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Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors.

The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting.

Sustainability and durability are the key requirements of pavement structure. Sustainability of asphalt pavement structure involves utilization of Warm Mix Asphalt (WMA) technologies with the addition of Reclaimed Asphalt Pavement (RAP), where durability of asphalt involves performance parameters like fatigue and fracture resistance properties etc. Utilizing the RAP content in asphalt mix increases the mixing and compaction temperature which may degrade the performance of asphalt. Hence, numerous studies have recommended different WMA technologies to decrease mixing and compaction temperature of asphalt mix containing RAP. The present research work evaluates the fatigue and fracture performance of WMA and Hot Mix Asphalt (HMA) with varying percentages of RAP and Sasobit. Different mixes of WMA and HMA were designed with varying percentages of RAP (0, 20, 40 and 60%) through Marshall Mix design. Sasobit (organic/wax-based additive) was used as WMA technology to prepare WMA at varying percentages (0, 2, 4 and 6%). The fatigue behavior of asphalt was evaluated using four-point bending test, where fracture resistance of asphalt was determined using Semi Circular Bending (SCB) test in the laboratory. Fatigue and fracture resistance of WMA were improved with the increase in percentages of Sasobit and RAP content, while the addition of RAP in HMA showed a decreasing trend of fatigue and fracture resistance due to the stiffer nature of RAP. Furthermore, WMA was identified as economical for construction besides other benefits like improved properties and environment friendly asphalt mix. Doi: 10.28991/cej-2021-03091741 Full Text: PDF

Road traffic accidents are among the top ten major causes of fatalities in the world, taking millions of lives annually. Machine-learning ensemble classifiers have been frequently used for the prediction of traffic injury severity. However, their inability to comprehend complex models due to their “black box” nature may lead to unrealistic traffic safety judgments. First, in this research, we propose three state-of-the-art Dynamic Ensemble Learning (DES) algorithms including Meta-Learning for Dynamic Ensemble Selection (META-DES), K-Nearest Oracle Elimination (KNORAE), and Dynamic Ensemble Selection Performance (DES-P), with Random Forest (RF), Adaptive Boosting (AdaBoost), Classification and Regression Tree (CART), and Binary Logistic Regression (BLR) as the base learners. The DES algorithm automatically chooses the subset of classifiers most likely to perform well for each new test instance to be classified when generating a prediction, making it more efficient and flexible. The META-DES model using RF as the base learner outperforms other models with accuracy (75%), recall (69%), precision (71%), and F1-score (72%). Afterwards, the risk factors are analyzed with SHapley Additive exPlanations (SHAP). The driver’s age, month of the year, day of the week, and vehicle type influence SHAP estimation the most. Young drivers are at a heightened risk of fatal accidents. Weekends and summer months see the most fatal injuries. The proposed novel META-DES-RF algorithm with SHAP for predicting injury severity may be of interest to traffic safety researchers.

In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’ end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’ walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.

The commuters’ circulation space in the multimodal transport interchanges comprises of a network of pathways and stairways and meant for the efficient transit of commuters from one mode to another transport mode. Transit Cooperative Research Program (TCRP) - Report 165 guidelines for the design of commuters’ circulation space in multimodal transport interchanges has several shortcomings. To aid the designers of multimodal transport interchanges, a Discrete-Event Simulation (DES) framework using Phase-Type (PH) distribution is developed for the analysis of commuters’ circulation space, based on commuters’ flow–density​ relationship. The performance measures such as average area occupied per commuters, blocking probability, average sojourn time and throughput are computed. The DES framework is then coupled with the Particle Swarm Optimization (PSO) to create a hybrid DES-PSO framework for the design of commuters’ circulation space under a required Level of Service (LOS). Experimental results show that the optimized widths are highly affected by both commuters’ arrival rates and randomness in the arrival flow. The commuters’ flow–density relationship considers both congested and uncongested conditions and therefore gives higher optimized width. The upstream stairways resulted in larger widths than elevated passageways due to slower movement of commuters. The sensitivity analysis under several factors reveals that commuters’ arrival rate and widths of the circulation space are highly dominant factors while the lengths of circulation space do not show any vital impact on performance measure values.

