Traffic Levels Research Articles

Traffic Traps: An Investigation into Road Congestion Management

Abstract: Urban traffic congestion presents a significant global challenge, necessitating effective traffic management for seamless transportation system operations. This study emphasizes the implementation of an Intelligent Traffic Management System (ITMS) capable of real-time monitoring and control. Focusing on Mattoor Junction in Ernakulam, Kerala, this research applies distinct algorithms in Python and MATLAB, introducing three key equations: Average Traffic Flow (ATF), Traffic Variability Index (TVI), and Time-of-Day Congestion Index (CI). The methodology involves a detailed survey of the route, traffic volume counts, spot speed studies, and roadside interviews to gather data. The ATF, TVI, and CI equations are used to analyze traffic patterns, variability, and congestion levels. The findings reveal significant congestion on critical routes intersecting at Mattoor Junction, particularly during peak hours. To address this, the study proposes the implementation of an automated traffic signal system, optimizing signal timings. This intervention aims to enhance traffic flow and reduce congestion. The research underscores the potential of ITMS for real-time traffic management, offering a data-driven approach to urban traffic challenges. The integration of theoretical models with empirical data provides a comprehensive framework for future ITMS strategies, contributing to more efficient and adaptive urban traffic management

Preliminary Research of Information Technology for Assessing the Level of Fatigue of Air Traffic Controllers

The work carried out a preliminary study of the development of the information technology concept for assessing the level of fatigue of air traffic controllers using special technical means, software, and expert opinion. For this purpose, it is proposed to construct an information technology that can consist of three consecutive stages: assessment of air traffic controllers' fatigue, based on the use of special technical means and software; expert evaluation of the level of fatigue of air traffic controllers; information technology that combines data from special technical means and experts' conclusions to derive a complex linguistic level of fatigue of air traffic controllers. Each stage has its permissible levels. Non-compliance with any of them leads to automatic notification of the decision maker (DM). The value of information technology is that it will consider data from special technical means and software on the one hand and, and the other hand, consider the opinions and experiences of experts on the fatigue of air traffic controllers. The technology works dynamically and involves experts only in case of need. For information technology, it is proposed to model uncertainties for technical and expert evaluation and use logical derivation to formalize knowledge. At the output, the comprehensive level of fatigue of the air traffic controller is obtained, in the opposite case, DM is signaled to immediately take measures to remove from official duties and replace the air traffic controller. The knowledge obtained about air traffic controllers' fatigue levels is important for ensuring flight safety. Proper management of fatigue can help reduce the risk of human error and improve working conditions for staff.

Conflict management at urban bus stops through cooperative trajectory planning

Conflicts due to frequent bus dwelling and entering/exiting maneuvers at bus stops have significantly undermined the efficiency, safety, and sustainability of the urban bus system. This paper proposes a cooperative trajectory planning strategy in a Vehicle-to-Infrastructure (V2I) environment for conflict management between buses and cars at bus stops. A dynamic multi-stage-based model along with solution algorithm is developed to search trajectories that eliminate the conflicts between cars and buses entering and leaving the bus stop in real time.The assessment of the proposed strategy is carried out at both the bus stop and corridor levels within a simulated environment. In-depth trajectory analyses during a typical weaving process at a bus stop reveal that the proposed strategy effectively eliminates bottlenecks and enhances operational performance across multiple approach lanes affected by the bus stop. A thorough evaluation involving multiple cycles of weaving among buses and vehicles at the bus stop reaffirms the strategy’s effectiveness under varying levels of bus occupancies, traffic demand, and bus stop locations. The case study at the corridor level illustrates that the proposed strategy successfully improves operational performance and bus schedule adherence across diverse scenarios, encompassing different roadway link lengths, traffic demands, bus headways and dwelling times, and bus stop locations. It notably excels in enhancing the performance of an urban corridor with longer link distances and near-side located bus stops, particularly under high traffic demand levels and shorter bus dwelling times.

EcoMRL: Deep reinforcement learning-based traffic signal control for urban air quality

As greenhouse gas (GHG) emissions from road networks significantly impact the urban environment, there has been a growing interest in integrating environmental factors into traffic controls. Traffic signal control (TSC) is an effective method to reduce air pollution by managing traffic congestion. Accordingly, several recent studies have applied artificial intelligence to TSC to improve traffic efficiency. Despite these efforts, there are a few limitations on the existing studies. First of all, a number of recent studies focus only on traffic performance rather than environmental effects. However, in some cases, the objective function related to traffic efficiency can have an adverse effect on the environment. Furthermore, the variable nature of emissions based on traffic demand is rarely considered. To address these issues, we propose a multi-agent deep reinforcement learning-based TSC to minimize GHGs in traffic networks. To this end, we first identify vehicle’s stop and delay as key factors in traffic efficiency that critically influence air quality. Subsequently, we integrate these factors into our model’s reward function. To validate the proposed model, we conduct experiments under various demand scenarios featuring different traffic volume levels. The results show that the proposed model outperforms comparative control methods in two aspects: traffic performance and environmental sustainability. Especially at a high congestion level, the proposed model reduces average delay time by 11.9–16.1% and increases total throughput by 11.5%. Additionally, it significantly lowers total CO2 emissions by up to 8.9% and average emissions by up to 18.3%.

