Road Type Research Articles

Effect of Binary Blended Fillers on the Durability Performance of Recycled Cold-Mix Asphalt

Cold-mix asphalt is a greener alternative to pavement construction, processed at 10–40 °C, which is typically lower than other techniques like warm-mix asphalt and hot-mix asphalt. Huge amounts of construction and demolition waste, such as broken bricks, recycled concrete aggregates, reclaimed asphalt pavement, ceramic waste, etc., are generated every year due to the acceleration in infrastructure development. The production of such massive amounts causes landfilling issues, and their disposal is a serious issue nowadays. This study examines the effect of binary blended fillers on the performance of cold asphalt mixes using emulsified binders and 50% reclaimed asphalt pavement materials. Moreover, three types of binary blended fillers (BBFs), cement, fly ash, and Stabil Road, were used at different dosages. Overall, 500 samples were prepared for the mix design, and the optimum emulsion content was determined as 11% and 9% for the CM and 50R mixes, respectively, based on the Marshall stability peak value and volumetric properties such as voids in the mineral aggregates, total voids, and dry density. The moisture susceptibility of the recycled cold-mix asphalt (RCMA) mixture was evaluated using the tensile strength ratio. Cantabro abrasion loss was used to assess the cohesion resistance of the mixtures. The dynamic response of the mixes to the applied load was evaluated using the resilient modulus. The results of the present study reveal that using BBFs in the RCMA improved the inter-particle bonding and strength. Furthermore, BBF incorporation enhanced the performance of the recycled cold-mix asphalt.

Tracking of microplastics distribution patterns and their characterisation in deposited road dust from Dhaka city, Bangladesh

The widespread presence of microplastics (MPs) in road dust has considerable concern regarding their potential risks to ecosystems and human health. Despite the massive production of plastic, the erudition of MPs distribution patterns in various sizes of deposited road dust is still limited around the globe. Thus, the aim of this research is to provide an unambiguous picture of MPs distributional pattern, identification, classification, quantification, and features from road dust in various zones and types of roadways in Dhaka, Bangladesh. This study examined MPs in road dust samples with particle sizes ranging from 300 to 150 μm (Group-A), 149-75 μm (Group-B), and <75 μm (Group-C). This work extracted MPs from road dust using 30 % H2O2, 1.6 g/cm3 ZnCl2, and 0.45 μm filter paper. A fluorescent microscope (Motic B410E, Germany), Motic Pictures, and 3.0 ML software were utilized to identify MPs visually. Additionally, FTIR and SEM were utilized to determine the chemical composition of MPs. Group-A Road dust samples had a significantly higher concentration of MPs (38945 items/kg) compared to Group-B and <75 μm Group-C dust (16720 and 5945 items/kg, respectively). The distribution hierarchy for total MPs on average by location and type of road is as follows: paved road (355 items/5 g) > unpaved road (325 items/5 g) > soil samples (294 items/5 g), roadside dust samples (284 items/5 g), and mid-road (283 items/5 g). By taking into account all sizes of road dust samples, the MPs were classified as fiber (70.26 %), fragment (26.12 %), beads (0.66 %), films (1.32 %), and foams (1.58 %). It was found that adults inhaled MPs an average of 1612 items/day, while they ingested an average of 880 items/day. Children had an inhalation rate of 1232 items/day and an ingestion rate of 10267 items/day, which was 4–17 times greater than in other countries. This study identified 22 MPs polymer types, and SEM results show that MPs surfaces are being weathered into nanoplastics, creating a more hazardous environment.

Research on the prediction of traffic accident by linear regression

Traffic accident is getting increasingly serious. Although previous researchers use a variety of methods to predict the traffic accident, there are numerous demerits that need to be improved. This article demonstrates 12 variables that impact the traffic accident with 679 samples of accidents in UK from 2012 to 2014. This paper first analyses the relevance between dependent and independent variables, and also two independent variables to show the correlation between each factor. By using the multiple linear regression, it is concluded that although some independent variables do not have relationship with the dependent variable urban or rural area, Accident Severity, Number of Casualties, Road Type, Speed limit, Junction Control show significant relationship with the dependent variable. The paper also considers the 95% confidence interval in order to compare the effective density of data. Overall, the prediction of traffic accident is based on a number of factors and a sizable sample of accidents to summarize the impact that traffic accidents bring.

