Two-lane Roads Research Articles

Novel application of low carbon limestone calcined clay cement (LC3) in expansive soil stabilization: An eco-efficient approach

This article presents the novel application of new ternary low carbon limestone calcined clay cement (LC3) to deal with construction problems associated with problematic expansive soils. Such soils exhibit high volumetric change behavior and cause extreme distress or failure to the overlying engineering structures. Various calcium-based admixtures such as lime and ordinary Portland cement (OPC) have been extensively used to mitigate its adverse effects, but have a huge environmental impact in terms of high carbon footprints. In this regard, recently developed low carbon LC3 has been applied in concrete technology for various purposes, however, its efficacy for expansive soil stabilization is still unknown. Therefore, this study for the first time investigates a wide spectrum of macro-micro properties of LC3-treated soil and reveals its underlying stabilization mechanism. LC3 addition exhibits complete mitigation of swelling behavior, reduced saturated hydraulic conductivity along with significant improvement in compressive strength considering the long-term curing period. The stabilization mechanism reveals that corresponding amelioration in engineering properties falls under three phases; 1) cation exchange; 2) flocculation or agglomeration, and 3) hydration products such as calcium aluminosilicate hydrate (CASH), coupled with AFt (ettringite) phases which act as a filler material by creating a high pH environment. Estimation shows that LC3 usage in place of OPC for expansive subgrade of the 1-km two-lane road can save consumption of approximately 350 GJ and 12594 kWh of thermal and electrical energies, respectively, and CO2 emission of around 27420 kg of C. Overall, the LC3 application in problematic expansive soil stabilization not only adds a new dimension but also provides a sustainable and environmentally friendly alternative to reduce the carbon footprints of traditional techniques.

Developing a New Driver Assistance System for Overtaking on Two-Lane Roads using Predictive Models

The complexity of an overtaking maneuver on two-lane roads merits a thorough method for developing an assistance system to prevent accidents, thus reducing the number of fatalities and the associated economic costs. This research aims to introduce a new Driver Overtaking Assistance System (DOAS). This system is based on the proactive prediction of the possibility of overtaking any preceding vehicle(s) both accurately and safely. To provide a comprehensive system, different factors related to the driver, the vehicle, the road, and the environment which have an impact on the maneuver have been taken into consideration. In addition to considering the main overtaking strategies including accelerative, flying, piggybacking, and the 2+. The proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity through Hello beacon messages and a set of sensors that are used as part of the reasoning process of the context-aware architecture to safely initiate the overtaking maneuver. A classification model was implemented for both the Artificial Neural Network (ANN) and Support Vector Machine (SVM) learning algorithms. A vehicle driving simulator STISIM Drive® was used to conduct driving experiments for 100 participants of different ages, gender, and levels of mental awareness. The results obtained from the DOAS show high accuracy in aiding a safe overtaking maneuver. The classification model shows promising results in the predictions, through perfect accuracy and a very low level of outcome errors.

Burn-Induced Acute Kidney Injury-Two-Lane Road: From Molecular to Clinical Aspects.

Severe burn injuries lead to acute kidney injury (AKI) development, increasing the mortality risk up to 28–100%. In addition, there is an increase in hospitalization days and complications appearance. Various factors are responsible for acute or late AKI debut, like hypovolemia, important inflammatory response, excessive load of denatured proteins, sepsis, and severe organic dysfunction. The main measure to improve the prognosis of these patients is rapidly recognizing this condition and reversing the underlying events. For this reason, different renal biomarkers have been studied over the years for early identification of burn-induced AKI, like neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, kidney injury molecule-1 (KIM-1), tissue inhibitor of metalloproteinase-2 (TIMP-2), interleukin-18 (IL-18), and insulin-like growth factor-binding protein 7 (IGFBP7). The fundamental purpose of these studies is to find a way to recognize and prevent acute renal injury progression early in order to decrease the risk of mortality and chronic kidney disease (CKD) onset.

Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions

With the growing rate of automated vehicles (AVs) at the lower level of automation, the experimental tests are also in progress with vehicles at higher levels. In the absence of extended digital infrastructures and deployment of level 5 full automated vehicles, the physical infrastructure is required to maintain a fundamental role to enable their introduction in public roads. This paper focuses on lane support system (LSS) whose operational design domain (ODD) is strongly connected to the road characteristics and conditions. An experimental test was carried out with a state of the art, and LSS and advanced technologies were used for road monitoring on different roads under various environmental conditions including dry, wet pavements and rain. We applied the generalized estimation equation for logistic regression to account within-cluster homogeneity which is induced by repeated measures on the same road sections. Statistical models allow the identification of variables that are significant for the LSS fault probability among various effects of road features including marking, pavement distress, weather conditions, horizontal curvature, and cross section. Results pointed out the relevance of the wet retro-reflection of marking (RLw) and the horizontal curvature in the definition of ODD for LSS. Threshold values have been proposed for the tested LSS. Wet pavement doesn't affect the LSS performance when compared to the dry condition. Rain was shown to be critical even with very good road characteristics. • Open on-road LSS test under various weather conditions such as dry, wet and rain. • Generalized estimating equation to highlight correlation among observations. • Average 3.9% fault probability of LSS with high variability in two-lane rural roads. • Comparatively rain increases the fault probability by a factor of 2.75 than dry weather condition. • Marking RLw and curvature 1/ R are the most relevant road factors.

Evaluation of the Influence of Road Geometry on Overtaking Cyclists on Two-Lane Rural Roads.

Road cycling, both individually and in groups, is common in Spain, where most two-lane rural roads have no cycle lanes. Due to this, and the difference in speed between drivers and cyclists, the overtaking manoeuvre is one of the most dangerous interactions. This study analyses how road geometry influences the overtaking manoeuvre performance. Field data of 1355 overtaking manoeuvres were collected using instrumented bicycles, riding along different rural road segments, and considering individual, medium and large groups of cyclists. The safety variables that characterise the overtaking manoeuvre are overtaking vehicle speed and lateral clearance. These variables have been correlated to geometric characteristics of the road, such as the type of centre line, the horizontal alignment, the speed limit, and the road cross section. Regression models have been fitted considering each cyclist group size and configuration. For individuals and medium groups, wider roads generate higher lateral clearances and lower overtaking speeds, while for large groups only the solid centre line was significant, generating lower clearances and higher speeds. Results suggest that other factors need to be considered, especially for large groups. Results offer a deeper understanding of the phenomenon by providing key points for improving road geometry design, such as widening the shoulders.

From the Vehicle-Based Concept of Operational Design Domain to the Road-Based Concept of Operational Road Section

Automated vehicles are designed to maintain both longitudinal and transverse control along a road section under certain favorable conditions. The conditions that ensure a safe operation of an automated vehicle are referred to as Operational Design Domain (ODD). So far, the concept of ODD has focused on the automated vehicle and how it is designed to operate autonomously and safely under certain conditions. However, Road Administrations and Operators would be probably more interested in formulating the concept of ODD from the road standpoint, identifying the sections that really allow an optimal and safe operation for all automated vehicles. Hence, this study introduces the concept of Operational Road Section (ORS), that is, defined as a road section, that is, compatible with all automated vehicles’ ODDs. This would result in very low disengagements of SAE Level 2 and 3 vehicles, and no disengagements at all of any SAE Level 4 vehicle, as long as their conditions do not vary. These road sections are determined as the overlapping of all the ODDs corresponding to all the automated vehicles traveling through a road segment. The concept of ORS will play a key role in the identification of the road features that promote a safe automated driving. With this information, Road Administrations and Operators could 1) establish improvement actions for extended automated operation and thus longer ORSs; and 2) manage driving restrictions to certain driving automation systems. Finally, a case study is presented showing the applicability of the ORS concept on a two-lane rural road segment.

Verification analysis of relationship between driving failure probability and traffic accident rate

To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.

An extended dynamic probabilistic risk approach based on a surrogate safety measure for rear-end collisions on two-lane roads

The rear-end collisions have significantly increased on two-lane roads because of drivers’ overtaking behaviors, and the time to collision (TTC) plays an effective role in these collisions. To prevent this type of collision, investigating the effect of overtaking behaviors on the TTC and predicting the risk probability of collisions are essential issues in road safety. To this end, the present study evaluated overtaking behaviors to find the effect of these behaviors on the TTC to determine a TTC threshold as a surrogate safety measure. Moreover, a dynamic probabilistic risk approach was developed based on binomial logistic regression and higher-order ordinary differential equations to examine the risk probability of the TTC threshold, which is then numerically solved by relying on Euler’s method. Accordingly, the importance weights of overtaking variables on the TTC were studied by multilayer perceptron to determine the importance weights in the risk value. The obtained data indicated that the TTC threshold was 2.4 s under the influence of overtaking behavior. Further, the results from dynamic risk probabilistic models revealed that space headway, time headway, and overtaking speed could raise the probability risk of the TTC threshold by 97, 95, and 93%, respectively. Finally, data related to the risk of the TTC for the proposed models indicated that the highest and lowest risks belong to space headway (1.3 times higher than other variables) and decision time (0.6 times less than other variables), respectively.

