Vehicle Density Research Articles

ANALISIS KONTRIBUSI KEPADATAN KENDARAAN BERMOTOR TERHADAP KONSENTRASI KARBON MONOKSIDA (CO) DAN TINGKAT KEBISINGAN DI JALAN SLAMET RIYADI SURAKARTA

<div class="WordSection1"><p><em>In the current era, the transportation is growing rapidly. It has an impact such as air and noise pollution. Carbon monoxide is one of the dangerous pollutants. While the noise comes from the sound of the vehicle engine, horn, and exhaust. Slamet Riyadi street Surakarta is a protocol road that has a high level of vehicle density. So it is necessary to conduct research with the aim of knowing the effect of motor vehicle density on carbon monoxide concentrations and noise levels. The results showed that the concentration of Carbon monoxide on Slamet Riyadi street Surakarta in the morning and evening exceeded the quality standard of 16038.26 µg/Nm<sup>3</sup></em><em>and 16141.58 µg/Nm<sup>3</sup></em><em>. During the day it is still below the quality standard, which is 9425.86 µg/Nm<sup>3</sup></em><em>. Meanwhile, the daytime noise level on Slamet Riyadi street Surakarta which is calculated by the Leq formula also exceeds the quality standard of 82.33 dBA.</em></p></div>

Development of the intelligent traffic light system based on image processing and fuzzy control techniques

In Viet Nam's current traffic conditions, congestion and jams—especially at intersections during peak hours—present major challenges. Traditional traffic light systems, which rely on fixed timing principles, often fail to manage traffic flow efficiently, particularly when vehicle density varies significantly across different directions. This research aims to develop an intelligent traffic light system where the signal timings automatically adjust based on the vehicle density at intersections. The study uses an object recognition algorithm to identify, classify, and count vehicles. The data was then fed into a fuzzy logic model to calculate the optimal signal timings. Experimental results demonstrate an accuracy of approximately 88% in vehicle detection. The fuzzy logic model and the programmable logic controller were able to effectively compute reasonable signal timings based on real-time vehicle density. Future developments include expanding the system's functionalities, creating a user-friendly interface, and developing a management application for mobile devices.

Linear Regression-Based Traffic Flow Simulation: Vehicle Density and Speed Analysis on Buah Batu Road

Traffic congestion has become an increasingly severe problem in many major cities around the world, including in the city of Bandung. Population growth and increased vehicle use exacerbate congestion. Jalan Buah Batu, one of the main roads in the city of Bandung, often experiences congestion due to high density. This study explains the traffic flow simulation using the Lighthill-Whitham-Richards (LWR) model with a speed-density function obtained from observation data on Jalan Buah Batu, Bandung. The data included the relationship between vehicle density and speed which was then analyzed using the linear regression method. The approximation of the velocity-density function obtained from linear regression is v(ρ)= -6.904+4.302. Traffic flow simulations were carried out with a road length of 60 meters, a total time of 5 minutes, and high resolution with 300 grid points. At the beginning of the simulation, a peak density of 0.70 occurred in a 15-25 meter road segment. Over time, the peak density shifted and decreased: 0.65 at 20-30 meters at 1.25 minutes, 0.60 at 25-35 meters at 2.5 minutes, and 0.50 at 30-50 meters at the end of the simulation (5 minutes). These results show the movement of vehicles that reduce congestion and improve the smooth flow of traffic. In conclusion, linear regression is effective in determining the velocity-density function.

Vehicle density and its impact on pavement wear in Guayaquil based on business development in port cities

Increasing vehicle density in Guayaquil has raised concerns about its impact on the quality of road infrastructure, particularly on the wear and tear of asphalt pavement. This study aims to assess the relationship between vehicular traffic and pavement deterioration in the city, focusing on several areas with different levels of density. Counting cameras and traffic sensors were used to record the volume of vehicles, categorising them into light and heavy, as well as visual inspections to measure pavement wear. The results revealed that areas with high vehicle density had a higher degree of pavement deterioration, with a significant increase in cracking and cracking. The data show that heavy traffic is a major contributor to accelerated road wear, which in turn can impact road safety and public health by releasing more pollutants into the air.

Dynamic Analysis Techniques for Road Bridges Under Large Traffic Flows

This work focuses on the dynamic analysis of road bridges under large microsimulated traffic flows, contributing an efficient dynamic analysis framework for future fatigue, comfort or driving safety studies in which realistic interactions between vehicles are considered. Different strategies to define the loading vector in the governing dynamic equations are presented for bridges discretized with beam and shell elements. The proposed interpolation techniques for beam-like models include a recursive binary search and a novel vectorized strategy based on shape functions expanded to the entire length of the deck. These are applied to a short simply supported bridge and a very long cable-stayed bridge under traffic flows generated with a cellular automata microsimulation model. The strategies presented here reduce the computational time required in conventional load interpolation algorithms, particularly in long bridges subject to dense traffic flows. Finally, the binary search algorithm is applied to the study of bridge decks discretized with shell elements under microsimulated traffic, demonstrating that high vehicle densities increase the contribution of the transverse vibration of the slab and the potential discomfort of pedestrians.

