Daily Traffic Volume Research Articles

Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data

As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.

Exploiting Surrogate Safety Measures and Road Design Characteristics towards Crash Investigations in Motorway Segments

High quality data on road crashes, road design characteristics, and traffic are typically required to predict crash frequency. Surrogate Safety Measures (SSMs) are an alternative category of indicators that can be used in road safety analyses in order to quantify various unsafe traffic events. The objective of this research is to exploit road geometry data and SSMs toward various road crash investigations in motorway segments. To that end, for this analysis, a database containing data on injury and property-damage-only crashes, road design characteristics, and SSMs of 668 segments was compiled and utilized. The results of the developed negative binomial regression model revealed that crash frequency is positively correlated with the average annual daily traffic volume, the length of the segment, harsh accelerations, and harsh braking. Moreover, four distinct clusters representing crash risk levels of the examined segments emerged from the hierarchical clustering procedure, ranging from more risk-prone, potentially unsafe locations to more safe locations. These four clusters also formed the response variable classes of a random forest model. This classification model used various road geometry data and SSMs as predictors and achieved high classification performance for all classes, averaging more than 88% correct classification rates.

FACTORS CAUSED ROAD DAMAGE BANDA ACEH - MEDAN STA 481+000 - STA 483+000

Roads are land transportation facilities that are very important in facilitating economic relations activities, both between one city and another. Good road conditions will facilitate the movement of people in relationships and other social activities. Meanwhile, if there is road damage, it will not only hinder economic and social activities, but accidents can occur. Many factors cause road damage, including weather, weather on the road surface when it rains, scorching heat in the dry season, average daily traffic volume (LHR), and vehicles that have a number of loads or overloads (Overloading). From the results of the study, the dominant type of damage on the Banda Aceh-Medan road STA 481+000-STA 483+000 sorted from the largest to the smallest percentage is Alligator Cracking, the second is Longitudinal Cracks, the third is Longitudinal Cracks. Edge (Cracked Edge).Keywords: Road, Overloading, Damage, Maintenance.

Integrating a Multi-criteria Route Optimization with ArcGIS for Gravel Road Data Collection

The ability to identify and collect low-volume roads’ data, especially gravel roads, is critical in transportation planning. Developing an effective method for gravel-road data collection allows both decision-makers and local agencies to efficiently obtain information regarding road conditions. In this study, a geographical information system (GIS)-based decision- support system was applied to assist local agencies in determining reliable access routes to all gravel roads in Wyoming. The routing criteria were developed using the Network Analyst tool of ArcGIS software based on the restrictions on average daily traffic (ADT) volume values, speed limits, driving distances and driving hours per day. Results indicated that the routing maps created by ArcGIS software were an easy-to-use method to plan and schedule data collection on gravel roads. The routing analysis provided a reliable means to minimize driving time and distance. The methodology developed in this study may be employed by local transportation agencies for road-maintenance purposes in rural areas. KEYWORDS: Low-volume roads, Gravel roads, Route optimization, ArcGIS, Data collection.

Edge Computing-Based Automated Vehicle Classification System Using the MobileNet V2 Model

The volume of traffic in one day is referred to as the average daily traffic volume. The Average Daily Traffic System (LHR) is also used to detect road damage caused by excessive vehicle loads. In the LHR system, vehicle data is still collected manually, with humans calculating the type and number of vehicles based on observations made and then divided into a time span. As a result, a system with a camera and deep learning data processing is required to automatically calculate the type and number of vehicles. The goal of this research is to develop edge computing systems by improving the system's performance in the calculation and classification of vehicles using the SSD MobileNet V2 model. The results of the MobileNet model scenario 5 have the lowest loss value of the five scenarios. The MobileNet V2 model can better classify vehicle types with a 65 FPS inference process.

