Percentage Of Trucks Research Articles

Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory

Nowadays, in order to improve the traffic conditions in mountainous areas, the demand of climbing lanes is increasing. In this paper, car–truck heterogeneous traffic flow characteristics on the highway with a climbing lane are studied up based on an improved Kerner–Klenov–Schreckenberg–Wolf (KKSW) cellular automation (CA) model. To depict the speed characteristics of vehicle’s climbing and lane-changing behaviors, additional evolution rules for trucks are introduced into the KKSW model. Specifically, the motion of trucks climbing on the slope is described as a deceleration process and an equilibrium process. Furthermore, we define the probability of a truck driver using the climbing lane as a function of the observed real-time occupancy on it. We have found that in accordance with Kerner’s theory, traffic breakdown is an F→S transition that occurs in metastable free flow with respect to the F→S transition. When the traffic demand increases, the bottleneck appears due to the merging of trucks from the climbing lane to the inner lane. Localized synchronized flow patterns (LSP) emerge on both the inner and outer lanes. When traffic demand increases further, wide moving jams emerge in synchronized flow and general pattern (GP) appears on the inner lane, whereas synchronized flow patterns remain on the outer lane only. In addition, the macroscopic traffic flow characteristics under varying truck percentages are analyzed. We have found that the improving effect of climbing lane on traffic efficiency is closely related to the truck percentage.

Evaluating safety performance of the offset diamond interchange design using VISSIM and surrogate safety assessment model

Many existing interchanges, which were built mostly in 1950s and 1960s based on old design guidelines, have operational and/or safety problems due to the inconsistency with current traffic and pedestrian demands. Therefore, upgrading existing designs based on recent policies and demands could result in improving the performance of old interchanges. This research evaluates traffic safety and pedestrian performance of the new offset diamond interchange (ODI) as a substitute for failing conventional interchanges. The ODI design, which showed potential in improving traffic operation in a past study by the authors, was compared to nine interchange designs using VISSIM and the Surrogate Safety Assessment Model (SSAM) in this study to examine the safety and pedestrian performance. Overall, 324 simulation scenarios were tested with various conditions of traffic and pedestrian volumes, turning traffic ratios, traffic distribution, and truck percentages. According to the results, the ODI showed potential to be a promising design in terms of safety and pedestrian performance. However, the diverging diamond interchange (DDI) resulted in fewer simulated conflicts compared to the new ODI. On the other hand, the ODI design had a better performance in terms of pedestrian performance.

Queue Analysis at Work Zones from Field-Collected Data

Estimation of the capacity of work zones is vital to manage the possibility of traffic flows exceeding capacity and resulting in unbearable queues during work zone lane closures. A plethora of research papers have studied several ways to estimate work zone capacity, with the Highway Capacity Manual (HCM) having its own methodology to estimate capacity based on various site characteristics. However, HCM always recommends validating its model with local data to reflect the actual driving behavior of the region. This study considered work zone capacity as a function of queue discharge rate (QDR), defined as the 15-min average flow rate immediately after breakdown, also known as postbreakdown flow rate. By collecting data from 10 different work zones within the state of Louisiana, the study estimated QDR and its corresponding duration at breakdowns. An average QDR of 1,664 pcphpl and an associated queue of 120 min average duration was found. Analysis of variance showed that average QDRs across all sites were not significantly different. The QDR prediction model revealed that a closed right lane and a work zone on linear roadways significantly increased the discharge rate. However, the presence of nearby exit ramps, daytime scenarios, and an increase in the speed ratio and truck percentages were found to decrease the discharge rate. A separate model for the duration of queue or breakdown found the time of day, change in the speed ratio, presence of entry ramp, location of work zones, and annual average daily traffic of the roadway to be significant variables.