Determinación de la densidad vehicular en ciudades intermedias por medio de visión por computador

The objective of this research work is to calculate the vehicle density of cities through the use of computer vision. To carry out the study, we captured videos of vehicular traffic at 8 intersections in the city of Loja using a drone and a high-resolution camera. Subsequently, we used the Python language and the YOLOv8 library to count vehicles of different categories, such as small vehicles, trucks, and motorcycles. Through mathematical formulas used in transportation engineering, the values ​​of vehicle density, vehicle flow rate, and average spacing were obtained. As results, we have that the machine learning model using YOLOv8 has an accuracy of 90% in detecting and classifying vehicles, and thanks to its use, the road with the highest vehicle density was identified. The practical applications of this work could improve vehicle flow and help competent organizations related to traffic management make decisions.

Atmospheric microplastic input into wetlands: Spatiotemporal patterns, drivers, and unique ecological impacts

Wetlands are major microplastic sinks with a large atmospheric input. However, many details of such deposited atmospheric microplastics entering into wetlands remain unclear, including temporal patterns of input and ecological effects. We monitored the aerial microplastics during four seasons in eleven economically developed cities along the lower reaches of the Yangtze River Basin, China. The average microplastic deposition rate was 512.31 items m−2 d−1, equivalent to an annual contribution of 17.46 metric tons of plastic to the surveyed wetlands with a total area of 1652 km2. These microplastics were predominantly composed of polyamide and polyethylene terephthalate with 61.85 ± 92.29 µm sized pellets, and we obtained similar results for microplastics intercepted on moss in wetlands. Microplastic input varied between wet and dry periods, primarily influenced by wind, rainfall and ozone concentration. Civilian vehicle density and textile industry were the primary socioeconomic factors driving microplastic deposition. Further indoor microcosm experiments revealed that moss phyllosphere bacterial community structure and function were influenced by microplastic abundance and size, exemplifying the unique ecological risks of aerially deposited microplastics to wetlands. These results indicate that mosses and their phyllosphere microbiota could serve as bio-indicators of aerial microplastic characteristics and impacts.

Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data

Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their number in various areas of the highway. This information is then processed to assess road congestion conditions, with the aim of detecting congestion. The data obtained were tested on two road scenarios and traffic conditions to evaluate the performance of the system. The results showed that the proposed system was able to detect congestion with an accuracy level of 59.2% from several experiments. The use of YOLOv8 enables real-time detection with efficient computing resources, making it a potential solution for large-scale deployment. This research shows that the incorporation of advanced object detection algorithms such as YOLOv8 with CCTV data can provide an effective solution for traffic management in large cities. This system is expected to improve response to congestion, help control traffic, and reduce the negative impact of congestion in urban areas

SROR: A Secure and Reliable Opportunistic Routing for VANETs

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols.

Correction: Adaptive learning-enhanced lightweight network for real-time vehicle density estimation

Correction: Adaptive learning-enhanced lightweight network for real-time vehicle density estimation

Synergizing shared micromobility and public transit towards an equitable multimodal transportation network

This paper assesses the equity impacts of shared micromobility and investigates regulatory policies that improve transport equity and promote synergy between public transit and shared micromobility. We consider a multimodal transportation network, where a micromobility platform deploys docking stations and operates a fleet of micromobility vehicles to provide shared micromobility services and a public transit agency offers transit services over a transportation network. A market equilibrium model is developed to capture the intimate interactions among access and egress times of shared micromobility services, waiting times of transit services, the spatial distribution of docking stations, passenger demand, platform pricing and fleet sizing, vehicle repositioning and the micromobility platform profit. The platform decision problem is cast as a high-dimensional non-convex program. A solution method is proposed to efficiently compute the solution through problem reformulation and dimensionality reduction. Based on the proposed framework, we evaluate spatial equity in transport accessibility using the Gini index, and find that although shared micromobility improves overall transport accessibility, the benefits are not fairly distributed across different geographic zones, which leads to enlarged spatial inequity gaps after introducing shared micromobility. To promote transport equity, we investigate three policy directions: (a) to impose a vehicle density floor on shared micromobility; (b) to offer a subsidy on shared micromobility rides for first/last-mile connections; and (c) to promote collaboration between public transit and shared micromobility. We show that different regulatory policies have advantages and limitations. The minimum vehicle density requirement can simultaneously improve spatial equity and passengers’ surplus, but has limited equity improvements. In contrast, the subsidy on bundled services could significantly mitigate spatial inequity, but it hurts passengers and the platform profit. Compared to the other two policies, the transit-micromobility collaboration can lead to higher equity improvement, higher passenger surplus, while offering a guarantee on the platform profit, which turns out to be the most cost-effective approach. These insights are validated through realistic numerical studies for San Francisco.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

