Development of an Integrated BIM and Traffic Simulation-Based Highway Alignment Planning and Optimization Framework

Abstract

Highway alignment optimization is critical for developing sustainable and resilient transportation infrastructure. Traditional alignment selection methods frequently fail to comprehensively account for all of the diverse factors, including geometric compliance, traffic efficiency, land use factors, environmental impacts, and cost considerations, ultimately resulting in suboptimal project outcomes. To address these challenges, this study proposes a building information modeling (BIM)-based alignment optimization framework that integrates diverse datasets, sophisticated modeling techniques, and stakeholder collaboration. The proposed framework systematically enables the user to model terrain, design geometric features, simulate traffic, and conduct cost analysis and environmental impact assessments. A case study of the Dera Ghazi Khan Northern Bypass project in Pakistan, a critical infrastructure project designed to ease congestion and enhance regional connectivity, is presented to validate the proposed framework. Three alignment alternatives were analyzed, with the optimized solution (Alignment Option 2) demonstrating a 30% reduction in congestion, a 20% decrease in travel time, and a 6.48% reduction in construction costs compared to the other alignment alternatives. These outcomes highlight the transformative potential of BIM-driven optimization to significantly enhance sustainability, cost-efficiency, and operational performance. This framework offers a scalable and adaptable model to guide future infrastructure development initiatives toward more sustainable outcomes.

1. Introduction

Transportation infrastructure is critical for the economic growth, regional connectivity, and sustainable development of any country. Highways facilitate mobility and trade, including lifecycle and lifeline functions, while linking the urban centers with industrial districts and rural localities [1]. However, increasing urbanization and increased vehicle volumes, traffic congestion, delays, and environmental problems are now major bottlenecks to efficient transportation [2]. Addressing these challenges requires advanced planning techniques capable of determining the best highway alignments with respect to a combination of diverse technical and financial factors. Highway alignment planning and design are conventionally conducted using segmented and isolated approaches, often resulting in conflicts and suboptimal project outcomes. Traditional methods neglect multidisciplinary factors such as terrain variability, traffic dynamics, built-up environments, etc., causing cost overruns in developing regions [3,4]. As these methods do not comprehensively incorporate different datasets concerning topography, traffic volumes, environmental factors, and utility information into a single framework, such methods result in suboptimal alignment designs, amplified construction costs, and considerable environmental impacts [5]. The current methods of traditional highway planning are inefficient with respect to either alignment optimization or overall decision-making. For example, Chong et al. [4] highlight that conventional planning practices frequently fail to integrate diverse datasets, resulting in fragmented decision-making processes. Similarly, Honarpisheh [6] and Arayici et al. [7] have documented the limitations of traditional methods in terms of coordination among different stakeholders and disciplines. In this context, building information modeling (BIM) has the potential to provide a comprehensive solution that integrates various kinds of data into a unified platform to visualize, simulate, and optimize infrastructure projects [8].

Multiple studies have demonstrated the implementation of BIM for achieving major operational advantages during highway planning and development. Azhar et al. [9] reported that BIM helps to reduce errors and improve coordination, while Zhao et al. [10] demonstrated the integration of BIM with GIS to optimize the highway alignments that simultaneously saved costs and improved operational efficiency. The authors validated the proposed framework in the context of Chinese highways. However, the authors focused primarily on geospatial factors, including geological data, geometric design factors, earthwork, etc. Furthermore, Vilutienė et al. [11] provided evidence that BIM implementation contributes to enhanced operational efficiency and cost savings, whereas Sabato et al. [12] empirically validated that BIM-supported simulations yield measurable improvements in safety and performance metrics. The Federal Highway Administration (FHWA) defines BIM for highway infrastructure as the collaborative delivery of capital projects, uniting planning, design, and construction and operational phases until the fulfillment of predicted infrastructure outcomes using digital processes instead of traditional ones [13]. BIM enables information systems within agencies to align their data through integrated operations, which disrupt data isolation and deliver cost reductions and enhanced outcomes across the entire lifecycle of built infrastructure.

BIM serves as a unified workspace, which enables professionals from multiple disciplines to create visualizations alongside simulations and analyses specifically for complicated infrastructure initiatives [14]. The technology allows engineers and planners to unite data from multiple sources inside a unified digital framework, which helps them perform precise alignment modeling while seeking optimal solutions. The infrastructure BIM-based software Autodesk InfraWorks 2024 enables this process through its capabilities, which include terrain modeling, traffic simulation, and cost analysis tools [15]. Through these features, decision-makers may gain the ability to examine diverse alignment possibilities and find ideal solutions while protecting the surrounding environment from disturbances. The application of BIM in highway infrastructure leads towards better decision outcomes while decreasing expenses [10]. BIM serves as a vital facilitator for collaborative infrastructure work by helping teams plan projects through data-driven simulations [11].

BIM implementation for highway projects shows limited progress, particularly in developing regions. These regions usually face obstacles from restricted resources coupled with limited technical expertise. Organizational development needs standardized BIM technology platforms that systematically and progressively overcome these obstacles. The proposed research proposes a comprehensive BIM-based framework that optimizes highway alignment processes as a solution to the present gap. The framework merges various discipline-oriented data while using advanced modeling abilities to evaluate options for alignment choices and supporting stakeholder engagement processes. The proposed framework aims to address all aspects of highway planning through the integration of vital characteristics, including traffic efficiency, geometric compliance, environmental impact, and cost-effectiveness. Unlike prior studies focusing on isolated aspects (e.g., geospatial data, earthwork quantities, cost, etc.), the proposed framework integrates BIM, traffic simulation, geometric, and built-up environment data into a single workflow, enabling real-time collaboration among stakeholders. This approach is particularly impactful in developing regions such as Pakistan, where fragmented data and limited technical expertise hinder large-scale projects.

