Traffic Performance Research Articles

Exploring the Impact of Distracted Pedestrians on Traffic Operations at Signalized Intersections in Central Florida

Exploring the Impact of Distracted Pedestrians on Traffic Operations at Signalized Intersections in Central Florida

Evaluating the effectiveness of rhythmic visual guidance technology for mitigating driving risks in highway tunnel groups: A simulation study.

Driving in highway tunnel groups necessitates frequent adaptation to drastic changes in the traffic environment, thereby increasing driving difficulty and risk. This study integrates drivers' preferences for rhythmic information with the inherent rhythmic characteristics of tunnel group structures to propose a new and adaptive method to mitigate driving risks using rhythmic visual guidance (RVG) technology. Unlike traditional visual guidance systems, which often rely on static signals, RVG utilizes dynamic, rhythmically varying cues to capture drivers' attention and improve situational awareness more effectively. By employing principles of fuzzy mathematics, the study quantifies the applicability of various rhythmic forms in visual guidance technology and establishes priority application principles for undulating and staggered rhythms. After verifying the accuracy of the simulation model, the effectiveness of RVG technology in mitigating driving risks in highway tunnel groups was analyzed using lateral offset, driving speed, and vehicle acceleration as evaluation metrics. The findings reveal that RVG technology significantly reduces vehicle lateral offset and enhances drivers' perception and control of tunnel sidewalls and driving trajectories. This effect is particularly pronounced under limited lighting conditions or in large tunnel groups with extended driving distances. Regardless of whether the lighting level is set at 0% or 100% of the standard brightness, the implementation of RVG results in reduced vehicle driving speeds. The variation in the 25th to 75th percentile distribution of driving speeds was insignificant, demonstrating that RVG technology effectively regulates driving speed and is not significantly affected by lighting conditions. Furthermore, when the lighting level is set at 100% of the standard brightness, the 25th to 75th percentile distribution interval of driving speeds is [89.576, 102.416], indicating the highest and least stable driving speeds suggests that blindly increasing tunnel lighting levels not only raises operating costs but may also adversely affect driving safety. This study provides novel insights into applying dynamic visual cues for highway tunnel groups' traffic operation and safety management. It has significant practical engineering value for guiding the low-carbon design of tunnel groups.

Traffic Operations Analysis of Seven New Alternative Intersections with Three-Phase Traffic Signals

Traffic Operations Analysis of Seven New Alternative Intersections with Three-Phase Traffic Signals

Selective Scale-Aware Network for Traffic Density Estimation and Congestion Detection in ITS

Traffic congestion detection in surveillance video is crucial for road traffic condition monitoring and improving traffic operation efficiency. Currently, traffic congestion is often characterized through traffic density, which is obtained by detecting vehicles or using holistic mapping methods. However, these traditional methods are not effective in dealing with the vehicle scale variation in surveillance video. This prompts us to explore density-map-based traffic density detection methods. Considering the dynamic characteristics of traffic flow, relying solely on the spatial feature of traffic density is overly limiting. To address these limitations, we propose a multi-task framework that simultaneously estimates traffic density and dynamic traffic congestion. Specifically, we firstly propose a Selective Scale-Aware Network (SSANet) to generate a traffic density map. Secondly, we directly generate a static congestion level from a traffic density map through a linear layer, which can characterize the spatial occupancy of traffic congestion in each frame. In order to further describe dynamic congestion, we simultaneously consider the dynamic characteristics of traffic flow, using the overall traffic flow velocity integrated with static congestion estimation for a dynamic assessment of congestion. On the collected dataset, our method achieves state-of-the-art results on both congestion detection and density estimation task. SSANet also obtains 99.21% accuracy on the UCSD traffic flow classification dataset, which outperforms other state-of-the-art methods.

Enhancing Mixed Traffic Flow with Platoon Control and Lane Management for Connected and Autonomous Vehicles

As autonomous driving technology advances, connected and autonomous vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for an extended period. The deployment of CAVs will alter traffic flow characteristics, making it crucial to investigate their impacts on mixed traffic. This study develops a hybrid control framework that integrates a platoon control strategy based on the “catch-up” mechanism with lane management for CAVs. The impacts of the proposed hybrid control framework on mixed traffic flow are evaluated through a series of macroscopic simulations, focusing on fundamental diagrams, traffic oscillations, and safety. The results illustrate a notable increase in road capacity with the rising market penetration rate (MPR) of CAVs, with significant improvements under the hybrid control framework, particularly at high MPRs. Additionally, traffic oscillations are mitigated, reducing shockwave propagation and enhancing efficiency under the hybrid control framework. Four surrogate safety measures, namely time to collision (TTC), criticality index function (CIF), deceleration rate to avoid a crash (DRAC), and total exposure time (TET), are utilized to evaluate traffic safety. The results indicate that collision risk is significantly reduced at high MPRs. The findings of this study provide valuable insights into the deployment of CAVs, using control strategies to improve mixed traffic flow operations.