SPATIOTEMPORAL OF NITROGEN DIOXIDE (NO2) CONCENTRATION IN THE URBAN ENVIRONMENT OF KLANG VALLEY, MALAYSIA

The high concentration of nitrogen dioxide (NO2) directly results in Klang Valley’s air quality deterioration, causing a public health risk. This study was conducted to analyse the daily-averaged and annual concentration of nitrogen dioxide (NO2) on a spatial-temporal scale at five continuous monitoring stations under the Department of Environment (DOE) in Klang Valley, namely, Klang, Shah Alam, Petaling Jaya, Kajang, and Cheras from 2000 to 2009 using Man-Kendall statistical analysis and interpolation technique in Geographic Information System (GIS). The result clearly showed that the Petaling Jaya station was identified as the most polluted compared to other stations, with an average concentration of more than 0.050 ppm every year and reaching the maximum concentration of 0.069 ppm where the mean was 0.030 in 2001. Based on the p-value derived from the Mann-Kendall statistical analysis, the Klang, Petaling Jaya, Shah Alam, and Cheras stations recorded a significant trend with p-values < 0.05 at 0.0001 and 0.020, respectively. The annual concentration of NO2 in all the stations was in the range of 0.015 to 0.04 ppm from 2004 to 2009, compared to 0.005 to 0.01 ppm from 2000 to 2003. The highest annual-averaged NO2 concentration was reported at the Petaling Jaya station between 0.035 and 0.004 ppm for all years except 2007 and 2009 when concentrations were in the 0.03 to 0.035 ppm. Notably, the Petaling Jaya station had the highest annual NO2 concentration, which ranged from 0.025 to 0.04 ppm due to emissions from motor vehicles. The major pressure on road infrastructure was recognised, mainly a lack of space to accommodate the effect of the maximum density of motor vehicles and traffic, resulting in traffic congestion in the city centre.

UDANet: An unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning

Unsupervised domain adaptive vehicle density estimation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, which has received extensive attention due to its practicality and effectiveness. However, the existing methods often rely on a large amount of expensive labeled data, and ignore the large differences between different data domains, making the network trained in one scene difficult to apply to the target scene. To solve these problems, this paper proposes an unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning (UDANet), which effectively extends the model to unlabeled target domain data by adding two domain adaptive components to the pre-trained backbone. Specifically, a multi-level feature discriminator is designed to align vehicle features from the image level and refine features from the pixel level by joint adversarial learning, thus achieving cross-domain transfer of features from the source domain to the target domain. In addition, to address the shared convolutional structure that incorrectly highlights irrelevant information, a feature correction module is designed to improve the adaptability of the network to target domain data by re-modeling the source and target data according to channel independence. Several well-known backbones are used to prove the superiority of the backbone and the mobility of the two domain adaptive components. The domain adaptation experimental results on four vehicle datasets show that UDANet achieves high estimation accuracy compared to current state-of-the-art unsupervised methods, and is suitable for the task of vehicle density estimation in complex scenes.

Effects of route guidance strategies on traffic emissions in urban traffic networks

Urban traffic emissions have significant environmental and health implications. Diverging from traditional research that primarily aims to improve traffic flow and efficiency, this study specifically focuses on the environmental impact on traffic emissions, conducting a comprehensive analysis within the Manhattan urban network through four route guidance strategies. The performance of these strategies is examined across various vehicle densities, and their impact on four traffic pollutant emissions (Carbon-dioxide, Nitrogen Oxides, Volatile Organic Compounds and Particulate Matter) is assessed. Moreover, our innovative approach analyzes emissions from the perspectives of both travel distance and trip frequency, placing special emphasis on trip frequency to provide practical insights with high real-world applicability. The results highlight the potential and limitation of the Congestion Coefficient Strategy. Under equal travel demands, the Congestion Coefficient Strategy showed promise in reducing carbon emissions. However, at lower vehicle densities, it led to a significant increase in emissions. This revelation pointed to the need for modifications to the strategy when applied in scenarios with lower traffic density. Recognizing this limitation, we introduced a modified strategy that achieved remarkable reductions in emissions across diverse vehicle densities, effectively overcoming the challenges posed by the original Congestion Coefficient Strategy. These findings offer valuable insights for policymakers and transportation planners in selecting optimal route guidance strategies to reduce pollutant emissions. Future studies will explore the efficacy of these strategies in road networks characterized by different topological configurations.