With the increase in demand of flexible pavements, due to their various advantages over rigid pavements, there is a need to improve the aging properties of the bitumen in order to enhance its resistance against different types of distresses such as rutting, fatigue cracking. This research focus on the use of one polymeric additive Polyethylene (PE) and one non polymeric additive Sulphur (S) to enhance the aging resistance of asphalt. These modifiers are evaluated for their effect on the aging mechanism in comparison with the unmodified bitumen. Aging of the original and modified bitumen is realized by the Rolling Thin Film Oven (RTFO) and Pressure Aging Vessel (PAV). Physical properties of the aged and unaged asphalt binders are evaluated through empirical testing like penetration, ductility and softening point test. Optimum content of the modifiers is obtained by comparing the results of conventional properties before and after aging. Fourier Transformed Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) are performed to bring out the chemical and morphological changes in the modified binder. Rheological properties of modified asphalt are evaluated with the help of a Dynamic Shear Rheometer (DSR). Results indicate improvement in physical properties of the modified asphalt even after the aging. Penetration index increased which shows less temperature susceptibility of the modified binders. Carbonyl and sulfoxide index are used as aging indicators which shows reduction in case of modified samples. Decrease in the sulfoxide and carbonyl index indicates better oxidation resistance of the modified samples. Morphological analysis proves good compatibility of the modifiers with asphalt binders. DSR results indicate improved viscoelastic properties of the modified binders. Hence it can be concluded that Polyethylene and Sulphur are good options to improve the aging resistance of asphalt in terms of their cost effectiveness and environment friendly nature.

The COVID-19 pandemic policies have had a significant impact on the daily commuter flow at the metro rail transit stations. In this study, we propose a modified state-dependent M(n)/G(n)/C/C queuing model for the analysis of commuter flow in the corridor of metro rail transit stations in the COVID-19 situation in order to ensure safe social distance. The proposed model is a finite capacity queuing system with state-dependent commuter arrivals and state-dependent service rates based on the flow–density relationship. First, a mathematical queuing model is developed by using the birth–death process (BDP) and the expected number of commuters, and average area occupied per commuter and blocking probabilities are computed. Then, the accuracy of the proposed model is verified by a discrete-event simulation (DES) framework. (1) The proposed model’s results are compared to those of the existing M/G(n)/C/C model. The proposed modified model’s sensitivity analysis revealed that the anticipated number of commuters in the corridor remains smaller when the arrival rate is state-dependent. (2) In accordance with COVID-19 protocol, when the facility is congested, commuters are discouraged from entering and a safe social distance is maintained between them. (3) No commuters are impeded, and adequate throughput is ensured from the corridor. The proposed model will assist the metro rail transit station operators in making intelligent decisions regarding the operations in the COVID-19 situation.

Multi-lane highways are used to channel the traffic between collector roads and freeways, such as urban arterial roads. Their capacity is moderate to high, and they are designed to balance traffic mobility with accessibility. Traditionally, the assessment of multi-lane highways’ level of service (LOS) is based on the Highway Capacity Manual (HCM). The HCM method, however, does not take into account randomness in vehicle flow as well as state-dependent vehicle speed on multi-lane highways, which does not represent actual conditions. A discrete-event simulation (DES) framework using the phase-type distribution (PHD) is developed to assist the traffic and highway designers in measuring average number of vehicles and dwelling time of vehicles on the multi-lane highway section as well as compute the level of service (LOS) in case of uniform and random traffic flow. Performance assessment by PHD-DES framework under different design parameter settings shows that the arrival rate of the vehicle, squared coefficient of variation (SCV) in the arrival interval and number of lanes affect the LOS of multi-lane highways considerably. The length of the multi-lane section also affects the performance measure and LOS, which the HCM method ignores. The proposed PHD-DES framework will assist the road traffic and highway designers in making smart decisions.

Road traffic accidents are one of the world’s most serious problems, as they result in numerous fatalities and injuries, as well as economic losses each year. Assessing the factors that contribute to the severity of road traffic injuries has proven to be insightful. The findings may contribute to a better understanding of and potential mitigation of the risk of serious injuries associated with crashes. While ensemble learning approaches are capable of establishing complex and non-linear relationships between input risk variables and outcomes for the purpose of injury severity prediction and classification, most of them share a critical limitation: their “black-box” nature. To develop interpretable predictive models for road traffic injury severity, this paper proposes four boosting-based ensemble learning models, namely a novel Natural Gradient Boosting, Adaptive Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, and uses a recently developed SHapley Additive exPlanations analysis to rank the risk variables and explain the optimal model. Among four models, LightGBM achieved the highest classification accuracy (73.63%), precision (72.61%), and recall (70.09%), F1-scores (70.81%), and AUC (0.71) when tested on 2015–2019 Pakistan’s National Highway N-5 (Peshawar to Rahim Yar Khan Section) accident data. By incorporating the SHapley Additive exPlanations approach, we were able to interpret the model’s estimation results from both global and local perspectives. Following interpretation, it was determined that the Month_of_Year, Cause_of_Accident, Driver_Age and Collision_Type all played a significant role in the estimation process. According to the analysis, young drivers and pedestrians struck by a trailer have a higher risk of suffering fatal injuries. The combination of trailers and passenger vehicles, as well as driver at-fault, hitting pedestrians and rear-end collisions, significantly increases the risk of fatal injuries. This study suggests that combining LightGBM and SHAP has the potential to develop an interpretable model for predicting road traffic injury severity.