Development of an IoT-based Traffic Signal Violation Detection System

This study tackles the pressing issue of traffic signal stop-line violations, which poses significant threat to road safety, by designing and implementing an intelligent traffic signal violation system (Internet-of-things (IoT) based technology). The system utilizes infrared (IR) sensors for vehicular detection and an ESP32 Camera to capture images of violating vehicles, which are then sent to a server via notification bots for media platforms. Also, the IR sensors are embedded with the ESP32 Camera and programmed microcontroller to detect traffic stop-line signal violations, capture images, and transmit them to designated authorities. The system was implemented using a prototype build and the result showed a 98% accuracy rate in detecting violations. The system reduces response time for violation detection by 60% success rate. In conclusion, this project demonstrates a reliable and efficient traffic signal violation system, leveraging IoT and Artificial Intelligence (AI) technologies to enhance road safety. The real-world significance of this system entails an enabled rapid intervention, identifying and disciplining offenders, enhance resource allocation, repository of data generated and enforcing legal actions thereby contributing to enhanced road traffic operations and user safety.

Affordable mobile application camera system to monitor residential societies vehicle activity

Residents of the community are faced with increasing challenges in managing traffic operations, ensuring safety, parking and improving traffic flow. Traditional methods, mostly manual or relying on expensive monitoring equipment, have difficulty meeting these needs. This study investigates investments in the use of video surveillance systems designed to monitor activities in residential areas, providing high-quality, efficient and cost-effective results. Integrated vehicle data using technology certification license. Provides residents and community management with a mobile application interface that provides remote access to live video, detailed traffic information and instant notifications about entry, illegal or unusual activities. The system eliminates the need for physical infrastructure by utilizing cloud- based storage, providing scalability and secure data management. Automatic entry simplifies long management and reduces manual errors for efficient operation. Keywords- Traffic operations management, video surveillance systems, residential traffic monitoring, cloud-based storage, traffic flow optimization, remote access application, vehicle performance data, automated entry systems, real-time notifications, scalable traffic management, smart parking solutions, technology certification license, paperless operations.

A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption.

Freeway continuous merging areas in a short distance exist continuous multiple ramps. In these areas, traffic flow and vehicle interactions are more complex, and traffic crashes and congestion are more frequent, which has been a major concern influencing traffic operation of freeways. Active traffic management (ATM) measures can improve traffic efficiency and reduce traffic risks in merging areas. Previous studies have focused on variable speed limit (VSL) control or ramp metering (RM) to address traffic problems in merging areas, whereas the problem of comprehensively ameliorating for traffic risks on mainlines and ramps by coordinating VSL and RM control strategies has rarely been explored. This study introduces a Bi-level Programming Model capable of coordinating controls of traffic risks (e.g., Crash Risk and Congestion Risk) in freeway continuous merging areas. The upper-level model aims to minimize the crash risk, the congestion risk, and vehicle energy consumption by VSL control. While the lower-level model focuses on the ramp control by minimizing the congestion risk and energy consumption of the ramp. Then an extended Cell Transmission Model (CTM) (it is based on VSL and RM control) is utilized to simulate the traffic flow of merging areas, based on which a traffic risk evaluation model and a Bi-level coordinated control model for the continuous merging areas are developed. The results demonstrate the constructed method outperforms other control strategies for improving the safety and efficiency of freeways. Specifically, the proposed control framework in the continuous merging areas of freeways reduces the average crash risk (ACR), average mainline congestion risk (AMCI), and average energy consumption (AEC) by 14.10%, 19.52%, and 8.86%, respectively. The research results could be potentially applied to active and coordinated traffic management of freeways.

Study on vortex-induced vibration response of large-scale two-lay steel trusses bridge under large wind angle of attack

With the advancement of urbanization, two-lay trusses bridges are widely used because of their good traffic capacity and structural performance. However, the aerodynamic behavior of this beam type is still in the exploratory stage. The local microclimate characteristics at the bridge site in mountainous cities are obvious, and it is easy to form a large wind angle of attack, which has a significant impact on the vortex-induced vibration (VIV) performance of the bridge. Therefore, this study takes a long-span two-lay steel trusses bridge in a mountainous city as the engineering background, and uses wind tunnel test and numerical calculation methods to study the changes of the static three-component force coefficient and VIV response of the main beam in the construction and completion state under the action of high wind angle of attack. The results show that the three-component force coefficient curves under different wind speeds are close to each other, and the Reynolds number effect is not obvious. The vibration test shows that the vertical bending VIV first occurs at +3° and +5°, and then two torsional VIV with different amplitudes occur. Both vertical bending and torsional VIV are simple harmonic vibrations with a single frequency, and the vertical bending VIV frequency is locked at 2.227 Hz, and the torsional VIV frequency is locked at 4.289 Hz, which are close to the natural frequency of the test model. Compared with +3°, the maximum amplitude of vertical bending VIV under +5° increases by 30.0 %, while the maximum amplitude of torsional VIV under high and low wind speed increases by 16.6 % and 12.7 % respectively, and the locking range is longer. It can be seen that the wind angle of attack has a significant effect on the VIV response of the main beam in the completion state. Especially, the trusses beam at a large angle is more sensitive to VIV, and it is more prone to large-scale and large-amplitude VIV. The research results can provide a theoretical basis for the aerodynamic shape optimization and provide a reference for the design of related bridges.

