proposed framework is tested through a microsimulation-based case study in Istanbul, Turkey. The results show that, despite varying MPRs, the safety effects of CAVs and FTC methods do not consistently reduce the number of conflicts in the traffic flow context. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. Cooperative vehicular platooning: a multi-dimensional survey towards enhanced safety, security and validation.
- Author
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Vasconcelos Filho, Ênio, Severino, Ricardo, Salgueiro dos Santos, Pedro M., Koubaa, Anis, and Tovar, Eduardo
- Subjects
- *
CYBER physical systems , *TRAFFIC engineering , *COMMUNICATION infrastructure , *TEST methods , *SAFETY , *ROAD safety measures - Abstract
Cooperative Vehicular Platooning (Co-VP) is a prime example of Cooperative Cyber-Physical Systems (Co-CPS), offering great potential for enhancing road safety by reducing human involvement in driving. However, this domain presents significant challenges, incorporating control theory, communications, vehicle dynamics, security, and traffic engineering. This survey explores recent advancements in Co-VP, covering control strategies, communication infrastructures, and cybersecurity. It also examines testing and validation methods, such as simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Lastly, it outlines open challenges within the Co-VP field. This comprehensive overview serves as a guide for further developments in this complex and critical area. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Short-term traffic flow prediction based on optimized MSTSAN model.
- Author
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Wu, Z., Huang, M., Yang, T., and Shi, L.
- Subjects
- *
TRAFFIC flow , *DEEP learning , *EXPRESS highways , *STANDARD deviations , *TRAFFIC engineering , *TRAFFIC monitoring , *MATHEMATICAL optimization - Abstract
Using historical traffic flow information to accurately and real-time predict future traffic flow information is particularly important for the management and control of traffic scenarios, and the guidance and planning of traffic travel. Short-term traffic flow prediction requires a large amount of forecast data and complex changes in traffic flow in space and time. However, the current prediction model cannot meet the requirements of real-time and accuracy of short-term traffic flow prediction. This paper proposes two traffic flow prediction models based on the changing trend and correlation of short-term traffic flow in time and space, combined with deep learning related theories. Firstly, this paper takes the traffic flow of the expressway as the research object, explores the basic parameters of traffic flow-flow, speed occupancy rate in time and space distribution law and change trend, and selects the speed as the research object, analyzes its temporal and spatial characteristics and correlation to provide data support for this study. Then, this paper introduces the related theories and frameworks of deep learning, mainly including neural network theory and hyperparameter optimization theory, as well as the deep learning model design framework. Pytorch and hyperparameter optimization framework Optuna, so as to provide theoretical foundation and technical support for this research. Finally, experiments of short-term traffic prediction based on single detection point and short-term traffic prediction based on multiple detection points are carried out on the PeMS dataset. Firstly, a multi-lane spatiotemporal convolutional network model (MSCTAN) based on traffic flow prediction at a single detection point is proposed to capture the spatiotemporal correlation of multi-lane traffic at a single detection point. Spatiotemporal Convolution and Spatiotemporal Attention Network Model (MSCSAN). For the two models proposed in this paper, comparative experiments were designed, and the mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) were used as evaluation indicators to verify their performance at a single detection point and multiple detection points. The results show that, whether it is a single detection point experiment or a multi-detection point experiment, the two models achieve the best performance compared with the comparison model. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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30. Characteristics of 2D Ultrasonic Vibration Incremental Forming of a 1060 Aluminum Alloy Sheet.