Towards Sustainable Urban Mobility: Voronoi-Based Spatial Analysis of EV Charging Stations in Bangkok

This study leverages the efficacy of Voronoi diagram theory within a mixed-methods approach to thoroughly examine the spatial distribution, service coverage, and optimal locations for expanding electric vehicle (EV) charging infrastructure in Bangkok. Drawing on data from field surveys and public data providers, our analysis unfolds in four key stages. Firstly, we delve into the spatial distribution of charging stations, scrutinizing density, proximity to various road types, and land use through the lens of Voronoi diagrams. Secondly, the application of Voronoi diagrams informs the evaluation of service boundaries. Thirdly, utilizing this effective tool, we assess population density per parking slot or charging head to anticipate future EV adoption. Finally, the study introduces an approach to identify suitable locations for new charging stations through the application of overlapping Voronoi circles. Findings reveal a clustered distribution of charging stations along roads, particularly in the central business district, showcasing the efficiency of Voronoi diagrams in spatial analysis. Residential areas and urban commercial zones also host significant charging station concentrations. Notably, service coverage in inner Bangkok surpasses that of middle and outer areas, highlighting underserved regions. Prospective areas for new charging stations, identified through Voronoi analysis, include Bang Khae, Phra Khanong, Min Buri, and Huai Khwang. This research, rooted in the application of Voronoi diagram theory, offers vital insights for various stakeholders involved in urban infrastructure planning. By employing Voronoi diagrams within Geographic Information Systems (GIS), the study contributes to strategically placing charging stations, optimizing spatial understanding, and enhancing resource allocation. This GIS-based approach not only supports the rise of electric vehicles but also promotes sustainable urban development practices through the efficient utilization of spatial data and analysis techniques.

Effects of Gravel Percentage to Compaction Density and Stability of Embankment

Embankments are frequently used in the construction of road, railway, airport, dams and other types infrastructure. Soil compaction is a crucial part of the construction process of embankment. Thus, soil compaction needs to be conducted and investigated in such a way so embankment will not experience large settlement that could lead to collapse. Generally, soil compaction density testing is performed using the sand cone method according to ASTM D-1556. The material used in this test is not carefully considered, which may lead to inaccurate results. In this study, the sand cone correction test according to ASTM D-4718 which consider the percentage of gravel is established to identify the compaction density of embankment and the effect to its stability. 16 secondary data of compaction density have been collected from the construction of embankment. According to the data, the compaction density of the embankment has met the specifications. The relationship between the gravel percentage and the compaction density is obtained. With a determination coefficient (R2) value of 0.805, it can be understood that these two variables have a highly significant correlation. Furthermore, to understand the relationship between the compaction density and the stability of the embankment, a slope stability analysis was conducted on the embankment using the Fellenius method. The result shows that when the compaction density of the embankment meet the specifications, embankment is stable with the safety factor (SF) of 1.511. Furthermore, both variables have a very strong relationship. The safety factor of the embankment is increased as the compaction density increases.

METHODS OF TRANSPORT MODELLING AS RESEARCH TOOLS IN CREATING TRAFFIC SAFETY MODELS IN URBAN AREAS

In a modern city with heavy traffic and increasing complexity of transportation systems, the issue of traffic planning to minimize the number of road accidents becomes particularly acute. Transport modelling is one of the tools used in the research and development of traffic models in urban areas. By using transport modelling, it is possible to identify potential hazards and evaluate the effectiveness of proposed safety measures that reduce accident risks and improve traffic safety in urban planning. Existing transport modelling methods, including microscopic, mesoscopic, macroscopic, dynamic, and static, have been examined. An analysis of the main characteristics of each method was conducted, along with the software that supports their implementation, evaluating the advantages and limitations of each method as research tools in creating traffic safety models in urban areas. As a result of the study, the simulation micromodelling method was proposed for assessing safety levels when analyzing existing solutions or during the design phase of new elements of the street and road network in cities. Key factors have been identified that justify the use of micromodelling when forming a traffic safety model. Based on the research, it was found that for assessing the safety level of existing solutions for street and road network elements and during the development of a traffic safety model in urban areas, the simulation micromodelling method is the most appropriate. At the same time, applying a set of measures to adapt the chosen implementation of this method to a specific type of street and road network element significantly improves the accuracy of traffic safety assessment results in cities.

Road traffic accidents and the contributing factors among drivers of public transportation in Mizan Aman town, Ethiopia: a Community-Based Cross-Sectional Study.