Quantitative study on the environmental impact of Beijing’s urban rail transit based on carbon emission reduction

Urban rail transit, as an efficient and eco-friendly mode of transportation, plays a pivotal role in mitigating traffic congestion and lowering urban carbon emissions. Despite the significant contributions by scholars in this area, debates surrounding the quantification of carbon emissions during the operational phase of urban rail transit persist, particularly in assessing its impact on reducing ground traffic congestion. This study examines the passenger flow during Beijing’s morning and evening peak hours, assuming that all passengers initially using urban rail transit switch to buses and taxis during these periods. A traffic congestion prediction model is developed based on the analysis of actual traffic operation data under this assumption. Through this model, the study calculates the potential congestion times across various scenarios, employing a bottom-up approach to carbon emission estimation to analyze the impact on carbon emissions. Results spanning 2015 to 2021 suggest that substituting urban rail transit with buses could increase congestion by 37–92 min and 46–59 min during morning and evening peaks, respectively, leading to a 24-82% and 27-56% surge in carbon emissions. The conversion of all these vehicles to taxis would result in a direct paralysis of Beijing’s road transport network, with a corresponding increase in carbon emissions of between 289% and 556% and 333% and 614%, respectively.These outcomes emphasize the substantial efficacy of urban rail transit in curbing traffic congestion and carbon emissions.

State Compensation Model in Adaptive Event-Triggered Predictive Control: A Novel Approach to Mitigating Moving Bottlenecks

Moving bottlenecks, characterized by their high frequency and unpredictability, pose significant challenges to timely response and management, often resulting in road congestion and increased risk of traffic accidents. To address these issues, this paper proposes an adaptive event-triggered variable speed limit (AET-VSL) method based on a state compensation model, which emphasizes the concept of symmetry in the optimization of multi-segment speed limits. This symmetry approach facilitates a balanced and efficient control strategy that adjusts speed limits in a way that harmonizes traffic flow across multiple road segments, reducing congestion and improving overall traffic stability. The state compensation model builds on the classical METANET traffic flow model, incorporating coordination between road segments to reduce congestion while minimizing disruptions to traffic flow stability. By dynamically adjusting speed limits using real-time traffic data, the AET-VSL method addresses fluctuations in traffic conditions and ensures adaptive control to manage bottlenecks efficiently. A simulation framework was employed to evaluate the proposed strategy across varying traffic scenarios. Results demonstrate that AET-VSL outperforms traditional methods, providing consistent improvements in traffic performance. For instance, under low-traffic-flow conditions, AET-VSL reduced waiting time (WT) by 41.36%, potential collisions (PCs) by 51.92%, and fuel consumptionfuel consumption (FC) by 34.07%. This study highlights the novelty and effectiveness of AET-VSL, offering a scalable and reliable solution for dynamic traffic management and showcasing its potential to enhance traffic safety and efficiency.

A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.

This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model's efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety.

Construction Control Technology and Monitoring Analysis of Walking Incremental Launching Construction of Small-Curvature Steel Box Girder Bridges Across Expressways

As urban areas keep growing, there are higher requirements for the carrying capacity of traffic operations, and there are more and more curve incremental launching projects with complex construction conditions. This paper builds upon the walking incremental launching project of a small-curvature steel box girder in Xuchang City and has developed a detailed construction method and monitoring technology. Due to the bridge’s longitudinal gradient being designed as a two-way slope and falling under the category of a small-radius steel box girder, the front end of the main beam exhibits significant lateral deviation, and linear control is difficult. It is necessary to carry out stress and displacement monitoring of the whole process of construction of the curved steel box girder and the guide girder to guide the construction process. The stress conditions of the incremental launching pier and the settlement of the concrete substructure are also studied, and we analyze the stress and displacement characteristics. Firstly, the finite element tool MIDAS Civil is adopted to build a model for the construction. The five most unfavorable working conditions are selected from the entire incremental launch process to analyze the internal force and displacement state of the steel box girder bridge, which is and then compared with the site monitoring value. It is demonstrated by the outcomes that the internal force and deflection of the steel box girder and the guide girder are within the safe construction range, which ensures the security of the incremental launching construction. In the maximum cantilever condition, the guide girder experiences significant stress, but the maximum value is not observed during the maximum cantilever condition of the guide girder. Therefore, whole-process monitoring should be carried during construction to maintain safety measures and quality management.