- Author
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Lv, Yuan, Wang, Yifan, Wang, Yan, Pan, Xixiang, Yi, Cong, and Dong, Meng'en
- Subjects
- *
ALUMINUM forming , *ALUMINUM sheets , *ULTRASONICS , *MICROHARDNESS testing , *TRAFFIC engineering , *ALUMINUM alloys - Abstract
Currently, 1060 aluminum alloy is widely applied in the electronics industry, construction, the aerospace field, traffic engineering, decorations, and the consumer goods market for its good chemical, physical, and mechanical properties. In general, excellent processing property is necessary and important for the manufacturing of complicated panels. In this paper, a special 2D ultrasonic vibration incremental forming method is designed to improve its plasticity and mechanical properties. Three kind of processing methods, including traditional single-point incremental forming, longitudinal ultrasonic vibration incremental forming, and 2D ultrasonic vibration incremental forming, are used for the flexible manufacturing of cones and cylindrical cups of 1060 aluminum alloy sheet. Then, micro-hardness tests, residual stress tests, and scanning electron microscopy tests are carried out to probe the changes in micro-structure and mechanical properties and to analyze the effects of different types of ultrasonic vibration on the plasticity and fracture characteristic of 1060 aluminum alloy. It is proven that 2D ultrasonic vibration facilitates the improvement of plasticity and surface qualities of 1060 aluminum alloy better than the other two processing methods. Therefore, the novel 2D ultrasonic vibration incremental forming process possesses substantial application value for the flexible and rapid manufacturing of complicated thin-walled component of aluminum alloy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. A dynamic traffic signal scheduling system based on improved greedy algorithm.
- Author
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Sun, Guangling, Qi, Rui, Liu, Yulong, and Xu, Feng
- Subjects
- *
GREEDY algorithms , *TRAFFIC signal control systems , *TRAFFIC signs & signals , *TRAFFIC engineering , *EMERGENCY vehicles , *TRAFFIC congestion , *CITY traffic - Abstract
Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Better value estimation in Q-learning-based multi-agent reinforcement learning.
- Author
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Ding, Ling, Du, Wei, Zhang, Jian, Guo, Lili, Zhang, Chenglong, Jin, Di, and Ding, Shifei
- Subjects
- *
DEEP reinforcement learning , *MACHINE learning , *DEEP learning , *REINFORCEMENT learning , *TRAFFIC signs & signals , *TRAFFIC engineering , *MARL - Abstract
In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning, many single-agent deep reinforcement learning algorithms have been extended to multi-agent scenarios. Overestimation in value estimation of Q-learning is a significant issue that has been studied comprehensively in the single-agent domains, but rarely in multi-agent reinforcement learning. In this paper, we first demonstrate that Q-learning-based multi-agent reinforcement learning (MARL) methods generally have notably serious overestimation issues, which cannot be alleviated by current methods. To tackle this problem, we introduce the double critic networks structure and the delayed policy update to Q-learning-based multi-agent MARL methods, which reduce the overestimation and enhance the quality of policy updating. To demonstrate the versatility of our proposed method, we select several Q-learning based MARL methods and evaluate them on several multi-agent tasks on the multi-agent particle environment and SMAC. Experimental results demonstrate that the proposed method can avoid the overestimation problem and significantly improve performance. Besides, application in the Traffic Signal Control verifies the feasibility of applying the proposed method in real-world scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. A probe-based demand responsive signal control for isolated intersections under mixed traffic conditions.
- Author
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Maripini, Himabindu, Vanajakshi, Lelitha, and Chilukuri, Bhargava Rama
- Subjects
- *
TRAVEL time (Traffic engineering) , *TRAFFIC engineering , *ROAD interchanges & intersections , *TRAFFIC signs & signals , *WAVE analysis , *SHOCK waves , *EDDY current testing - Abstract
The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. THE TRAFFIC ENGINEERING FUNDAMENTAL KNOWLEDGE CURRICULUM EDUCATE TO CHILDREN FOR ROAD SAFETY.
