Heavy Vehicle Volume Research Articles

Cycle fatalities in Delhi and their risk factors

There is a lack of research that investigates the risk factors of cycling in low- and middle-income countries. We present descriptive epidemiology of crashes that involved at least one fatal cyclist, over a three-year period (2016–2018) in Delhi, India. Next, we used an unmatched case-control approach to identify road design risk factors of cycle fatalities. Cases were road segments with at least one cycle fatality, and controls were those with none. We developed logistic regression models with cases and controls as binary outcomes to estimate the odds ratio of site characteristics. There were 167 crashes involving at least one cyclist fatality over the study period. Fatal cyclists were almost all males. They were less likely to be children or young adults and less likely to be residents of high-income localities, compared to the general population. One in ten crashes included more than one occupant on a cycle and 5% of fatal victims were pillion riders. Seventy percent crashes occurred at midblock, and majority were backend collisions. Regression shows that road width, traffic speed, and volume of heavy vehicles have strong positive effects on the fatality risk of cyclists. These results can inform strategies to design interventions for safety of cyclists.

The effect of traffic volume on the noise caused by motorized vehicles in front of Karya Bhakti Pratiwi Dramaga Hospital, Bogor

Transportation is the movement or transfer of both people and goods from a place of origin to a destination. In this movement or movement, of course, using transportation facilities in the form of vehicles which in operation cause noises such as engine sound coming out through the muffler or horn. At a certain level, these sounds can still be tolerated in the sense that the consequences they cause are not a nuisance, but at a higher level, the sound generated by these vehicles is already a nuisance or pollution, which is called noise. The activity of the population of Bogor district a suburb of the capital city of Jakarta in Indonesia is relatively high. Along with economic development in the Bogor district, the number of motorized vehicles operating both private and public vehicles has also increased. The purpose of this study was to determine the average volume of motorized vehicles that pass through the Dramaga highway, especially in front of Karya Bhakti Pratiwi Hospital, and also to determine the level of noise generated due to the volume of motorized vehicles. The method used in this study is a quantitative method by calculating the number of vehicles and the average volume of vehicles and data analysis. The data taken is vehicle volume data every 15 minutes and noise levels using a Sound Level Meter (SLM). In this study, three SLMs were used which were placed at three points. The results showed that the volume of vehicles affects the noise level quite significantly. The results of data analysis show that the effect of volume on noise is 12.6%, where the equation is as follows, y = 72.898 - 0.003 x1 + 0.220x2 - 0.054x3. This equation means that if there is no decrease in motorcycle volume, light vehicle volume, and heavy vehicle volume, the noise level in SLM 1 is 72.898 dBA. However, if there is an increase in the number of light vehicles by 0.220, it is found that the number of motorcycles and heavy vehicles is decreased by 0.22, so the noise level will increase by 0.003 dBA.

A Model for Identifying Road Risk Class

Abstract In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

Analisa Pengaruh Volume Kendaraan dan Kecepatan Terhadap Tingkat Kebisingan Lalu Lintan di Jalan Cikuray, Garut

Cikuray street, Garut is a collector road located 1 km from Garut Square, and on this road, there are many offices and shops for facilities and services. Therefore, Cikuray street experiences an increase in a traffic flow which causes noise. This study aims to analyze the level of traffic noise due to the volume and speed of vehicles. The method used in this research is a direct survey of the field and traffic noise modeling analysis using multiple linear regression analysis with the help of SPSS (Statistical Package of the Social Sciences) software version 26 for Windows. From the analysis, the highest volume of vehicles occurred on Saturday at 11,605 pcu/hour, with the average speed ranging from 34.00 – 38.00 km/hour and for a noise level of 84.55 dB(A) which means it has exceeded the standard. limit. permissible noise level. The relationship between volume and vehicle speed to traffic noise levels on Jalan Cikuray Garut using mathematical modeling is shown in the equation Y = 56.777 + 0.020X1 + 0.043X2 + 0.214X3 – 0.090X4 with R2 = 0.443. This shows that traffic noise (Y) can be explained by the volume of motorcycles (X1), the volume of light vehicles (X2), the volume of heavy vehicles (X3), and traffic speed (X4).

