Annual Average Daily Traffic Volume Research Articles

Influence of Land use and Traffic on Road Safety Along Jimeta Bypass

Many roads in some urban areas in developing countries, such as Nigeria, are characterized by a high level of traffic and, because of limited resources, receive inadequate forms of protection and inappropriate maintenance strategies. Some of the roads have become part of a landfill dump embankment, which cause defects that contribute to road accidents. The study sought to investigate the effect of land use and traffic on road safety along the Jimeta Bypass Road. Vehicle enumeration was carried out by manual counting. Road traffic accident data for the analysis was obtained from the Federal Road Safety Commission in Yola. Physical inspection of the pavement section revealed that it was in an unsuitable condition for riding and safety. The findings also revealed that the road pavement had an annual average daily traffic volume of about 1470 vehicles per day, with tricycles having the highest traffic volume and luxurious buses having the lowest traffic volume. Analysis of variance showed that there was no significant variation in traffic volume between periods (morning, afternoon, and evening); traffic only differs between vehicle types. Accident data collected was found to highly correlate with traffic volume, an indicator that the high traffic volume along the Jimeta Bypass Road could be responsible for the occurrence of road traffic crashes along the road. The study findings also indicate there is need to adopt the maintenance culture by Road Authorities and relevant stakeholders so as to address the failure condition at the onset.

Do Expressway Interchanges Increase Crash Injury Severities? Insights Using Temporal Instability and Unobserved Heterogeneity

Expressway crashes in interchange areas are a critical concern in China, posing significant economic and social challenges. Utilizing three years of crash data from the Beijing–Shanghai Expressway, this study investigates the transferability and heterogeneity of crash characteristics between interchange and noninterchange areas, as well as the temporal shifts in factors influencing injury severity levels. The research employs four series of random parameters logit models to estimate three potential crash injury severity outcomes of severe injury, minor injury, and no injury (based on the most severely injured individual in each crash) and to identify key determinants, encompassing driver, vehicle, roadway, environmental, temporal, traffic, and crash attributes. Likelihood ratio tests and out-of-sample predictions are utilized to assess the temporal stability and transferability of crash area characteristics. Additionally, the marginal effects of various determinants are calculated to understand their influence across different year periods and crash types. Key variables such as overspeed, single-vehicle, AADT (annual average daily traffic volume), Ls min , and other crash type indicators are identified as significant random parameters, demonstrating heterogeneity in means and variances. Notable distinctions are observed between interchange and noninterchange crashes, indicating nontransferability, with most significant indicators revealing temporal instabilities. Furthermore, factors such as multivehicle involvement, buses, and nighttime conditions are identified as risk indicators, notably increasing the likelihood of severe injuries. These insights are invaluable for expressway designers and decision-makers, aiding in understanding the contributing mechanisms of various elements. This study suggests that stricter enforcement measures are crucial to prevent random lane changes, particularly at interchange entrances and exits. Additionally, effective management strategies and enforcement countermeasures should be implemented to mitigate crash injury outcomes in both interchange and noninterchange areas.

Developing Economic Loss-Based Thresholds for Improving Context-Specific Crash Prediction Models

Negative binomial-based safety performance functions (SPFs) have been extensively used by United States Department of Transportation professionals for predictive crash analysis. Recently, the Florida Department of Transportation (FDOT) has developed a context classification approach and incorporated it into crash prediction models, which has the potential to significantly enhance their accuracy and reliability. The additional modeling contexts and parameters make it more challenging to diagnose and remedy modeling problems, however. Particularly for roadway segments with low annual average daily traffic (AADT), short lengths, or low counts of severe crashes, the SPF models significantly underestimate the actual number of crashes. This uncertainty in SPF predictions can lead FDOT practitioners to reach misleading conclusions, such as failing to detect sites with genuinely high crash rates. This project intends to establish thresholds for certain SPF parameters to ensure reliable crash predictions are obtained across various context classes. For this purpose, we (a) developed a functional statistical model that quantifies economic loss relative prediction errors as a function of AADT volume and (b) calculated the minimum context-specific AADT threshold for each segment length group, roadway category, context classification, and crash severity combination. Employing the developed AADT thresholds confirmed up to 89% reduction in SPF prediction errors for the most represented context class. In light of the results obtained, we are able to conclude that context-specific AADT thresholds perform well in significantly reducing prediction errors for the thresholded segments and contexts on Florida roadways.

