Traffic Density Levels Research Articles

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in discretionary lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose the Parameterized Soft Actor–Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis with Parameterized Soft Actor–Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining zero collision rates. However, at high traffic density levels, although both algorithms result in collisions, PASAC-PIDLag has a much lower collision rate than PASAC.

Inventarisasi Kepadatan Perlintasan Sebidang Kereta Api Berdasarkan Warna Peta Jalan pada Aplikasi Google Maps di Kota Madiun”

Through this research, it is expected to know the characteristics of the level of traffic density around railway crossings and contribute to improving the safety and efficiency of railway crossings in Indonesia.How is the inventory of vehicle traffic density at railway crossings in Madiun City? Based on the color condition of the highway map in the Google Maps application. The method carried out is to collect historical data on traffic conditions on the google map application at a specified JPL point with a certain period of time, namely during the morning, afternoon and evening rush hours, traffic density condition data is shown with certain color indications. As comparison data, samples of live-traffic data are taken on Google Maps which will be analyzed and identified the level of density of railway crossings. Data analysis was carried out to identify railway crossing points that are prone to congestion in the city of Madiun. Through analysis of railway crossing density data, crossing points that have high, medium and low density levels will be identified. Color condition data shows that the plot crossing on JPL 138 has a frequency percentage of 50.37% and 47.71% of the distance is green, with the level of vehicle traffic density is not dense to rather dense. JPL 138 crossing on the side of Jalan Yos Sudarso – Pahlawan has a higher level of traffic density.

Spatial Identification and Hotspots of Ecological Risk from Heavy Metals in Urban Dust in the City of Cartagena, SE Spain

In the present work, a study has been carried out on the contamination of heavy metals in urban dust deposited on the roads of the city of Cartagena (Spain) in order to know the content of metals such as Ni, Zn, Pb, Cd, Cr and Cu. Likewise, the possible relationship between the concentration of heavy metals and the color of the sample, level of magnetism and traffic density was studied. Contamination was evaluated using several indices such as contamination factor (CF), enrichment factor (EF), geo-accumulation index (Igeo), pollutant load index (PLI) and ecological risk index (RI). A total of 88 samples were taken in the urban area of Cartagena, and the metals were determined by acid digestion and measured by inductively coupled plasma mass spectrometry. The concentration of heavy metals in urban dust from Cartagena was Zn (672 mg kg−1) > Cu (248.9 mg kg−1) > Pb (227 mg kg−1) > Cr (82.7 mg kg−1) > Ni (47.7 mg kg−1) > Cd (4.1 mg kg−1). Contamination levels were high in Pb, Zn, Cd and Cu, according to environmental indices. A correlation was found between magnetism and metal concentration, which was repeated for all metals except Cd. Dark-colored samples contained higher metal concentrations than light-colored samples. Meanwhile, streets with medium and low traffic intensity were found to have higher concentrations of heavy metals. This study’s objective was to identify pollution hotspots caused by heavy metals in dust in the urban ecosystem of Cartagena city.

Evaluasi Kinerja Operasional Bus Trans Banyumas pada Koridor 1 Pasar Pon – Terminal Ajibarang

The Trans Banyumas Bus is a mode of mass transportation owned by the Banyumas Regency Transportation Agency which is managed by PT. Banyumas Raya Transportation with the Buy The Service (BTS) system.. It is hoped that this mode of transportation can reduce the use of private vehicles so as to reduce the level of traffic density. The purpose of this study was to determine the level of operational performance of Trans Banyumas corridor 1 buses in terms of load factor, headway, circulation time, travel speed, downtime and availability of transport and then compared with the minimum service standards contained in the Decree of the Director General of Land Transportation Number 687 year 2002, Regulation of the Minister of Transportation of the Republic of Indonesia No. 10. Year 2012, and Regulation of the Minister of Transportation of the Republic of Indonesia No. 98. Year 2013. This research was conducted using dynamic survey methods and static surveys which were carried out on Sunday, 17 July 2022 and Wednesday, 20 July 2022 starting at 05.00 am until 09.00 pm. From the research results on the two survey days, the operational performance results for each indicator reviewed and which met the minimum service standards were circulation time, travel speed, downtime and transportation availability. Meanwhile, the operational performance indicators that have not met the minimum service standards are load factor and headway. So of the six indicators reviewed, two of them have not met the minimum service standards based on the Decree of the Director General of Land Transportation No. 687 of 2002, Regulation of the Minister of Transportation of the Republic of Indonesia No. 10. Year 2012, and Regulation of the Minister of Transportation of the Republic of Indonesia No. 98. Year 2013.