Analisis Tingkat Pencemar Carbon Monoksida di Udara Akibat Lalulintas dengan Model Polusi Udara Skala Mikro di Jalan MT Haryono Kota Kendari

Density of motorized vehicle users in Kendari City is one of the factors that affect ambient air quality. This is the reference for conducting this research using a field survey methode for the number of vehicle volumes and ambient air sample testing for air quality in Kendari City with carbon monoxide parameters. The results of this study indicate the daily traffic volume at Jalan MT. Haryono (in front of Pasar Baru). The volume of motorized vehicles of 10,699 units/day (motorcycles), 4,078 units/day (cars) and 272 units/day (heavy equipment). Based on the results of laboratory tests, the CO concentration of the MT. Haryono waypoint (in front of Pasar Baru) Kendari City obtained as follows: in the morning at 4000 μg/m³, in the afternoon at 10350 μg/m³, and in the afternoon at 8000 μg/m³.f The results of measurements carried out the concentration of Carbon Monoxide at points on the MT. Haryono (in front of Pasar Baru). For the morning and evening it is still below the threshold based on the quality standard according to Government Regulation Number 22 of 2021 concerning the Implementation of Environmental Protection and Management. Meanwhile, during the day, the quality standard has passed due to the condition of air temperature, humidity and wind speed which causes the CO concentration to increase.

ANALYSIS OF TRAFFIC ROAD CAPACITY ON ROAD SECTION OF SEI LIAT PANGKALPINANG CITY BOUNDARY

The increasing number of vehicles in Bangka Belitung Province has caused several congestion points on several roads, one example in the Road Section of Sei Liat - Pangkalpinang City Boundary which is the main route connecting Pangkalpinang City with Sungailiat City. For the purpose of analyzing the existing capacity of the Sei Liat - Bts Road Section in Pangkal Pinang City, a traffic survey was conducted for seven days using CCTV as the data source. The average daily traffic volume value obtained on the Road Section of Sei Liat - Pangkalpinang City Boundarywas 11,156 vehicles/day or 902 pcu/hour. The value of Road Traffic Capacity obtained on the Road Section of Sei Liat - Pangkalpinang City Boundary was 2,977 pcu/hour with a degree of saturation of 0.3 which means that the Road Section of Sei Liat - Pangkalpinang City Boundary is included in the safe/unsaturated category and capable of serving vehicles that pass through this road every day.

Transferability of the highway safety manual freeway model to EU countries

IntroductionIn roadway safety management processes, Accident Prediction Models (APMs) represent the best available tools to analyse potential safety issues, to identify safety improvements and to estimate the potential effect of these improvements in terms of crash reduction. The Highway Safety Manual (HSM) provides consistent predictive methods for estimating the predicted average crash frequency, but an appropriate calibration is necessary to use them in contexts different from the ones where they were developed. MethodThe present study provides a contribution in this field of research providing a European APM based on the one proposed by HSM and introducing a new methodology to transfer the HSM to different European rural freeways. Specifically, a new set of jurisdiction-specific (JS) base safety performance functions (SPFs) have been developed as a function of annual average daily traffic volume and roadway segment length, considering JS base conditions specific for each different national network, different from the HSM base conditions. These new SPFs were then used for the calibration of the full models, and the results compared with those obtained applying the HSM predictive model. This allows to evaluate the potential benefits of calibrating jurisdiction-specific base SPFs, with different base conditions. ResultsThe results showed that the local SPFs development approach allowed to obtain a better fit than the HSM predictive model for the analysed European countries. ConclusionsThe findings suggest that the development of jurisdiction-specific base SPFs will offer more suitable APMs for countries that differ from the USA, thus a more reliable prediction could be obtained by applying this procedure. Practical ApplicationsThe use of JS SPFs allowed developing European APMs based on those proposed by HSM but applicable to different conditions. This represents a useful starting point for further analysis and improvements in accident prediction modelling.