- Author
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Suwanno, Piyapong, Theerathitichaipa, Kestsirin, Seefong, Manlika, and Kasemsri, Rattanaporn
- Subjects
- *
ROAD safety measures , *TRAFFIC engineering , *LEARNING ability , *EDUCATIONAL standards , *ROAD users , *CHILD trafficking , *TRAFFIC safety - Abstract
This research aims to examine the traffic road safety curriculum, which is the extraordinary curriculum for children in Thailand. This educates about traffic road safety to children based on traffic engineering knowledge. The entire content of the curriculum is based on case studies that depict both hazardous and safe situations. It covers all road users, including pedestrians, intersection rules, private vehicle users, and public transportation users. In this research, animations are used instead of actual crash recordings to minimize the negative impact of severity. As this curriculum is new and not a part of the standard curriculum for children in Thailand, ensuring an effective curriculum and learning processes is crucial. Therefore, assessing the curriculum from actual users is one of the approaches to check and maintain the effectiveness of the curriculum. Direct assessments, both formative and summative, are conducted to evaluate children's learning abilities regarding traffic safety before and after training. Statistical analysis of the data reveals that the fundamental knowledge of traffic safety in children increases by 20.28 percent after training. The study shows that students are more likely to change their behaviors towards increased safety, with an average change of 34.30 percent. When evaluating satisfaction with the traffic safety training activities, most participating students express a high level of satisfaction with the teaching materials and lesson contents. It is evident that the curriculum is highly effective in enhancing learning skills while providing an enjoyable and participatory experience. Ultimately, the ones who benefit from this curriculum are the teachers who implement it and the children who will come to learn and absorb the knowledge and practices related to safety. The hope is that this curriculum will help increase awareness of safety among children and have a positive impact on their road usage behavior, making it safer in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. 100 Years of the Ubiquitous Traffic Lights: An All-Round Review.
- Author
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Kulkarni, Ashish R., Kumar, Narendra, and Ramachandra Rao, K.
- Subjects
- *
AUTONOMOUS vehicles , *TRAFFIC signs & signals , *TRAVEL delays & cancellations , *RESEARCH personnel , *TRAFFIC engineering - Abstract
Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. An optimal cooperative relay selection strategy for delay aware image transmission in large scale multi-radio multi-channel multimedia wireless sensor network.
- Author
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Devulapalli, Praveen Kumar, Boppidi, Srikanth, Sake, Pothalaiah, Matta, Jagadeesh Chandra Prasad, Gopal, Dhanalakshmi, and Maganti, Sushanth Babu
- Subjects
- *
WIRELESS sensor networks , *WIRELESS channels , *IMAGE transmission , *SMART devices , *TELECOMMUNICATION systems , *POWER transmission , *TRAFFIC engineering - Abstract
High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs' dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method's performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach.
- Author
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Su, Gang and Yang, Jidong J.
- Subjects
- *
TRAFFIC signs & signals , *TRAFFIC signal control systems , *TRAFFIC engineering , *DEEP reinforcement learning , *REINFORCEMENT learning , *TRAFFIC flow , *CYBER physical systems - Abstract
The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight's remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. Phenibut screening and quantification with liquid chromatography–tandem mass spectrometry and its application to driving cases.
- Author
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Dziadosz, Marek, Rosenberger, Wolfgang, Bolte, Katarina, Klintschar, Michael, and Teske, Jörg
- Subjects
- *
LIQUID chromatography-mass spectrometry , *DRUGGED driving , *BLOOD alcohol , *AMMONIUM acetate , *TRAFFIC engineering , *MASS transfer coefficients - Abstract
An analytical strategy for identification by an LC–MS/MS multitarget screening method and a suitable LC–MS/MS based quantification were developed for the psychotropic drug phenibut. The samples analyzed were collected during traffic control and were associated with driving under the influence of drugs. A positive sample for phenibut was identified in a single case of driving under the influence. The quantification revealed a drug concentration of 1.9 μg/mL. An interaction with blood alcohol (BAC = 0.10%) was discussed as the explanation of the way of driving and deficit manifestations observed (swaying, nystagmus, quivering of the eyelid, and reddened eyes). According to the available information, the quantified phenibut concentration could be explained by an intake of four tablets (self‐reported) during the day containing 250 mg of the drug. Chromatography was performed with a Luna 5 μm C18 (2) 100 A, 150 mm × 2 mm analytical column, and a buffer system consisted of 10 mM ammonium acetate and 0.1% acetic acid (v/v) included in mobile phases marked as A (H2O/methanol = 95/5, v/v) and B (H2O/methanol = 3/97, v/v). An effective limit of detection (LOD = 0.002 μg/mL) could be achieved for the multitarget screening method. The quantification of phenibut was performed on a second LC–MS/MS system with LOD/LOQ values of 0.22/0.40 μg/mL. Since phenibut quantification data are rare, the presented information can be used with caution for evaluation of positive cases in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. A Framework for Elephant Flow Detection for SDNs Based on the Classification.