ANALISIS DAN SIMULASI KECEPATAN LALU-LINTAS PADA SIMPANG JALAN MENGGUNAKAN PERANGKAT LUNAK PTV VISSIM STUDENT VERSION (STUDI KASUS: SIMPANG BERGOTA, KOTA SEMARANG)

One of the intersections that has a significant role in the city of Semarang is the Bergota Intersection. The level of traffic density and crowds at this point of the road is quite significant because it is a route from Jalan Pandanaran and towards the Central Java Regional Police, which passes Jalan Kyai Saleh—a transportation movement system of various types and characteristics of the traffic that occurs. The method used in this study is observation by collecting data as input at PTV Vissim, such as geometric data at intersections, vehicle routes, composition, vehicle volume, vehicle speed, and traffic time cycle using the Traffic Counting method at Bergota Intersections based on analysis of recorded results. The drone video then enumerates the traffic flow by counting the vehicles passing through the survey point. Modeling and simulation results using PTV Vissim in the existing conditions at 11:10-11:20 at the Bergota Intersection using vehicle volume data taken from the field show that the highest speed is 20.27 Km/hour on Jalan Minister Supeno. The lowest speed is 14.59 km/hour on Jalan Kyai Saleh Bergota. Modeling and simulation results using PTV Vissim in the existing conditions at 11.10-11.20 Simpang Bergota using vehicle volume data taken from the field show that the highest delay is 41.19 seconds on Jalan Kyai Saleh Bergota - Jalan Kyai Saleh Veteran. At the same time, the lowest delay is 5.59 seconds on Jalan Dr. Kariadi – Jalan Kyai Saleh Bergota.

Air Pollution Burden and Three Population Health Indicators in 58 Counties Comprising the State of California, USA

Air pollution is a major risk factor in human health causing premature death disease and ranks fifth among the top five leading causes of death worldwide. In California, air pollution attracts significant attention due to increased pollution from fossil fuel burning, industry, transportation, and the state’s drought record and fire-proneness. Data used in this analysis was obtained from the CalEnviroScreen pollution monitoring tool. There is significant variation in air pollution across counties and racial groups. Hispanic populations, followed by Caucasians, predominantly occupy areas with the highest pollution levels above 40%. Most counties had low average scores in population parameters that determine population health. Pollution burden predicted asthma and CVD prevalence but not low-birth-weight babies. Asthma was closely associated with traffic density, PM2.5, cleanup sites, and ozone levels; low-birth-weight babies were associated with traffic density and release of toxic substances; and CVD was associated with PM2.5, toxic release, and cleanup sites. Low HPI scores (<40%) were associated with incidences of asthma and CVD but not low-birth-weight babies. The increased population health compromises in California caused by the effects of air pollution call for a paradigm shift in the way population health is evaluated and effects of air pollution mitigated.