Economic feasibility of electrified highways for heavy-duty electric trucks

New solutions to decarbonization in the transport sector are prominently required to replace oil consumption. Full battery electric vehicles (BEVs) are usually limited to light duty and medium duty vehicles due to their limited battery energy density and cost. The aim of this study is to evaluate the economic feasibility of heavy-duty electric trucks that are supplemented by electrified highways (eHighways), instead of using conventional diesel trucks and full BEV trucks, and address the technical aspects of eHighways. The battery is the most expensive component of electric vehicles. The principle of eHighway is that electricity is supplied to electric vehicles directly from the electric grid as they travel along the road. The eHighway concept is being developed with two primary methods to convey the power source to the vehicle: conductive power transfer (CPT) where electric connection to the vehicle is provided through electric wires from above the vehicle and inductive power transfer that is in-motion wireless power transfer (WPT). If eHighways are installed on the major links that connect main cities, the eHighway technologies will be suitable for long-distance journeys. This research evaluates the applications of both CPT and in-motion WPT technologies. A case study is conducted, and various costs are calculated and analyzed. Results show that the driving cost (i.e. break even selling price of electricity) for a heavy-duty electric truck on the eHighway using CPT technology ranges from $ 0.242 to 0.666 per km and the driving cost of a heavy-duty electric truck on the eHighway using in-motion WPT technology ranges from $0.279 to 1.031 per km, depending on daily traffic volume. If fuel and vehicle prices evolve as predicted between now and 2050, eHighways could become an economically feasible form of road transport, especially for the heavy-duty trucks segment, resulting in energy savings and thus significant reductions in CO2 emissions. Moreover, a sensitivity analysis is conducted to evaluate which parameters have a greater impact on the economic viability of the eHighway systems.

Tyre additive chemicals, tyre road wear particles and high production polymers in surface water at 5 urban centres in Queensland, Australia

Plastics pollution is a global issue impacting every part of our environment. Tyre road wear particle (TRWP) plastics pollution is thought to be one of the largest pollution sources in urban environments. These plastics are also of concern due to the presence of additive chemicals, incorporated during manufacture, that can be released into the surrounding environment. This study aimed to provide information on concentrations of a range of anthropogenic plastics related pollutants in the Australian environment through a scoping study of surface water in 5 key urban centres around Queensland, Australia. Samples were analysed for a suite of 15 common tyre additive chemicals, TRWPs and 6 common high production polymers, and included the new transformation product of concern 6PPD-quinone which has recent reports of causing mass mortality events in certain aquatic species. The additives were ubiquitously detected (2.9–1440 ng/L) with 6PPD-quinone concentrations lower than in previous studies (<0.05–24 ng/L) and TRWPs detected at 18 of the 21 sites (<MDL to between 690 and 1990 μg/L). Of the high production polymers, polyethylene and polypropylene were detected at the highest concentrations (16–1750 and <0.7–37 μg/L respectively) with profiles highly variable between sites. A traffic related additive profile was determined at 7 sites, which all had nearby traffic related sources. Concentrations of additive chemicals were significantly correlated with average daily traffic volumes (p = 0.006), although concentrations of TRWPs were not correlated. Generally, concentrations were in line with or lower than concentrations in other geographical regions, although it is noted samples were collected during the dry season and further sampling during the tropical wet season would be of interest.

ANALYSIS OF TRAFFIC ROAD CAPACITY ON ROAD SECTION OF SEI LIAT – PANGKALPINANG CITY BOUNDARY

The increasing number of vehicles in Bangka Belitung Province has caused several congestion points on several roads, one example in the Road Section of Sei Liat - Pangkalpinang City Boundary which is the main route connecting Pangkalpinang City with Sungailiat City. For the purpose of analyzing the existing capacity of the Sei Liat - Bts Road Section in Pangkal Pinang City, a traffic survey was conducted for seven days using CCTV as the data source. The average daily traffic volume value obtained on the Road Section of Sei Liat - Pangkalpinang City Boundarywas 11,156 vehicles/day or 902 pcu/hour. The value of Road Traffic Capacity obtained on the Road Section of Sei Liat - Pangkalpinang City Boundary was 2,977 pcu/hour with a degree of saturation of 0.3 which means that the Road Section of Sei Liat - Pangkalpinang City Boundary is included in the safe/unsaturated category and capable of serving vehicles that pass through this road every day.

