Traffic Levels Research Articles

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.

Field investigation of material layer properties for emulsion-treated base layer application in low-volume roads

ABSTRACT The study presents the determination of pavement design input parameters-structural layer coefficients and back-calculated layer moduli for Emulsion-Treated Base (ETB) layer materials in a Low-Volume Road (LVR) application. Seven test sections were constructed on a stretch of LVR with varying emulsion contents and 50% Recycled Asphalt Pavement (RAP) aggregate incorporation in ETB mixes. Structural performance of the test sections was periodically observed using a Benkelman Beam Deflectometer (BBD) and the data was used for the estimation of the structural layer coefficient of ETB, based on the effective structural number of the pavement. The deflection bowl data of the test sections were also captured using a Falling Weight Deflectometer (FWD) and the modulus of ETB layer was back-calculated using a back-calculation software. The study proposes a structural layer coefficient of 0.23–0.29 and back-calculated moduli of 578–919 MPa for ETB mixes characterised by Indirect Tensile Strength (ITS) values ranging between 140 kPa and 245 kPa. Traffic level-based ETB mix specifications are also proposed in the study, which enables a mix designer to proportion the mix for a selected design traffic level. For a design traffic level of 1 million standard axles, the target dry ITS specification was found to be 188 MPa.

Measuring Traffic Congestion with Novel Metrics: A Case Study of Six U.S. Metropolitan Areas

Quantifying traffic congestion is a critical task for transportation planning and research. Numerous metrics have been developed, mainly focusing on changes in vehicle speeds, their extents, and travel time. In this study, new metrics are presented using the Hägerstrand’s space-time cube that has been studied from time geography perspectives since the 1960s. Particularly, the product of distance and time, i.e., distanceTime, is proposed as a base metric to measure traffic congestion amounts. Using the base metric such as mileHours, metrics of weighted congestion and normalized congestion amounts were also developed. New metrics were applied to six metropolitan areas and their vicinities in the United States (Atlanta, Chicago, Washington, D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), and congestion amounts were calculated and compared. The Google Traffic Layer API was used to obtain traffic congestion datasets for six months (April–September 2022), and GIS (geographic information systems) was used for delineating road features and traffic intensity levels. Among the six areas, New York and its vicinity showed the largest congestion when only heavy congestion was used. Los Angeles and its vicinity showed the largest congestion when all congestion levels were considered. This study shows that the proposed metrics are very effective in summarizing traffic amounts and broadly applicable for further analyses of traffic congestion phenomena by associating various other factors, such as weekdays, months, or gas prices. The new metrics developed in this research may help transportation researchers and practitioners by providing them with a set of metrics applicable to summarizing congestion amounts by synthesizing congestion intensity, extent, and duration.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

BackgroundExisting studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents. MethodsThe traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models. Results(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %. ConclusionsCompared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

Monitoring of traffic noise pollution in urban Patna, Bihar, India

Road traffic noise is a major environmental issue that has been faced by many people around the world, including in Patna city. There is a need to investigate noise regularly because of the rapidly increasing traffic volume. This study focuses on the estimation of the urban traffic noise level in Patna, Bihar, India. Based on a preliminary survey of traffic volume, 12 stations were selected. A sound meter was used to measure the “A”-weighted noise level. Noise parameters like L10, L50, and L90 were evaluated from the collected noise data. And these noise parameters were used for the calculation of noise pollution indices like Noise Climate (NC), Equivalent Continuous Noise Level (Leq), Noise Pollution Level (NPL), and Traffic Noise Index (TNI). These noise indices compare with the standard limit of the Central Pollution Control Board (CPCB). The equivalent continuous noise level (Leq) measured at different locations in the commercial cum residential zones varies from 77.7 dBA to 92.5 dBA, which is higher than the prescribed noise level limit of the CPCB. Other noise indices, such as the NPL and TNI, are found to range from 88.2 dBA to 111.5 dBA and 77.9 dBA to 125.9 dBA, respectively, at various sampling locations and are also higher than the prescribed limit of the CPCB. The noise level of all stations was found to exceed the prescribed limit of the CPCB. There is a strong correlation exists between NPL with TNI and NC (r > 0.5). Also, there is a strong correlation exists between Leq with NPL (r > 0.5). Using NPL as a dependent variable and TNI, NC, and Leq as independent variables, the regression model was completely validated.