Bayesian Approach to Developing Context-Based Crash Modification Factors for Medians on Rural Four-Lane Roadways

Rural four-lane roadways provide important transportation accessibility and mobility to populations in rural areas. It is a challenge for practitioners to determine cross-section types when both benefits and costs need to be considered. Crash Modification Factors (CMFs) are developed to evaluate the safety effectiveness of alternative designs. However, safety effectiveness could vary significantly across contexts. Thus, this study aims to estimate CMFs for alternative cross sections of rural four-lane roadways under different contexts characterized by traffic volume, truck percentage, and access point density. Using Georgia state-wide crash data, this study developed Safety Performance Functions (SPFs) to predict crash frequencies for different contexts. Considering linearity and independence assumptions of traditional negative binomial SPFs, this study adopts Bayesian generalized negative binomial modeling approaches to relax those assumptions and only follows the Bayes rule to form SPFs for CMF estimation. This study focuses on four typical cross-sections including: (1) non-traversable medians; (2) two-way-left-turn lanes; (3) 4-ft flush medians; and (4) undivided roadways with double-yellow lines (the base cross-section design). The results show that CMFs vary significantly across different contexts. Compared with the base cross-section design, safety benefits of the other three designs can be either positive or negative under different traffic or road conditions. For example, 4-ft flush medians are found to have positive safety benefits (CMF < 1) under lower average daily traffic volumes (e.g., ≤ 6,000) and negative benefits (CMF > 1) under greater average daily traffic volumes (e.g., ≥ 15,000). The findings suggest that, to enhance roadway safety, practitioners should vary cross-section designs for different rural contexts.

A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

An analysis of crashes occurring in 252 unidirectional Italian motorway tunnels over a 4-year monitoring period is provided to identify the main causes of crashes in tunnels. In this paper, we propose a full Bayesian bivariate Poisson lognormal hierarchical model with correlated parameters for the joint analysis of crashes of two levels of severity, namely severe (including fatality and injury accidents only) and non-severe (property damage only), providing better insight on the available data with respect to an analysis based on severe and non-severe independent univariate models. In particular, the proposed model shows that for both of severity levels the crash frequency increases with some parameters: the average annual daily traffic per lane, the tunnel length, and the percentage of trucks, while the presence of the sidewalk provides a reduction in severe accidents. Also the presence of the third lane induces a reduction in severe accidents. Moreover, a reduction in the crash frequency of the two crash-types over years is present. The correlation between the parameters might offer additional insights into how some combinations can affect safety in tunnels. The results are critically discussed by highlighting strength and weakness of the proposed methodology.

Evaluation of Hybrid, Electric and Fuel Cell Powertrain Solutions for Class 6-7 Medium Heavy-Duty Vehicles

<div class="section abstract"><div class="htmlview paragraph">Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine. However, vocational applications can realize a significant benefit from electrified powertrains due to their lower vehicle speeds, frequent start-stop driving and shorter operating range requirements.</div><div class="htmlview paragraph">As the heavy-duty industry deals with solving challenges around the application of electrified powertrains, there are multiple pathways that can be explored to meet future regulatory requirements. This paper is the second part of a two-paper series that focuses on evaluating electrified solutions for Class 6-7 medium heavy-duty vehicles in the 2027 and beyond time frame. Using a model-based approach, this paper presents a comprehensive analysis that compares the baseline diesel powertrain against multiple alternative powertrain configurations. These configurations include, an optimized parallel diesel hybrid, all-electric, diesel range extender, gasoline range extender, Compressed Natural Gas (CNG) range extender, and fuel cell range extender; which are evaluated on the basis of cost of ownership, fuel efficiency, emissions, payback period, lost payload opportunity, range, and battery life. The analysis shows that for a near-term less-disruptive scenario wherein the conventional diesel powertrain layout is preserved, a 48V P3 parallel hybrid vehicle configuration is the most cost-effective solution, while for a near-term more-disruptive scenario, the Internal Combustion Engine (ICE) based Range Extender Electric Vehicles (REEVs) have the most potential to reduce greenhouse gas emissions while achieving lower cost of ownership than the baseline vehicle. For a longer-term and more-disruptive scenario, the all-electric vehicle configuration can achieve parity with the baseline vehicle cost of ownership provided the vehicle driving range requirement is limited to 100-150 miles. The analysis further highlights that the overall benefit of an all-electric powertrain solution depends significantly on the future battery technology advancements and vehicle range requirements.</div></div>