THE EFFECT OF VEHICLE VOLUME ON DAMAGE TO PINE ROAD IN PALANGKA RAYA CITY

Along with the level of traffic density that crosses the Pinus road, there are several obstacles that cause damage, one of which is the damage that occurs in the road construction section, which is caused by the load of vehicles with excessive loads such as trucks, which further causes damage to the road, namely an increase in volume. heavy traffic every day. Asphalt pavement is generally used on roads that have a fairly dense traffic volume, with the increase in the number of vehicles resulting in damage in a relatively short time than planned. The purpose of this study is to determine the effect of the volume of vehicle types on road damage on asphalt pavement, it can be predicted earlier the value of road damage that will occur, the method used in this study is the method of analyzing vehicle volume on road damage with the regression method. Used to obtain the relationship function with the value of R² (coefficient of determination) which shows the magnitude of the effect of changes in the volume of vehicle types on changes in the value of road damage, this study was carried out on the Pinus road, Palangka Raya City. There is a relationship between the volume of vehicle types and the value of road damage, the results obtained are R² = 0.744 which shows that road damage is influenced by the volume of heavy vehicles, light vehicles and motorcycles with a percentage value of 74.4%. From the results of the equation between heavy vehicles (X1), light vehicles (X2), motorcycles (X3), and the value of road damage Y = 0.159 X1 + 0.034 X2 + 0.017 X3 + 14,189. From these equations it can be described as follows, the regression coefficient (X1) (a) = 0.159 means that a heavy vehicle of 100 vehicles/day will increase the level of road damage by 5.9, the regression coefficient (X2) (b) = 0.034 means a light vehicle of 100 vehicles/day will increase the level of road damage by 3.4, Regression coefficient (X3) (c) = 0.017 means that 100 motorbikes/day will increase the level of damage by 1.7, constant (c) = if there are no vehicles driving passing a road segment, the road will experience road damage of 14,189.

Impact of Overloading Vehicle towards the Level of Service on Freeway Segment (Case Study: JORR KM 27 to KM 23)

Abstract The congestion problem that occurs on the road is one of the indicators to determine the level of service. Approximately 20% of all traffic congestion is due to the existence of shockwave jams or phantom jams. One of the factors causing the shock wave phenomenon is the speed differences between heavy vehicles and light vehicles (Garber & Hoel, 2009). On the other hand, the Over Dimension and Over Loading (ODOL) Vehicle worsened the situation by reducing operational speed due to the exceeding load. Indonesia Traffic Police stated that throughout 2019 there were 136.470 vehicles out of 1.376.956 offences that were ODOL vehicles. This research aims to identify the effect of overloading vehicles and the volume of heavy vehicles on creating shock wave phenomenon that led to the lower level of service. Microsimulation modelling is carried out to obtain the resulting level of service from heavy vehicles volume using the primary data from the data collection. The result shows that the large volume of heavy vehicles exacerbated by the presence of ODOL vehicles can cause a temporary change in road speed and lead to a decreased level of service.

A random parameter negative binomial model for signalized intersection accidents in Seoul, Korea

A variety of statistical models were generally considered to better understand the relationship between crash occurrences and diverse factors. However, most of statistical models adapted fixed parameters which cannot incorporate time variation or sement-specific effects. To relieve this problem, this study focuses on a traffic accident frequency model using a random parameter negative binomial approach. This method allows for the consideration of unobserved heterogeneity in accident data that current popular methods such as Poisson or Negative Binomial models cannot account for. A four-year (2007-2010) continuous panel of accident histories at 95 signalized intersections in Seoul, Korea, was used to estimate the random parameter negative binomial model with traffic volumes and various geometric characteristics at intersections. Results show that the presence of a left-turn exclusive lane on a major road, the existence and length of a median barrier, and the existence of a pedestrian island on a major road are random parameters, and an additional ten variables significantly affected the safety at the intersections as fixed parameters. The fixed parameters were associated with major and minor roadway heavy vehicle volume, exclusive turn lane presence on major and minor roadway, taxiway lane presence, median barrier presence, as well as the number of lanes on major and minor roadway. The insights from this study indicate the need for broader analysis of lane channelization, lane exclusion and lane geometry effects as potential random parameters in intersection accident propensities.