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.

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.

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.

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.

Development and application of an innovative approach to predicting pollutant concentrations in highway runoff

Recognising the challenges and limitations of current methodologies to predict highway runoff concentrations, this paper presents a novel approach based on the derivation of pollutant emission factors for twelve different types of vehicle. Published emission factor data and properties of differing vehicles types are combined with annual average daily traffic volume (AADT), highway characteristics and rainfall data to determine the pollutant distributions associated with differing highway and traffic types. In this paper, the method is applied to 126 sections of highway in the Greater London Borough of Enfield (United Kingdom; UK) and results are comparable with values reported in the literature. The approach is used to identify the level of AADT predicted to result in an exceedance of environmental quality standards (EQS), with results suggesting that runoff from highways experiencing AADT values as low as 5000 may require treatment prior to discharge to receiving waters. Future scenario analyses indicate that the impact of progressively replacing petrol and diesel vehicles with electric vehicles will have negligible impact on concentrations of zinc (Zn), copper (Cu), cadmium (Cd) and total suspended solids discharging from highway environments. The approach enables identification and ranking of urban highways in terms of their pollution runoff potential and provides an important support to users in prioritising locations for the installation of sustainable drainage options in order to protect receiving water environments.

Environmental and economic comparison of semi-rigid and flexible base asphalt pavement during construction period

Sustainable design of pavements is necessary for achieving carbon neutrality, which is the important goal for green pavements. In order to investigate the impacts of major design parameters on the environmental and economic performances of asphalt pavement, process life cycle assessment (LCA) and process life cycle cost (LCC) methods were used. And this paper mainly focuses on the effects of pavement type, resilient modulus of subgrade, thickness of adjustable layer, traffic level and the bidirectional annual average daily traffic volume of two-axis six-wheel and above vehicles (AADTT) on environmental and economic performances of asphalt pavement. Then energy consumption, greenhouse gas (GHG) emission, construction budget and environmental economy cost of heavy traffic and medium traffic asphalt pavement were quantitatively analyzed during construction period. The results indicate that the energy consumption, GHG emission, construction budget and environmental economy cost of each layer of semi-rigid and flexible base asphalt pavement are the largest in the materials preparation stage, followed by the transportation stage and the construction stage. The above environmental and economic indicators are mainly influenced by AADTT, adjustable layer thickness and pavement type of asphalt pavement during construction period, while the resilient modulus of subgrade has minor effect on environmental and economic indicators. Overall, the energy consumption and GHG emission of the flexible base asphalt pavement are lower than that of the semi-rigid base asphalt pavement, although flexible base asphalt pavement has higher construction budget and environmental economy cost.

Estimation of Annual Routine Maintenance Cost for Highway Tunnels

In highway management, the prediction of the routine maintenance cost of tunnels is an important issue in saving tunnel maintenance costs due to its uncertainty, and the influencing factors should be carefully selected because too many variables could not be involved in the model. The complicated relationship between variables may lead to the inconsistency of model coefficients with the actual situation even though the goodness of fit of the model constructed with more variables is higher. This paper presents an approach in which quantitative analysis is combined with qualitative analysis to quickly select the independent variables of the tunnel routine maintenance cost (TMC) model. Based on the routine maintenance data collection of nine highway tunnels in Shaanxi province from 2007 to 2016, the independent variables of the models are determined with one‐way ANOVA, Pearson correlation, partial correlation, and hierarchical regression. Afterwards, a fixed‐effect regression model which can reflect the overall regional features is developed. Results show that tunnel age (Age) and tunnel length proportion (PET) have less effect on TMC among the main influencing factors such as district, Age, annual average daily traffic volume (AADT), truck traffic volume proportion (PTT), PET, and number of ventilation facilities (NVF), while the NVF makes a positive contribution to the TMC. Compared with grouped regression models, the fixed‐effect regression model has higher fitting accuracy and a better regression coefficient significance. The quick independent variable selection method can shorten the time of establishing the model and determine the influencing factors of the research object effectively. The established model is suitable for forecasting the TMC and budget arrangement. In addition, the elastic analysis results of regression coefficients are helpful to the decision of maintenance strategy and the allocation of maintenance funds.

