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Keywords = annual average daily traffic volume (AADT)

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21 pages, 6868 KiB  
Article
Impact Assessment of Socio-Economic Scenarios on a Water Quality Swale: An Exploratory Analysis with WinSLAMM
by Sujit A. Ekka, Jon M. Hathaway and William F. Hunt
Sustainability 2024, 16(24), 10857; https://doi.org/10.3390/su162410857 - 11 Dec 2024
Viewed by 1108
Abstract
Sustainable long-term performance of water quality swales, a common stormwater control measure (SCM), requires a futuristic view that considers the impact of socio-economic conditions. The impact of five socio-economic scenarios on a water quality swale in Knightdale, North Carolina, USA, was assessed using [...] Read more.
Sustainable long-term performance of water quality swales, a common stormwater control measure (SCM), requires a futuristic view that considers the impact of socio-economic conditions. The impact of five socio-economic scenarios on a water quality swale in Knightdale, North Carolina, USA, was assessed using WinSLAMM, a stormwater quality model. Scenarios included changing annual average daily traffic (AADT) and maintenance regimes mimicking environmental protection and degradation. Statistical performance evaluation criteria (e.g., RMSE, R2) were used to assess model suitability and calibration for runoff volume and sediment. Results indicated that sediment delivery to the swale increased with AADT, and reduced maintenance negatively impacted swale performance. While the reduced AADT during the COVID-19 pandemic provided short-term water quality benefits, a lack of maintenance impacted treatment through the swale. SCM inspection and maintenance is critical for accommodating increased AADT and enhancing swale life-cycle. This exploratory impact assessment focused on the socio-economic axis of climate change scenario framework and underscored the importance of sound environmental policies for sustainable swale performance. Future studies are needed in other areas to influence local environmental policies. Full article
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19 pages, 9417 KiB  
Hypothesis
Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
by Dae Cheol Han
Appl. Sci. 2024, 14(20), 9436; https://doi.org/10.3390/app14209436 - 16 Oct 2024
Cited by 2 | Viewed by 2249
Abstract
Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed [...] Read more.
Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010–2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model’s resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications)
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22 pages, 823 KiB  
Article
Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models
by Muhammad Wisal Khattak, Hans De Backer, Pieter De Winne, Tom Brijs and Ali Pirdavani
Sustainability 2024, 16(4), 1537; https://doi.org/10.3390/su16041537 - 11 Feb 2024
Cited by 2 | Viewed by 2122
Abstract
The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash [...] Read more.
The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID. Full article
(This article belongs to the Collection Emerging Technologies and Sustainable Road Safety)
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16 pages, 4574 KiB  
Article
The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach
by Sangwan Lee, Jicheol Yang, Kuk Cho and Dooyong Cho
Urban Sci. 2023, 7(3), 91; https://doi.org/10.3390/urbansci7030091 - 25 Aug 2023
Cited by 4 | Viewed by 3398
Abstract
This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected [...] Read more.
This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications. Full article
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14 pages, 7098 KiB  
Article
Imputing Missing Data in Hourly Traffic Counts
by Muhammad Awais Shafique
Sensors 2022, 22(24), 9876; https://doi.org/10.3390/s22249876 - 15 Dec 2022
Cited by 8 | Viewed by 3039
Abstract
Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs [...] Read more.
Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit. Full article
(This article belongs to the Section Vehicular Sensing)
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14 pages, 2395 KiB  
Article
A Novel Safety Assessment Framework for Pavement Friction Evolution Due to Traffic on Horizontal Curves
by Guilong Xu, Jinliang Xu, Chao Gao, Rishuang Sun, Huagang Shan, Yongji Ma and Jinsong Ran
Sustainability 2022, 14(17), 10714; https://doi.org/10.3390/su141710714 - 28 Aug 2022
Cited by 3 | Viewed by 1854
Abstract
The friction coefficient is one of the dominant parameters affecting vehicle driving stability on horizontal curves. However, there is no comprehensive framework to assess the traffic safety on the horizontal curve with the evolution of the friction coefficient caused by the traffic flow. [...] Read more.