The capacity design of passengers’ circulation areas at the transfer stations is a crucial issue from the designers’ point of view. An adequate design reflects the better performance while inadequacy leads to the congestion issues at the transfer stations. To overcome the shortcomings in the design guide (TCRP-165 Report) and existing exponential-based (M/G(n)/N/N) design model, this paper reports a novel Discrete-Event Simulation (DES) model based on the Phase-Type (PH)-distributed random variates for the circulation areas (passageways and stairways), integrated with the Differential Evolution (DE) optimization approach. This hybrid simulation-optimization model is an automated framework that simultaneously works to provide optimal widths of the passengers’ circulation areas, under a desirable Level-of-Service (LOS) and blocking probability criteria. The passengers’ arrival and service time pattern of the circulation areas in the DES model is described by the PH distribution, which accurately takes into account the variation of passengers’ arrival at different instants as well as randomness and state-dependent service time of the circulation areas. The comparison of proposed model with exponential-based (M/G(n)/N/N) model and TCRP-165 Report reveals that: (1) the optimal widths of circulation areas obtained by proposed model are greater than both M/G(n)/N/N and TCRP-100 Report; (2) the optimal width increases with the increase in squared coefficient of variation for all the passengers’ arrival rates, LOS and lengths of the circulation areas; (3) the optimal width values drop from LOS B towards D; (4) our proposed simulation-DE optimization outperforms the simulation-GA optimization approach in term of the speed of convergence. This novel hybrid simulation-DE optimization framework is an effective tool for the designers of transfer stations in decision making process and analyzing the complex facilities with ease.

The passengers’ transfer zone in the transit centers is the interface among various transportation modes, where passengers transfer from one mode to another. The Transit Cooperative Research Program (TCRP)-Report 165 presents the capacity analysis technique for the passengers’ transfer zone in the transit centers. However, the TCRP-Report 165 procedure is based on the fixed values of passengers’ arrival flow and service time of facilities, which do not depict the real scenario. To aid the designers of transit centers as well as to capture the randomness in the arrival flow and service time, a discrete-event simulation (DES) framework based on the PH-distributed random variates is developed. The DES framework represents the passengers flow in the transit centers and also takes into account the randomness in passengers’ arrival flow and service time. Sensitivity analysis is conducted under different settings of passengers’ arrival flow, coefficient of variation (CV) and dimensional features (length and width of transfer zone). The results showed that the passengers’ arrival flow, CV and width of the transfer zones are highly influential parameters, while the length of transfer zones has no significant influence. Therefore, during the design phase of transit centers, emphasis should be given to influential parameters as they illustrate the actual conditions in the transit centers.

This paper proposes an integrated discrete-event simulation (DES) using phase-type (PH) distribution and optimization method for the performance assessment, as well as optimal configuration of metro rail transit (MRT) station service facilities. The MRT facilities (including ticket facilities and elevator) are represented as a queuing network system with the passengers’ arrival flow and the service time based on the PH distribution, which considers the flow and service time fluctuation. The necessary performance measures include facility utilization, mean number of passengers and waiting time of passengers in the facilities are obtained by PH-based DES model of service facilities. The model is then coupled with a genetic algorithm that works simultaneously to give the optimal configuration of service facilities. The results of PH-based DES model are first verified by existing PH-based analytical models and then compared with other existing exponential-based and deterministic-based queuing models. The results reveal that increase in squared coefficient of variation of arrival interval causes an increase in the performance measure values and high number of ticket facilities compared to existing models. The existing models therefore underestimate the results. The squared coefficient of variation is an important parameter and cannot be ignored in analysis and design of service facilities. This research provides a more realistic and novel PH-based DES, as well as PH-based simulation-optimization method that will assist the planners and designers of MRT stations to make intelligent decisions regarding analysis and design.