Correction: Flight Regimes and Environmental Impact: A Linear Regression Analysis of co2 Emissions for En-Route Air Traffic Operations

Correction: Flight Regimes and Environmental Impact: A Linear Regression Analysis of co2 Emissions for En-Route Air Traffic Operations

Enhanced Cognitive Load Detection in Air Traffic Control Operators Using EEG and a Hybrid Deep Learning Approach

Enhanced Cognitive Load Detection in Air Traffic Control Operators Using EEG and a Hybrid Deep Learning Approach

Maintaining Predictable Traffic Engineering Performance under Controller Failures for Software-Defined WANs

Maintaining Predictable Traffic Engineering Performance under Controller Failures for Software-Defined WANs

Fuzzy Reliability Theory Analysis of Traffic Signal Lamp Performance

Fuzzy Reliability Theory Analysis of Traffic Signal Lamp Performance

자율차-일반차 혼재상황에서 돌발상황 대응 교통운영전략 USE CASE 시나리오 효과분석

This study aims to analyze the effects of providing traffic operation strategies to autonomous vehicles in response to incidents in mixed traffic situations involving autonomous vehicles and manual vehicles. The use case scenario of providing detour information for incidents in a living lab where autonomous vehicles are expected to operate is intended to derive the effects of traffic operation strategies through comparison of average travel speeds. In order to analyze the effectiveness of traffic operation strategy in traffic situations where autonomous vehicles and manual vehicles coexist, three scenarios were designed for peak and non-peak traffic conditions, and the average travel time of autonomous vehicles for each autonomous driving service was calculated and analyzed as an indicator for judging the effectiveness of the information provision scenario for detours. The simulation analysis results for scenario 2, where an incident occurs, showed that the average travel speed increased compared to scenario 1, and this was analyzed to reflect the bottleneck caused by the incident (reducing operation from 3 lanes to 1 lane). In scenario 3, which simulates the case where detour information is provided as part of the incident response traffic operation strategy, the average travel time of autonomous vehicles decreases compared to scenario 2 for the occurrence of an incident. Therefore, it is judged that the traffic conditions improves as autonomous vehicles select the detour as a strategy for providing detour information in response to an incident, thereby reducing the demand for vehicles passing through the incident location. This study is significant in that it analyzed the effectiveness of traffic operation strategy by implementing an actual traffic environment as a simulation program for the use case of traffic operation strategy that can be provided to autonomous vehicles when an incident occurs in a mixed situation of autonomous vehicles and manual vehicles, and as an initial study in preparation for the era of autonomous driving, it is a study on mixed situations of autonomous vehicles in urban areas.

Traffic Safety Management System

Traffic safety is a critical concern for civic and pastoral surroundings likewise, with the thing of minimizing accidents, enhancing road-stoner safety, and icing effective transportation systems. This paper presents a comprehensive ‘Traffic Safety Management System’ (TSMS) designed to ameliorate road safety through the integration of advanced technologies and datadriven approaches. The system combines real-time business monitoring, predictive analytics, and proactive measures to address safety challenges. Crucial features of the TSMS include the use of Internet of effects (IoT) bias for real-time data collection, machine learning algorithms for accident vaticination, and Geographic Information Systems (Civilians) for hotspot identification. By using smart detectors surveillance systems, and communication networks, the system provides practicable perceptivity for policymakers and transportation authorities to apply targeted interventions. Also, TSMS promotes public mindfulness through stoner-friendly interfaces and dynamic feedback mechanisms. Traffic Management is one of the major issues which is arising fleetly because of significant increase in number of vehicles. To address this there is the need of a smart traffic operation system which will enable the smooth traffic flow. Case studies and simulations demonstrate significant advancements in reducing business-related losses and enhancing road safety compliance. The proposed result underscores the eventuality of integrating technology and policy for erecting safer, more sustainable transportation ecosystems.

Analysis of the Impact of Intelligent Traffic Signals on Reducing Urban Traffic Congestion

Based on the urban traffic congestion problem, this study takes 143 intersections in Haidian District and 289 intersections in Futian District as practical objects, constructs an intelligent traffic signal evaluation index system, and uses multi-source data collection and machine learning analysis methods to quantitatively evaluate the implementation effect of the system. The study finds that the average travel time is reduced by 32.5%, the energy consumption index is decreased by 28.7%, and the annual economic benefit is 4.43 billion yuan. The survey data shows that the public satisfaction rate reaches 87.6%, and the system reliability reaches 99.2%. The evaluation results indicate that the intelligent traffic signal system has significant effects on improving traffic operation efficiency and enhancing social benefits.