Keeping the above in view, it can be concluded that despite advancements, existing BIM applications in highway planning remain limited in two key areas. First, prior studies primarily focus on geospatial and geometric factors but lack integration with dynamic traffic simulation systems, hindering holistic optimization. Second, limited data availability and technical expertise in developing countries restrict large-scale implementations of such context-sensitive solutions. This study bridges these gaps by proposing a BIM framework that integrates traffic dynamics, environmental constraints, and stakeholder collaboration and is tailored to resource-constrained settings.

The main goal of this study is to design and prove the effectiveness of a BIM system for highway alignment optimization that addresses current limitations. To validate the proposed framework, this study presents a case study based on the Dera Ghazi Khan Northern Bypass–Pakistan project. Dera Ghazi Khan serves as a critical transportation hub in Pakistan; however, its current road infrastructure is inadequate, leading to significant traffic congestion due to a lack of modernization. The bypass project case study helps to show the real-world applicability of the proposed BIM-based solution. This research demonstrates the BIM-enabled advancements in infrastructure planning by providing valuable guidance to engineers, planners, and decision-makers. The current study brings theoretical insights closer to real-world practice, thus advancing the practical knowledge related to sustainable roadway development.

2. Literature Review

The integration of BIM has transformed infrastructure project management across all lifecycle stages, enabling multidisciplinary collaboration and reducing cost overruns and delays [3,4]. For highway projects, BIM’s ability to integrate spatial, environmental, and geological data has been empirically validated. Zhao et al. [10] demonstrated that BIM–GIS integration reduced earthwork costs and ecological disruptions in Chinese highway projects using genetic algorithms for alignment optimization. Inzerillo et al. [16] reported improvements in asset management efficiency through BIM-driven infrastructure and structures information modeling (ISIM). However, challenges such as limited technical expertise and interoperability barriers persist in developing regions [17].

BIM serves as a comprehensive project simulation platform, connecting 3D models with planning, design, construction, and operations data [9]. Parametric modeling ensures automatic updates, improving documentation accuracy. The integration of BIM with integrated project delivery (IPD) enhances efficiency and reduces project expenses by fostering collaborative decision-making [18]. A case study at Savannah State University showed that joint stakeholder collaboration using BIM led to significant cost savings and accelerated project completion [18].

Infrastructure projects in remote environments face logistical challenges, resource management issues, and stakeholder coordination difficulties. BIM addresses these by providing a unified platform for communication and real-time resource tracking, as demonstrated by Arayici et al. [7]. Visualization functions enhance stakeholder interconnectivity, facilitating execution strategies and supply chain management. BIM further supports real-time monitoring, improving efficiency and transparency [10].

Emerging technologies such as geographic information systems (GISs), the Internet of Things (IoT), and big data are increasingly integrated with BIM to enhance lifecycle management and sustainability [19]. Cepa et al. highlight BIM–GIS synergies that improve spatial context, monitoring, and data acquisition for infrastructure planning [19]. Despite these advancements, BIM adoption in developing regions remains slow due to high implementation costs and limited government support [17]. This research aims to demonstrate BIM’s role in improving design efficiency and collaboration in highway planning and development, supporting sustainable infrastructure progress.

The convergence of BIM with GIS into ISIM has emerged as a transformative approach to infrastructure management. ISIM combines BIM’s engineering precision with GIS’s spatial analytics, optimizing project planning and execution. Inzerillo et al. [16] applied ISIM to Italian highway projects, achieving cost reductions through real-time spatial data updates. Zhao et al. [10] demonstrated its role in mitigating ecological disruptions in China by identifying sensitive zones during alignment planning. ISIM facilitates proactive management throughout a project’s lifecycle, enhancing design choices and maintenance efforts while reducing costs [20].

BIM also enhances safety and asset management in transportation infrastructure. Sabato et al. [12] documented significant improvements in environmental risk identification during highway design phases. Cepa et al. [8] showed that BIM-driven lifecycle assessments reduced carbon emissions by optimizing earthwork logistics, aligning with FHWA benchmarks that report reduced requests for information (RFIs) due to improved interdisciplinary coordination [13]. However, software interoperability and high upfront costs remain major barriers to widespread adoption [7,17]. Standardized approaches and localized software improvements are needed to address these challenges [21]. Recent advances in BIM–GIS integration have further enhanced highway planning. Miao et al. [22] demonstrated BIM’s role in fostering collaborative decision-making for highway projects in China, while Pérez-García et al. [15] emphasized the need for standardized protocols to streamline BIM adoption in public infrastructure. Roumyeh and Badenko [20] showcased BIM’s utility in dynamic risk assessments, aligning with this study’s focus on environmental constraints. Industry–academic collaborations can strengthen BIM training programs, equipping professionals with essential skills.

BIM’s role in transportation infrastructure continues to expand, streamlining project coordination, reducing errors, and enabling multidisciplinary teamwork. Studies by Biancardo et al. demonstrate how BIM improves design precision and operational efficiency, such as in modeling guardrails and retaining walls. The Harbor of Naples project exemplifies BIM’s capability in managing complex infrastructure developments [3].

In summary, while BIM’s potential in highway alignment design and optimization is well-documented, sustained efforts are required for its implementation in developing regions. Future frameworks should integrate sustainability, cost-effectiveness, and technical performance to meet modern infrastructure needs. This research proposes a BIM-based system for optimizing highway routes, addressing current knowledge gaps.

3. Methodology

This research study implements building information modeling (BIM) using Autodesk InfraWorks software to create a data-driven highway alignment optimization process. The approach is organized into five major steps: data acquisition, base model development, evaluation of alignment alternatives, simulation and visualization, and final optimization and selection. The research process employs international engineering standards such as AASHTO guidelines on highway geometry for BIM data management [23]. This approach also employs ISO 19650-1:2018 guidelines for the fusion of various forms of data with simulation cycles and sophisticated visualization tools [24]. The formalized framework facilitates technical standard compliance and ensures cost-effectiveness and sustainability. Figure 1 illustrates the methodological framework proposed in this study.