- Author
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Çavdar, Tuğrul, Aymaz, Şeyma, and Aymaz, Samet
- Subjects
- *
FEATURE extraction , *ELEPHANTS , *TRAFFIC engineering , *QUALITY of service , *DATA warehousing - Abstract
Recently, there has been considerable interest in the classification of flows among existing applications in SDN, such as traffic engineering, service quality, and network management. Particularly, the classification methods used for elephant flow detection play an essential role. With elephant flow detection, the high bandwidth provided by data centers is used effectively, and traffic routing can be performed on the network without delay. The most important goals in elephant flow classification are to detect elephant flows in the shortest possible time and with high accuracy. This study aims to lead further studies by analyzing different classifiers for elephant flow detection, which is the preliminary step of elephant flow routing in SDN. For this purpose, a new framework is proposed for elephant flow detection. The framework is tested with six different classifiers; most have not been used in the literature before in elephant flow detection on SDN. The proposed framework's first step is to convert UNI1 and UNI2 datasets containing actual network data into flowing datasets using the Flowrecorder tool. The OTSU thresholding method is used for ground-truth labeling. Then, feature extraction is made for the six different classifiers. In the proposed framework, 11 various features are extracted for each flow. In addition, the most suitable parameters for each classifier are determined by the grid search method. The success of classification techniques is calculated using accuracy, precision, recall, F-score, and running time measures. When the results are examined, the proposed framework obtained pretty successful results in SVM and decision tree methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Design of experiment and simulation approach for analyzing automated guided vehicle performance indicators in a production line.
- Author
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Eduardo, Salazar Javier and Tseng, Shih-Hsien
- Subjects
- *
EXPERIMENTAL design , *AUTOMATED guided vehicle systems , *MATERIALS handling , *ANALYSIS of variance , *TRAFFIC engineering , *FACTORS of production - Abstract
Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory's physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. 基于驾驶员脑电微状态分析的草原公路交叉口 交通设施组合研究.
- Author
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屈冉, 苏杭, 李航天, and 戚春华
- Subjects
- *
TRAFFIC engineering , *GRASSLANDS , *ROADS , *FACILITIES - Abstract
In order to solve the existing problem of the combination of traffic signs at typical grassland highway intersections, the combination setting of intersection traffic engineering facilities was optimized. Through simulation experiments, the electroencephalogram (EEG) signals of 40 drivers were collected, clustered into 5 (MS1—MS5) microstate topographic maps, and the reaction time and duration, coverage, frequency of occurrence and conversion probability of the drivers were statistically analyzed. The experimental results showed that the default network and the dorsal attention network in the driver's EEG microstate played a major role in the cognitive process of the combination of traffic facilities at the grassland highway intersection. The duration of MS4 and the conversion probability of MS1—MS3 increased with the increase of transportation facilities, which can be used as direct indicators to evaluate driver's cognitive load. Microstate indicators and reaction time trend analysis found that at the information level C, that was, the combination of four traffic engineering facilities, the driver's brain state had the best performance, the strongest cognitive ability, the smaller load and the fastest response. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Detecting road network errors from trajectory data with partial map matching and bidirectional recurrent neural network model.
