Truck Factor Research Articles

Analisa Kerusakan Jalan Akibat Beban Kendaraan di Gerbang Masuk Kota Jambi Paal 10 – Simpang 4 Paal 10 Kota Jambi

The Palembang - Jambi highway is a very busy route. Along the road at the entrance to Jambi City, Paal 10 - Simpang 4 Paal 10, Jambi City is passed by many vehicles because this road is one of the roads that is open to pass through heavy vehicles carrying loads. This is also one of the causes of rapid damage to flexible road pavement. The aim of this research is to determine the average daily traffic volume of vehicles passing through the asphalt pavement at the entrance gate of Jambi City, Paal 10 - intersection 4 Paal 10, Jambi City, to find out the effect of vehicle load on the level of road damage on the entrance gate of Jambi City. Paal 10 – Simpang 4 Paal 10 Jambi City. This research method uses guidelines from Bina Marga Pd T-14-2003. Average daily traffic data (LHR) on the entrance road to the city of Jambi Paal 10 – intersection 4 Paal 10 Jambi City on Saturday is 6,702 vehicles, on Sunday it is 6650 vehicles and on Monday it is 6719 vehicles. From this data, the total LHR from the vehicle analysis results is 1115 vehicles/hour. and the results of the Truck Factor calculation are 3.775 > 1, where this value shows that the condition of the damage to the road at the entrance to the city of Jambi Paal 10 - intersection 4 Paal 10 Jambi city is due to the excessive load of vehicles passing through it. The excessive load factor that occurs causes 2 types of damage, namely groove distortion with a high level of damage and alligator cracks.

Analisis Pengaruh Beban Berlebih terhadap Sisa Umur Perkerasan Lentur pada Jalan Kawasan Industri Driyorejo dengan Metode MDPJ 2017

Flexible pavement is designed with regard to the load that will be received in order to achieve optimal performance throughout its service life. However, conditions in the road reveal indications of overloading by each category of vehicles, especially overload vehicles. Driyorejo industrial area road is a provincial road that connects the main industrial areas in Gresik Regency. With traffic dominated by overload vehicles, especially large trucks carrying a lot of goods, indications of damage to the pavement are becoming increasingly clear and obvious. This research aims to assess the impact of traffic load on the remaining life expectancy of flexible pavement, focusing on the load of vehicles crossing the road in the Driyorejo industrial area as the study variable. The calculation method used is the Bina Marga 2017 Method. The data used are Average Daily Traffic (LHR) data and vehicle load data. From the calculation results, the actual traffic growth (i) value is 32.7%. Vehicle overloading causes a decrease in the planned life of the road as indicated by the truck factor value of 1.06. The Remaining Life (RL) calculation represents a 14% decrease from the standard RL value of 69.3% to 55.3% under overload conditions, so it can be concluded that the overload significantly reduces the life of the road which was originally designed for 10 years to only 7 years.

Revealing the impact of losses on flexible pavement due to vehicle overloading

Pavement overload has become a significant concern in Indonesia due to its damaging effects on the pavement system, where most roads use flexible pavements. Meanwhile, overloading is also typical in other developing countries, causing everything from damage to accidents to the detriment of road users and operators. The current problem is that there is no definite knowledge of handling these overloaded vehicles, which has adverse effects. Therefore, this study aims to reveal the impact of losses caused by overloaded vehicles on flexible pavement. This research uses vehicle traffic load data to examine actual conditions in the field, which are converted to axle load values for each vehicle and used to determine pavement condition using the truck factor approach, mechanistic-empirical design, pavement smoothness level, and the impact of overloading due to fuel consumption by vehicles on flexible pavement. The results show that overloaded vehicles harm the flexible pavement. The increase in truck factor values for all vehicles in overloaded vehicles beyond the maximum allowable load results in more damage to the flexible pavement with an increase in truck factor values up to 83 %. The effect of overloading can be mitigated by increasing the thickness of the asphalt layer and the modulus of asphalt, so it is essential to pay attention to the quality of asphalt in anticipation of this overloading. To anticipate the overload phenomenon, it was found that the thickness of the asphalt overlay in the range of 170–205 mm could mitigate this detrimental effect. In addition, overloading affects the flexible pavement roughness and vehicle fuel consumption. Increased roughness and fuel consumption will increase road maintenance costs and affect driving comfort and safety. In addition, excessive fuel consumption can pollute the surrounding environment

Analisa Kerusakan Perkerasan Jalan Aspal

The road Kari Taluk Kuantan Terminal is a cross-road with flexible pavement that has suffered severe damage. The research objective was to find the remaining life of the pavement plan and the degree of damage to the asphalt pavement, using the Bina Marga method. During the service period, the pavement will experience repeated loading which causes damage, especially on the road section of the Terminal Kari Teluk Kuantan, Kuantan Singingi Regency STA 2 + 700 to 4 + 175. The results of the analysis that have been done, that: The remaining life of the pavement for the next 10 years = 12%, with a Truck Factor 0.465 <1, which means that the road is not fit to pass, The degree of damage for light front wheel vehicles is 0.0001, bus 0.0246, Truck 2 as 0.0178. The degree of damage for light vehicle rear wheels is 0.0001, buses 0.1245, Truck 2 as 0.0900, meaning that the average degree of damage for light vehicles and heavy vehicles, is less than one axis, in this case the damage is classified as collapse due to damage, with vehicle load normal which does not really affect the degree of damage to the road.