Anti-seismic Technology in Suspension Bridge Designs: A Review

Suspension bridges are vital transportation infrastructures that support high traffic levels. It is of great importance for suspension bridges to remain operational following hazardous earthquake motions. Seismic devices preserve crucial bridge elements and aid in the control of ground motion response through energy dissipation and damping. This literature review introduces and evaluates the performance of widely-applied anti-seismic technologies such as tuned mass dampers, fluid viscous dampers, and seismic isolators in suspension bridge designs. The paper condenses and compares the results of analytical literature surrounding the effectiveness of seismic devices. Conclusions from current research indicate that the implementation of fluid viscous dampers is optimal for deck displacement damping in suspension bridges. Less effectiveness is found for seismic isolators and almost negligible success is found for tuned mass dampers under high seismic motions. The generalization of the results and the potential performance discrepancies due to spatial variability are noted.

Quantifying Uncertainty with Pavement Performance Models: Comparing Bayesian and Non-Parametric Methods

An important part of pavement management systems is accurately estimating the performance-time-degradation relationship. One common approach to establishing this relationship is to use performance family curves. These curves are developed by collecting performance data at specific points in time and collectively shifting pavements of various ages to identify the probable underlying function. This paper compares two alternative methods for characterizing such a family curve function. First, a Bayesian method (Method-A) is used, which fits both the family curve and the shift factor function in parallel by assuming a Beta distribution for pavement performance condition rating (PCR). Second, a non-parametric method (Method-B) is developed, which fits the model in two steps; (1) by fitting the family; and (2) by horizontal shift to minimize the error. PCR values from flexible pavements in North Carolina (NC-PCR) are used for this comparison. These data include a total of 30,988 pavement sections segregated according to surface type and traffic level. Data from 2013 to 2015 are used for model calibration, and data from 2016 are used for model validation. The root means square error and k-fold cross-validation test are used to conduct the comparison, and Method-A is found to be preferred. The uncertainty in both models is quantified and compared. On the basis of this uncertainty, the Bayesian method is preferred, but in cases with large data sets, a non-parametric method does result in lower uncertainty.

A platoon-based cooperative optimal control for connected autonomous vehicles at highway on-ramps under heavy traffic

To improve traffic efficiency at highway on-ramps under heavy traffic, this study proposes a platoon-based cooperative optimal control algorithm for connected autonomous vehicles (CAVs). The proposed algorithm classifies CAVs on both mainline and on-ramp into multiple local platoons (LPs) according to their initial conditions (i.e., spacing and speed), which enables the algorithm to adapt to time-varying traffic volume. A distributed cooperative control for multiple LPs is designed which projects on-ramp LPs onto mainline to transform the complex 2-D multi-platoon cooperation problem into a 1-D platoon following control problem. An optimal control is applied to further consider the strict nonlinear safety spacing constraint and state limitations (e.g., maximum speed and acceleration), and an analytical solution to the optimal control is derived based on Pontryagin’s maximum principle. The consensus of intra-platoon and inter-platoon are analyzed, and sufficient conditions of the consensus are mathematically deducted based on Lyapunov stability theorem. Numerical simulations are conducted for different traffic demand levels and demand splits to verify the effectiveness of the proposed algorithm. The sensitivity analysis of maximum platoon sizes for mainline and on-ramp LPs is performed. A comparison with a baseline virtual platooning merging strategy is conducted, and results show that the proposed algorithm could significantly improve the average travel speed and traffic efficiency, and reduce total travel time.

Modeling of Restriping of Waterborne Paints Using Transverse Test Deck Data in Hot and Humid Climate

The National Transportation Product Evaluation Program (NTPEP) database is a promising tool for U.S. state agencies to evaluate the performance of waterborne paint pavement markings. Out of the total 50 U.S. states, 29 states (57%) utilize the NTPEP data to assess the performance of waterborne paints to be included in the approved materials list. Yet, this dataset is based on transverse retroreflectivity (RL) measurements, which are significantly different from the longitudinal RL measurements in real-world conditions. As such, the key objective of this study was to develop a methodology that converts the transverse RL measurements of waterborne paints to the corresponding longitudinal measurements in hot and humid climate. This approach could be used by transportation agencies to predict the service life of their waterborne paints and make reliable restriping decisions. To achieve this objective, three road sections were selected in Louisiana, U.S., and four different waterborne paints were installed in the transverse and longitudinal directions. These road sections consisted of seven subsections with varying traffic levels and pavement surface types. The data collected included durability readings and longitudinal and transverse RL measurements at five time periods between October 2020 and April 2022. Based on the collected data, two non-linear regression models (for asphalt and concrete surfaces) were developed, with reasonable accuracy, to predict the longitudinal RL of waterborne paints based on the corresponding transverse RL. The predicted longitudinal RL could then be used to determine the expected service life of waterborne paints and make reliable restriping decisions.