Urban risk assessment model to quantify earthquake‐induced elevator passenger entrapment with population heatmap

AbstractThe seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city‐scale earthquake‐induced elevator passenger entrapment. The model integrates big data and physics‐based approaches. A novel mapping method was developed to estimate city‐scale elevator traffic level based on population heatmap data and deep learning. A process‐based parallel computing scheme was designed to accelerate the assessment. The applicability was demonstrated based on a real‐world urban area comprising 619 buildings. The findings reveal that as the time of the earthquake varies, the risk exhibits significant fluctuations. Additionally, this study highlights that a simplistic correspondence between seismic intensity and passenger entrapment risk can lead to erroneous estimations.

Aplicación de la minería de datos espacial basado en técnicas de agrupamiento para detectar los niveles de congestionamiento del tráfico vehicular en la ciudad de Trujillo

Aplicación de la minería de datos espacial basado en técnicas de agrupamiento para detectar los niveles de congestionamiento del tráfico vehicular en la ciudad de Trujillo

Assessment of traffic noise pollution in Arau, Perlis

Abstract Traffic noise pollution has become an important factor in the growth of society. The noise from a motor vehicle can cause severe damage to human health and reduce labour productivity. The study was conducted in December 2020 in Arau, Perlis. The data collected of traffic composition, volume, traffic flow, traffic noise level (Leq ) and traffic speed were analyzed using Microsoft Excel to elucidate the pattern of roadside noise generated by the vehicle’s movement during peak hours. From the observation, the noise level for both studied areas exceeds DOE guidelines due to increased traffic volume and speed. Traffic volume for both studied areas was increased during morning peak hour, especially in school session. Another factor may contribute to noise level are road condition, wind speed and vehicle speed. Driving a car at a higher speed pass through a yellow line or poor road condition contribute to produce high level noise, especially for heavy vehicles. Furthermore, the correlation in SPSS found that traffic noise level is associated with traffic speed but not traffic volume. Field measurement was taken simultaneously to interpret the relationships among the traffic characteristics using various models, the Burgess model, Fagotti model, and NAISS model. The NAISS model was better than the other models used in term of field measurements.

Critical radius prediction for small radius curved section of two-lane secondary highways based on speed characteristics

Small radius curved sections of two-lane secondary highways have low technical indicators, complex alignments, and frequent accidents. In order to improve the safety of traffic operation, the article investigates the curved sections of two-lane secondary highways with different radii and obtains the travelling speeds of different vehicle types at different section locations. By studying the trajectory characteristics and speed characteristics of vehicles travelling in curves, the velocity difference ratio, which responds to the continuity of driving speed, was defined. Then, based on this, this paper investigates the vehicle motion law of small radius curved road sections. The results show that the speed change of large cars in small radius curves is similar to a “U" shape, while the speed change of small cars is similar to a “V" shape. The speed adjustments of the larger cars occur mainly within the range of gentle curves, whereas the speed changes of the smaller cars are large throughout the range of curves. There is a significant exponential function relationship between curve radius and 85 % percentile speed, from which the curve radius threshold can be predicted. This provides a basis for engineering construction of curved road sections and optimization of curve design indicators, which greatly improves the traffic safety level of curved road sections.

Congestion and throughput optimization protocol for providing better quality of service and experience

<p>Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.</p>

IR Sensor based Smart Parking System

Nowadays congestion of traffic level increased with the increase in population. As the population increases, the utilization of vehicles also increases. It's becoming a difficult task to find a slot to park the vehicle. To overcome this problem, a smart parking system is introduced. Previously, there were some approaches to this system where Bluetooth and GPS tracker were used to find the empty slots, and different sensors were used for indoor and outdoor parking. In some approaches there was no security for the user’s details. Whereas in the proposed system, every slot has a number which makes it easier for the user to find out the empty slot. IR sensors are used to detect the vehicle and the empty slot is displayed on LCD as well as cloud server where the user’s details are secured. Smart parking system is an IoT based system which includes Raspberry pi, LED display, Cloud Server, Monitor and IR Sensors that are available in each parking slot. The proposed system leverages the advantages of IR sensor technology to accurately detect the presence of vehicles in parking spaces, provides real-time information to the users, and improves overall parking efficiency. The paper discusses the system architecture, sensor deployment strategies, and implementation, thereby showcasing the potential benefits of IR sensor-based smart parking systems. Index Terms: camera, cloud, parking, Raspberry Pi, sensors, vehicles