RTA-IR: A runtime assurance framework for behavior planning based on imitation learning and responsibility-sensitive safety model

Current research on artificial intelligence (AI) algorithms in safety–critical areas remains extremely challenging due to their inability to be fully verified at design time. In this paper, we propose an RTA-IR architecture, which bypasses the formal verification of the AI algorithm by incorporating runtime assurance (RTA) and provides safety assurances for the AI controllers of complex autonomous vehicles (such as those obtained using neural networks) without excessive performance sacrifice. RTA-IR consists of a high-performance and unproven advanced controller and two verifiable safety controllers and a decision module designed based on the Responsibility Sensitive Safety Model (RSS). The advanced controller is designed based on attention generating adversarial imitation learning(GAIL), which can imitate the efficient policies of experts from a set of expert demonstrations. RSS provides verifiable safety criteria and switching logic for the decision module, and RTA-IR provides safety for autonomous vehicles when the advanced controller produces unsafe control, as well as restoring control of the vehicle by the advanced controller under conditions that confirm safety. We tested and evaluated RTA-IR separately for two levels of traffic density in one driving task. Experiments have shown that RTA-IR exhibits superior performance in terms of both safety and efficiency compared to the baseline method.

Simulasi Numerik Model Arus Lalu Lintas dengan Pengaruh Kepadatan Kendaraan dan Kondisi Jalan

Traffic jams are common in developing countries. This is generally caused by traffic density, road conditions, and the driver's personality. This phenomenon can be simulated using a mathematical model, allowing it to be seen how these factors affect traffic conditions. The goal of this study is to simulate the effect of traffic density and road conditions using the Lighthill-Whitham-Richard (LWR) model. This model is used to describe macroscopic traffic flow conditions, which are affected by traffic density and speed. The Greenshield model with the influence of road condition parameters added is used for the speed. In numerical solutions, the Upwind method is used to evaluate midpoint values. The simulation results demonstrate the model's ability to describe traffic conditions that are affected by traffic density and road conditions. The level of traffic density is inversely proportional to road conditions and traffic speed.

Improving drivers’ merging performance in work zone using an in-vehicle audio warning

Work zone poses a great impact on traffic safety, and one of the important reasons is the increased merging behavior caused by lane closure. This study focuses on drivers’ lane-merging process in work zones, considering both individual driving behaviors and interaction risks with surrounding vehicles. An intelligent in-vehicle audio warning was proposed to improve the merging behaviors, and its efficacy was tested in a driving simulator experiment. Forty-two participants comprising 22 males (12 professional drivers vs. 10 non-professional drivers) and 20 females (10 professional drivers vs. 10 non-professional drivers) took part in the study. Five traffic density levels and five different locations to release the audio warning were set to collect the behavioral and trajectory data. The results showed the audio warning could reduce the merging risks evidently. However, its effectiveness was affected when the warning information was released too late. A high traffic density was associated with poor driver behavior and high merging risks. Male drivers were more likely to have interaction risks with the lead vehicles than female drivers. Professional drivers had more stable vehicle operating and lower merging risks during lane-merging than non-professional drivers. The research shed some lights on work-zone planning and management strategies.

Indicator of Highway Conditions on Traffic Density Levels

Traffic problems, especially congestion which is the impact of high levels of traffic density, are problems that have not been properly resolved, especially in big cities in Indonesia. In this article, 7 features of road conditions are proposed on the level of traffic density. These features include the number of vehicles, the average speed of the vehicle, the area of the main road, the length of traffic that has been determined, the duration of traffic congestion, and the time of traffic congestion. Determining the level of traffic density can be determined by utilizing the MCDM hybrid technique to produce information that can be used to support decision-making in traffic management or other policies such as formulating policies on the buying and selling of private vehicles. The number of indicators related to computer vision for presenting information on the level of traffic density requires a large amount of time and memory. Besides that, the perception of a decision maker in determining priority weights also influences the results of the information on the level of traffic density.

Lane-Change in Dense Traffic With Model Predictive Control and Neural Networks

This article presents an online smooth-path lane-change control framework. We focus on dense traffic where intervehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes model predictive control (MPC) with generative adversarial networks (GANs) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman filter to mitigate sensor noise. Simulation studies are investigated at different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.