- Author
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Yang, Can, Yue, Peng, Gong, Jianya, Li, Jian, and Yan, Kai
- Subjects
- *
RECURRENT neural networks , *FEATURE extraction , *DATA mapping , *TRAFFIC engineering , *URBAN planning , *CITY traffic - Abstract
Ensuring the correctness of road network data is critical for navigation, traffic control and urban planning. Errors like missing roads and absent connections can hinder its quality. Trajectory data emerges as a cost-effective source to uncover such errors. Existing methods often analyze the mismatches between trajectories and road networks to identify specific errors. They heavily rely on manually established rules and fail to fully leverage the diverse patterns of trajectories and the underlying road network structure. The article introduces a sequential classification approach to detect diverse road network errors. It starts with partial map matching (PMM) to associate trajectories with a road network, allowing unmatched portions. Context features are subsequently extracted by encoding patterns in the map matching (MM) outputs, raw trajectories and road network. Finally, a bidirectional recurrent neural network (BiRNN) model is trained to identify the network error category for each trajectory point. Experiments were performed on detecting errors in OpenStreetMap (OSM) road network with a real-world trajectory dataset. It demonstrates that the proposed method achieves accuracy over 96%, significantly surpassing four baselines. An ablation study confirms the necessity of considering different types of context features. This method advances error detection by effectively utilizing trajectories in identifying diverse network errors. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Regulatory options for vehicle telematics devices: balancing driver safety, data privacy and data security.
- Author
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Truby, Jon, Brown, Rafael Dean, and Antoine Ibrahim, Imad
- Subjects
- *
TELEMATICS , *MOTOR vehicle industry , *DATA privacy , *DATA security , *ELECTRONICS in motor vehicles , *ROAD safety measures , *TRAFFIC engineering - Abstract
This article seeks to address the issue of the regulation of telematics in vehicles. The objective is to navigate the need to protect data privacy and data security while enhancing road safety through telematics. Vehicles telematics devices utilizing analytical and predictive technology can help identify and reduce the risk of dangerous driving. Such devices are a growing tool in the insurance industry and amongst vehicle manufacturers, allowing safe driving to be rewarded whilst dangerous driving can be penalized. Data generated through telematics can also be of use to traffic authorities and governments to help with traffic management and planning. As such, the EU is planning to mandate the use of such devices. The growing use of telematics has, however, faced major data privacy and data security concerns. The article evaluates regulatory responses from the US and EU, highlighting specific European countries. The purpose is to find an effective balance through comparative analysis between driver safety, data privacy and data security. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Behavioural characteristics influencing walking speed of pedestrians over elevated facilities: A case study of India.
- Author
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Banerjee, Arunabha, Das, Sanhita, and Maurya, Akhilesh Kumar
- Subjects
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WALKING speed , *PEDESTRIANS , *TRAFFIC engineering , *PEDESTRIAN areas , *SOCIODEMOGRAPHIC factors , *SKYWALKS - Abstract
Walking speed is an essential parameter used in determining pedestrians' walking behavioural characteristics, the performance of a pedestrian facility, the capacity of the system, and several traffic engineering policy-related applications. While several studies have focussed on understanding walking behavioural patterns over at-grade facilities, there is still a paucity of research on understanding the influence of pedestrians' individual and group behavioural characteristics on walking speed over elevated facilities. This study uses extensive video data collected from different foot-over bridges (FOBs) and skywalks to comprehensively understand walking speed characteristics and their variations over both elevated facilities. The results of the study demonstrated the importance of considering socio-demographic factors with the associative activities involved in walking, group characteristics, and land use type in the estimation of pedestrians' walking speed. The outcome of the study results would provide relevant policy-related strategies to designers and planners for improving infrastructures, developing realistic crowd behavioural simulation models, and estimating overall crowd dynamics. • Mean walking speed was higher over skywalks compared to FOBs. • Lane formation and leader-follower observed towards side of facility. • Pedestrians in faster-is-slower effect showed reduced walking speeds. • Squeezing effect significantly reduced the walking speeds. • Pedestrians engaged in texting/calling/listening to music reduced speeds. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