The proposed framework aims to develop and streamline various systematic methods for enhancing highway alignments by utilizing BIM technology. The methodology integrates data interoperability with technical modelling approaches to tackle highway design challenges. Unlike conventional approaches that majorly prioritize cost or geometric compliance in isolation [4], this study integrates five conflicting criteria into a unified BIM workflow:

• Construction costs (earthwork, utility relocation);
• Traffic efficiency (congestion reduction, travel time);
• Environmental impact (water bodies, ecological zone disruption);
• Land acquisition (social and economic costs);
• Geometric compliance (AASHTO design standards).

3.1. Data Acquisition

This step of the proposed framework involves the collection of extensive multidisciplinary data, which are necessary to develop the desired BIM models. The acquired data also need to be validated through the already available literature and government reports. Data authenticity may be ensured through cross-verification with government records and field surveys. In addition, data collection must follow ISO 19650-1:2018 standards [4] and FHWA guidelines [14]. As per the proposed framework, key datasets include the following.

3.1.1. Topographical Data

Survey data, including contour levels, elevation points, and land features, are obtained through techniques such as drone and satellite imagery and validated through GPS and total station surveying. These data enable the creation of a detailed digital terrain model (DTM).

3.1.2. Traffic Data

Traffic surveys are conducted to capture traffic volume, vehicle classifications, peak hours, and origin–destination data. These data help simulate traffic flow within the model and evaluate the impact of each alignment on traffic congestion and flow.

3.1.3. Environmental Data

Data related to agricultural lands, urban settlements, water bodies, ecologically sensitive zones, and protected areas are gathered to ensure minimal environmental impact.

3.1.4. Land Acquisition and Utilities Data

Information on existing built-up areas, green spaces, agricultural land, and industrial zones within the project area is collected through satellite imagery and field surveys. This includes identifying areas of land that need to be acquired, assessing the impact of land acquisition on the project’s cost, and considering socioeconomic implications. Moreover, details about existing utilities, including water lines, power grids, and sewer systems, are obtained from satellite data and relevant authorities. Areas with dense urban development are documented to ensure that alignment planning minimizes disruptions to these regions. Special attention is given to avoid or mitigate impacts on residential, commercial, and industrial zones.

3.1.5. Design Standards

The geometric design parameters are adopted from AASHTO guidelines, which explain the parameters for building horizontal and vertical curves, along with minimum sight distances, design speeds, and other geometric design factors. These standard benchmarks establish a unified foundation for assessing and further improving the alignment mobility design process.

3.2. Base Model Development

After the acquisition of required data, the next step in the proposed framework is to integrate the collected datasets for building a dynamic and detailed base model. For this purpose, software such as Autodesk InfraWorks 2024 is utilized. Autodesk InfraWorks provides the option to import topographical data with DEMs to build a georeferenced three-dimensional terrain model and detailed base model. Existing road networks and proposed alignments are overlaid, adhering to AASHTO geometric standards [23]. This model serves as a digital representation of the study area, enabling advanced simulation and visualization capabilities. This process involves the following:

• DEMs and topographical data are imported into Autodesk InfraWorks to construct a georeferenced 3D terrain model. The tools for elevation profile visualization, slope gradient analysis, and terrain contour mapping allow the identification of possible design challenges;
• The integration of road networks, such as existing road infrastructure, including arterial highways and local roads, is mapped onto the terrain model. The initially proposed alignment, including the corresponding right-of-way, is created with reference to the existing road network;
• GIS layers for ecological zones, existing utilities, and urban settlements are integrated to visualize constraints, while traffic simulations are calibrated using methodologies validated by [4,17], aligning with real-world congestion patterns [12]. The integrated datasets allow researchers to identify potential conflicts and analyze all possible options for the study area from each aspect.

The developed base model serves as a dynamic platform for evaluating alternative alignments while supporting iterative design changes and analysis. An overview of the InfraWorks software interface is presented in Figure 2.

Model Review and Validation

The review and validation processes are essential in ensuring that the base model accurately represents the real-world conditions of the project area and serves as a reliable foundation for further analysis and decision-making. The model review and validation processes involve several steps to confirm the accuracy of integrated data, alignment with design standards, and usability for project stakeholders. Autodesk InfraWorks provides multiple tools and capabilities to facilitate a thorough review, enabling adjustments as needed to enhance the model’s precision and functionality. The model is verified by checking the spatial accuracy, attribute details, and connectivity between various data components in the model. Moreover, each element in the digital model, including topographic data, utility placements, and built-up area representations, is cross-checked against aerial survey data, ground survey data, and satellite imagery. This comparison ensures that the digital terrain model (DTM) matches the terrain accurately, with no deviations in elevation or contour lines that could affect alignment analysis.

Additionally, all utilities, such as water lines, gas pipelines, sewer systems, and power lines, are reviewed for correct spatial placement. Validation is performed by comparing the locations within the model to utility maps provided by local agencies. The location, size, and attribute details of structures in built-up areas, such as buildings, roads, and other significant landmarks, are confirmed to match real-world dimensions and layouts.

3.3. Development of Alignment Alternatives

Once the base model is developed and validated, the proposed framework enables users to generate numerous alignment alternatives that potentially optimize traffic flow along with cost-effectiveness, sustainability standards, and road construction protocols. Alignment options are iteratively refined, which enables the user to explore feasible solutions. Autodesk InfraWorks alignment design tools are utilized to develop the alignment options. The methodology for alignment development includes the following steps:

• Preliminary alignments are generated using InfraWorks’ alignment design tools. The software generates preliminary layouts through its automated design process based on terrain conditions and geometric requirements;
• Each alignment is refined iteratively by adjusting the horizontal curvature, vertical gradients, and sight distances, as per the requirements, to achieve optimal results. The unique features of Autodesk InfraWorks modeling packages allow real-time adjustments and validation against AASHTO standards;
• Alignment options are evaluated for constructability, environmental impact, and operational performance. The assessment procedure verifies that all alternative designs fulfill the standards for safety and sustainability, together with cost-efficiency criteria.