Verification analysis of relationship between driving failure probability and traffic accident rate

To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.

Carpooling and the reduction of urban traffic

The article presents the results of research that quantitatively confirm the well-known thesis on the impact of carpooling (measured by the declared carpooling indicator, DCI) on a significant reduction in the intensity of urban traffic flows. Of course, it is about joint journeys by passenger cars, which (especially in medium-sized and large cities) are an important source of transport congestion, and the connection with the intensity of traffic is obvious. The working hypothesis was verified by conducting observations and measurements in the central area of a medium-sized city in Poland. It is the city of Radom, 100 km south of Warsaw, with a population of 215,000. residents. To verify the hypothesis, the average daily traffic volume, ADTV were compared with the average reduced average daily traffic volume, RADTV. These were determined - by simulating carpooling, in which the main control variable (although not the only one) was the indicator, DCI, i.e. the number of additional passengers in a passenger car. It turned out that RADTV values decreased significantly (therefore congestion decreased) when DCI values increased. This is illustrated by the DCI - RADTV dependence graphs for different days of the week and different measurement locations. This relationship was confirmed by calculating the Spearman’s rank correlation index between the DCI and RADTV variables.

Daily electricity demand assessment on the example of the Turkish road transport system – a case study of the development of electromobility on highways

The aim of this study is to investigate how the daily electricity demand from road transport related to the implementation of an electric road system on the eight roads with the highest traffic flow connecting the seven largest cities in Turkey varies according to time and location. Intercity highway route O-7, O-5, O-21, D715, D687, E96, and E87 in Western Turkey was used as a case study. The daily electricity demand on the eight roads working on the full electrification of the existing traffic flow can be increased by 3.7% in the case of the reference point. However, if all roads in Turkey are converted to an electric road system and all land vehicles use this system, the corresponding peak power increase will be 100%. The daily electricity demand along the roads is derived from the available measuring points for the daily road traffic volumes. The study also compares the CO₂ reduction potentials and energy demands of the electrified road system with the use of fossil fuels to achieve the same transportation volume. The results show that an electric road system application on eight Turkish roads with considerable traffic flow can reduce 18.8 million tons of CO₂ emissions from the road transport sector. The research can find practical application in assessing the validity of developing a strategy for the development of electromobility on highways in Turkey.

Injury severity assessment of rear-end crashes via approaches based on generalized estimating equations

Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, and average annual daily traffic volume were identified as contributing factors. However, significant differences were observed between the elasticity effects measured by different models, especially in terms of minibus and foggy weather. Thus, this study verifies that GEE-based models account for a greater amount of unobserved heterogeneity, yield better performance in terms of precision, and exhibit more consistent explanatory power compared to traditional models.

Exploring the impact of seasonal weather factors on frequency of lane-departure crashes in Maine

Lane departure crashes account for approximately 34% of all roadway crashes and over 70% of all roadway fatalities in Maine. Despite an 18% decrease in average daily traffic volume, the half of the year with colder weather, from November to April, comprises over 64% of the yearly lane-departure crashes. The purpose of this study is to explore to what extent seasonal (i.e., winter vs. non-winter) and monthly weather variations impact lane departure crashes on rural Maine roads. We used a negative binomial model with panel data to analyze monthly crashes on Interstates, minor arterials, major collectors, and minor collectors from 2015 to 2019 for winter and non-winter periods. The data include monthly average daily traffic, geometric characteristics, and weather variables. The research results indicate that the seasonal variability as reflected in various weather variables significantly impact the frequency of lane-departure crashes during the winter period. The marginal effect analysis shows that as the number of days with more than 1 inch of snowfall, or rainfall increases by 1%, the average number of lane-departure crashes increases approximately by 0.51% and 0.09% on Interstate roadways, respectively.