The friction coefficient is one of the dominant parameters affecting vehicle driving stability on horizontal curves. However, there is no comprehensive framework to assess the traffic safety on the horizontal curve with the evolution of the friction coefficient caused by the traffic flow. In light of this, this paper developed an integrated risk-assessment framework to evaluate the safety on the horizontal curve with the friction coefficient evolving under different traffic characteristics. The speed distribution on the horizontal curve of the freeway is obtained through field experiments that serve as the basic parameters of the model. A new multi-vehicle risk index (MRI) is introduced to assess the traffic safety risk for the horizontal curve by coupling the reliability theory and negative binomial. Three traffic characteristics are considered in the analysis: cumulative traffic volume (CTV), annual average daily traffic (AADT), and average daily traffic of heavy goods vehicles (AADTHGV). The results show that the AADT and AADTHGV have a considerable impact on the road risk level. When the truck traffic volume is less than 1000 veh/d, the risk of horizontal curves changes less as road operational time goes. The research results can provide a reference for the road maintenance department to determine the timing of road maintenance. Full article
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15 pages, 4838 KiB  
Article
Managing Traffic Data through Clustering and Radial Basis Functions
by Heber Hernández, Elisabete Alberdi, Heriberto Pérez-Acebo, Irantzu Álvarez, María José García, Isabel Eguia and Kevin Fernández
Sustainability 2021, 13(5), 2846; https://doi.org/10.3390/su13052846 - 5 Mar 2021
Cited by 9 | Viewed by 2959
Abstract
Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic [...] Read more.
Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT. Full article
(This article belongs to the Special Issue Road Traffic and Pavement Engineering toward Sustainable Development)
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11 pages, 943 KiB  
Article
Evaluation of Traffic Density Parameters as an Indicator of Vehicle Emission-Related Near-Road Air Pollution: A Case Study with NEXUS Measurement Data on Black Carbon
by Shi V. Liu, Fu-Lin Chen and Jianping Xue
Int. J. Environ. Res. Public Health 2017, 14(12), 1581; https://doi.org/10.3390/ijerph14121581 - 15 Dec 2017
Cited by 36 | Viewed by 7209
Abstract
An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as land use regression [...] Read more.
An important factor in evaluating health risk of near-road air pollution is to accurately estimate the traffic-related vehicle emission of air pollutants. Inclusion of traffic parameters such as road length/area, distance to roads, and traffic volume/intensity into models such as land use regression (LUR) models has improved exposure estimation. To better understand the relationship between vehicle emissions and near-road air pollution, we evaluated three traffic density-based indices: Major-Road Density (MRD), All-Traffic Density (ATD) and Heavy-Traffic Density (HTD) which represent the proportions of major roads, major road with annual average daily traffic (AADT), and major road with commercial annual average daily traffic (CAADT) in a buffered area, respectively. We evaluated the potential of these indices as vehicle emission-specific near-road air pollutant indicators by analyzing their correlation with black carbon (BC), a marker for mobile source air pollutants, using measurement data obtained from the Near-road Exposures and Effects of Urban Air Pollutants Study (NEXUS). The average BC concentrations during a day showed variations consistent with changes in traffic volume which were classified into high, medium, and low for the morning rush hours, the evening rush hours, and the rest of the day, respectively. The average correlation coefficients between BC concentrations and MRD, ATD, and HTD, were 0.26, 0.18, and 0.48, respectively, as compared with −0.31 and 0.25 for two commonly used traffic indicators: nearest distance to a major road and total length of the major road. HTD, which includes only heavy-duty diesel vehicles in its traffic count, gives statistically significant correlation coefficients for all near-road distances (50, 100, 150, 200, 250, and 300 m) that were analyzed. Generalized linear model (GLM) analyses show that season, traffic volume, HTD, and distance from major roads are highly related to BC measurements. Our analyses indicate that traffic density parameters may be more specific indicators of near-road BC concentrations for health risk studies. HTD is the best index for reflecting near-road BC concentrations which are influenced mainly by the emissions of heavy-duty diesel engines. Full article
(This article belongs to the Section Global Health)
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