Average Daily Traffic Volume Research Articles

Pollutant concentration measurement and emission factor analysis of highway tunnel with mainly HGVs in mountainous area

In recent years, with the rapid growth of traffic flow in highway tunnels, the in-tunnel environmental pollution problem has aroused widespread concern in China. Previous studies mainly focused on the emission factors in tunnels dominated by passenger cars (PCs) and light commercial vehicles (LCVs). However, this study concentrated on the characteristics of traffic flow dominated by heavy-goods vehicles (HGVs), environmental monitoring and assessment in highway tunnels in mountainous areas. To explore the emission characteristics of vehicle pollutants in highway tunnels in such regions, a field measurement was conducted in Qinling No. 3 Tunnel in China, focusing on the traffic volume, air velocity, and pollutant concentration. Measurement results show that the average daily traffic volume in Qinling No. 3 Tunnel is 22,218 pcu/d, with the HGVs comprising more than 65%. The average air velocity in case tunnel reaches 5.09 m/s. The average concentrations of NH3, SO2, CO, and NOx in the tunnel are 4.54 ppm, 0.06 ppm, 6.22 ppm, and 4.39 ppm, respectively. The single vehicle emission rates of measured pollutants are calculated based on collected data, among which, the emission factor of NOx is the largest (average: 9.37 g/(km·veh)), while that of SO2 is the lowest (0.09 g/(km·veh)). The results show that the dilution of NO2 in the tunnel should be considered in the ventilation design of highway tunnels.

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.

Replication of Daily and Monthly Freeway Demand Variations for Travel Time Reliability Procedures

The sixth edition of the Highway Capacity Manual (HCM) incorporates a travel time reliability assessment procedure for freeways and urban streets. Several demand adjustment factors, referred to by demand multipliers, are used to capture traffic demand variation across different days and months. These factors are currently produced by referencing the average daily traffic volume of each day-month combination to a base daily volume. However, practitioners usually perform traffic analyses during specific times of the day, for example, peak periods, off-peak periods, or even peak hours, demand multipliers may therefore replicate demand variation more accurately if they are based on traffic volumes concurred in time intervals narrower than a day. This paper investigates six criteria or periods to derive demand multipliers: full-day, pre AM-peak, AM peak-period, midday, PM peak-period, and post PM-peak. The study explores how these periods affect the scale of demand multipliers and the travel time reliability assessment. It was found that the main statistics of demand multipliers, that is, the mean, range, and standard deviation, greatly differ across the different multiplying periods. If analyzing peak periods on oversaturated corridors, the adoption of daily-volume multipliers was found to significantly overestimate the mean travel time index and planning time index during both the AM and PM peak periods, the accuracy of the travel time reliability estimation was considerably influenced. The study concludes with major findings and recommendations for possible enhancements to the HCM travel time reliability procedure.

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.

A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records.

Highway Development and Capacity Utilisation in Ogun State, Nigeria

Abstract Highway development in Nigeria pioneered other modes of transport including rail, air, water and pipeline. It serves as the most efficient means of distributing agricultural products, locally-made products and natural resources. As a result of this, highways requires adequate planning and periodic maintenance for effective and efficient performance. This study examined traffic situation and capacity utilisation of highways in Ogun State, Nigeria with particular reference to Lagos-Ibadan, Lagos-Abeokuta and Sagamu-Benin Highways. Manual traffic count method was employed for the estimation of traffic volume and flow pattern. The count took 12 hours a day for three consecutive days (Tuesday, Thursday and Saturday) of a week. The traffic data gathered were analyzed and interpreted using descriptive and inferential techniques to determine Average Daily Traffic Volume (ADTV), flow situation and capacity utilization rate of each highway through thorough observation of inbound and outbound traffic. Findings revealed significant variation in traffic flow situation observed on Tuesday, Thursday and Saturday of selected highways. Findings also revealed that Lagos-Ibadan Highway (2,085 vehicles/hour/lane) is well utilized while Abeokuta-Lagos and Sagamu-Benin Highways are underutilized with 820 and 1,184 vehicles/hour/lane respectively. Improvement measures and strategies to address traffic flow situation including route development and utilisation issues on the highways were proposed.

Analysis of Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

The main objective of this study was to investigate the impact of factors linked to the geometric, roadway and traffic flow characteristics on traffic accident occurrence at Hazardous Road Locations (HRL) in the region of Sousse, Tunisia using longitudinal data of 52 HRLs for an 11-year monitoring period from January 1, 2004 to December 31, 2014. The results of estimation of Negative Binomial model and the Random Effects Negative Binomial model reported that the different variables representing the presence of rural segment, the curved alignment, the visibility, the average daily traffic volume, the presence of public lighting, the number of lane, the presence of vertical/horizontal sign, the presence of drainage system, the presence of paved shoulder, the roadway surface condition, and the presence of major road were the main significant variables influencing accident occurrences. Therefore, the results could be useful for deciders in the transport sector in order to implement effective measures to improve road safety by adopting proper sound use practices, establishing efficient transportation systems and formulating appropriate traffic policies and legislation.