This methodology helps in creating the alignment options, which can be further refined and optimized accordingly.

3.4. Simulation and Visualization

Dynamic simulation, along with advanced visualization capabilities, functions as a fundamental tool for conducting evaluations of the alignment alternatives developed using the proposed framework. Autodesk InfraWorks software provides users with a powerful platform to visualize and evaluate alignment performance metrics. Autodesk InfraWorks employs mesoscopic and microscopic traffic simulation algorithms, enabling efficient modeling of travel behavior at both the network and individual vehicle levels. InfraWorks supports dynamic origin–destination (OD) matrix inputs, allowing real-time traffic flow modeling based on changing demand patterns. The simulation module models lane-based vehicle movements and basic car-following behavior, allowing a detailed analysis of congestion levels, delay times, and overall network performance under different alignment alternatives. The integration with BIM workflows ensures that design modifications are instantly reflected in updated traffic models, supporting iterative optimization. Together, these features provide a comprehensive and dynamic evaluation of highway alignment alternatives in this study.

Key simulation activities for optimizing the alignment alternatives include the following:

• Autodesk InfraWorks is used to simulate traffic movements across each alignment, accounting for peak-hour conditions, vehicle throughput, and intersection efficiency. A process of iterative design improvements is conducted based on identified bottlenecks and delays;
• Simulations are conducted to evaluate geometric compliance. This includes factors such as sight distances, curvature, and gradient compliance. These parameters are validated based on the AASHTO standards to ensure operational efficiency and safety;
• Built-up areas, utilities, and environmental data layers are integrated to visualize the potential impact of alignment options on water bodies, utility relocations, agricultural areas, and other sensitive zones. Urban settlement disruptions and ecological habitat concerns are also identified through the visualization functions to enable appropriate mitigation measures to be taken.

3.5. Optimization Process

The optimization procedure aims to establish optimal conditions that balance performance outcomes alongside cost reduction and ecological resilience. This iterative approach is based on the following:

• Key performance indicators (KPIs), such as geometric design efficiency, construction cost, congestion reduction, travel time savings, and environmental and socioeconomic impacts, are quantified;
• Alignments are refined iteratively based on simulation feedback and stakeholder consultations. Adjustments are implemented in InfraWorks to satisfy design standards and maximize operational productivity. In the optimization phase, adjustments are made to curves, gradients, and route proximity to sensitive areas.

It is important to mention here that specific weights can be associated with various KPIs based on expert judgment, stakeholder priorities, or other formal methods such as the analytical hierarchy process (AHP), etc. The choice of weighting method depends upon the project’s characteristics and sensitivity. It reflects the project’s emphasis on cost-effectiveness and sustainability, consistent with regional infrastructure goals. Ultimately, the optimization procedure results in an alignment choice that optimizes technical requirements, economic factors, and environmental standards. The proposed BIM-based framework provides a streamlined process for improving the traditional highway alignment selection methods. The process underscores the value of a data-driven, BIM-based approach, demonstrating that simulation and optimization can lead to an alignment that meets both immediate project needs and long-term infrastructure goals.

4. Case Study

DG Khan is a central trading and transport city in Pakistan, situated at the intersection of National Highway N-70 and Indus Highway N-55. These two highways permit the movement of both goods and passengers all over the country. The location of these roads within the city leads to heavy traffic congestion, longer journey times, and increased road safety concerns. These issues have resulted in a considerable increase in environmental degradation and socioeconomic inequality, which have greatly reduced the quality of life of residents and users of the roads on a daily basis. In this context, the DG Khan Northern Bypass is an important infrastructure project that aims to mitigate the growing congestion and other traffic-related concerns in the city.

The government of Pakistan, through the National Highway Authority (NHA), showed its intent to initiate the DG Khan Northern Bypass project by issuing a tender for review and updating the initial design. The document requesting proposals from consultancy services contains all of the details of the initially proposed alignment and other design factors [25]. This proposed bypass is meant to improve regional connectivity and road safety and reduce environmental impact by rerouting the through-traffic. This project is strategically significant because it could drive economic development, foster sustainable urban expansion, and make the national transportation system more effective.

The DG Khan Northern Bypass was chosen as a case study to validate the proposed framework. The selected real-world case study is well-suited for framework validation as it provides a complex scenario to balance multiple highway alignment-related factors, including technical, cost, environmental, and socioeconomic elements. Combining a well-structured research methodology with BIM technology helps with highway alignment optimization. The key activities carried out in this study comprised the following:

• Comprehensive data collection and integration into a BIM platform;
• Development and evaluation of multiple alignment alternatives;
• Simulation and visualization of traffic performance and environmental impacts;
• Iterative optimization to achieve the best balance of performance, cost, and sustainability.

4.1. Data Acquisition for Case Study

It was critical to adopt a comprehensive data collection strategy to ensure a robust foundation for the case study. Data collection involved gathering geospatial data, traffic volume statistics, and road network maps pertinent to the case study areas, including the N-55 and N-70 highways.

4.1.1. Topographical Data (DG Khan Case Study)

Topographic surveys were conducted to gather detailed information about the elevation, contours, and features of the land in the study area. High-resolution digital elevation models (DEMs) and detailed topographical surveys were employed to capture the terrain’s physical characteristics. DEMs were created using topographic survey data to represent the elevation of the terrain across the study area. DEMs were also used to analyze terrain features such as hills, valleys, and ridges. Surveying equipment, such as drones, total stations, and GPS devices, was used to collect accurate topographic data. High-resolution satellite imagery was obtained from Google Earth to acquire land use and utilities data. Satellite imagery was analyzed to identify different land use categories, such as residential areas, agricultural land, industrial zones, and natural features.