Using satellite imagery to estimate heavy vehicle volume for ecological injury analysis in India

A major limitation of road injury research in low- and-middle income countries is the lack of consistent data across the settings, such as traffic counts, to measure traffic risk. This study presents a novel method in which traffic volume of heavy vehicles – trucks and buses – is estimated by identifying these vehicles from satellite imagery of Google Earth. For Rajasthan state in India, a total of ∼44,000 such vehicles were manually identified and geo-located on national highways (NHs), with no distinction made between trucks and buses. To estimate population living in proximity to NHs, defined as those living within 1 km buffer of NH, we geocoded ∼45,000 villages and ∼300 cities using Google Maps Geocoding Application Programming Interface (API). We fitted a spatio-temporal Bayesian regression model with the number of road deaths at the district level as the outcome variable. We found a strong Pearson correlation of 0.84 (p < 0.001) between Google Earth estimates of heavy vehicles and freight vehicle counts reported by a national-level study for different road sections. The regression results show that the volume of heavy vehicles and rural population in proximity to highways are positively associated with fatality risk in the districts. These effects have been estimated after controlling for other modes of travel.

Performance Analysis of a Roundabout and a 3 leg Intersection Under Heterogeneous Traffic

This paper addresses the analysis of the operational performance of a roundabout and a 3-legged intersection located in quite a busy area of the Aligarh city. The city has an urban population of around 0.9 million people. The roundabout and 3-legged intersection are located in the close proximity of busy commercial areas and schools. Roundabout that has been taken under consideration is un-signalized and 3-legged intersection is priority controlled. The Current study has been undertaken analyze the operational execution of the two intersections and to pave the way for forthcoming investigations related to improvement of the intersections in the Aligarh District region. Traffic data was accumulated on weekdays during peak periods (5:30 pm to 6:30 pm). Video recording was taken in consideration to accomplish this task. The traffic was categorized in 3 classes; light vehicles, heavy vehicles and bicycles. To execute the evaluation of functioning performance of both intersections, SIDRA INTERSECTION software has been used. Results have shown that both the roundabout and 3-legged intersection are operating nearly at their maximum capacities and Level of Service (LOS) is not adequate for such amount of traffic influx into both the intersections. Volume to capacity (v/c) ratio has revealed that both the roundabout and 3-legged intersection are in an unstable state and roundabout condition is worse than the 3-legged intersection as the heavy vehicle volume influx is quite higher for the roundabout.

ANALYSIS OF THE IMPACT OF MAXIMUM VEHICLE LOAD AT THE AGE OF FLEXIBLE PAVEMENT (CASE STUDY OF THE ROAD SECTION OF HERA DILI TIMOR-LESTE

The road network is a land transportation infrastructure that plays an important role in the transportation sector, especially for the balance of the distribution of goods and services, both from other regions. In this study, an analysis was conducted to find out how the influence of heavy vehicle volume increase on the service life of the road, while the method used was Bina Marga, the study was conducted by taking a case study on the Hera road section until Dili. This research is expected to find out how the service life of the road if the volume of heavy vehicles continues to increase. If the% of heavy vehicles increases, the thickness of the required surface layer will also be even greater. When% of vehicles or carrying heavy traffic loads are increased, the thickness of the existing layer cannot meet, because the calculation results with% of heavy vehicles increase by 10%, surface thickness 17.2 cm / This will cause the service life of the road to decrease because it is confirmed that the road will be damaged before the service period ends due to excessive load increase in heavy vehicles.