Traffic accidents on the road is an accident is a terrible accident that causes death, injury, and property damage. However, limited studies were addressed to investigate the prevalence of traffic accidents on the road and the contributing factors among drivers that help in developing strategies to cop-up the incidence within the research domain in Ethiopia, particularly in the study area. This study aimed to assess the prevalence of road traffic accidents and the contributing factors among drivers of public transportation in Mizan Aman town, Ethiopia. A community-based cross-sectional survey was employed among 376 drivers of public transportation. Every research subject was selected by using a simple random sampling technique. Semi-structured and open-ended questionnaires which comprised demographic characteristics, risky personal behaviors and lifestyles, driver's factors, vehicle condition, and environmental conditions were used to gather data. And then after, data was collected through interviewer-administered using KoBo Collect tools. Completed data were edited and cleaned in the Kobo collect toolbox and then exported for additional analysis to a statistical tool for social science statistics version 26. The descriptive statistics were displayed as figures, tables, and texts. Binary logistic regression was analyzed to identify the contributing factors. Statistically significant was decided with a p-value of ≤ 0.05. The results showed that the prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was 17%. The study identified factors influencing traffic accidents on the roads including marital status (being single), employee condition (permanent), monthly income (1001-2500 Ethiopia Birr), alcohol use, vehicle maintenance (not), road type (non-asphalt), and weather conditions (being windy). The overall prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was relatively low. Despite this, sociodemographic characteristics, driver factors, vehicle conditions, and environmental conditions [road type and weather conditions] were the predicting factors of traffic accidents in town. Therefore, reduction strategies should be the highest priority duty for concerned bodies like Mizan Aman town road and transport office, Bench Sheko zone transport and logistics office, and Southwest Ethiopia People Regional State (SWEPRS) transport bureau in the study area.

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

Geological Disaster Susceptibility Evaluation Using Machine Learning: A Case Study of the Atal Tunnel in Tibetan Plateau

Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.

Analysis of E-scooter Risk Factors by Road Types on Different Speed Levels

Analysis of E-scooter Risk Factors by Road Types on Different Speed Levels

Video analysis of bicyclist and pedestrian movement on shared-use paths under daylight and electric lighting conditions—Method exploration

This study introduces a progressive methodology for examining the travel behaviour of bicyclists and pedestrians under both daylight and electric lighting conditions. By analysing data from naturalistic/undisturbed settings, we aimed to assess the representativeness of participant behaviour in an invited context compared to that of typical road users. This paper expands upon the methodological exploration initiated in the original Video Analysis of Pedestrian Movement (VAPM) study by analysing the microscopic movements of both pedestrians and bicyclists in outdoor environments under both natural and electric lighting conditions. Furthermore, the paper investigates bicyclists’ bi-directional interactions in a naturalistic setting, adding a valuable dimension to the study's comprehensive analysis. Our research introduces an effective approach to data collection through the use of drone technology to obtain microscopic data from video analysis. This method has the potential to enhance travel quality for bicyclists during the hours of twilight and darkness and increase the likelihood of short motor vehicle trips being replaced by active, sustainable forms of transport.

Состояние наземных путей сообщения и дорожное строительство на юге Дальнего Востока России во второй половине XIX в. – начале ХХ в.

The article examines the initial period of road construction and the formation of transportation routes in the southern Far East of Russia following its annexation to the Russian Empire until the early XX century. The term “communication routes” encompasses various types of roads designed for the transportation of people and goods, including postal routes, pack trails, dirt roads, and clearings. The construction of roads in the Lower Amur region near the city of Nikolaevsk, in the Amur Oblast, as well as in the Primorsky Krai, is considered. In the Lower Amur region, the construction of transportation routes was driven by strategic defense needs. In the Amur Oblast, the primary incentive for creating transportation infrastructure was economic needs, driven by the development of gold deposits. Postal routes, which operated in the region’s uninhabited conditions, played an important role in road construction. By the late XIX century, the construction of dirt roads in the Primorsky Krai was greatly influenced by the construction of the Ussuri Railway. Conclusion dwells upon the fact that the development of transportation routes in the region corresponded to the capabilities of its time.