- View/download PDF
45. An Attention Reinforcement Learning–Based Strategy for Large-Scale Adaptive Traffic Signal Control System.
- Author
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Gengyue Han, Xiaohan Liu, Hao Wang, Changyin Dong, and Yu Han
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TRAFFIC signal control systems , *TRAFFIC engineering , *REINFORCEMENT learning , *TRAFFIC signs & signals , *DEEP reinforcement learning - Abstract
This paper proposes a reinforcement learning (RL)-based traffic control strategy integrated with attention mechanism for large-scale adaptive traffic signal control (ATSC) system. The proposed attention RL integrates attention mechanism into a multiagent RL model, namely multiagent proximal policy optimization (MAPPO), so as to enable more effective, scalable, and stable learning in complex ATSC envi)ronments. In the attention RL, decentralized policies are trained using a centrally computed critic that shares an attention model, while the attention model selects relevant intersections for each agent to estimate the global critic. This framework effectively reduces the computa)tional complexity and stabilizes the training process, enhancing the ability of RL agents to control large-scale traffic networks. The proposed control strategy is tested in both a large synthetic traffic grid and a large real-world traffic network of Yangzhou city using the microscopic traffic simulation tool, SUMO. Experimental results demonstrate that the proposed approach learns stable and sustainable policies that achieve lower congestion level and faster recovery, which outperforms other state-of-art RL-based approaches, as well as a gap-based actuated controller. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning.
- Author
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Heyao Gao, Hongfei Jia, Ruiyi Wu, Qiuyang Huang, Jingjing Tian, Chao Liu, and Xiaochao Wang
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TRAFFIC flow , *TRAFFIC engineering , *SPEED limits , *DEEP learning , *MARKOV processes , *TRAFFIC safety - Abstract
With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. Multicriteria Planning Framework for Regional Intersection Improvement Using Telematics Data of Connected Vehicles.
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Khadka, Swastik and "Taylor" Li, Pengfei
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REGIONAL planning , *TELEMATICS , *ROAD interchanges & intersections , *CITY traffic , *TRAFFIC engineering , *CITIES & towns - Abstract
This paper presents a novel approach to intersection improvement planning utilizing telematics data from connected vehicles to generate performance measures for mobility, safety, and emissions. Congestion, crashes, and emissions are three major issues in urban areas, particularly at intersections, and agencies often struggle to prioritize improvement plans because of a lack of objective data. Traditional infrastructure sensors provide limited information at selected locations, but it is not feasible to deploy them at all intersections. The use of telematics data from connected vehicles provides a high granularity of information on driving events and trajectories that can be used in conjunction with vehicle emission modeling to efficiently generate performance measures for all intersections. In a case study of over 300 intersections in Arlington, Texas, the Pareto front method was used to evaluate and rank intersections based on multiple criteria. Intersections falling on the Pareto front were identified as having at least one outstanding (poor) performance measure and were required to be given priority for improvement. The results were cross-validated with historical crash reports and the judgments of city traffic engineers, demonstrating the effectiveness of the proposed framework in generating objective and reliable intersection performance measures. This approach has the potential to significantly improve intersection safety, mobility, and environmental impact, and can serve as a valuable decision-support tool for transportation agencies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. System and method for recognition of car number plate using computer vision technique.