Assessment of Traffic Data for Road Rehabilitation Design: A Case Study of the Korogwe-Mombo Road Section in Tanzania

Abstract Traffic loading is one of the key inputs for new and rehabilitation designs of pavement. Heavy vehicles cause the most structural damage to pavements; hence, as part of the pavement design process, heavy vehicle volume and axle load surveys are typically carried out to assist with the accurate estimation of the cumulative traffic loading over a design period. Traffic volumes, axle loads, and the ultimate cumulative traffic loading often fluctuate because of factors such as varying motorist population and economic activities along the length of the road. This article presents a comparative assessment of traffic loading estimated for the rehabilitation design in 2005 and the actual measured site-specific traffic loading in 2015. The design traffic loading was also compared with the projected future traffic loading for the Korogwe-Mombo road section along the T2 trunk road that connects Tanzania’s business hub of Dar es Salaam with the northern regional cities. The study found, inter alia, that the cumulative traffic loading based on the 2015-measured site-specific traffic data is approximately 2.8 times higher than the design traffic loading based on the 2005 traffic data, which illustrates the importance of using accurate and reliable site-specific traffic data during pavement design. The implications of the findings for rehabilitation design are presented in this article, along with discussions on the contribution of heavy traffic loading to rutting/permanent deformation that occurred on the surface asphalt layer along the Korogwe-Mombo road section. To improve the accurate determination of traffic loading, traffic studies should ideally be conducted over a long period (typically over 1 year). However, this is not practical and cost effective when traditional manual methods are used. Hence, it is recommended that road agencies should consider the use of portable automated traffic and Weigh-In-Motion monitoring systems.

Determining the Effect Rate of Speed, Volume of Passenger Cars, Heavy Vehicles and Light Non-Passenger Car Vehicles on Likelihood of Accidents on Urban Highways

In this article, the role of speed and volume of traffic on occurrence of accidents on urban highways is investigated using Fuzzy models and the accident data of Tehran urban highways is used as case study. To fuzzify the variables in scatter diagram, the notion of statistical percentiles is used for assigning the linguistic terms. To produce rules in each model, one or more variables are deemed effectively in occurrence of accidents. The evaluated number of accidents by developed models is compared with the number of observed accidents. The results of comparison represent the accuracy of each model. The model with the highest value of R2 is the best model and the variables deemed effective for that model are those which do play a role in occurrence of accidents. Comparing the effect of elements of traffic volume indicates that after the average speed, volume of light non-passenger car vehicles is more effective on occurrence of accidents on urban highways than volume of heavy vehicles and passenger cars. After that the part of volume of heavy vehicles is more prominent than volume of passenger cars in the likelihood of more severe accident. The opposite is true for no injury accidents. After prioritization of variables in terms of influence on occurrence of accidents, the authors employed the models best fitting the data with the highest value of goodness of fit to do the sensitivity analyse. Sensitivity analyse specifies the effect rate of each variable on likelihood of accidents.

Modeling roadway traffic noise in a hot climate using artificial neural networks

Road traffic noise is the most prevalent form of environmental noise pollution, and it can either be measured in the field or predicted through verified mathematical models. While many road traffic noise models are available around the world, these models cannot be simply generalized because local conditions affecting such noise (e.g., vehicle type and weather) vary from one locality to another. In this paper, the Artificial Neural Network (ANN) technique was employed to model road traffic noise in a city with known hot climate, namely Sharjah City in United Arab Emirates. Toward this end, field data were collected from three different road sites which resulted in more than 420 hourly measurements of noise level, traffic volume and classification, average speed, and roadway temperature. Overall, a total of 16 feed-forward back-propagation ANN models with one and two hidden layers were undertaken in this research. The best-performing models were compared with two conventional models: The Basic Statistical Traffic Noise model (BSTN) and the Ontario Ministry of Transport Road Traffic Noise model (ORNAMENT). In general, results showed that ANN models outperformed the conventional models. Furthermore, the performance of both ANN and conventional road noise models was further improved by including roadway temperature. While the reported work emphasizes the importance of adapting models to different local conditions, it also encourages others to consider the roadway temperature when modeling traffic noise, especially in areas of hot climate. Given that ANN models are often designated as “black-box,” a few techniques were utilized to expand the capabilities of ANN models to offer explanatory insights in modeling the inter-relations between the variables considered in this study. Generally, it was found that the ANN model was capable of identifying these relations as expected. In-depth analysis showed that the distance from the edge of road was found to be the most significant contributing factor whereas heavy-vehicle volume was surprisingly found to be the least.

Time series trends of the safety effects of pavement resurfacing

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time.