Analisa Pengaruh Beban Kendaraan Terhadap Kerusakan dan Umur Rencana Jalan

The Km-18 of Padang Bypass Road is an Arterial road class III-A. This road is burdened by heavy vehicles which carrying loads that exceed the permitted capacity. This activity can lead to road damage and a reduction in road life. This study aims to analyze the effect of traffic loads that exceed permitted capacity on road conditions, including the remaining life of the pavement and the degree of road damage. The primary data used is data on road damage conditions such as the discharge of road surface grains, potholes, and grooves. Secondary data used is in the form of LHR data for 2017 and 2018, also, the vehicle weight data obtained from P2JN West Sumatra Province. The analytical approach uses the 1993 AASHTO method in the form of a cumulative analysis of the 10-year ESAL plan, by the traffic design for flexible pavement. The result of this study indicates that the Km-18 of Padang Bypass Road has decreased 7.58% of its design life in 2020. The cumulative plan ESAL value is 70,792,016.05 and the cumulative ESAL overloading value is 192,394,000.9. The Truck factor (TF) in normal condition is 0.8176692, but in overloading condition, it is 6.1375255. The road life predicted to be ended in year 4 (in 2024), although it planned to end in 2027.

Algorithms for estimating truck factors: a comparative study

Software development is a knowledge-intensive industry. For this reason, concentration of knowledge in software projects tends to be very risky, which increases the relevance of strategies that reveal how source code knowledge is distributed among team members. The truck factor (also known as the bus factor) is an increasingly popular concept—proposed by practitioners—that indicates the minimal number of developers that have to be hit by a truck (or leave the team) before a project is incapacitated. Therefore, it is a measure that reveals the concentration of knowledge and the key developers in a project. Due to the importance of this concept, algorithms have been proposed to automatically compute truck factors, using maintenance activity data extracted from version control systems. However, we still lack large studies that assess the results of truck factor algorithms. To fulfill this gap in the literature, this paper describes the results of three empirical studies. In the first study, we validate the results produced by three algorithms to estimate truck factors. To this purpose, we build an oracle of truck factors, gathered via a survey with 35 open-source project teams. In the second study, we provide a comparison between truck factors and core developers, a related concept commonly used to denote the key developers of open-source projects. Our results indicate that truck factor developers are in most cases a subset of core developers. Finally, as the algorithms proposed so far are based in commit data, in the third study, we investigate other factors that may impact the computation of truck factors.

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R2 > 0.8 and classification tree – R2 > 0.6) and ANN (continuous response – R2 > 0.8 and categorical response – R2 > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).

The impact of payload truck factor use in mine performance reports for an open pit copper mine in Brazil

This article aims to evaluate the use of a truck factor for off-highway trucks in open pit mine operations, thus reducing error on material movement and mine production data entry. This method is current for a copper mine located in South East Pará, Brazil. With a dispatch system, the truck factor is calculated using three data inputs called measured t, dump t and excavator load time. This dispatch system, designed to upload real time data from each truck, acquires measured load information by a truck weightometer and provides the basis for long-term, medium-term and mainly short-term planning. Due to the significant impacts toward mine planning, through data error, a payload truck factor system provides data assurances in place of potential failure of onboard weighing. However, when using a system that is reliant on actual data, caution must be applied when replacing information with assumed fixed figures, thus forming the discussion on which this article attempts to review by providing overview on both the positive and negative impacts of implementing a truck factor system to an open pit mining using a dispatch system.

Performance study of artificial neural network modelling to predict carried weight in the transportation system

The major aim of this study is to model and predict the amount of carried weight based on the five direct impact factors in the transportation system. In this study, artificial neural network (ANN) has been incorporated for developing a predictive model. Three different training algorithms, namely Levenberg-Marquardt-LM, batch backpropagation-BBP and quick propagation-QP, were used to train. The input parameters are the aforementioned five transportation factors plus two timing factors namely number of weeks and seasons while the carried weights is the output. The next purpose of this study is comparing the mentioned learning algorithm's performance based on predicting ability. The results showed that the QP algorithm with 7-4-1 network topology exhibited the highest predictive power. The available data have been trained by ANN (QP-7-4-1) and the responses were predicted. Moreover, the truck factor plays a slightly more dominant role in the prediction of carried weighs.

Evaluation of Truck Factors for Pavement Design in Developing Countries

The traffic represents a fundamental parameter used in the analysis and design of pavement structures. In order to simplify the characterization of the traffic variable for pavement structural analysis and design, the vehicle axles are converted to a number of 80kN Equivalent Single Axle Loads (ESALs) through the Load Equivalency Factors (LEF). A Truck Factor represents the ESALs applications per commercial vehicle. This study determined the Truck Factors for commercial vehicles operating in Colombia. The high Truck Factor values found in this investigation could be used to explain the current critical condition of Colombian road infrastructure.