Width is crucial to the performance of circulation network in urban rail transit station. However, poor performance has been observed in most existing circulation systems. In fact, randomness and state-dependence exist in circulation network where blocking and feedback cannot be ignored. In this paper, we develop a PH( n )/PH( n )/ C / C state-dependent queuing network model with an analytical solution. This model describes the random and state-dependent arrival interval as well as service time by phase-type distribution. Feedback is also taken into account. The existing M/M( n )/ C / C is a special case of the proposed PH( n )/PH( n )/ C / C queuing network model, and the existing M/G( n )/ C / C and D/D/1/ C models can be approximated by the proposed network model. Then we present a programming formulation for circulation network design with blocking probability control based on the queuing network model. Finally, we illustrate the applicability of the proposed design method by comparing it with the existing design methods. The results show that: 1) the blocking probability is quite small and evenly distributed in the network designed by the new method, while much bigger and fluctuating blocking probabilities exist in networks designed by the other two methods; 2) other performance measures, like area per passenger, dwell time and throughput, are also considerably improved in the new method; 3) performance measures of the proposed method enjoy high performance-cost elasticity compared with the other two methods. An interesting insight is also obtained that the squared coefficient of variation for arrival interval plays an important role in determining the optimal width for circulation network.

Width design of the urban rail transit stations circulation facilities is a vital issue. The existing width design approach failed in fully considering the essential factors such as fluctuation in passengers’ arrival process, fluctuation and state-dependence in passengers walking speed and the blocking when passengers’ demand exceeds the capacity of facilities. For this purpose, a PH-based simulation-optimization approach is proposed that fully considers the fluctuation, the state-dependence, Level of Service (LOS) and blocking effect. This novel approach provides automatic reconfiguration of the widths of circulation facilities by a concurrent implementation of a PH-based Discrete-Event Simulation (DES) model and the Genetic Algorithm (GA). The proposed PH-based simulation- optimization approach and the existing design approaches based on the exponential and deterministic models are applied to design the widths of circulation facilities. The results reveal that the circulation facilities designed by the proposed approach have larger widths. Similarly, increase in the SCV of arrival interval results in increasing the widths designed by the proposed approach increase while the widths of the other two approaches stay the same. The width designed of the proposed approach increase at faster rate than that of the other two approach when the passengers’ arrival rate increases.

The optimal design of the walkway at an urban rail transit station is a vital issue. The Transit Capacity and Quality of Service Manual (TCQSM) TCRP-100 report for the design of urban rail transit station walkway and the existing design models neglect the important factors such as randomness in the passenger arrival rate, randomness and state-dependent service time of the walkway and blocking phenomenon when the passenger flow demand exceeds the walkway capacity. There obviously exists a need to develop a design approach that overcomes these shortcomings. For this purpose, this paper details a simulation-based optimization approach that provides width design through automatic reconfiguration of walkway width during the simulation–optimization process based on phase-type (PH) distribution. The integrated PH/PH(n)/C/C discrete-event simulation (DES) model and optimization method that uses the genetic algorithm (GA) work together concurrently to obtain optimized (design) widths for different passenger flow and level of service (LOS) The numerical experiments are conducted to compare the proposed model with the existing design methods. It reveals that: (1) The width obtained by our proposed model is higher than the existing width design models; (2) when squared coefficient of variation of passenger arrival interval increases, the walkway width increases more for our proposed model than the existing design models; (3) when the arrival rate increases, the walkway width of our proposed model increases faster than the existing design models; (4) the increase in the length of walkway has no significant effect on the walkway width.

Multi-lane highways have moderate to high capacity and designed to provide a balance between traffic mobility and accessibility. The assessment of Level of Service (LOS) of multi-lane highways is traditionally based on the Highway Capacity Manual (HCM). However, the HCM method does not takes into account randomness in vehicular flow as well as state-dependent speed of vehicles on multi-lane highways, which do not depict actual conditions. To aid the traffic and highway designers as well as to capture the randomness in the vehicular arrival flow and state-dependent speed, a Discrete-Event Simulation (DES) framework using the Phase-Type Distribution is developed. The Phase-Type distribution takes into account randomness in the vehicular flow. The LOS computation under different settings of design parameters shows that the vehicular arrival rate, Squared Coefficient of Variation (SCV) in the inter-arrival time and number of lanes significantly affects the LOS of multi-lane highways. The length of multi-lane section also has an influence on the LOS, which is ignored by the HCM. The proposed DES framework will assist the traffic and highway designers to make smart choices in LOS computation.