Effects of the COVID-19 Pandemic on the Air Quality of the Metropolitan Region of São Paulo: Analysis Based on Satellite Data, Monitoring Stations and Records of Annual Average Daily Traffic Volumes on the Main Access Roads to the City

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series.

Brief design and numerical calculations for a secondary road

Based on the traffic volume survey, the standard traffic volume conversion is considered, and then the road traffic volume is predicted. Through the calculation, the maximum annual average daily traffic volume in the design life is obtained, and then the road grade and design speed of the design road are determined. The Annual Average Daily Traffic (AADT) is converted into Directional Design-Hour Volume (DDHV), and then the number of lanes of the road is determined. Collect the terrain data of the route area through the field survey, and then carry on the interior design. The route schemes are compared and the optimal route scheme is selected. AUTOCAD and a professional road design software were used to assist in the design of road line selection, horizontal curve design, vertical section design, cross section design and drainage design. Through the calculation, the design parameters such as the maximum synthetic slope, the average slope and the minimum length of superelevation transition section are obtained. Afterwards, the parameters in horizontal curve, vertical curve and superelevation-widening were checked manually.

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

Surveying annual average daily traffic volumes using the trip connectivity function of vehicle GPS in an urban road network

ABSTRACT Vehicle-trajectory big data, collected through vehicle-GPS systems, is one of key information sources for road traffic volumes because the vehicle-trajectory volume represents a certain portion of the total traffic volume. This renders a promising opportunity in surveying annual average daily traffic (AADT) volumes. Devising viable means of surveying AADT volumes for all road segments with limited budgets and resources remains one of the main challenges in urban transportation studies. This paper proposes a new methodology for directly surveying AADT volumes using vehicle-GPS trajectory data. The methodology consists of two sub-methods: a nonlinear spatial clustering method based on trip connectivity between observed road sections and a target road section for selecting effective road sections from a road network, and a direct conversion method to understand the nonlinear relationship between the annual average daily probe (AADP) volumes and the observed AADT volumes for the selected road sections, with the subsequent expansion of the AADP volume of the target road section into the AADT volume. In a case study with real-world vehicle-GPS trajectory data, the performance of the method was found to be highly acceptable for actual applications in terms of its estimation accuracy. Therefore, it appears that the proposed method is and will be feasible in the present and near future.

Left turn crash risk analysis: Development of a microsimulation modeling approach

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection.Safety impacts for 750 unique combinations of intersection geometry, traffic, and signal timing parameters were simulated and the number of left-turn conflicts per hour noted. Results of the simulation analyses were used to develop statistical models that expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn phasing mode and prevailing conditions during that hour. The study was motivated by the recent widespread application of the flashing yellow arrow (FYA) which provides the opportunity to vary left-turn phasing mode by time of day—potentially leading to more efficient traffic operations at signalized intersections. In this regard, the study addresses a basic need for tools that predict how the risk for left-turn crashes might vary at a more disaggregated level than that provided by existing crash prediction models, which typically predict yearly totals of left-turn crashes, often based on annual average daily traffic volumes. Potential application of the model to the implementation of a time-variable safety-based left-turn phasing selection scheme using FYA was successfully demonstrated.

The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments

A promising methodology is proposed to estimate reliable annual average daily traffic (AADT) volumes for no-surveyed road sections using probe volumes collected by a vehicle global positioning system (GPS). This research was inspired by the obvious concept that probe counts are a direct portion of AADT from the viewpoint of vehicle trip behavior. The method converts the probe volume of target road section to AADT using the nonlinear relationship between geographical neighborhoods composed of observed AADT volumes and annual average daily probe volumes. The relationship is determined with a locally weighted power-curve model. A feasibility of the proposed method was demonstrated through a case study using real-world data. Analysis results show that the proposed method is a practical and cost-effective way to estimate reliable AADT for unmeasured road segments. This indicates that there exists a strong relationship between AADT values and vehicle-GPS probe values from the trip characteristics of a road network.