Geocells for Unpaved Roads: Analysis of Design Methods from the Literature

Most forest roads are unpaved, connecting rural and forest areas and enabling access for firefighting and commercial purposes. Low traffic levels lead to reduced functional demands, while rapid development of deformations results in frequent maintenance. Using geocells as reinforcements reduces deformations, minimizing maintenance needs. Herein, geocell-reinforced soil design methods were collected and categorized based on their result: increase in confining pressure; bearing capacity; height of the base layer. The goal was to compare methods reported in the literature, from a user perspective and within each category, using a base scenario. The methods were analysed to better understand their differences and application conditions. Methods that estimate the increase in confining pressure refer to static or cyclic loading, leading to results that are not directly comparable; often, the reinforcement contribution is represented by an apparent cohesion, with no physical meaning and misleading. Methods that estimate the increase in bearing capacity due to geocell consider its contribution differently (lateral resistance, vertical stress dispersion, and membrane effects) and distinct combinations. For geocell reinforcement, the membrane effect can be neglected. Methods that estimate the height of the base layer can be used directly for an expedite design of unpaved roads. When geocell reinforcement is adopted, the minimum height of the base layer should coincide with that of the geocell. Thus, while current methods contribute and support the design of unpaved roads, further work is essential to develop methods that are of simple and of expedite application for forest engineers, adaptable to local conditions and requirements.

PENERAPAN LOGIKA FUZZY TSUKAMOTO BERBASIS WEB UNTUK KLASIFIKASI DAERAH RAWAN KECELAKAAN DI KOTA JAMBI

This study aims to apply web-based Tsukamoto fuzzy logic in the classification of accident areas in Jambi City. This method was developed by researchers at POLSERSTA Jambi city with the aim of increasing understanding of the risks of traffic accidents and helping make more effective decisions. Tsukamoto's fuzzy logic was chosen because of its ability to deal with uncertainty in the context of a classification involving various factors. The proposed system is able to take input data such as traffic levels, weather conditions, and road types, and convert them into a risk classification using fuzzy logic. The results of this system can provide guidance for traffic security officers and related agencies in managing potential accident areas. This research shows that web-based Tsukamoto fuzzy logic has the potential to be a useful tool in identifying traffic accident risk areas. However, improvements in data validation, optimization of fuzzy models, and collaboration with related parties need to be made to ensure the accuracy and usefulness of this system in improving traffic safety in Jambi City

Assessment of genotoxicity of air pollution in urban areas using an integrated model of passive biomonitoring

Atmospheric pollution is a major public health issue and has become increasingly critical for human health. Urban atmospheric pollution is typically assessed through physicochemical indicators aligned with environmental legislation parameters, providing data on air quality levels. While the effects of pollution on sensitive organisms serve as a warning for public health decision-makers, there remains a need to explore the interpretation of environmental data on pollutants. The use of species adapted to urban environments as sentinels enables continuous and integrated monitoring of environmental pollution implications on biological systems. In this study, we investigated the use of the plant species Tradescantia pallida as a biomonitor to evaluate the genotoxic effects of atmospheric pollution under diverse vehicular traffic conditions. T. pallida was strategically planted at the leading urban intersections in Uberlândia, Brazil. During COVID-19 pandemic lockdowns, we compared indicators such as physical, biological, and traffic data at different intersections in residential and commercial zones. The reduction in vehicular traffic highlighted the sensitivity of plant species to changes in air and soil pollutants. T. pallida showed bioaccumulation of heavy metals Cd and Cr in monitored areas with higher traffic levels. Additionally, we established a multiple linear regression model to estimate genotoxicity using the micronucleus test, with chromium concentration in the soil (X1) and particulate matter (PM) in the atmosphere (X2) identified as the primary independent variables. Our findings provide a comprehensive portrait of the impact of vehicular traffic changes on PM and offer valuable insights for refining parameters and models of Environmental Health Surveillance.

Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council’s open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables.