Investigation of Skew Angle and Other Factors Influencing Crash Frequency at High-Speed Rural Intersections

Intersections experience a disproportionate share of traffic crashes, injuries, and fatalities because of the increased number of conflicting traffic movements at these locations. These issues are particularly pronounced at rural locations where speeds are generally higher and most intersections are either stop- or yield-controlled. Ideally, intersecting roadways should be oriented at as close to a 90-degree angle as possible. However, intersection design can diverge from this preferred configuration and result in a skewed intersection. Skewed intersections have been found to cause safety and operational issues for road users. To determine the effect of intersection skewness on crash frequency, crash prediction models were estimated for rural stop-controlled intersections on high-speed two-lane highways throughout the state of Iowa. Separate analyses were conducted for three-leg and four-leg intersections. In both cases, crash frequency was estimated as a function of annual average daily traffic, skew angle, and other salient geometric characteristics. The results consistently showed crash frequency to increase with skew angle. A 10-degree deviation from 90 degrees would result in 3% more crashes at three-leg intersections and 4% more crashes at four-leg intersections. In addition, crashes were affected by the volume of heavy vehicles, whether the approaches were paved or unpaved and whether intersection lighting was present.

Potential Effects of Heavy Vehicles on Operations of Super 2 Highways

Increasing volumes, specifically heavy truck volumes, have an increasingly greater effect on operations on the nation’s two-lane highways. Various strategies have been implemented to reduce delay and increase capacity, particularly the use of marked passing zones and the addition of periodic passing lanes to allow faster vehicles to pass slower vehicles and to disperse platoons. However, much existing guidance and previous research focused on those design features in the context of passenger vehicles. As the volume of heavy vehicles increases, particularly in high-demand corridors such as those related to energy exploration or manufacturing distribution, so does the number of individual trucks and truck convoys that can further erode operational characteristics on two-lane highways. This paper summarizes existing guidance and recent literature on passing treatments and design guidelines for passing sight distance, passing lanes, and passing zones. This paper also presents results from a proof-of-concept exercise to demonstrate potential effects of trucks on extending the lengths of passing sight distance for certain scenarios.

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.

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patterns of this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990–2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

Challenges and Impact of Open Market Concepts in Haulage Industry

This article explores the challenges and the impact of open market concept to haulage industry in Malaysia. The objectives of this article are to identify the challenges and the impact of open market concepts in haulage industry. The review of the literature in haulage industry reveals that there are not many studies conducted to examine the challenges and the impacts of open market concept to the haulage industry in Malaysia. The challenges identified are divided into two main challenges which consist of operational challenges and green goal challenges. Whereas the impact of the open market are high and heavy vehicle volume, unhealthy competition and pollution.

A novel method to monitor bicycling environments

A bicycle is a promising human-powered and emission-free transportation mode to address growing transportation and environmental problems. Bike-friendly environments should be constructed to innovatively increase the use of bicycles as a significant transportation mode. From this perspective, the scientific and effective monitoring of bicycling environments is of keen interest. An important technical challenge for monitoring is to evaluate the performance of bicycling environments. This study proposes a novel monitoring method that can be used for evaluating bicycle performance in terms of safety and mobility. An instrumented probe bicycle (IPB), which is equipped with a set of sensors including a global positioning systems (GPS) receiver, accelerometer, and gyro sensor, was used to develop the proposed monitoring method. The IPB provides useful bicycle maneuvering data for identifying longitudinal, lateral, and vertical maneuverings of the bicycle, which are affected by environmental factors such as heavy vehicle volume, surface conditions, grade, crossings, humps, and curbs. Regarding safety monitoring, an index to predict bicyclist’s perceived safety and comfort with the predictors derived from the measurements by the IPB was developed. A questionnaire survey was conducted to obtain actual responses from bicyclists for perceived safety and comfort during the field experiment. In addition, a method to evaluate the bicycle mobility using GPS speed data was devised. Then, a fault tree analysis (FTA) technique, which is a well-known technique for risk analysis, was adopted to integrate safety and mobility monitoring. As a result, the bicycling monitoring index (BMI) was proposed. Data derived from the proposed method is expected to be effectively used for promoting the bicycle use by supporting the development and evaluation of various bicycle-related policies.