Short-term impact of noise, other air pollutants and meteorological factors on emergency hospital mental health admissions in the Madrid region

BackgroundA number of environmental factors, such as air pollution, noise in urbanised settings and meteorological-type variables, may give rise to important effects on human health. In recent years, many studies have confirmed the relation between various mental disorders and these factors, with a possible impact on the increase in emergency hospital admissions due to these causes. The aim of this study was to analyse the impact of a range of environmental factors on daily emergency hospital admissions due to mental disorders in the Madrid Autonomous Region (MAR), across the period 2013–2018. MethodologyLongitudinal ecological time series study analysed by Generalised Linear Models with Poisson regression, with the dependent variable being daily Emergency Hospital Mental Health Admissions (EHMHA) in the MAR, and the independent variable being mean daily concentrations of chemical pollutants, noise levels and meteorological variables. ResultsEHMHA were related statistically significantly in the short term with diurnal noise levels. Relative risks (RRs) for total admissions due to mental disorders and self-inflicted injuries, in the case of diurnal noise was RR: 1.008 95%CI (1.003 1.013). Admissions attributable to diurnal noise account for 5.5% of total admissions across the study period. There was no association between hospital admissions and chemical air pollution. ConclusionNoise is a variable that shows a statistically significant short-term association with EHMHA across all age groups in the MAR region. The results of this study may serve as a basis for drawing up public health guidelines and plans, which regard these variables as risk factors for mental disorders, especially in the case of noise, since this fundamentally depends on anthropogenic activities in highly urbanised areas with high levels of traffic density.

Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding

Vehicular traffic in urban areas faces congestion challenges that negatively impact our lives. The infrastructure associated with intelligent transportation systems provides means for addressing the associated challenges in urban areas. This study proposes an effective and scalable vehicular traffic congestion avoidance methodology. It introduces a traffic thresholding mechanism to predict and avoid vehicular traffic congestion during route computation. Our methodology was evaluated and validated by employing four road network topologies, three vehicular traffic density levels and various traffic light configurations, resulting in 26 urban traffic scenarios. Using our approach, the number of vehicles that can run in free flow can be increased by up to 200%, whereas for traffic congestion scenarios, the time spent in traffic may be reduced by up to 69% and CO2 emissions by up to 61%. To the best of our knowledge, in the vehicular traffic flow prediction domain, this is the first approach that covers a set of road network topologies and a large and representative set of scenarios for simulated urban traffic congestion testing. Moreover, the comparative analysis with different other solutions in the domain, showed that we obtained the best driving time and CO2 emission reduction.

PROTOTYPE SMART TIME SCHEDULER LAMPU LALU LINTAS MENGGUNAKAN ALGORITMA HAAR CASCADE

The traffic light functions as a traffic controller at crossroads and also as smoothness at intersections to avoid congestion. The current application of conventional traffic lights still has weaknesses in determining the duration of traffic lights which are not adjusted to the number of vehicle volumes which often change every time. Especially when there is a dense flow of vehicles at intersections, especially at certain times. Based on observations of traffic density at the intersection that connects Soekarno - Hatta road and Arifin Achmad road in Pekanbaru City. There is often a high density of vehicles from the direction of Jalan Soekarno-Hatta (Morning Market) to Jalan Jenderal Sudirman during working hours so that the duration of the traffic lights cannot reduce the amount of queue density in the light traffic. The implementation of the Smart Time Scheduler is a solution for adjusting the duration of traffic lights based on the level of traffic density, by building a tool to detect and calculate the number of vehicle queues at the Traffic Light and then enter the calculation results into 3 categories, namely Normal, Medium and Dense Density. The system uses Haar Cascade with the open cv library on the Raspberry pi, the results of the system testing that has been carried out can count the number of vehicles and instruct Arduino to set the duration of the traffic lights where in the category of normal vehicle queues, the green traffic light gets a waiting time of 10 seconds in countdown, Moderate category for 15 seconds, and Solid Category, for 20 seconds, while the calculation error value uses the Error Percentage equation formula, namely comparing the results of vehicle calculations by the system with Manual Calculation Results multiplied by 100% Then the System Calculation Error is 16% and the accuracy level of the tool is 100% - 16% = 84%, the size of the error value is influenced by the quality of the light intensity, and the distance of the camera's detection of the object when performing the detection.