Development of a New Procedure for Assessing Road Signs Maintenance Priorities for the Tuscan Road Network

Road network safety management is a challenging process for Road Administrations (RAs) in Italy. It requires planning their activities, identifying operational actions, and implementing maintenance activities. For an effective road management system, all these activities should be based on a replicable monitoring procedure which relies on technical criteria to identify critical sites requiring attention. To develop and implement a reliable and effective maintenance strategy, Road Administrations need inexpensive and user-friendly procedures that can be applied to the entire road network. Road signs and road markings plays an important role in providing visual guidance for road users, which in turn influences road safety. This paper proposes and discusses a standardized and systematic monitoring procedure for the condition assessment of road signs and markings. The procedure will be applied to identify damaged, old, or unreadable signs and markings and a section of a two-way two-lane rural road has been used to test and calibrate the proposed procedure.

Implementation of the sight distance passing on two-lane roads in Colombia

La Distancia de Visibilidad de Adelantamiento (DVA) requerida para un tramo de carretera se obtiene de los manuales de diseño geométrico de vías y de señalización vial de cada país; en Colombia se tienen el Manual de Diseño Geométrico de Carreteras del 2008 [1] y el Manual de Señalización Vial del 2015 [2] para obtenerla. El objetivo de este trabajo es aplicar y cotejar las metodologías establecidas en cada uno de ellos para calcular la DVA requerida y para medir la DVA disponible en planos. Al comparar éstas dos últimas se indican los tramos donde es permitido el adelantamiento para señalizar el eje de la carretera con línea discontinua. Cuando se calcula la DVA requerida en cada uno de los manuales de Colombia, se obtienen valores diferentes. Para entender el efecto de estas diferencias en la señalización, se aplican los valores y la metodología de cada uno de ellos a la carretera Túnel de Occidente – San Jerónimo, que es una carretera de dos carriles representativa y ubicada en el departamento de Antioquia, Colombia. Luego de obtener la respectiva señalización se detectaron vacíos y contradicciones en la reglamentación colombiana de diseño vial y de señalización.

A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a prominent role in the decision-making process of autonomous driving (AD), which enables autonomous vehicles (AVs) to learn an optimal driving strategy through continuous interaction with the environment. This paper proposes a deep reinforcement learning (DRL)-based motion planning strategy for AD tasks in the highway scenarios where an AV merges into two-lane road traffic flow and realizes the lane changing (LC) maneuvers. We integrate the DRL model into the AD system relying on the end-to-end learning method. An improved DRL algorithm based on deep deterministic policy gradient (DDPG) is developed with well-defined reward functions. In particular, safety rules (SR), safety prediction (SP) module and trauma memory (TM) as well as the dynamic potential-based reward shaping (DPBRS) function are adopted to further enhance safety and accelerate learning of the LC behavior. For validation, the proposed DSSTD algorithm is trained and tested on the dual-computer co-simulation platform. The comparative experimental results show that our proposal outperforms other benchmark algorithms in both driving safety and efficiency.

A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads

Two-lane roads are the most significant part of the road network in many countries, and are widely used for systematic and non-systematic daily travels. Traffic on a two-lane road typically involves a high level of interaction among vehicles, with the formation of platoons. As a part of the road network of a country, they represent a crucial development factor from a social and economic point of view, because they ensure the close accessibility of the innermost areas and local markets, and favor the connection between the nodal points of the logistic system and the last mile of the supply and distribution chain. Thus, the estimation of the presence of vehicle platoons makes it possible to develop significant indicators for performance analysis on this type of road, which in turn is the basis for planning, upgrading, and improving transport programs to find a sustainable balance between environmental, social, and economic qualities. This paper presents a statistically based model, the Two-Lane Roads Statistical Platooning Model (TLR-SPM), which allows for evaluating the percentage of vehicles which are free to travel at the desired speed and of non-free vehicles constrained to travel in platoons at lower speeds than desired, as a function of the traffic flow. Based on a data-driven methodological approach, TLR-SPM allows for going beyond the critical threshold value for time headways, such as the widespread 3 s threshold, but lacks the need to hypothesize, identify, or estimate the probability laws for speed and time headway. From the formal treatment of the general statistical method, the paper shows the data processing procedure through its application to a real case. As shown by the application case and the comparisons with the results of other methods, the proposed model can significantly adapt to the experimental data and can support in analyzing a two-lane road in its operating conditions to promote its safety and efficiency as part of a sustainable transport system.