Analysis of the Damage of Rigid Pavement Road by Using Pavement Condition Index (PCI)

Bogasari Road, which is located in Citeureup sub-district, Bogor Regency, within the PT Indocement Tunggal Prakarsa industrial area, is a road that uses rigid pavement. The road is always passed by heavily loaded vehicles that affect road pavement conditions and the level of comfort and safety for road users. The study's purpose is to determine the average daily traffic volume, determine the value of conditions in the rigid pavement, and provide input to relevant agencies in terms of solutions and road repair costs. The method used in the analysis is the Pavement Condition Index (PCI). The study shows the average daily traffic of 2,883 vehicles/hour/day and the average pavement condition value of 66.57 with a good rating, or in good condition. It also provides a method of repair by functional means.

Injury Prediction Models for Onshore Road Network Development

Abstract Integrating different modes of transport (road, rail, air and water) is important for port cities. To accommodate this need, new transport hubs must be built such as airports or sea ports. If ports are to grow, they must be accessible, a feature which is best achieved by building new roads, including fast roads. Poland must develop a network of fast roads that will provide good access to ports. What is equally important is to upgrade the network of national roads to complement fast roads. A key criterion in this case is to ensure that the roads are efficient to minimise time lost for road users and safe. With safety standards and safety management practices varying vastly across the EU, Directive 2008/96/EC of the European Parliament and of the Council was a way to ensure that countries follow procedures for assessing the impact of road projects on road safety and conduct road safety audits, road safety management and road safety inspections. The main goal of the research was to build mathematical models to combine road safety measures, i.e. injury density (DI) and accident density (DA), with road and traffic factors on longer sections, all based on risk analysis. The practical objective is to use these models to develop tools for assessing how new road projects will impact road safety. Because previous research on models to help estimate injuries (I) or injury density (DI) on long sections was scarce, the authors addressed that problem in their work. The idea goes back to how Poland is introducing procedures for assessing the effects of infrastructure on safety and developing a method to estimate accident indicators to support economic analysis for new roads, a solution applied in JASPERS. Another reason for the research was Poland’s insufficient and ineffective pool of road safety management tools in Poland. The paper presents analyses of several models which achieved satisfactory results. They are consistent with the work of other researchers and the outcomes of previous research conducted by the authors. The authors built the models based on a segmentation of national roads into sections from 10 to 50 km, making sure that they feature consistent cross-sections and average daily traffic volumes. Models were built based on the method described by Jamroz (Jamroz, 2011). Using the available road traffic volume data, each section was assigned variables defining geometric and traffic features. Based on studies conducted on road sections, the variables were either averaged over the entire length of the section or calculated as a percentage of the variable occurring over the entire length: related to traffic volume, roadside environment or cross section

Development of Delay Claim with a Traffic Volume Approach of the Road National Improvement Project

The objective of the road infrastructure improvement project is to enhance the economy of the community. However, during the construction period, there could be a negative impact for the community of road users and the surrounding environment. Negative impacts on road users can be delays due to traffic jams. The impact of congestion on road users that is often calculated is the loss of road users cost. The loss of the road users cost of a project link road varies. Similarly, the value of the contract of execution of this project also varies. If the contractor is late in completing the project, then, according to the contract is subject to claim delay of 0.1% of the contract value. In this paper, a model of delay claims that the loss of road user costs with variable generators is worth the average daily traffic volume. The descriptive method used in this research. The case of this study is the execution of the two-lane two-way National road in the province of Bali. The results have found that the loss of road user costs is aligned with traffic volume. The logarithm model has found a relationship between the claim delay in contract and the traffic volume. The delay claim charges 0.23% of contract amount of traffic volumes smaller than 11,400 pcu/day, and the maximum claim 0.34 % of contract amount of traffic volume more than 72,700 pcu/day.