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Bindushree, Mohit, Dev, Gokul, Prajapat, Govind, and Kumar, Akshay
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INDIANS (Asians) , *TRAFFIC engineering , *COMPUTER vision , *TOLL collection , *TAX collection , *TOLLS , *AUTOMOBILE license plates , *APPLICATION software - Abstract
Every country's traffic control and vehicle owner identification has become a major issue. It can be difficult to identify the owner of a vehicle that violates traffic rules and drives too fast at times. As a result, it is impossible to apprehend and punish such individuals because traffic officers may be unable to retrieve the vehicle number from a moving vehicle due to the vehicle's speed. As a result, one of the solutions to this problem is to develop a Vehicle Number Plate Recognition system. Because of the increased use of vehicles on a daily basis, this system plays an important role in today's hectic world. This software's applications include unmanned parking slot, automatic tolls tax collections, security, and safety. People in India are currently breaking the rules of the toll and fleeing, which can lead to a variety of serious issues such as accidents. This system detects the vehicle number from real-time images using efficient algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Smart approach for vehicle detection and counting.
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Mahayash, Muskan, Punia, Neelakshi, Tripathi, Nishtha, and Sarvamangala
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TRAFFIC signs & signals , *TRAFFIC congestion , *TRAFFIC density , *TRAFFIC flow , *TRAFFIC engineering , *CITY traffic - Abstract
Developing countries such as India face many problems when it comes to the management of existing traffic system and its inefficient planning and scheduling leads to traffic congestion, increased pollution level and transit delays. This requires a significant amount of attention to build a smart traffic management system. Taking this into consideration a framework is proposed to determine traffic density and use the corresponding data to further modulate the traffic signal proficiently. This will be carried out by applying two different methodologies namely Background Subtractor MOG and TensorFlow Object Detection API. A video from surveillance camera is taken as input which will be converted into frames using OpenCV and after processing the detected vehicles are counted, thus the density is obtained. This is followed by comparing the results obtained by the mentioned methodologies. A vital application of the determined traffic densities can be in controlling traffic signals in a smarter way, therefore leading in decreased traffic congestion and uniform flow of traffic. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Traffic engineering provisioning of multipath link failure recovery in distributed SDN controller environment.
- Author
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Kelian, Virakwan Hai, Mohd Warip, Mohd Nazri, Ahmad, R. Badlishah, Ehkan, Phaklen, Zakaria, Fazrul Faiz, and Ilyas, Mohd Zaizu
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TRAFFIC engineering , *NETWORK operating system , *OPENFLOW (Computer network protocol) , *SOFTWARE-defined networking , *NETWORK performance , *DATA packeting - Abstract
A revolutionary networking technology called Software-Defined Networking (SDN) enables better networking flexibility. In contrast to the conventional network, it provides another option for network development. SDN is characterized by the separation of the control and data planes in network architecture, implementation, and management. The central component of the network is the controller, which constitutes the control plane. The appropriate selection of a controller, along with determining the number and placement of controllers, plays a crucial role in optimizing resource utilization and guaranteeing network availability and network performance. Since SDN is still in its beginnings of development, it is virtually certain that further study will be needed in areas like design, particularly on the control plane, since the architecture directly affects the network's total performance. Furthermore, despite its intended purpose of managing networks on a large scale, SDN still presents challenges in effectively addressing network dynamics, such as the occurrence of link failures. This study presents a concept for the implementation of an SDN architecture. The proposed approach involves utilizing an Open Network Operating System (ONOS) open-source distributed SDN controller. The purpose of this implementation is to analyze network performance metrics and assess network availability. This study investigates the distributed SDN controller's performance on different scale networks: NSF, AEON, and TM topologies. Several metrics have been analyzed, including throughput, link failure detection, and Round-Trip-Time (RTT). The experiments use Mininet for emulation and Wireshark for real-time data packet capture and analysis. According to the study results, there is a positive correlation between network design complexity and controller load. The experiment emphasizes the resilience of distributed controllers, such as ONOS, in effectively recovering from link failures. This research will help academics and businesspeople who use distributed SDN controllers choose a controller and evaluate its effectiveness on the analyzed network architectures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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