Assessment on the crash risk factors of a typical long-span bridge using oversampling-based classification method and considering bridge structure movement

Abstract In comparison to ordinary highways, traffic accidents on long-span bridges have unique characteristics due to large percentages of large trucks, inclement weather conditions, and dynamically moving bridge structures. Presently, there is a research significant gap in the existing literature about the traffic safety of long-span bridges due to those difficulties. Meanwhile, the structure movement data of bridge is difficult to obtain. In this paper, real-time data related to the bridge crashes including surrounding environment, traffic status and especially structural movement were obtained from monitoring system of a long-span bridge. An oversampling-based classification method was utilized to explore the risk factors of the long-span bridge crashes. The results indicate that higher maximum wind speed and volume prior to a crash tend to increase the likelihood of the occurrences of the crash, while higher temperature, humidity, average vehicle speed and truck percentage are found to decrease the likelihood. Moreover, the structure movement indicators including horizontal vibration acceleration and deformation are found to have significant adverse effects on the traffic safety of the long-span bridge, and we recommend that those factors should be considered at the design stage.

Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata

An improved cellular automata model based on car–truck combination effect oriented to faithfully reproducing the characteristics of heterogeneous traffic flow is presented in this paper. The model focuses on reflecting complex car-following and lane-changing behavior of cars and trucks by incorporating the difference of physical performance and driving behavior between cars and trucks and the behavioral change caused by car–truck interaction. The update rules of cars and trucks were constructed separately based on the analysis results of NGSIM and SP survey. The impact of the truck in front was introduced into the model, which can affect the lane-changing probability, safety expectation and randomization probability. As revealed by the simulation results, the driving of vehicles in the heterogeneous traffic (i.e. car–truck, truck–car, car–car and truck–truck) is reproduced properly through the model. In addition, the model is capable of reproducing the hysteresis phenomenon and identifying three typical traffic states, namely free flow, synchronized flow and jam. Under certain conditions, trucks can help to eliminate the propagation of congestion waves. Moreover, the percentage of trucks and truck impact have a significant influence on the fundamental diagram, congestion rate, lane-changing rate and traffic stability. The findings of this study can provide a basis for further research on the heterogeneous traffic.

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

This paper examines the nature of traffic loading in recurrent congested traffic conditions on a long-span suspension bridge. Traffic flow and percentage of trucks are extracted from image data and a cluster analysis performed to classify the data into four clusters. One cluster (MTHF, medium truck percentage and high flow) is identified that incorporates almost 50% of the hours of traffic data scattered throughout the day. Site-specific load assessment confirms that this MTHF cluster is the most critical for the bridge considered, the Forth Road Bridge in Scotland. For non-recurrent congestion, another cluster (HTLF, high percentage of trucks and low flow) is shown to govern but this finding is highly site-specific, depending on the relative frequency of the different types of congestion. A comparison of the maximum hourly/daily MTHF load effect of the cable force for five notional bridges shows that a 100% increase in the bridge span generates an increase of about 65% in the characteristic load effect.

Exploratory Analysis of the Relationships between Congestion, Travel Time Reliability, and Freight-Related Performance Management Measures and Their Associativity with the Roadway Attributes

The purpose of this study is to investigate the performance management measures related to highway system reliability, freight, and traffic congestion in light of the federal rulemaking that establishes these performance measures. The study conducts an exploratory analysis to understand their inter-relationships and examines their (un)common underlying attributes to discover their associativity with each of the performance measures. In doing so several traffic and roadway related characteristics of each reporting segment of the National Highway System (NHS) of South Carolina were processed and modeled for the travel time reliability and peak hour excessive delay using generalized linear models with a log-link function. The results from the study indicate that the unreliable Interstate segments contributed to about 87% of the excessive delays on the entire Interstate. It was also found that more than half of the non-Interstate NHS segments that experienced excessive delay, were reliable and they contributed to approximately 52% of the entire peak hour excessive delay of the non-Interstate NHS. The results from the model indicate that the directional annual average daily traffic (AADT) and the urban areas are the two most important attributes positively associated with all three performance measures, while the number of through lanes was found to be negatively associated with all three performance measures. The length of the reporting segments was positively associated with the excessive delays but negatively associated with the travel time reliabilities. The percentage of single trucks was unique to the Interstate delays and positively associated.