On the basis of topographic surveys, satellite imagery, and on-site photographs, the amount and location of major features and existing structures were identified. Georeferenced drone imagery acquired during surveys was employed to accurately record major features (e.g., road crossings, river/canal/nullah crossings, towns, settlements, shrines, mosques, etc.). Additionally, the data were also validated through satellite imagery for the area of interest (AOI). The collected datasets were rigorously validated through secondary source cross-referencing and stakeholder review procedures to establish their accuracy and reliability levels.

4.1.2. Traffic Data (DG Khan Case Study)

In order to conduct traffic analysis, detailed classified traffic counts, origin–destination surveys, and growth projections over 10- and 20-year periods were utilized. These datasets provided insights into peak-hour volumes, vehicle classifications, and future traffic demand, forming the basis for flow simulations and congestion analyses. The traffic data for the DG Khan Bypass were collected through an extensive field study conducted over a continuous 72 hours period (3 days) at three strategically selected locations, i.e., the N-55 near DG Khan (towards Taunsa Road), the N-70 near DG Khan at PSO Pump (towards Multan), and the N-70 near DG Khan at Al Ghazi Tractor Ltd. (towards Sakhi Sarwar). The data were collected under normal weather conditions, avoiding rainfall and extreme weather events to ensure data consistency. This study captured a comprehensive range of vehicle types (classified traffic counts), including motorcycles, rickshaws, cars, minibuses, buses, and various classes of trucks (classified by axle configuration), as well as tractor trolleys. To validate the accuracy of the data, a manual cross-check of 10% of the sample (randomly selected) was performed, and the error margins were controlled to within ±5%. The data were analyzed to derive both average daily traffic (ADT) and average annual daily traffic (AADT) volumes. In addition to these counts, origin–destination (OD) surveys were conducted to assess travel patterns. Dynamic origin–destination (OD) matrices developed from field surveys were used to model traffic redistribution onto the proposed bypass corridor. For future traffic forecasting, this study adopted a 4.5% annual growth rate for cars and buses and a 4.0% growth rate for trucks based on historical trends, vehicle registration data, and GDP projections.

4.1.3. Environmental, Land Acquisition, and Utilities Data (DG Khan Case Study)

Environmental constraints were identified through geographic information system (GIS) overlays, including details about ecologically sensitive areas, agricultural lands, and urban settlements. The relevant data were acquired from government authorities, including the Punjab Environmental Protection Agency (PEPA) and the Punjab Land Record Authority (PLRA), and were further complemented by extensive field surveys. The PEPA datasets provided critical spatial insights on ecologically sensitive zones, agricultural lands, and urban settlements. In parallel, the PLRA offered detailed records on land ownership and land use information. Extensive field surveys further validated and enriched these datasets, resulting in a robust evaluation of environmental constraints and their potential impacts.

Systematic mapping of utility layouts helped identify potential interference with gas pipelines, power lines, and water distribution networks. The assessment process included the systematic cataloging of minor elements, ranging from tubewells to roadside trees and drainage networks, to ensure the complete coverage of necessary utility relocations. Utility layouts were verified through on-site inspections and cross-referenced with the government databases, ensuring minimal interference during construction.

4.2. Base Model Development and Validation

The collected data were integrated into Autodesk InfraWorks to create a comprehensive 3D base model of the D.G. Khan Northern Bypass project area. This model served as the foundation for analyzing and optimizing the alignment options.

The topographic data were imported into InfraWorks, generating a DTM that provided an accurate representation of the terrain. Contours and slopes were visualized to aid in designing the alignment options. GIS layers for ecological zones, existing utilities, and urban settlements were also integrated into the base model. Environmental and land use data, including water bodies, agricultural land, and built-up areas, were incorporated to identify impact zones. In the later phases, this helped to assess the environmental implications of each alignment alternative and prioritize routes that minimized disruption.

Additionally, utilities data were overlaid on the terrain model to highlight water, power, and sewage lines. This layering enabled the immediate detection of potential conflicts, such as alignment segments crossing utility lines, which would require additional planning for either relocation or protection. Moreover, the traffic data for existing scenarios were also incorporated into the base model.

After the development of the base model in Autodesk Infraworks, the model was extensively validated to ensure its spatial accuracy, attribute details, and data coherence.

4.3. Alignment Alternatives

Using the base model developed in Autodesk InfraWorks, three main alignment alternatives were created and analyzed to achieve the project objectives with the least disruption. As shown in Figure 3 below, the three alternatives included the initial alignment proposed by the NHA [25], Alignment Option 1, and Alignment Option 2.

4.3.1. Alignment Proposed by the NHA

The first alignment, initially proposed by the NHA, followed a direct route from approximately 5 km from Airport Road on N-70 to an endpoint on N-55. This alignment spanned 18.885 km and aimed to minimize the land acquisition and complexity of construction while maintaining close proximity to the existing road network. However, it did have steep gradients, sharp curves, and high earthwork volumes, creating safety issues, lower traffic efficiency, and increased environmental impacts. Sensitive ecological areas and agricultural lands were also impacted, which caused sustainability issues.

4.3.2. Alignment Option 1

The second alternative, named “Alignment Option 1”, focused on improving the initially proposed alignment by enhancing the geometric design and reducing the environmental impact. Through improvements in horizontal and vertical gradients, the curves became smooth, thereby increasing road safety and the flow of traffic. By adjusting the geometry and gradient, the earthwork quantities, i.e., cut and fill volumes, also decreased. However, Alignment Option 1 passed through urban developed zones, causing land acquisition costs to increase. It moderately reduced the construction cost, although environmental and socioeconomic factors required further adjustments.