Modeling Roadway Link PM2.5 Emissions with Accurate Truck Activity Estimate for Regional Transportation Conformity Analysis

The impact of fine particulate matter (PM) on public health has long been a concern. The primary mobile sources of fine (PM) (PM2.5) are diesel trucks. In practice, accurate roadway link-based modeling of the truck emissions remains a major challenge because of aggregated and unreliable truck activity data. The advanced emission model MOVES has been recommended by the U.S. Environmental Protection Agency for estimating emission factors, but supplying accurate and detailed truck activity-related inputs has become another challenge. Daily truck traffic activity is usually not estimated accurately and cannot be disaggregated to hourly activity with traditional methods. To address this problem, two innovative econometric methods were successfully enhanced in this study to predict accurate truck activity-based inputs for emission estimation. The models for truck factor spatial panel and multinomial probit hourly vehicle miles traveled were improved and tested with regional traffic data from the greater Cincinnati, Ohio, area. The application of those models indicates that using MOVES default input data underestimates the regional PM2.5 inventory. The proposed methodology enables plotting the spatiotemporal distribution of PM2.5 emissions in a subarea. Such an integrated method provides a useful decision support tool for practitioners because they can also model PM2.5 emissions at a detailed level as required by project-level conformity analysis. The methodology presented is scalable and transferable and holds technical promise in its application to different regions and different pollutants.

Determination of Truck Factor from WIM Data in Thailand

Determination of Truck Factor from WIM Data in Thailand

Genetic Programming to Investigate Design Parameters Contributing to Crash Occurrence on Urban Arterials

Nonlinear models were developed to estimate crash frequency on urban arterials with partial access control. These multilane arterials consist of midblock segments joined by signalized and unsignalized intersections (or access points). Crashes included in the analysis are of three major types: rear-end, angle, and head-on. Each crash type is further sorted into mutually exclusive categories on the basis of the roadway element responsible for the crashes: midblock segment, signalized intersection, and access point. Genetic programming (GP) is adopted for predicting crash frequency. GP, which is primarily based on genetic algorithms, uses the concept of evolution to develop models through the processes of crossover and mutation. The GP modeling approach gives independence for model development without restrictions on distribution of data. The models developed were compared to the basic negative binomial models. Morning and afternoon peak periods are observed to have fewer occurrences of rear-end crashes at all roadway elements. Higher traffic volume results in an increased number of angle crashes. Instances of angle crashes have increased at signalized intersections, even at lower maximum posted speeds. A higher average truck factor increases the instances of head-on crashes on midblock segments and at signalized intersections.

Effect of Including the Empty-adjusted Truck Factors on Pavement Design: A Case Study on Riyadh Area Road Network

Truck traffic is one of the major inputs to any pavement design procedure. In almost all pavement design procedures, truck traffic is represented in terms of the accumulated Equivalent Axle Load (EAL) applications. The level of EAL applications is mainly dependent on the prevailing values of truck factors. Thus efforts have to be made to accurately estimate truck factor values based on local loading conditions. The current practice of the Ministry of Communications (MOC) involves recording axle weights of loaded trucks only without including empty trucks. This, in turn, leads to higher truck factor estimates and consequently over-designed pavement structures. The main objective of this study is to quantify the effect of excluding empty trucks when estimating truck factors, on highway pavement design. The truck factors associated with loaded trucks were determined from MOC data files. The empty truck factors were determined from a field sample consisting of 4000 empty trucks. The empty-adjusted truck factors were calculated as the weighted average of the two truck factor values. An analysis has been conducted to study the effects of using the empty-adjusted truck factors on pavement design instead of loaded truck factors, using the AASHTO design procedure. The analysis indicated that the average truck factor for empty trucks is about 1.15, the average truck factor for loaded trucks is about 9.87 and the average empty-adjusted truck factor is about 6.63. It also showed that excluding empty trucks would result in over-designs with an average of about SR 1.0 / m2 or about SR 3500 / km-lane extra construction cost.

Estimating Truck's Critical Cornering Speed and Factor of Safety

Fully laden trucks are prone to rollover when exceeding a critical cornering speed. After an accident, authorities, both highway engineers and the police, often need to establish a vehicle's speed from tire marks and other physical evidence. This paper outlines the relative precision of equations of varying complexity used to estimate a truck's critical rollover speed based on tire marks. An error analysis is compared with a limited tachometer data base to evaluate the accuracy of the speed-estimating equations. The study shows that for most situations with fully laden, rigid trucks, the simple lumped-parameter model gives an acceptable estimate of the rollover speed for highway design engineers and the police. Also, a review of the lateral acceleration generated by a vehicle negotiating a minimum-radius curve found that the acceleration is very close to the level needed to tip over a truck. Based in part on these findings a method for developing a reasonable estimate of level of safety is discussed.