Evaluasi Kinerja APILL di Simpang Empat Bundaran Burung Kota Palangka Raya

Burung Roundabout is an important roundabout for mobilizing the activities of people entering and leaving the city of Palangka Raya, with the increase in population and the rise in community activities the density of vehicles also increases significantly every year. Traffic problems can occur if there is a lack of coordination in each arm of the roundabout. The research objective is to know the performance of Weaving Area and the performance of APILL at Burung Roundabout. The obtained data consists of quantitative data from field survey data. The data is analyzed using Indonesian Road Capacity Manual (MKJI, 1997). The research result using the calculations from (MKJI, 1997) for the Weaving Area section show that the maximum Capacity (C) obtained is 11,125 smp/h at the DA section. The average traffic delay at the roundabout is 0,77 second/smp with the highest delay index (DS) is 0,22 at the CD section. Consequently the service level during peak hours is classified as grade A. The analysis of APILL at Burung roundabout has good traffic flow result. The highest capacity on the southern arm is 1566 smp/hour and the highest delay is 21 second/smp. The highest queue length is 22 meters on the Southern with a highest saturation degree of 0,44 on the Southern with indicates level of service index C.

Enhancing privacy-preserving in vehicular cloud through an incentive-based strategy

The literature has extensively explored vehicle ad hoc networks (VANETs) and vehicular clouds, with a common assumption in these studies being incorporating onboard units (OBUs) in vehicles. OBUs are used to collect and disseminate information between vehicles. Furthermore, numerous studies assume the presence of road infrastructure for communication. Implementing a vehicular cloud can play a vital role in aggregating data on events such as weather conditions, traffic information and accidents. This information is distributed to other vehicles, allowing drivers to make informed decisions and ensure safe driving practices. To protect privacy within the vehicular cloud, pseudonyms serve as a means of communication between vehicles and roadside units (RSU). Numerous existing approaches suggest more frequent updates to vehicle pseudonyms to reduce the likelihood of linking transmitted messages by vehicles. However, some of these strategies overlook situations where vehicle density is low, and vehicles have limited willingness to engage in the pseudonym-changing process. This article introduces an architecture that encourages vehicles to participate in the pseudonym-changing process to enhance vehicle privacy. This is achieved by issuing rewards to vehicles that can be used to access cloud services.

Analysis of Carbon Monoxide on Transportation Along the Eastern Crossroad of Jambi

The Eastern Crossroad is one of the national roads that connect the city of Jambi with other cities, districts, and provinces, resulting in relatively heavy traffic and frequent congestion on this road. This has led to the concentration of carbon monoxide (CO), which can result in a decline in ambient air quality. The research used a quantitative descriptive approach aimed at depicting the ambient air quality of CO on Mendalo Darat Road, which is part of Eastern Crossroad. The research revealed the highest vehicle density of 17,954 units in the morning on Tuesday, with the highest emission rate of 114,290 µg/m.s, and the lowest density on Sunday morning with 6,568 units and an emission rate of 44,207 µg/m.s. The highest accumulation of CO emission levels occurred on Tuesday evening, reaching 38,536.44 µg/Nm3. Overall, the ambient air quality of CO on Mendalo Darat Road tends to exceed the quality standards. The accumulation of increased CO emissions correlates closely with the road's national status, increased vehicle density due to high community activity, changes in the day's status (working day), and road conditions with traffic signal devices.

Optimized flow rate and efficient shaft water cooling method for EV's PMSM temperature-dependent energy system output power increase

One possible way to enhance the power density of electric vehicle (EV) powertrains is by utilizing temperature-sensitive permanent magnet synchronous motors (PMSMs) managed through liquid cooling. The management of benefits and costs from EV PMSM liquid cooling versus power are carried out for energy system output power (ESOP) increase. This study examines the effects of optimizing flow rate into housing water jackets (HWJ) and implementing compound shaft water (SWJ) cooling as management strategies using numerical multiphysics simulations and experiments. The PMSM with higher temperature at higher speed shows larger profit space brought by enhanced cooling. The optimized HWJ flow rate is a highly-efficient strategy to increase ESOP when overall pumping efficiency e1 for HWJ is less than 5 %, increasing more than 5.6 %, 2.1 % and 2.4 % ESOP at 1500, 3500, and 7000 rpm continuous working point. ESOP is insensitive to the condition of HWJ flow rate below 6 L/min or e1 over 30 %. The SWJ cooling is proved to be a low-cost and high-yield energy management strategy roughly independent of SWJ flow rate and EV's cooling system arrangement. ESOP is further increased by up to 3.9 %, 6.2 % and 4.4 % with SWJ cooling at 1500, 3500, and 7000 rpm.

Adaptive learning-enhanced lightweight network for real-time vehicle density estimation

Adaptive learning-enhanced lightweight network for real-time vehicle density estimation