4.3.3. Alignment Option 2

The third alignment, named “Alignment Option 2”, was positioned further north, away from dense urban areas, agricultural land, and water bodies. This alignment spanned approximately 20 km, making it the longest route of the three alignment options, but it minimized social and environmental disruption by avoiding the most sensitive areas. This alignment had an improved geometric design, with better curves and gradients for increased traffic flow and reduced construction costs. Alignment Option 2 avoided densely populated areas and agricultural lands to minimize the land acquisition cost. Alignment Option 2 also considered the relocation of utilities to minimize cost. Therefore, this alternative aimed to balance technical, environmental, and cost-related factors.

4.4. Optimization of Alignment Options

The proposed framework served as a foundation for developing and assessing various alignment options. The topographical survey data, along with data on traffic counts, land acquisition, ecologically sensitive areas, and utility layouts, were integrated into a base model using Autodesk InfraWorks. This model enabled users to both visualize and simulate all options for alignment.

InfraWorks delivered automated design functionality that combined alignment geometry with standard engineering requirements and terrain-specific capabilities while adhering to environmental boundary restrictions. Multiple rounds of design refinements took place to enhance horizontal and vertical alignment geometry and minimize earthwork expenses while maintaining AASHTO compliance. Technical simulations for traffic flow in conjunction with capacity assessments and stakeholder feedback strengthened the alignment modifications to meet project specifications. By using a streamlined approach, the framework generated clear side-by-side comparisons, which led to data-based decision-making and the selection of optimal, sustainable, and efficient schemes. It is pertinent to mention that all the KPIs evaluated in the presented case study carried equal weightage.

5. Results

The DG Khan Northern Bypass alignment options were evaluated against geometric design standards, traffic performance factors, environmental impacts, and construction costs.

5.1. Geometric Design Compliance Results

The geometric design of the alignments was evaluated and aligned with AASHTO requirements. Major important factors, such as horizontal curvature, vertical gradients, and sight distances, were analyzed to evaluate the performance of alignments against these factors. The balance between safety and operational requirements was achieved by referring to AASHTO’s Policy on the Geometric Design of Highways and Streets [23]. The detailed AASHTO guidelines for highway design are provide in Appendix A

Table A1. AASHTO Guidelines for Highway Design.

Design Parameter	AASHTO Guidelines	Application in Study
Design Speed	Recommended speed based on road type and terrain (typically 80–120 km/h for highways in level terrain) [23].	Alignments were designed for a design speed of 100 km/h, ensuring safety and operational efficiency.
Horizontal Curvature	Minimum radius varies with design speed; for 100 km/h, minimum radius is approximately 250 m [23].	Alignment Option 2 utilized optimized curves, meeting or exceeding the minimum radius to ensure smooth traffic flow.
Vertical Gradients	Maximum gradient of 3% for highways in flat or rolling terrain [23].	Steep gradients in the NHA-proposed Alignment were reduced in Alignment Option 2 to maintain compliance.
Stopping Sight Distance	Minimum stopping sight distance for 100 km/h is approximately 185 m [23].	Sight distances in Alignment Option 2 exceeded the minimum, enhancing safety at critical points like intersections.
Super-Elevation	Maximum superelevation of 6% is recommended to counter centrifugal force on curves [23].	Superelevation adjustments were incorporated in Alignment Option 2 to maintain stability on curves.
Lane and Shoulder Widths	Minimum lane width of 3.65 m and shoulder width of 2.5 m for rural arterials [23].	Lane and shoulder dimensions adhered to these standards, ensuring driver comfort and operational efficiency.
Cross-Slope	Typical cross-slope of 2% for pavement surface drainage [23].	Applied to the pavement cross-section to prevent water accumulation and maintain road safety.
Vertical Clearance	Minimum clearance of 5.2 m for overpasses and 4.5 m for underpasses [23].	Ensured vertical clearances were sufficient for vehicular and pedestrian traffic under bridges.
Transition Lengths	Proper transition lengths between curves to minimize abrupt changes in alignment [23].	Incorporated into Alignment Option 2 to improve driving comfort and reduce vehicle wear.
Right-of-Way Width	Minimum right-of-way width of 60 m for highways [23].	Evaluated during land acquisition to ensure sufficient space for construction and future expansions.

whereas the results for the alignments’ performance against geometric features are provided in

Table 1. Results of the geometric design analysis.

Feature		Alignment Proposed by the NHA	Alignment Option 1	Alignment Option 2
Horizontal Geometric Design	Length (km)	18.9	18.3	20
	Average Curve Radius (m)	250	300	450
	Horizontal Curvature	More weaving and sharp curves	Moderate curves with weaving	Straighter, fewer curves
Vertical Geometric Design	Average Gradient	4%	3.5%	1.5%
	Vertical Gradients	Steep	Moderate	Gentle
Sight Distance Compliance		60% (Limited)	80% (Adequate)	97% (Excellent)
Safety Compliance		Higher need for guardrails at sharp curves (Partial)	Moderate safety features in weaving areas (Good)	Reduced need for extensive safety features (Excellent)

Sight distance compliance: percentage of alignments meeting minimum AASHTO stopping sight distances (185 m for a 100 km/h design speed).

.

Figure 4 below shows a snapshot of the sight distance visualization report produced in Autodesk InfraWorks.

Alignment Option 2 performed excellently against geometric factors. It displayed smooth gradients and optimized curves and met the sight distance requirements of AASHTO. This performance aligns with global findings on highway safety optimization, reinforcing that Alignment Option 2 is the best choice that adheres to geometric standards [6].

5.2. Traffic Analysis Results

Traffic flow analysis for the D.G. Khan Northern Bypass project was conducted to evaluate each alignment’s effectiveness in managing existing and projected traffic volumes. Using traffic data inputs, InfraWorks simulations modeled the performance of each alignment option under peak and off-peak conditions, as well as for projected future demand.

The dynamic simulation process analyzed traffic performance from all three alignment possibilities through an examination of essential measures of performance focused on travel time optimization, vehicular congestion elimination, and level-of-service (LOS) criteria. The analysis results presented in

Table 2. Results of the traffic efficiency analysis.