OffRoadSynth Open Dataset for Semantic Segmentation using Synthetic-Data-Based Weight Initialization for Autonomous UGV in Off-Road Environments

This article concerns the issue of image semantic segmentation for the machine vision system of an autonomous Unmanned Ground Vehicle (UGV) moving in an off-road environment. Determining the meaning (semantics) of the areas visible in the recorded image provides a complete understanding of the scene surrounding the autonomous vehicle. It is crucial for the correct determination of a passable route. Nowadays, semantic segmentation is generally solved using convolutional neural networks (CNN), which can take an image as input and output the segmented image. However, proper training of the neural network requires the use of large amounts of data, which becomes problematic in the situation of low availability of large, dedicated image data sets that consider various off-road situations - driving on various types of roads, surrounded by diverse vegetation and in various weather and light conditions. This study introduces a synthetic image dataset called “OffRoadSynth” to address the training data scarcity for off-road scenarios. It has been shown that pre-training the neural network on this synthetic dataset improves image segmentation accuracy compared to other methods, such as random network weight initialization or using larger, generic datasets. Results suggest that using a smaller but domain-dedicated set of synthetic images to initialize network weights for training on the target real-world dataset may be an effective approach to improving semantic segmentation results of images, including those from off-road environments.

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment.

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the need for assessing these measurements in a scalable manner. We explore collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches - a standard collocation assessment technique where the mobile platform is parked near the fixed regulatory site for a period of time and an expanded approach using measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed reference site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, Colorado, USA and in the San Francisco Bay Area, California, USA. Our one-month Denver dataset includes both parked collocation periods near the fixed regulatory sites as well as general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, as the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (year-long) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40-hour running median converges to within ±4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be used during large-scale monitoring campaigns using small sensors to identify potential measurement biases.

Validation of stamper device to measure international roughness index IRI

Measuring International Roughness Index (IRI) is one of the tasks in the process of checking, monitoring and evaluating road surface quality. In Vietnam, the types of IRI measuring devices are still limited. There are specialized devices with high accuracy and speed, but the cost is high. Therefore, they are not commonly used for regular monitoring. To measure IRI regularly and conveniently, it is necessary to use device that is easy to use, convenient, with high speed, high accuracy, but at a reasonable cost. The STAMPER (System with Two Accelerometers for Measuring Profile, Enabling Real-time data collection) from Taisei Rotec company (Japan) has been used to measure on different types of roads in Hanoi: city road, expressway and national highway. Data will be collected many times, with speed ranges and by many types of vehicles. The results show that STAMPER can measure IRI quickly and stably. It is easy to install on many type of vehicles and gives accurate, reliable measurement results

Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions.

Cumulative effect of driver’s historical violations on traffic accidents: New evidences from China

This study explores the impact of long-term traffic violations on accident risk, a frequently overlooked aspect in existing research. Analyzing violation data from Guilin, China, over a decade (2010-2020), including accident records from 2016-2020, the study categorizes drivers into three groups based on their cumulative violations. Given the challenges posed by high-dimensional data and the need for interpretability, the study employs a combination of CatBoost and SHAP models to assess factors influencing accident severity across these driver groups. Results indicate that for drivers without cumulative violations, road type, vehicle type, household registration, and age significantly influence accident risk. Among those with 1–18 cumulative violations, road type, cumulative penalty points, vehicle type, and cumulative fines emerge as significant factors. Drivers with more than 19 cumulative violations are affected by road type, vehicle type, driving experience, and terrain. Notably, penalty points are more effective than fines in reducing the risk of serious accidents, and fines prove significant only for those with fewer than 19 cumulative violations over a decade. In summary, the cumulative violation count effectively distinguishes driver groups, highlighting marked variations in accident risk factors. This research offers valuable insights for devising region-specific measures to enhance road safety.

A road surface reconstruction dataset for autonomous driving

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

An Approach of Multi Criteria Decision Making in Analyzing Rural Road Safety in Nashik District

Abstract: Rural road safety evaluations play a significant role in the transportation industry, and most fatal accidents happen on these types of roads. Road Safety Analysis (RSA), a process to evaluate a road's safety criteria and support overall road management decision-making, ensures the avoidance of loss of life and property damage. The "Pradhan Mantri Gram Sadak Yojana," which is primarily utilized in rural areas, is the main emphasis of this. In order to identify the most vulnerable roadways and implement mitigation measures, roads must be prioritized depending on safety ratings. This document presents the results of a study conducted to rank the safety criteria of Pradhan Mantri Gram Sadak Yojana (PMGSY) roads, a particular category of rural roads. A questionnaire survey was used for the study's analysis, and three MCDM methods were used to examine the relationships and interactions between the various factors. The study's findings indicated that the based on input from PMGSY engineers and contractors. From the perspective of the engineers in the Nashik area, we can determine the safety impact factor that influences the safety assessment of the PMGSY route.