Extracting Information from Vehicle Registration Plate using OCR Tesseract

The increase in population and vehicle ownership causes high levels of traffic density. The very high mobility of the population ultimately has an impact on routine congestion which is felt to be getting worse over time. There are several factors that cause traffic jams to get worse. One of them is due to the increasing number of motorized vehicles. The number of vehicles that continues to increase is not in accordance with the road capacity that can accommodate these cars. For this reason, a system is needed that can categorize cars that can run on certain days according to the number plate (eg odd or even) to reduce congestion. This built system can extract the license plates of vehicles passing on the highway using image processing and character recognition methods. This research paper proposed an Automatic Number Plate Recognition ANPR system is an image processing and character recognition system that is used to recognize a car's license plate using Optical Character Recognition (OCR). The inputted license plate is automatically localized, segmented, and recognized using the OCR algorithm provided in the Tesseract library. The experiment shows 83.3% accuracy due to the difference in license plate format, background, and fonts.

Analisis Parameter Particulate Matter 10 (PM10) pada Udara Ambien di Pasar Baruga Kota Kendari

The problem of air pollution is a global problem that almost everyone experiences. Nearly 90% of people in the world live in an environment where the air is not healthy. One of the places that may affect the pollutant is, where the market has a level of crowd and traffic density that can have an impact on the environment. One of them is the central market of Kendari City.this study aims to determine the concentration of particulate matter (PM10) in the baruga market in kendari city and the amount of traffic volume at three predetermined times.the results obtained are the concentration of PM10 in ambient air in the baruga market, kendari city at three times, namely morning, afternoon and evening. 26,92 μg/Nm3 with a temperature of 33,5oC, in the afternoon the results obtained are 51,67 μg/Nm3 with a temperature of 35,25oC and in the afternoon the results are 18,50 μg/Nm3 with a temperature of 32,33oC which still meets the PP quality standard. No.41 of 1999.

A Robust Environment-Aware Driver Profiling Framework Using Ensemble Supervised Learning

Driver profiling is the real-time process of detecting driving behaviors and computing a driver’s expected risk based on detected behaviors. Predicting risk based solely on the inclusion of detected behaviors may not be accurate because this method of predicting ignores the environmental (e.g., weather conditions, traffic density level) context of detected behaviors. Moreover, coupling detected behaviors with their environmental context can be leveraged towards creating personalized risk profiles for drivers in each driving environment. These profiles can be utilized in various ITS applications including personalized safety-based route planning. In this paper, a novel driver profiling environment-aware framework is presented. In the proposed framework, data processing is distributed over three computational layers to enhance the overall reliability of the system. A risk prediction model is hosted on the edge/fog to determine the driving risk while considering the joint effect of the in-vehicle detected behaviors and their environmental context. Risk values along with a driver’s compliance to warnings are both utilized to compute the risk profile on the cloud. Using SHRP2 Naturalistic Driving (ND) dataset, the development of a novel risk prediction model is presented herein with the underlying sub-processes of data preprocessing, error analysis, and model selection. Then we analyze both the performance of the developed risk prediction model and the overall performance of the proposed system. Validation results for the developed model indicate a good compromise between bias and variance. Moreover, the results of the overall risk scoring model reflect its robustness and reliability in assigning accurate risk scores.