Modern procedures for capacity and Level of Service analysis of two-lane roads

This paper describes the concepts of capacity and Level of Service of two-lane roads. The paper also includes an overview of different methodologies for capacity and Level of Service analyisis of Two-lane road, such as HCM 2010, HCM 2015, HBS 2001, HBS 2015 and procedure developed in Republic of Serbia. In addition to the methodologies themselves, the differences between them are presented, which is emphasized through a comparative analysis of procedures and typical test examples, which show the difference between the obtained results of capacity and the level of service on specific examples. Examples are such that some of them differ from each other by only one parameter, which shows the impact of this parameter on the change in the values ​​of capacity and Level of Service of the two-lane road. Comparative analysis includes the analysis of the impact of certain road and traffic characteristics on the capacity and Level of Service of the two-lane road.

A Predictive Approach to Identify Geometrically Hazardous Road Segments and Evaluate the Relative Safety Effects of Design Alternatives

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).

Assessment of Performance and Maintenance of Flexible Pavement Using KENLAYER and HDM-4

This paper describes the performance of flexible pavement using the KENLAYER and Highway Development and Management (HDM-4) software for urban roads. This study includes the study area of four-lane and two-lane roads at Hyderabad city, such as Gandi Maissama to Bachupally (GB), Bachupally to Nizampet (BN), Miyapur to Bachuapally (MB), JNTU to Pragathi Nagar (JP). The distress data like rutting, fatigue, pothole, patches, and edge drop data of each roads section were taken by adopting the manual methods. The pavement deterioration model was selected and developed based on the available distress using both the software KENLAYER and HDM-4. The current study aims to forecast the performance of specific roads in order to calculate the damage ratio. It also provides the design life of the roads as per the current and future traffic based on the distress model. Further, the maintenance of the particular roads is carried out using the HDM-4 models. It is also noticed that major damage was found in Gandi Maissama to Bachupally than the other road section. This study concluded that the comparative study of different roads section and governing factors for distress along with the allowable limits of distress in each section of the flexible pavement were found. Finally, the output of KENLAYER and HDM-4 has been compared.

Infrastructure-to-Vehicle Visible Light Communications: Channel Modelling and Performance Analysis

Visible light communication (VLC) has emerged as a potential wireless connectivity solution for infrastructure-to-vehicle (I2V) communications. In this paper, we investigate the performance of I2V VLC systems with access points in the form of streetlights. Particularly, we consider a typical two-lane highway road where the light poles are located at both roadsides and uniformly separated from each other. Based on non-sequential ray tracing simulations, we first propose a closed-form path loss expression as a function of transceiver and infrastructure parameters. Then, we statistically analyze the path loss and derive a closed-form expression for its probability distribution function (PDF). Utilizing the derived PDF, we derive an approximate closed-form bit error rate (BER) expression. We confirm the accuracy of derived BER expression through comparison with Monte Carlo simulation results and demonstrate the effect of transceiver and infrastructure parameters such as receiver aperture, pole height, car height, lateral shift, and spacing between light poles on the BER performance.

Field Determination of Unsaturated Permeability and Flow Properties through Subgrade Instrumentation

Due to the representation of a particular field condition of soil rather than the real time scenario from laboratory experiments, the selection of unsaturated permeability and flow parameters becomes challenging when conducting numerical modeling. Keeping this in mind, the objective of the study was to determine the permeability in both directions along with the unsaturated flow parameters from field data. Although it is conventional to determine the flow parameters from the curve fitting of laboratory results, a novel approach was carried out during the course of study, wherein field soil water characteristic curves were used to determine the unsaturated flow parameters. Two two-lane roads in Kaufman County and Ellis County, Texas were selected for data acquisition and monitoring in this study. For the investigation of in situ moisture content and matric suction, soil moisture and suction sensors were installed at up to a depth of 4.5 m into the ground, while the precipitation was recorded using rain gauges installed at the sites. Field determination yielded hydraulic conductivity values in the range of 10−4 to 10−5 m/s, representing the rapid flow of water due to desiccation cracks on expansive soil. Field-generated unsaturated flow parameters also indicated variability while constructing the SWCC. Finally, PLAXIS 2D was used for the transient flow analysis. The close agreement of the FE results with the direct field measurements validated the estimated flow parameters. The approach described in the study can be used for determining permeability and unsaturated flow parameter values from field data, which offers a dynamic situation in contrast to the static laboratory condition.