Criteria		Alignment Proposed by the NHA	Alignment Option 1	Alignment Option 2
Congestion Analysis	Initial Congestion Reduction (%)	20	25	30
	Projected 10-Year Congestion Increase (%)	15	10	8
	Projected 20-Year Congestion Increase (%)	25	20	18
Travel Time Analysis	Average Savings per Trip (Minutes)	5	8	10
	Travel Time Reduction (%)	12	15	20
LOS Analysis	Initial LOS (Years 1–5)	LOS C	LOS B	LOSA
	Projected LOS at 10 Years	LOS D	LOS C	LOS B
	Projected LOS at 20 Years	LOS E	LOS D	LOS C

LOS: level of service (A: free flow, B: reasonably free flow, C: stable flow, D: approaching unstable flow, E: unstable flow).

establish the travel time benefits, traffic congestion reduction, and LOS for each alignment option. Alignment Option 2 turned out to be the most efficient and resilient, delivering a 30% reduction in congestion and a 20% improvement in travel times while maintaining at least LOS-C for long-term traffic demands. These impressive results were due to its optimized geometric design, which greatly improved traffic flow and minimized delays. Supporting this, studies by Chong et al. [4] also demonstrated that refined geometric designs can significantly boost highway traffic performance. Figure 5 below provides a snapshot of the traffic simulation scenario developed in Autodesk InfraWorks.

5.3. Cost Analysis Results

Construction costs for each alignment were calculated by considering factors such as earthwork volumes, utility relocations, and structure requirements. The cost analysis aimed to optimize construction expenses without compromising the alignments’ geometric and traffic performance. It is important to mention that the construction costs were estimated based on the NHA’s composite schedule of rates, whereas land acquisition and utilities shifting costs were estimated based on the information acquired from government sources.

Table 3. Results of the cost analysis.

Criteria	Alignment Proposed by the NHA	Alignment Option 1	Alignment Option 2
Cut Volume (cu.m.)	118,310	125,270	0
Fill Volume (cu.m.)	3,368,151	3,174,248	2,779,851
Structures (Culverts, Underpasses, and Subways) (PKR Million)	795	737	722
Structures and Bridges (PKR Million)	982	953	914
Drainage and Erosion Works (PKR Million)	274	257	241
Shifting of Utilities (PKR Million)	143	115	107
Land Acquisition Cost (PKR Million)	297	285	261
Construction Cost (PKR Million)	7412	7265	6932

PKR: Pakistani Rupees (1 USD ≈ 278 PKR).

presents a comparative construction cost analysis for each alignment option.

Alignment Option 2 proved to be the most cost-effective, with construction expenses totaling PKR 6932 million. This represents a 6.48% reduction compared to the initially proposed alignment. The cost savings were achieved by minimizing earthwork volumes and the number of required structures and by avoiding extensive land acquisition and utility relocation costs. These strategies align with cost-saving methods highlighted in similar studies [22].

5.4. Environmental Impact Assessment

Environmental impact assessments were carried out with a special focus on potential disruptions to agricultural lands, urban settlements, and ecologically sensitive areas, including water bodies and protected areas. The aim of the assessment was to ensure environmental sustainability by evaluating the environmental impact of each alignment option.

Table 4. Environmental impact analysis.

Criteria	Alignment Proposed by the NHA	Alignment Option 1	Alignment Option 2
Agricultural Land Impact	High	Moderate	Low
Urban Settlement Disruption	Substantial	Moderate	Minimal
Ecological Zone Impact	Substantial	Reduced	Minimal

presents a comprehensive summary of the environmental impact analysis findings, highlighting the effects on agricultural lands, urban settlements, and sensitive ecological zones.

As evident from Table 4 above, Alignment Option 2 was the least disruptive to the environment, avoiding densely populated and sensitive areas. This approach aligns with the sustainability principles advocated by Inzerillo et al. [16], which emphasize the importance of environmentally responsible infrastructure design.

6. Discussion

The findings of this research highlight the transformative potential of using BIM technology in highway alignment optimization. The proposed framework, based on the utilization of BIM tools such as Autodesk InfraWorks, effectively demonstrates the data-driven systematic approach to developing, evaluating, and refining various alignment options. Autodesk InfraWorks not only helps with decision-making but also stakeholder collaboration through dynamic visualization and simulation.

To validate the framework, a case study is presented for the DG Khan Northern Bypass project. Key aspects, including traffic efficiency, geometric compliance, cost reduction, and environmental sustainability, are analyzed, providing significant insights into the broader implications of BIM in highway infrastructure projects.

Among the various alignment options, the optimized alignment (named “Alignment Option 2”) demonstrated substantial improvements in traffic efficiency, with a 30% reduction in congestion and a 20% decrease in travel time compared to alternative options. These results align with existing studies, such as Zhao et al. [10], which highlights BIM’s capacity to improve transportation performance by enabling enhanced alignment designs. By simulating real-world traffic conditions in a virtual environment, BIM identified bottlenecks and allowed iterative refinements to the alignment geometry, ensuring optimal traffic flow and safety.

In terms of geometric compliance, Alignment Option 2 adhered closely to AASHTO standards, offering a superior horizontal curvature, vertical gradients, and sight distances. This ensures improved road safety and driving comfort while meeting international safety guidelines, consistent with the findings of Halim et al. [17]. Furthermore, the advanced visualization and analysis capabilities of BIM played a crucial role in aligning the design with regulatory standards.

Cost-effectiveness and environmental sustainability emerged as key findings of this study. Alignment Option 2 emerged as the most cost-efficient alignment, with a construction cost of PKR 6932 million, a 6.48% reduction compared to the initially proposed alignment. Additionally, this alignment minimized environmental disruptions by strategically avoiding urban settlements, agricultural land, and ecologically sensitive zones, a balance that is often challenging in conventional approaches. These outcomes align with the findings by Biancardo et al. [3], emphasizing the dual benefits of cost reduction and sustainability achievable through BIM integration.