THE EFFECT OF VEHICLE VOLUME ON DAMAGE TO PINE ROAD IN PALANGKA RAYA CITY

Along with the level of traffic density that crosses the Pinus road, there are several obstacles that cause damage, one of which is the damage that occurs in the road construction section, which is caused by the load of vehicles with excessive loads such as trucks, which further causes damage to the road, namely an increase in volume. heavy traffic every day. Asphalt pavement is generally used on roads that have a fairly dense traffic volume, with the increase in the number of vehicles resulting in damage in a relatively short time than planned. The purpose of this study is to determine the effect of the volume of vehicle types on road damage on asphalt pavement, it can be predicted earlier the value of road damage that will occur, the method used in this study is the method of analyzing vehicle volume on road damage with the regression method. Used to obtain the relationship function with the value of R² (coefficient of determination) which shows the magnitude of the effect of changes in the volume of vehicle types on changes in the value of road damage, this study was carried out on the Pinus road, Palangka Raya City. There is a relationship between the volume of vehicle types and the value of road damage, the results obtained are R² = 0.744 which shows that road damage is influenced by the volume of heavy vehicles, light vehicles and motorcycles with a percentage value of 74.4%. From the results of the equation between heavy vehicles (X1), light vehicles (X2), motorcycles (X3), and the value of road damage Y = 0.159 X1 + 0.034 X2 + 0.017 X3 + 14,189. From these equations it can be described as follows, the regression coefficient (X1) (a) = 0.159 means that a heavy vehicle of 100 vehicles/day will increase the level of road damage by 5.9, the regression coefficient (X2) (b) = 0.034 means a light vehicle of 100 vehicles/day will increase the level of road damage by 3.4, Regression coefficient (X3) (c) = 0.017 means that 100 motorbikes/day will increase the level of damage by 1.7, constant (c) = if there are no vehicles driving passing a road segment, the road will experience road damage of 14,189.

Analisis Konsentrasi Particulate Matter (PM10) pada Udara Ambien di Pasar Sentral Kota Kendari

The problem of air pollution is a global problem that almost everyone experiences. Nearly 90% of people in the world live in an environment where the air is not healthy. One of the places that may affect the pollutant is, where the market has a level of crowd and traffic density that can have an impact on the environment. One of them is the central market of Kendari City. This study aims to determine the concentration of particulate matter (PM10) in the central market of Kendari City and the amount of traffic volume at three predetermined times. The results obtained are PM10 concentrations in ambient air at the central market of Kendari City at three times, namely morning, afternoon, and evening, laboratory test results in the morning are 4.3521 g/Nm3 with a temperature of 280C and a wind speed of 2.2 m /s, in the afternoon the result is 2.2868 g/Nm3 with a temperature of 320C and a wind speed of 2.5 m/s, and in the afternoon the result is 4.8524 g/Nm3 with a temperature of 290C and wind speed 1.94 m/s but all sample test results still meet the ambient air quality standards according to the Government Regulation of the Republic of Indonesia No. 22 of 2021 and the results of calculating the volume of vehicles at three times, namely at 08.30 – 09.30 WITA, 11.30 – 12.30 WITA, and 14.30 – 15.30 WITA, the results are that in the morning the daily traffic volume is 1480 vehicles/hour, during the day as much as 1284 vehicles/hour, and in the afternoon as many as 1523 vehicles/hour.

Towards Multi-Model Big Data Road Traffic Forecast at Different Time Aggregations and Forecast Horizons

Due to its usefulness in various social contexts, from Intelligent Transportation Systems (ITSs) to the reduction of urban pollution, road traffic prediction represents an active research area in the scientific community, with strong potential impact on citizens’ well-being. Already considered a non-trivial problem, in many real applications an additional level of complexity is given by the large amount of data requiring Big Data domain technologies. In this paper, we present the first steps of a novel approach integrating both classic and machine learning models in the Spark-based big data architecture of the H2020 CLASS project, and we perform preliminary tests to see how usually little-considered variables (different data aggregation levels, time horizons and traffic density levels) influence the error of the different models.