Considering demographics of other involved drivers in predicting the highest driver injury severity in multi-vehicle crashes on rural two-lane roads in California

The injury severity of a driver in a crash is significantly related to the driver’s age and gender and vehicle characteristics. Previous studies have used only information about the most severely injured driver to represent the crash severity, ignoring other drivers involved in the crash, which can also be important to explain the crash severity. This study uses demographic information of all drivers involved in a multi-vehicle crash to predict the injury severity of the most severely injured driver using a partial proportional odds model. Models incorporating demographic information and vehicle characteristics of all drivers and vehicles involved in a crash were compared with models considering only information about the most severely injured driver in terms of significance of factors and prediction accuracy. The results indicate that although young drivers are likely to have lower levels of injury severity compared to working-age drivers, injury severity increases if the proportion of young drivers increases in a multi-vehicle crash. Drivers indicated to be not at fault frequently were more severely injured than drivers at fault. Finally, the inclusion of all drivers’ demographic information shows an improvement in the prediction accuracy of crash severity of the most severely injured driver.

Crash Distribution Dataset: Development and Validation for the Undivided Rural Roads in Oromia, Ethiopia

Abstract Predicting the number of crashes that may occur as a result of specific highway features is critical in evaluating different treatment or design alternatives. Since different highway geometric characteristics can influence crash distribution datasets, Highway Safety Manual’s (HSM’s) predictive method encourages users to predict crashes based on their severity and collision type proportions. This study used crash data from rural two-way two-lane road segments in the Oromia region over seven years to develop Oromia’s fixed crash distribution dataset on Interactive Highway Safety Design Model (IHSDM) software. The crash distribution dataset has two parts; the crash severity proportions and the collision type percentages. The developed Oromia’s fixed crash distribution dataset was compared and validated against the default HSM crash configuration. As a result, the Crash Prediction Model (CPM) evaluation results confirmed that the developed crash severity proportion (the first part of the crash distribution dataset) estimates are more accurate and closer to the observed values. Furthermore, the findings show that crashes in the Oromia region are severer than in the states where the HSM crash configuration was developed. According to the second part of the crash distribution dataset evaluation (collision type percentage), the developed fixed crash distribution dataset outperforms the default HSM configuration in most collision type proportions, but not in all. For instance, from the ten collision type proportions developed, Right-Angle and sides-wipe collision proportions are predicted more precisely by the default HSM configuration. This points to the need for developing collision type proportion (the second part of the crash distribution dataset) as a function rather than a fixed configuration for a better result, based on the availability of complete crash data (i.e. crash location). In general, the study revealed that in order to exploit the full potential of HSM’s predictive approach, researchers must develop a jurisdiction crash distribution dataset using local crash data. The methodology demonstrated in this study to develop the jurisdiction’s crash distribution dataset has been validated as true thus, safety practitioners are encouraged to adopt it.

A note on estimating safety performance functions with a flexible specification of traffic volume

In this note, a flexible approach to allow for variation in the impact of traffic volume in the estimation of Safety Performance Functions (SPFs) is proposed. The approach generalizes a recently proposed approach by Gayah and Donnell (2021) (GD) titled “Estimating safety performance functions for two-lane rural roads using an alternative functional form for traffic volume”. GD approach proposes a multiple regime structure for AADT impact while explicitly constraining the impact at the regime threshold to be the same. While the GD approach provides a flexible structure, the framework as proposed calls for careful judgement for threshold selection and additional model estimation complexity for the AADT constraint. The current note establishes the equivalence of the proposed approach with the GD approach and subsequently presents a more flexible model structure that improves on the GD approach. Subsequently, we document the advantages of our proposed approach in terms of model estimation, parameter significance testing, flexibility to consider multiple traffic volume ranges and ease of accommodating random parameters for analysis. Finally, we present potential directions for future research.