Modeling highly imbalanced crash severity data by ensemble methods and global sensitivity analysis

Crash severity has been extensively studied and numerous methods have been developed for investigating the relationship between crash outcome and explanatory variables. Crash severity data are often characterized by highly imbalanced severity distributions, with most crashes in the Property-Damage-Only (PDO) category and the severe crash category making up only a fraction of the total observations. Many methods perform better on outcome categories with the most observations than other categories. This often leads to a high modeling accuracy for PDO crashes but poor accuracies for other severity categories. This research introduces two ensemble methods to model imbalanced crash severity data: AdaBoost and Gradient Boosting. It also adopts a more reasonable performance metric, F1 score, for model selection. It is found that AdaBoost and Gradient Boosting outperform other benchmark methods and generate more balanced prediction accuracies. Additionally, a global sensitivity analysis is adopted to determine the individual and joint impacts of explanatory factors on crash severity outcome. Vertical curve, seat belt use, accident type, road characteristics, and truck percentage are found to be the most influential factors. Finally, a simulation-based approach is used to further study how the impact of a particular factor may vary with respect to different value ranges.

Driveway Access Spacing Considerations for Rural Highways with High Truck Volumes

Increased traffic and heavy truck percentages associated with energy exploration in West Texas have placed unprecedented demands on the region’s highway network as well as driveway access to and from that network. Access management principles have proven to be effective in improving operations and safety in numerous locations, but many of those locations have been in urban or suburban locations where land uses and traffic patterns are different from those typically found in rural West Texas, so guidelines for driveway spacing and other access management treatments in rural, high-speed areas are not as commonplace. This paper describes the study of a corridor with high volumes, high truck percentages, high speeds, high turning volumes, and high demand for access. The study team reviewed the applicable guidelines and policies for driveway spacing in Texas, as well as relevant findings from other guidelines and research, to develop a set of recommended driveway spacing values for cars and for trucks on high-speed rural roads.

Two-way left turn lane or raised median? A truck safety based study

Introduction: Due to their size and weight, trucks require more space and time to make left turns when exiting or entering a roadway. Therefore, appropriate median treatments are critical for roadways with substantial truck traffic. The two-way left-turn lane (TWLTL) and raised median (RM) are the two types of median most commonly used to improve roadway mobility and manage roadway accessibility. However, previous studies on these median treatments have focused primarily on the general traffic conditions and geometric roadway features without considering the truck traffic impact. Method: To fill this gap, this study investigates the truck impacts on TWLTL and RM by considering two major influencing factors – truck percentage and roadway access point density. First, a negative binomial regression is developed to analyze the relationship between crash frequency and various influencing factors. Next, the crash rate difference analysis between the TWLTL and RM is conducted to identify critical points for these two factors. Results: The findings indicate that, compared with RM, TWLTL has significantly higher crash frequency, especially for roadways with a higher percentage of trucks. This suggests that the percentage of trucks should be taken into consideration when selecting an appropriate type of roadway median.

Smart vs conventional motorways: Environmental impact assessment under realistic traffic conditions