This study reports a novel hybrid simulation based Particle Swarm Optimization (PSO) technique using PH-Distribution for the design of facilities at transit stations. First, a Discrete-Event Simulation (DES) model for the network of stairs and passageway is developed that incorporates all influential factors that were ignored by the existing design codes (TCRP Report-165). The DES model is coupled with the PSO technique to build an automated framework for the area of network of stairs and passageway. The experimental results by hybrid simulationoptimization reveal that: (1) increase in randomness (SCV) of passengers’ arrival causes increase in the breadth of both passageways and stairs; 2) For the same arrival rate and SCV, the stairs breadths are more than passageway breadths; 3) The breadth decreases from Level of Service (LOS) B towards D.

Concrete is the most widely used construction material in the world. Surely, it is extensively employed in the construction of rigid pavements. Repetitive loads on the pavement cause cracks which allow the ingress of harmful compounds and thus reduce the durability of concrete. Hence, this research presents an ecofriendly solution to impart self-healing capability to concrete by using bacteria as a healing agent and Recycled Brick Aggregate (RBA) as an immobilizer for bacterial spores. Utilization of RBA is considered to offer a sustainable resource consumption. The bacteria used in this study is Bacillus subtilis and the calcium source used is calcium lactate. Specimens were prepared for four mixes, mix 1 was control mix, mix 2,3 and 4 contained 30%, 50% and 70% RBA respectively. The number of bacterial spores was kept same in mix 2,3 and 4 which was 106 cells/ml. The results showed slight reduction in compressive and flexural strength with the increasing percentage of RBA in the mix. Self-healing tests were also conducted which showed that samples containing 50% RBA and bacterial spores showed maximum crack healing of up to 0.92 mm with strength recovery of 69% when samples were cracked after 7 days and crack healing of 0.68 mm with strength recovery of 57% when samples were cracked after 28 days. Moreover, XRD and SEM analysis also confirmed the calcite precipitation.

Summary Metro station corridor and passengers are described as a G/G(n)/C/C state-dependent queuing system with a general random arrival interval G and a general random and state-dependent service time G(n) to offset the shortcomings in existing design methods. The corresponding G/G(n)/C/C state-dependent discrete event simulation model is developed, and its high-fidelity is tested. Then the optimization algorithm based on the simulation model is designed to determine corridor width. The proposed simulation optimization method and the existing analytical optimization methods, based on M/G(n)/C/C and D/D/1/C queuing models, are applied to design corridor width in a numerical example of 48 combinations of passenger flow rates and level of service (LOS). The designed corridor widths are tested in a micro-simulation model, and the performance measure is compared. The result shows that the corridor widths obtained by the new method are 0.357 m (7.4%) larger than that of the other two methods on average; the area per passenger of the new method increases 10.53% and 11.63%, respectively, compared with that of the other two methods; the widths designed by the new method satisfy the requirement of LOS under various passenger flows, whereas 93% of the corridor widths obtained by the other two methods fail to meet the requirement of LOS, and the corridor widths designed by the new method have high elasticity coefficients of LOS-width. Copyright © 2015 John Wiley & Sons, Ltd.

The codes for analysis and design of subway station circulation facilities neglect the important factors such as passenger flow randomness, state-dependent service time of circulation facilities, and passengers blocking when demand exceeds capacity. This paper proposes a new Discrete-Event Simulation (DES) method using Phase-Type distribution to analyze the performance of circulation facilities of subway station as a queuing network. The proposed method is compared to the existing methods and it is observed that existing methods underestimate the performance measures values by neglecting the important factors. The new DES method will allow the planners and designers of subway station to make intelligent decision regarding circulation facilities design as well as when and how to upgrade the circulation facilities.

Rutting is a common phenomenon on pavements in Pakistan. Resilient modulus is one the important properties used in the mechanistic analysis of pavement response under dynamic traffic loads as well as used to study the rutting behavior of pavement. This research study investigates the effect of specimen diameter, bitumen grade and compactive effort on the resilient modulus (stiffness) and Indirect tensile strength (Strength) of asphalt concrete. The analysis of experimental results revealed that both the strength and stiffness values for Polymer Modified Bitumen grade were greater than that of unmodified penetration grade bitumen. Similarly, the resilient modulus and indirect tensile strength values of specimens prepared with 100 mm diameter and compacted with Marshall Hammer were more than that of specimens prepared with 150 mm diameter and compacted with gyratory compactor. This research will help the highway agencies in modifying their design methods for asphalt concrete pavements.