Statistical evaluation of noise due to changes in pandemic traffic patterns

Vehicular traffic noise for free-flowing roadways and highways generally follows statistically predictable flow rates and vehicular mix. The authors’ earlier work in this area examined long-term variations in traffic noise level with the purpose of establishing maximum hourly levels for vehicular sources (“Defining vehicular noise levels to manage risk associated with exterior façade design,” LoVerde, Dong, Rawlings, Internoise 2014 Melbourne) and general average sound level (“Noise prediction of vehicle sources on arterials using measured sound data,” LoVerde, Dong, Rawlings, ASA 2014 Providence; “Methods for estimating the variance in traffic noise distribution from short-duration measurements,” LoVerde, Dong, Rawlings, ICSV 2015 Florence). When the COVID-19 pandemic resulted in lockdowns, traffic patterns at the measurement location in Southern California were affected. The authors’ examination of traffic data revealed that the changes in traffic patterns did not affect noise level significantly, but variability in sound level across the 24-h period increased substantially (“Changes in statistical traffic noise descriptors during COVID-19,” LoVerde, Dong, Edwards, Rawlings, ASA 2021). For this paper, the authors have undertaken comparison of pre-, during-, and post-lockdown vehicular traffic noise levels over a several-month period for the purpose of understanding how measurement of traffic noise is implemented and interpreted.

The Echoes of Noise: Residential Exposure to Traffic and Risk of Tinnitus.

The Echoes of Noise: Residential Exposure to Traffic and Risk of Tinnitus.

Comparative analysis of noise levels available on simple and rubber granule asphalt-concrete coating roads

In this article, a scientific study was conducted and studied on the effect of road surface on traffic noise level. The experiment was conducted on two different paved roads and the noise levels were based on international standards. The results of research conducted on asphalt-concrete paved roads with the addition of rubber granules found that the noise levels on high-quality bitumen-paved asphalt concrete paved roads differ from the noise sources. By giving several recommendations for the organization of noise protection have been developed.

Road Traffic Noise and Incidence of Primary Hypertension: A Prospective Analysis in UK Biobank

BackgroundThe quality of evidence regarding the associations between road traffic noise and hypertension is low due to the limitations of cross-sectional study design, and the role of air pollution remains to be further clarified. ObjectivesThe purpose of this study was to evaluate the associations of long-term road traffic noise exposure with incident primary hypertension; we conducted a prospective population-based analysis in UK Biobank. MethodsRoad traffic noise was estimated at baseline residential address using the common noise assessment method model. Incident hypertension was ascertained through linkage with medical records. Cox proportional hazard models were used to estimate hazard ratios (HRs) for association in an analytical sample size of over 240,000 participants free of hypertension at baseline, adjusting for covariates determined via directed acyclic graph. ResultsDuring a median of 8.1 years follow-up, 21,140 incident primary hypertension (International Classification of Diseases-10th Revision [ICD-10]: I10) were ascertained. The HR for a 10 dB[A] increment in mean weighted average 24-hour road traffic noise level (Lden) exposure was 1.07 (95% CI: 1.02-1.13). A dose-response relationship was found, with HR of 1.13 (95% CI: 1.03-1.25) for Lden >65 dB[A] vs ≤55 dB[A] (P for trend <0.05). The associations were all robust to adjustment for fine particles (PM2.5) and nitrogen dioxide (NO2). Furthermore, high exposure to both road traffic noise and air pollution was associated with the highest hypertension risk. ConclusionsLong-term exposure to road traffic noise was associated with increased incidence of primary hypertension, and the effect estimates were stronger in presence of higher air pollution.

Remote acoustic sensing for urban air quality assessment

In this work, sound pressure levels and air quality are measured around near roadways in urban areas. The measured sound pressure level data are compared to the air quality index to investigate the relationship between roadway traffic levels and particulate matter concentration. Additionally, the effects of wind speed and direction are investigated. Of particular interest are the upwind and downwind conditions. The results of this work will be presented and discussed.

Analyzing multiple COVID-19 outbreak impacts: A case study based on Chinese national air passenger flow

The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi-centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future.

The effects of exposure to road traffic noise at school on central auditory pathway functional connectivity

As the world becomes more urbanized, more people become exposed to traffic and the risks associated with a higher exposure to road traffic noise increase. Excessive exposure to environmental noise could potentially interfere with functional maturation of the auditory brain in developing individuals. The aim of the present study was to assess the association between exposure to annual average road traffic noise (LAeq) in schools and functional connectivity of key elements of the central auditory pathway in schoolchildren. A total of 229 children from 34 representative schools in the city of Barcelona with ages between 8 and 12 years (49.2% girls) were evaluated. LAeq was obtained as the mean of 2-consecutive day measurements inside classrooms before lessons started following standard procedures to obtain an indicator of long-term road traffic noise levels. A region-of-interest functional connectivity Magnetic Resonance Imaging (MRI) approach was adopted. Functional connectivity maps were generated for the inferior colliculus, medial geniculate body of the thalamus and primary auditory cortex as key levels of the central auditory pathway. Road traffic noise in schools was significantly associated with stronger connectivity between the inferior colliculus and a bilateral thalamic region adjacent to the medial geniculate body, and with stronger connectivity between the medial geniculate body and a bilateral brainstem region adjacent to the inferior colliculus. Such a functional connectivity strengthening effect did not extend to the cerebral cortex. The anatomy of the association implicating subcortical relays suggests that prolonged road traffic noise exposure in developing individuals may accelerate maturation in the basic elements of the auditory pathway. Future research is warranted to establish whether such a faster maturation in early pathway levels may ultimately reduce the developing potential in the whole auditory system.

Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?

Urbanization leads to an increased demand for urban housing, which can be met by building dwellings closer to streets. Regulations often limit equivalent sound pressure levels which do not account for changes in time structure that occur when decreasing the road distance. This study investigates the effect of such temporal changes on subjective workload and cognitive performance. A group of 42 participants performed a continuous performance test as well as a NASA-TLX workload test under three different sound conditions, i.e., close traffic, far traffic, both with the same equivalent sound pressure level of LAeq≈40 dB, and silence. Additionally, participants answered a questionnaire regarding their preferred acoustic environment for concentrated working. Significant effects of the sound condition on the multivariate workload results as well as on the number of commission errors in the continuous performance test were found. Post hoc tests showed no significant differences between the two noise conditions, but there were significant differences between noise and silence. This indicates that moderate traffic noise levels can influence cognitive performance and perceived workload. If there is a difference in the human response to road traffic noise with constant LAeq but different time structures, the used methods are not suitable to detect them.

Development and Evaluation of a Cellular Vehicle-to-Everything Enabled Energy-Efficient Dynamic Routing Application.

Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings).

Outdoor residential noise exposure and sleep in preadolescents from two European birth cohorts

ObjectiveTo examine whether outdoor residential exposure to annual average road traffic and multiple (i.e., road traffic, railway, aircraft, industry) noise levels is related with preadolescents' sleep using maternal-reported and wrist-actigraphy data in two European birth cohorts. MethodsThis cross-sectional study used data of 1245 preadolescents from the Dutch Generation R Study and 232 from the Spanish INMA-Sabadell cohort with a mean age of 12.3 years old. We used noise maps to assess average outdoor road traffic and multiple noise levels (day-evening-night noise indicator, LDEN) at each child's residential address for the year before the sleep assessment. Sleep disturbances were reported by mothers through the Sleep Disturbance Scale for Children and objectively recorded using GeneActiv wrist-actigraphy during seven subsequent days. Linear and Poisson regression models adjusted for several potential confounding variables were performed. ResultsThe mean (SD) exposure to road traffic noise was 53.2 dB (7.3) in the Generation R Study and 61.3 dB (5.9) in the INMA-Sabadell cohort. Exposure to road traffic was related with reduced total sleep time and longer wake after sleep onset (e.g. −3.62 min (95%CI -6.87; −0.37) and 6.88 min (95%CI 1.15; 12.61) per an increase of 10 dB in road traffic noise, respectively) collected by wrist-actigraphy. We observed no association between road traffic exposure and maternal-reported sleep disturbances. Results were similar for multiple noise exposure. ConclusionsThese findings indicate that sleep may be compromised for preadolescents living in areas highly exposed to outdoor residential noise. Future studies using longitudinal designs to further explore these associations during the different stages of sleep development across childhood and adolescence are warranted. Also, wrist-actigraphy measurements which provide more accurate information and may be complementary to the parental- and self-reported data should be considered.

Influence Area at Signalized and Stop-Control Intersections: Operational Analysis

Understanding the influence of intersections on operating conditions along connecting roadway segments is important for the analysis of highway facilities and corridors. This study aims at assessing the influence area at signalized and stop-control intersections along rural corridors. The study used speed as a performance measure in examining the spatial extent of operational effects at intersections. High-fidelity connected vehicle (CV) trajectory data, collected at 11 different sites in Florida, was used in this study. The CV trajectory data consists of individual waypoints that included timestamps and location coordinates along with other attributes. Drivers’ speed profiles while driving through the intersection were established and analyzed to determine the length of upstream and downstream influence areas. Quantile regression models were developed to estimate the 50th, 70th, and 85th percentiles of upstream and downstream influence areas separately for signalized and stop-control intersections. Study results indicate that the upstream influence area is longer for a signalized intersection than for a stop-control intersection for comparable segment running speeds. Further, the downstream influence area is significantly longer than the upstream influence area at signalized intersections, and this was consistent at all study sites. Traffic flow level did not have a significant effect on the upstream or downstream influence area; however, midblock running speed, percent heavy vehicles, and facility type (multilane versus two-lane) were found to significantly affect the upstream and downstream influence areas at signalized intersections.