The case study demonstrates the scalability and flexibility of the proposed approach. The results prove that the research methodology framework can be effective in addressing complex infrastructure issues. Keeping in view the optimization results presented in this study, it can be concluded that Alignment Option 2 consistently outperforms the alternatives due to its favorable geometry and minimal environmental footprint, thereby reinforcing the robustness and practical value of the proposed framework in real-world applications. However, it is important to acknowledge that alignment selection is inherently sensitive to several project-specific parameters. One key factor is projected traffic growth, where substantial increases in future traffic demand may necessitate higher-capacity alignments with greater expansion potential, while lower growth projections could favor cost-effective and minimally invasive routes. Similarly, land acquisition costs, often influenced by local property values, ownership complexity, and legal constraints, can shift the preference toward alignments that minimize land use or utilize existing corridors. Environmental considerations also play a critical role, especially when alignments intersect with ecologically sensitive areas, protected zones, or habitats. In such cases, environmentally optimized alternatives may take precedence over options that are otherwise optimal from a cost or geometric perspective. Although a detailed quantitative sensitivity analysis was beyond the scope of this study, the proposed framework is designed to flexibly accommodate variations in these parameters. This adaptability ensures that alignment decisions remain responsive and context-sensitive, enhancing the reliability and generalizability of the proposed methodology across diverse project scenarios.

Furthermore, the presented case study strengthens the need to adopt BIM to streamline the processes involved in infrastructure planning and design. Despite these advantages, challenges in adopting BIM were identified, including issues related to limited data availability, difficulties in integrating diverse datasets, increased costs related to data acquisition and processing, resistance from stakeholders due to unfamiliarity with new workflows, and software interoperability issues when exchanging information between BIM platforms and specialized external analysis tools. Precise BIM modeling demands high-resolution data (e.g., DEMs, etc.), land use data, utility maps, etc. Moreover, detailed traffic data are required to develop and integrate traffic simulation models with the BIM models. Finally, the element of stakeholder adoption also requires training programs to bridge technical skill gaps, particularly in public sector agencies reliant on traditional practices. Addressing these challenges through standardized protocols for data exchange and enhanced user training is essential for the wider adoption of BIM in infrastructure projects.

Finally, this study highlights the significant differences between BIM-based methodologies and traditional practices. Conventional highway alignment methods often rely on static processes that fail to account for dynamic factors such as dynamic traffic patterns and environmental constraints. By contrast, BIM’s integrated approach enabled real-time simulations, iterative design refinements, and stakeholder collaboration, resulting in superior and more sustainable design outcomes. However, in future studies, specifically for more complicated scenarios, the analytical hierarchy process (AHP) or other formal weighting methods may also be explored.

7. Conclusions and Recommendations

This study demonstrated the critical contribution of BIM technology to highway alignment optimization. The proposed framework, based on a data-driven, structured approach, significantly enhanced the process of highway alignment optimization. The DG Khan Northern Bypass case study was presented to validate the proposed framework. This study illustrated the advantages of utilizing BIM towards the attainment of sustainable, resilient, and affordable highway designs.

The evaluation and selection of the most suitable alignment for the DG Khan Bypass was conducted using Autodesk InfraWorks. Each alignment alternative was evaluated using various KPIs. The strengths and weaknesses of all alternatives were quantified, providing a clear basis for decision-making. Alignment Option 2 proved to be the best option, with the highest performance in traffic efficiency, geometric design adherence, and cost-saving efficiency, and the least environmental impact. The use of BIM software, i.e., Autodesk InfraWorks, allowed us to adopt a dynamic and iterative design process with integrated multidisciplinary data and simulations. These features helped to optimize the design components and guide decision-making.

The findings underscored the potential of BIM in addressing traditional challenges associated with highway alignment, such as stakeholder conflicts, limited collaboration, and static design processes. By encouraging transparency and collaboration among stakeholders, the proposed framework ensured that project objectives were met while adhering to international standards and sustainability principles.

However, this research also identified certain challenges in the real-world adoption of BIM technologies in infrastructure projects. Issues such as data availability, data integration, associated costs, and stakeholder acceptability were identified as major hurdles. These issues need to be addressed in the future through approaches such as the implementation of standard protocols, increased training, and technological improvements to achieve the optimal benefits of BIM for infrastructure planning. Furthermore, to promote broader adoption of BIM in public infrastructure planning, several practical measures are recommended. First, advocating for national BIM standards and open data protocols can ensure consistency, interoperability, and transparency across projects. Second, government procurement policies should mandate BIM usage in major infrastructure tenders, thereby institutionalizing its application and driving industry-wide adoption. Finally, dedicated capacity-building programs and training modules for public sector engineers and planners are essential to equip stakeholders with the skills needed for effective BIM integration. Collectively, these initiatives can enable a more sustainable, data-driven, and future-ready infrastructure development process.

In conclusion, this study demonstrated the applicability of BIM technology for highway alignment optimization by providing a robust framework that balances technical, economic, and environmental considerations. The proposed framework presented a reproducible model for highway alignment optimization, allowing data-driven and cost-efficient decision-making. However, this study can be extended in multiple directions to address the current challenges and further strengthen the application of BIM in infrastructure projects. Firstly, the applications of the proposed framework can be further evaluated by conducting large-scale case studies and employing more complex and diverse datasets.

In the future, the BIM and GIS-based integration of real-time traffic data and hydrological data with environmental and land use information can be executed to enhance dynamic optimization of alignments under extreme climatic events and other varying conditions. Multicriteria optimization approaches with specific weights for each criterion can also be employed to further streamline the selection of various factors. Moreover, detailed cost–benefit analyses and lifecycle assessments of various alignment options can also be performed using the proposed methodology. Finally, BIM implementation on highway projects would benefit from standardized protocols and rules to simplify their application. In this context, collaborative studies can be conducted to produce these standards, thereby guaranteeing consistency and large-scale use.