Crowdsourcing-based traffic simulation for smart freight mobility

Crowdsourcing is emerging as a powerful tool in transportation applications as it aims to provides possible solutions to problems by soliciting inputs from the crowd - usually people, objects or entities on an individual level. The primary sources of ‘big data’ in transportation (which includes social media, mobile applications and connected vehicles) facilitate this evolution through crowdsourcing; however, limited applications of crowdsourcing have been found in literature for the freight operations. In this paper, a simulation framework is developed combined with probabilistic modeling to make freight truck a ‘smart freight’ truck - with improved mobility by being able to detour to avoid a downstream congestion on the path. Smart freight trucks have access to crowdsourced data on an imminent congestion on its route and can detour leveraging this privilege. A discrete-time Markov chain (DTMC)-based simulation model is developed which describes the detour process through the nearest exit ramp of a freeway. The detour occurs with the help of crowdsourced information on a downstream congestion location. Simulation analyses carried out with both average daily passenger cars and truck traffic volumes using interstate 405 (I-405) and I-605 in the Southern California Region substantiate the effectiveness of the model. Simulation analysis shows that the efficiency improvements in detouring freight trucks under free flow traffic conditions and congested scenarios are 52% and 31%, respectively. Since connected vehicle technology (CVT) relies heavily on crowdsourced information from other vehicles in the fleet, simulation findings of this research can have large-scale implications in determining the success of CVT applications in freight operations. This crowdsourced data-based simulation framework can be used within traffic simulation software packages to enhance both passenger and truck freight operations.

Impact of Road Infrastructure Land Use and Traffic Operational Characteristics on Pedestrian Fatality Risk: A Case Study of Kolkata, India

Pedestrian safety in urban India is an issue of growing concern. While an extensive body of literature is available on the identification of elements related to the pedestrian’s risk, the studies are mainly conducted in urban areas of developed countries, where the condition of infrastructure, built environment, road user’s behavior and perception are very different from Indian cities. As a result, findings from these studies may only be partially applicable to the cities of developing countries such as India. The present study makes an attempt to fill the gap by analyzing historical crash records obtained from Kolkata police and identifies the hazardous corridors and network locations posing the highest risk to the pedestrian. Subsequently, the binary logistic regression models are developed to identify the risk factors associated with road infrastructure, land use, and traffic operational characteristics. Based on the statistical assessment carried out in this study, there is evidence that factors such as road width, land use type, the absence of designated bus stop facility, inadequate sight distance, average daily traffic and pedestrian volume, and pedestrian–vehicular interaction considerably affect pedestrian safety at the intersections. On the other hand, road width, land use type, inadequate sight distance, the absence of designated bus stop facilities significantly influence pedestrian safety at midblock road segments. The knowledge of the risk factors specific to a location on the road network is helpful for incorporation and/or modification of the particular planning and design elements to reduce risk.

Technical Approaches to Developing a Highway Noise Programmatic Agreement

There are many instances in which noise studies were completed for projects that were not likely to result in noise impacts. In this paper, the authors provide an analysis from their conducted study, validating a programmatic agreement (PA) intended to reduce the need for unnecessary noise analyses. The technical study considered a range of average daily traffic volumes between 500 and 28,000. The parameters of the PA rely solely on calculated noise levels at specified traffic volume, mix, speed, and roadway designs using the FHWA Traffic Noise Model (TNM®) version 2.5 to develop a series of TNM cases, testing impact thresholds at various speed and slope conditions. Using this data, the authors developed a regression model to enable the PA expansion and provide answers for a finer grid of traffic volume. Using the regression model results, the authors will develop a graphical user interface to assess project eligibility for the PA.

A Bayesian Regression Model for Estimating Average Daily Traffic Volumes for Low Volume Roadways

Common Average Daily Traffic (ADT) estimation models use Linear Regression and a collection of socio-economic and roadway variables. While linear regression is widely understood, it is not always optimal for developing prediction models as the regression techniques don’t have the ability to account for data distributions, or variability of the point estimates. To overcome this limitation, this paper presents a study that utilizes a Bayesian Regression model to develop a model to estimate ADT values for low volume roadways. The need for ADT estimates is critical as roadway traffic counts are the backbone of maintenance, safety and construction designs. While significant investment is made in collecting ADT values for higher functionally classified and high volume roadways, low volume roadways are often neglected in the traffic count program due to budget limitations and the misguided notion that there is limited return on investment in counting these facilities. This research developed a technique to estimate ADT for local roads in Alabama incorporating variables used in previous studies and a Bayesian Regression model. The final Bayesian Regression model relies on four independent variables: number of households in the area, employment in the area, population to job ratio and access to major roads. The model was used to generate ADT estimates on low-volume rural, local roads for 12 counties in Alabama. The paper concludes that the model can be used to predict the ADT for low-volumes roadways in Alabama for future applications.

Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation

The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model framework was used to estimate the prediction model based on field collected crash data for 26 existing roundabouts across the country. Peak hour traffic distribution was simulated with AIMSUN, and peak hour conflicts were then estimated with the SSAM applying the filters identified by calibrating AIMSUN and VISSIM. The crash prediction model was based on the assumption that the crashes per year are a function of peak hour conflicts, the ratio of peak hour traffic volume to average daily traffic volume and the roundabout outer diameter. Goodness-of-fit criteria highlighted how well the model fitted the set of observations also better than the SSAM predictive model. The results highlighted that the safety assessment of any road unit may rely on surrogate safety measures, but it strongly depends on microscopic traffic simulation model used.

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.

Statistical Investigation of Truck Type Distribution on Cold Region Highways During Winter Months

Recent research on impact of weather interaction on classified traffic volume variations on provincial highways in cold regions showed that the total truck volume is not affected with severity of snowfall and cold temperature. However, no research is conducted to analyze the variations in truck distributions, despite of its importance for truck counting and monitoring program. Described in this paper is the statistical investigation of association of truck type distribution on cold region highways during severe winter months and seasons in a year. The investigation is based on weigh-in-motion data collected from six sites located on five provincial highways in Alberta, Canada. Trucks were classified into three types such as straight unit, single trailer, and multi trailer using the FHWA vehicle classification scheme. Two statistical tests namely Chi-squared test and Binomial probability test are applied to analyze the distributional change in three different truck classes during high snowfall and low temperature conditions. The analysis suggested that the truck type distribution does not change from winter to non-winter season for regional commuter road (Highway 2A) and long distance roads (Highway 2 and Highway 16). Also, no change in truck distribution from month to month was noticed during sever winter months. Consistent results were not found for special roads such as Highway 44 due to difference in road user characteristics. The study findings have practical implications for rationalization of the length and frequency of traffic counts including classified traffic monitoring programs throughout the year. The knowledge about independency of truck type distribution with various seasons is likely to help in effective traffic monitoring and estimation of the highway planning and design parameters like Truck Annual Average Daily Traffic (TAADT), Truck Average Daily Traffic (TADT) and Design Hour Truck Volume etc.

Safety Evaluation of the SafetyEdge Treatment for Pavement Edge Drop-Offs on Two-Lane Rural Roads

This study estimated crash modification factors (CMFs) for the SafetyEdge paving technique that is applied for the treatment of pavement edge drop-offs on two-lane rural highways. An empirical Bayes observational before-after evaluation based on installation data in Iowa, North Carolina, Ohio, Pennsylvania, and Florida found that the SafetyEdge treatment was associated with statistically significant reductions in fatal and injury (FI), run-off-the-road (ROR), opposite-direction (head-on and sideswipe), and drop-off-related crashes. The ROR CMF was also statistically significant for both horizontal curved and tangent sections. A disaggregate analysis examined the variation of the CMF for ROR with factors such as the travel lane width, traffic volume, presence of a horizontal curve, posted speed limit, and the pre-treatment ROR crash frequency. The results of that analysis indicated, for example, that the SafetyEdge paving technique appears to have a greater ROR safety benefit on two-lane rural roadway segments with average annual daily traffic volumes greater than 3,000 vehicles per day, relative to roadway segments with lower traffic volumes. A crash modification function (CMFunction) was calibrated with expected pre-treatment ROR crashes as the independent variable to simultaneously capture the relationship of the CMF for ROR crashes to multiple factors. An economic analysis found that the treatment is highly cost-effective.

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.

Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference

This study investigates factors that significantly contribute to the severity of driver injuries resulting from single-vehicle run-off-road (SV ROR) crashes. A mixed logit model approach is employed to explore the potential unobserved heterogeneous effects associated with each age group: young (ages 16–24), middle-aged (ages 25–65), and older drivers (ages over 65). Likelihood ratio tests indicated that the development of separate injury severity models for each age group is statistically superior to estimating a single model using all data. Based on the crash data collected from 2009 to 2013 in North Carolina, a series of driver, vehicle, roadway, and environmental characteristics are examined. Both parameter estimates and their elasticities are developed and used to interpret the models. The estimation results show that contributing factors which significantly affect the injury severity of an SV ROR crash differ across three age groups. Use of restraint device and horizontal curves are found to affect crash injuries and fatalities in all age groups. Reckless driving, speeding, distraction, inexperience, drug or alcohol involvement, presence of passengers, and driving an SUV or a van are found to have a more pronounced influence in young and middle-aged drivers than older drivers. Compared to the passenger cars, older drivers are less likely to experience possible injuries in a large-size vehicle (e.g., truck or bus). The average annual daily traffic volume and lighting conditions are also found to influence the resulting injury severity of an SV ROR crash specific to young drivers.