An Improved Algorithm for Highway Design Hourly Volume Calculation

The designed hour traffic volume is an important parameter in the construction of highway toll station. It is usually calculated by using the ratio of the 30th traffic volume and annual average daily traffic volume. Based on toll data of Shaanxi highway, the method of calculating 30th hour traffic volume was improved in the paper. After deducting the national legal holiday period for the free passage of small passenger cars ， the ratio of hourly traffic volume in smooth and annual average daily traffic was obtained as the designed hour traffic volume coefficient. This algorithm is more scientific and accurate and easy to implement. It meets the requirement of highway toll station traffic volume forecast.

Reliability Evaluation of a PSC Highway Bridge Based on Resistance Capacity Degradation Due to a Corrosive Environment

This paper presents a method for evaluating the reliability of an in-service highway bridge that considers the resistance capacity loss due to various corrosive environments. To demonstrate the application of the suggested method, a pre-stressed concrete-I (PSC-I) type girder was selected as a sample bridge. An analytical procedure was developed to quantitatively evaluate the performance degradation of a PSC-I girder bridge considering the traffic conditions, corrosive environment, and crack damage. The bridge performance was evaluated by considering traffic conditions, including the annual average daily traffic volume, heavy vehicle volume, and corrosive environment (mild, normal, and severe). To calculate the resistance capacity, all variables regarding the materials and sections were considered through probabilistic variances, Monte Carlo simulation, and the statistical characteristics of the resistance. The results showed that the performance degradation is sensitive to the important parameters of the traffic conditions and corrosive environment, which may decrease the structural reliability and lead to bridge failure. Cracks in a PSC-I girder may accelerate the performance degradation and affect the reliability level of the bridge. Therefore, a maintenance plan should be rationally considered depending on the site environment.

A site-specific traffic load model for long-span multi-pylon cable-stayed bridges

This paper proposes a site-specific traffic load model for long-span multi-pylon cable-stayed bridge. Structural effects are primarily investigated based on influence lines, which are identified as either global effect (GE) or partial effect (PE) depending on the effective influenced region. GEs are further categorised as sensitive effect (SE), insensitive effect (ISE) or less sensitive effect (LSE), considering sensitivity to unbalanced traffic loading. Three on-bridge traffic states are simulated, and Weibull extrapolations are utilised to predict the extreme responses. These responses are analysed and compared with several design codes. Results indicate the maximum response is only 75% of the value calculated based on the design code of China (D60), and even lower than other codes. The responses show strong positive correlation with traffic parameters of annual average daily traffic volume and heavy vehicle proportion, and the on-bridge traffic states have significant influence on the responses. Further, the identified effects of ISE, SE and LSE present different responses, which indicate specific load models are needed accordingly. Finally, a site-specific traffic load model consisting of load form, loading pattern, multi-lane factor and load value is recommended, which gives an accurate illustration on the structural effects and traffic responses.

Verification of Methodical Procedure for Determining the Traffic Volumes Using Short-term Traffic Surveys

The traffic volume is frequently used measure of road capacity. Traffic volumes are suitable for urban planning, construction and upgrading of roadways or land-use planning decisions, while it is necessary to plan the construction of road infrastructure in the next few decades ahead on the basis of prognosis of road traffic.This paper outlines the problems of calculating the traffic volumes in Czech Republic. Especially the authors focus on the verification of methodical procedure for the determination of resulting traffic volumes using the short-term traffic surveys. The methodology TP 189 for determining the Annual Average Daily Traffic volumes (AADT) reflects the conversion coefficients which were statistically derived from long-term measurements of traffic intensities. It can be assumed that different traffic characters exist on different types of roads, for which no coefficients are set yet. Authors of the paper use data obtained from real long-term measurements of traffic volumes on the various categories of roads and try to determine categories of roads for which current conversion coefficients to determine AADT cannot be applied