Transaction Analysis for Operational Optimization in Transjakarta Public Transportation System

This study presents a comprehensive exploratory data analysis (EDA) of transaction data within the Transjakarta public transportation system, focusing on routes, passenger flow, and congestion patterns. Leveraging tap-in and tap-out transaction records collected through payment cards, the research aims to provide actionable insights for optimizing operational efficiency and enhancing service quality. By analyzing transaction data, the study identifies busy routes, peak hours, and passenger distribution across different modes of transportation, including big bus (BRT), medium and big bus (non-BRT), and mini-bus (Mikrotrans). Moreover, the research investigates correlations between transaction volume and traffic congestion levels, enabling the identification of areas prone to delays and bottlenecks. The findings of this analysis offer valuable information for route planning, scheduling adjustments, and infrastructure improvements within the Transjakarta system. By optimizing resources based on data-driven insights, the study seeks to contribute to the enhancement of public transportation services, promoting accessibility, reliability, and sustainability in urban mobility.

Relationship between the Significant Solutions of Static Traffic Assignment Problems for Mixed Traffic Flow of Connected and Automated Vehicles and Human-Driven Vehicles

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology’s efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads.

Improving Short-Haul Intermodal Transit Times on Low-Density Rail Corridors with Platoons of Self-Propelled Autonomous Railcars

U.S. freight railroads consume in excess of 13 billion liters of diesel fuel yearly. Although efforts to improve energy efficiency and reduce emissions have made substantial progress in the last decades, the benefits are relatively incremental and will not be sufficient to realize full decarbonization. Several independent efforts are underway to develop alternative locomotive fuels and propulsion technologies designed to fit within the typical locomotive envelope. In addition to these more conventional efforts, multiple startup companies are working to develop self-propelled autonomous railcars (SPARCs) that can achieve targeted decarbonization levels while also disrupting the traditional railroad operating paradigm. SPARC operations with multiple platoon lengths, as well as a control scenario with a conventional train length of 110 railcars, were simulated on an example corridor linking a port with an inland intermodal terminal. This line was single track with short sidings on inconsistent intervals, which is typical of U.S. branch lines serving few local customers without heavy through traffic. For all traffic levels simulated, SPARCs had a much lower transit time than the conventional trains. Optimal platoon length varied between 15- and 38-vehicles per platoon depending on the traffic demand, well under North American standards of over 100 railcars per train.

A cellular automaton model for mixed traffic flow considering the size of CAV platoon

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed.

Mechanical and Environmental Analysis of Granular Subbase with Fine Crumb Rubber

The increasing manufacturing of tires, driven by the automotive sector, has led to various sustainability and waste management issues. Consequently, it is necessary to explore alternatives for the reuse of these wastes. Rubber has demonstrated properties that could make it an attractive material for use as a granular aggregate. The aim of this study is to evaluate the influence of crumb rubber (CR) on the mechanical behavior of a granular subbase (GSB) by replacing fine CR at 10% and 15%. An environmental analysis of the proposal was conducted using the SimaPro tool, and a series of laboratory tests were performed to assess physical and mechanical characteristics. The strength results indicate a decreasing trend as the CR content in the GSB increases. Specifically, in abrasion tests, the natural material showed an average result of 30.86%, while the mixes with 10% and 15% CR exceeded the maximum limit (50%), achieving results of 59.24% and 53.98%, respectively. In terms of the California Bearing Ratio (CBR), only the natural samples compacted at maximum energy exceeded the minimum requirements for low and medium traffic levels (30%). The highest CBR value for samples containing CR was 5%, whereas the natural GSB reached a maximum CBR of 33%. A similar trend is observed in environmental outcomes, with increases of up to 17% in abiotic depletion for the sample with 15% CR. Overall, the environmental analysis suggests that incorporating CR into the GSB could lead to an increase in various environmental impacts.

Long-term exposure to air pollution and road traffic noise and incidence of dementia in the Danish Nurse Cohort.

We examined the association of long-term exposure to air pollution and road traffic noise with dementia incidence in the Danish Nurse Cohort. Female nurses were followed for dementia incidence (hospital contact or medication prescription) from 1993/1999 to 2020. Air pollution and road traffic noise levels were estimated at nurses' residences, and their associations with dementia were examined using Cox regression models. Of 25,233 nurses 1409 developed dementia. Particulate matter with a diameter of ≤2.5µm (PM2.5) was associated with dementia incidence, after adjusting for lifestyle, socioeconomic status, and road traffic noise (hazard ratio [95% confidence interval] 1.35 [1.15-1.59] per interquartile range of 2.6µg/m3). There was no association of PM2.5 with dementia in physically active nurses. Association with road traffic noise diminished after adjusting for PM₂.₅ (1.02 [0.93-1.11] per 7.6dB). Long-term exposure to air pollution increases risk of dementia, and physical activity may moderate this risk. Long-term exposure to air pollution was associated with increased risk of dementia among female nurses from the Danish Nurse Cohort. Association of air pollution with dementia was independent of road traffic noise. Association of road traffic noise with dementia diminished after adjusting for air pollution. Physical activity moderated adverse effects of air pollution on dementia.