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current day traffic volume is used as the dependent variable and the previous 7-day traffic volume is used as the independent variable for model learning. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model.

Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany

Mass movements are linked to increasing amounts of damage and disruptions to transportation infrastructures. A valid risk assessment in order to reduce future costs is not always appropriate, as adequate information on landslide data is missing. The presented study estimates the rockfall susceptibility on a rural road network in the Harz mountains using a bivariate statistical method (information value method). The model is validated using a receiver operating characteristic (ROC) analysis. In addition, the vulnerability of the road network is estimated using vulnerability indicators. The susceptibility model assigns a high or very high susceptibility to 23% of the area in the road network corridor. The relevant road sections are linked to high slope values, NE orientations of road sections, and low-to-moderate vulnerability values. The highest vulnerability values can be found on marginal road sections with high average daily traffic volumes. The combination of the presented methods proposes an easily applicable estimate of vulnerability where conventional methods (i.e., vulnerability curves, matrices) cannot be implemented.

The Effect of the COVID-19 Pandemic on Traffic Flow Characteristics, Emissions Production and Fuel Consumption at a Selected Intersection in Slovakia

The surveyof traffic intensity is used to obtain information on the number of vehicles on roads during the day. Subsequently, it is possible to derive from this the daily, weekly, and other road traffic intensity information. This survey represents the basis for the calculation of the annual average daily traffic volume and the basic characteristics of traffic flow. The COVID-19 pandemic has caused extensive economic and social damage around the world. These damages have also affected traffic. Changes in traffic behavior have mainly affected the reduction in traffic intensity on road networks. Thanks to the reduction in the demand for transport, there has also been a significant reduction in traffic delays, fuel consumption and emissions. An examination of changes in traffic intensity took place at a selected intersection in 2019, 2020 and 2021. This paper describes the effects of reducing the traffic intensity, fuel consumption and emissions obtained by microsimulation. The results obtained confirmed the reduction in traffic, which also contributed to a significant reduction in vehicle delays.

Traffic emission dominates the spatial variations of metal contamination and ecological-health risks in urban park soil

Metals in urban park soil are closely related to traffic emissions, which adversely affect soil quality and human health. However, little is known about the quantitative impacts of traffic on the spatial variations of metals in park soil after the banning of leaded gasoline. Herein, concentrations of Cu, Pb and Zn in surface soil of four recreational parks of Sydney (Ashfield, Robson, Lamberts and Leichhardt) were measured to evaluate their spatial characteristics in contamination, ecological and health risks and relationships with traffic emissions. Contamination of metals are assessed by contamination factor (CF). Normalized metal concentrations (<63 μm) in the park soil were 24–614, 23–3520 and 99–3060 mg kg−1 for Cu, Pb and Zn, respectively, and CFs ranged from 1.4 to 207, whose variations inter- and intra-parks were related to traffic volumes. Traffic emission accounted for 72–84% of metals contamination in soil of Ashfield, Robson and Lamberts by sites, whereas the values were 25–70% for Leichhardt due to the absence of a surrounding arterial road. In Ashfield and Robson Parks, metal concentrations from traffic decreased exponentially with distance from arterial roads. Metals in Lamberts Park and in areas near arterial roads in Ashfield and Robson Parks may raise ecological risk, and traffic sources contributed to 61–81% of the risk. The ranges of ecological risk zones away from arterial roads and average daily traffic volumes showed an exponential relationship. Copper and Zn in soil of the four parks have no non-carcinogenic health risk for children and adults, and Pb has negligible health risk for adults. Lead in Lamberts Park and in sites near arterial roads of Ashfield and Robson Parks may raise non-carcinogenic risk for children (HI > 1) due to traffic emissions. These results emphasize the remarkable influence of traffic emissions on urban soil metal, which can be predicated quantitatively by traffic volume.