This research aims at assessing the environmental impacts exerted by a smart motorway compared to those of a traditional motorway. The study has global policy implications: it takes into account the impacts due to the construction and maintenance of the infrastructure and the environmental effects produced by the traffic emissions, taking into account smart technologies and truck platooning regulation. Through a classical LCA approach, 1 km-long smart motorway with 2 m-high embankment was assumed as the functional unit for the analysis. A realistic traffic condition has been considered. A comparison between environmental effects produced by the use of virgin material and by Reclaimed Asphalt Pavement was made by assuming two maintenance plans. Thanks to C-ITS systems the greater safety featured by smart motorways has a significant effect on the environmental impact, compared to conventional motorways. The impact produced by safety barriers during the life cycle was also estimated. For smart motorways the impact categories GWP, POCP, AP and EP are observed to be considerably reduced in the maintenance phase of zinc-coated steel safety barriers and in those associated to traffic emissions. It must be noted that in smart motorways vehicle emissions are markedly influenced by the percentage of heavy vehicles travelling in Truck Platooning mode. The results show that concomitant use of lime stabilization and RAP leads to a significant reduction in energy consumption (up to 35%) and pollutant emissions (up to 34% of CO2) than in case of exclusive use of virgin material. The accidents reduction produce a 30% decrease of GWP, POCP, AP and EP related to safety barriers maintenance phase than the corresponding values of traditional motorway. Truck platooning mode generates GWP reduction close to 4%. The environmental advantages of a smart motorway increase progressively with the increase of AADT, platooning truck percentage and heavy vehicles percentage.

Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

Shared resources in collaborative vehicle routing

The logistics industry is known to suffer from inefficiencies. Recently published numbers on the percentage of trucks running idle or almost idle on European road networks are alarming. Collaborative transportation offers opportunities to share capacities and thus reduce inefficiencies and environmental pollution considerably. It is therefore a highly relevant topic for both researchers and practitioners. We survey up-to-date literature in the field of shared resources in collaborative vehicle routing. We report that, in the last 3 years alone, more than 40 relevant articles were published. These studies can be classified according to the underlying level of information sharing. If a fully informed decision maker exists, we refer to this as centrally planned collaboration. In decentralized collaborations coalition partners agree on mechanisms, where no or only a limited amount of information has to be revealed. We elaborate on recent findings and identify topical research gaps for both centralized and decentralized collaborations.

Passenger Car Equivalent Estimation Methods of Trucks in Traffic Stream

In Most Of Developing Countries, The Traffic Is Heterogeneous In Nature Consisting Of Wide Variety Of Vehicles Having Different Dynamic And Static Characteristics. Passenger Car Unit (PCU) / Passenger Car Equivalent (PCE) Values Show A Vital Job In Changing Over Heterogeneous Traffic Stream Into Comparable Homogeneous Traffic, Which Comprises Of Traveller Vehicles As It Were. PCE Values Are Vital In Rush Hour Gridlock Stream Investigations. This Paper Reviews The Previous Researches Carried Out About The Estimation Of Passenger Car Equivalents With Different Performance Measures At Mid-Block Sections And Summarizes PCE Variation With Percentage Of Trucks And Flow Rates In The Tabular Form.

A combined approach of multiple-criteria decision analysis and discrete-event simulation: lessons learned from a fleet composition study

This study proposes a framework for a decision of the fleet composition problem based on a set of qualitative and quantitative criteria, which is a hybrid method for decision-making support that combines MCDA and simulation techniques in a multicriteria scenario for fleet composition. The proposed framework is able, using simulations, to address the complexity of criteria considered in a fleet composition problem in order to increase the preciseness of the decision-making process. From the solutions evaluated, there was a change in the preference of the alternative initially chosen after simulation, which is considered to refine the solution provided by the MCDA method. Considering only the MCDA method, the cost is prioritised and the solution uses exclusively high capacity trucks. Using the simulation and a different weighing process, the service level has a greater importance and a percentage of smaller trucks is admitted as part of the fleet.

A combined approach of multiple-criteria decision analysis and discrete-event simulation: lessons learned from a fleet composition study

This study proposes a framework for a decision of the fleet composition problem based on a set of qualitative and quantitative criteria, which is a hybrid method for decision-making support that combines MCDA and simulation techniques in a multicriteria scenario for fleet composition. The proposed framework is able, using simulations, to address the complexity of criteria considered in a fleet composition problem in order to increase the preciseness of the decision-making process. From the solutions evaluated, there was a change in the preference of the alternative initially chosen after simulation, which is considered to refine the solution provided by the MCDA method. Considering only the MCDA method, the cost is prioritised and the solution uses exclusively high capacity trucks. Using the simulation and a different weighing process, the service level has a greater importance and a percentage of smaller trucks is admitted as part of the fleet.

Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes.