Enhancing Energy Efficiency in Road Transport Systems: A Comparative Study of Australia, Hong Kong and the UK

Abstract

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems.

1. Introduction

Transport system design plays an important role in shaping global energy policy, influencing energy consumption patterns, emissions, and the overall sustainability of urban and regional mobility systems. The transport sector is one of the largest consumers of energy worldwide, accounting for approximately 28% of final energy consumption globally and over 60% of petroleum use in many developed economies [1,2]. More than 90% of global transport remains dependent on fossil fuels, including gasoline and diesel, making the sector particularly vulnerable to both energy supply volatility and environmental challenges [1]. This interdependence between transport and energy shows the necessity of aligning transport design with sustainable energy objectives.

Transportation is also a major contributor to global greenhouse gas (GHG) emissions, producing roughly 14–16% of worldwide GHG output [1]. Achieving global climate commitments, such as the Paris Agreement and Net Zero by 2050, requires continuous annual reductions in CO₂ emissions from the transport sector [3]. Strategic transport system design can significantly influence this trajectory by supporting the integration of cleaner technologies and renewable energy sources [4]. In parallel, governments in both developed and developing economies are promoting cleaner and more efficient transport technologies to address energy security, climate, and environmental challenges and to maintain or enhance their competitive position within an evolving global automotive and mobility value chain [5,6]. These global transport-transition dynamics demonstrate the need to understand how design standards and emerging technologies together shape energy performance.

The design and organization of transport systems fundamentally influence energy consumption through their effects on route structure, infrastructure efficiency, operations, and modal choice. Previous studies indicate that compact cities with well-connected, mixed-use developments tend to promote shorter trip distances and non-motorized or public transport modes, thereby reducing energy use and emissions [5,7]. Prioritizing public transportation systems, which are substantially more energy-efficient per passenger-kilometer than private vehicles, can dramatically improve system-wide energy performance [7]. In this context, we understand Intelligent Transport Systems (ITS) as integrated combinations of sensing, communication, control, and information technologies that support traffic management, traveler information, and multimodal coordination. The deployment of ITS, optimized traffic flow designs such as roundabouts and adaptive signal control, and multimodal networks can directly enhance energy efficiency by reducing congestion, stop–start conditions, and idle time [2]. Other provisions in design manuals, such as safety features, accessibility standards, or urban design treatments, may have more indirect or emerging implications for energy outcomes, for example by influencing speeds, mode choice, or network reliability over time. Distinguishing between these direct, indirect, and more speculative effects is important for interpreting how design standards contribute to energy performance.

Transport system design is both an instrument and an outcome of energy and climate policy. It decides the patterns of mobility that either reinforce or mitigate energy dependence. By adopting a system-level approach and aligning design decisions with energy efficiency goals, renewable integration, and behavioral change, policymakers can foster sustainable, low-carbon transportation networks [4,8]. This study builds on that understanding that undertaking a cross-jurisdictional comparison of road-related design manuals and guidelines in three contrasting contexts: Australia, Hong Kong, and the United Kingdom. These regions were selected because they represent diverse urban forms, governance structures, and transport–energy profiles (e.g., density, modal split, and vehicle fleets), and because each has articulated explicit decarbonization or net-zero commitments that increasingly shape transport infrastructure policy. Their principal government-endorsed design frameworks: The Austroads Guide to Road Design [9], Hong Kong’s Transport Planning and Design Manual (TPDM) [10], and the United Kingdom’s Design Manual for Roads and Bridges (DMRB) [11] provide authoritative references linking road and transport system design to safety, operational performance, and, to varying extents, environmental and energy outcomes.

Against the above-mentioned background, the study pursues three main aims:

• Comparing how the three manuals address energy-relevant aspects of transport system design, including geometric design, multimodal planning, and provisions for renewable-energy integration and ITS.
• Assessing how far explicit and implicit provisions in the manuals can be interpreted as supporting transport energy efficiency, distinguishing between direct mechanisms (e.g., speed management, traffic control, network geometry) and more indirect or emerging mechanisms (e.g., safety-driven design, urban form, or resilience measures).
• Synthesizes the comparative insights into a conceptual, AI-enabled benchmarking framework that can guide the assessment and future enhancement of energy-efficient road and transport design.

These aims are addressed through answering the following research questions:

• How do the Austroads Guide to Road Design [9], Hong Kong’s TPDM [10], and the UK’s DMRB [11] incorporate provisions that directly or indirectly influence transport energy efficiency and related environmental performance?
• In what ways do similarities and differences across these manuals reflect their respective policy orientations, institutional contexts, and stages of transport-energy transition?
• How can insights from this comparative analysis inform a conceptual, AI-driven framework for benchmarking and enhancing the energy performance of road-based transport systems?

By addressing these questions, the paper makes three main contributions to the literature and to practice. Conceptually, it advances understanding of how road and transport design standards embed, or omit, energy-efficiency considerations across multiple dimensions of network planning, geometry, operations, and technology. Methodologically, it develops and demonstrates a structured, document-based comparative approach that distinguishes direct, indirect, and speculative links between design provisions and energy outcomes. Practically, it proposes an AI-enabled benchmarking framework that can support policymakers, designers, and operators in evaluating existing standards and prioritizing future revisions to accelerate low-carbon transport transitions.

The relationship between transport and energy efficiency is also fundamentally connected to the United Nations Sustainable Development Goals (SDGs). Improving energy efficiency in the transport sector supports SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities) by reducing energy waste, enabling cleaner mobility systems, and improving the reliability and inclusiveness of access to opportunities [6]. In addition, consistent with the broader global transport-transition landscape discussed above, these SDG-aligned efforts are increasingly pursued not only for climate and environmental reasons but also as part of competitiveness and innovation strategies, as jurisdictions seek to strengthen their position in evolving low-carbon mobility and automotive value chains while enhancing accessibility to goods, services, and employment.

The remainder of this paper is organized as follows. Section 2 presents the research materials and methodology, providing an overview of the key design frameworks from the three case regions: the Austroads Guide to Road Design [9], Hong Kong’s TPDM [10], and the UK’s DMRB [11], and outlining how these instruments were systematically analyzed in relation to energy efficiency and environmental performance. Section 3 summarizes the main findings of the comparative analysis, identifying convergences and divergences in design approaches, standards, and energy-related criteria, and clarifying which provisions have direct versus indirect implications for energy use. Section 4 discusses the results, examining policy implications, technological integration (including AI, ITS, and smart infrastructure), and the potential for harmonization or mutual learning across different jurisdictions. Finally, Section 5 concludes by highlighting key insights, the scope and limitations of the study, and recommendations for advancing energy-efficient and sustainable road transport systems internationally.

2. Materials and Methods

Recent literature has examined transport energy efficiency from multiple perspectives, including vehicle technologies, urban form, public transport systems, and Intelligent Transport Systems (ITS) [1,5,6,7]. Collectively, these studies demonstrate that improvements in operational efficiency, modal shift, and digital traffic management can significantly reduce transport energy consumption and associated emissions. However, comparatively limited attention has been devoted to how such energy-relevant outcomes are embedded within authoritative road-design standards and manuals that govern real-world planning, design, operation, and maintenance processes. Existing research has tended to focus on individual interventions or modeling exercises, rather than on the institutional design logic and regulatory role of technical standards themselves [4,5].

This review addresses this gap by systematically comparing how three major road-design frameworks incorporate direct, indirect, and emergent energy-efficiency mechanisms, thereby complementing and extending prior work on transport–energy transitions, infrastructure governance, and digital mobility systems. Specifically, three principal road-design manuals are examined alongside their associated supporting documents, guidance notes, and relevant research literature. The aim of this document-based comparison is to identify how each manual embeds provisions that influence transport-energy performance and to distill best practices linking energy-efficient road design with sustainable economic development.

The analysis applied four evaluation criteria aligned with international energy and sustainability frameworks [12]:

• Energy Efficiency Integration: The extent to which design provisions minimize energy waste and reduce system-level energy demand.
• Renewable and Alternative Energy Utilization: The adoption of renewable-energy systems and support for low-carbon vehicle and infrastructure operations.
• Economic and Policy Alignment: The consistency between design standards and national energy-transition goals, with attention to life-cycle cost and carbon implications.
• Technological and AI (Artificial Intelligence) Advancement: The integration of digital, intelligent, and adaptive systems that enhance operational efficiency and support innovation.

The four evaluation criteria were chosen because they reflect the core pillars of contemporary transport-energy transition frameworks, including global climate agreements, sustainable-infrastructure standards, and international best practices in road and mobility system design [3,8]. Each criterion maps onto well-established assessment themes used by organizations such as the IEA, IPCC, UN SDGs, ISO sustainability standards, and OECD transport-policy frameworks [1,2]. These criteria were applied systematically to the three manuals and relevant supplementary documents, enabling a comparative assessment of direct, indirect, and emerging energy-related design mechanisms.

2.1. Australia’s Austroads Guide to Road Design

The Austroads Guide to Road Design [9] provides Australia’s authoritative technical framework for road-infrastructure planning and design. Its emphasis on context-sensitive and performance-based design supports both energy and economic efficiency. Provisions encouraging smart, renewable-powered systems contribute to reduced long-term fuel consumption and enhanced productivity across freight and passenger networks, while sustainability components strengthen maintenance efficiency and stimulate investment in low-carbon transport technologies [13,14].

The Guide comprises a suite of interconnected parts. This study focuses on the sections most relevant to energy-efficiency outcomes:

• Part 1: Objectives of Road Design (2025) embeds energy-efficient principles by emphasizing multimodal integration, performance-based design, whole-of-life cost considerations, and the Safe System philosophy—a design approach centered on human limits, stable speeds, and predictable, forgiving environments [8,9]. It also highlights the need for compatibility with emerging low-carbon transport technologies, ensuring that road-design decisions support both current and future energy-transition objectives.
• Part 4A: Unsignalized and Signalized Intersections (2023) optimizes intersection layouts, sight distances, and turning movements to reduce idling, queuing, and stop-start operations. Its performance-based provisions allow flexibility for constrained urban contexts and support heavy-vehicle and active-transport efficiency.
• Part 6A: Paths for Walking and Cycling (2021) advances systemwide energy efficiency by supporting mode shift through continuous, direct, and comfortable walking and cycling networks integrated with public transport.
• Part 7: New and Emerging Treatments (2021) introduces innovative interventions—such as turbo and mini-roundabouts, raised platforms, and road diets—that stabilize speeds and reduce fuel waste through smoother traffic flow [14].

The Guide also highlights opportunities to integrate ITS technologies such as Variable Message Signs (VMS)-based variable speed management and expanded Closed-circuit television (CCTV) networks to support more adaptive and energy-efficient operations [15].

2.2. Hong Kong’s TPDM

TPDM [10] integrates energy efficiency into high-density transport planning by prioritizing public transport, strengthening intermodal connectivity, and applying data-driven operations. Its scope spans highways, signals, PTIs, and pedestrian systems, linking mobility efficiency to economic resilience through congestion reduction, reliable travel, and optimized land use. Across the nine volumes, the manual embeds ITS and real-time traffic management to enhance operational efficiency and system continuity. This study reviews the following volumes of TPDM, the most relevant to energy-efficient system design:

Volume 1—Transport Planning;

Volume 2—Highway Design Characteristics;

Volume 3—Traffic Signs and Road Markings;

Volume 4—Road Traffic Signals;

Volume 6—Traffic and Environmental Management;

Volume 9—Public Transport.

Volume 1 establishes an integrated, low-carbon planning framework at territorial, regional, and district levels. It aligns land-use and transport to shorten trips and reduce energy demand; promotes compact, transit-oriented development with rail as the backbone; and applies Comprehensive Transport Study 3 (CTS-3) and Base District Transport Models (BDTMs) to forecast demand, evaluate performance, and optimize flow. Appraisal criteria cover operational, environmental, economic, and acceptance factors, with public transport, carrying a very high share of motorized trips, prioritized through network rationalization, real-time management, and progressive electrification.

Volume 2 operationalizes this strategy with geometric and network design that smooths flow, lowers idling, and supports multimodal, low-emission operations. Advanced modeling underpins demand-led service design and reliability improvements, while coordinated pedestrian, cycling, and electrified public transport facilities align network performance with carbon goals. Building on this, recent research [16,17,18,19] highlights AI, ITS, and renewable-energy microgrids to optimize operations and support fleet electrification; embedding energy-performance indicators and life-cycle assessment strengthens accountability and resource efficiency.

Volume 3 complements these outcomes by enhancing legibility and driver compliance through coherent road signing and marking, thereby reducing stop-start behavior and operational errors. It reinforces compact, rail-anchored, and transit-oriented spatial planning that shortens travel distances and encourages a shift toward public transport, an approach validated by previous studies [4,7,15].

Volume 4 advances corridor-level efficiency via signal coordination and adaptive control. Optimized cycles, offsets, and junction geometry cut idling and stop-and-go losses; vehicle-actuated control and Area Traffic Control (ATC), e.g., SCOOT/SCAT/TRANSYT, balance flows and trim delays. Priority for high-occupancy and critical services improves energy use per passenger-kilometer. LED aspects, auto-dimming, and smart monitoring lower power consumption and maintenance.

Volume 6 strengthens system efficiency through one-way and circulation schemes, bus priority, calibrated speed management, and walkability. These measures reduce conflicts, smooth speeds, and foster modal shift to high-occupancy and non-motorized modes, lowering corridor energy intensity.

Volume 9 aligns service design, infrastructure, and operations for energy-efficient public transport. Rail forms the backbone, with franchised buses as feeders and taxis/minibuses serving niches only, minimizing duplication and vehicle-kilometers. Energy-saving PTI/terminus designs (e.g., saw-tooth/peripheral layouts, column-free and well-ventilated halls) reduce reversing, idling, and queueing; new facilities are encouraged to provide power for charging and adopt efficient lighting/ventilation. Real-time control, information systems, and bus-priority measures stabilize speeds and reduce dwell and waiting.

TPDM functions as a technical and strategic blueprint for urban mobility: integrating land use and transport, applying modeling and ITS/AI for predictive control, provisioning for electrification, and embedding energy metrics, life-cycle assessment and traffic management [20]. The result is lower emissions, improved reliability, and a pragmatic pathway toward a smart, low-carbon transport system consistent with international climate and SDG commitments.

2.3. The UK’s DMRB

The DMRB [11] is the United Kingdom’s principal technical standard for the planning, design, operation, and management of its strategic road network. Encompassing motorways, trunk roads, drainage, lighting, structures, and digital systems, the DMRB provides the structural and procedural framework required to deliver a safe, efficient, and resilient national asset. Although energy efficiency is not stated as a primary design objective, the manual’s geometric, operational, and materials standards inherently support low-carbon performance by smoothing traffic flow, stabilizing speeds, and embedding whole-life decision-making across design and maintenance activities. Recent revisions further reinforce whole-life performance and carbon assessment, particularly through governance and environmental documents such as Governance documents (GG-series) and Environmental and sustainability documents (LA) 104—requiring designers to assess life-cycle environmental and carbon impacts while maintaining long-term safety, durability, and asset resilience [21].

Within this framework, the Design Standard (technical design requirements) (CD) 109 (Highway Link Design) promotes fuel-efficient operation by optimizing horizontal and vertical alignment, gradient management, and sight-distance consistency to reduce braking, acceleration, and idling [22]. It also allows the integration of ITS that dynamically manage capacity and speed. Complementary standards on traffic control, signage, and lighting, such as CD 120, CD 146, and TD 501, advance low-energy operation through LED and solar-powered lighting, high-performance reflective materials, and recyclable substrates that extend service life and reduce maintenance-related emissions [23]. These provisions improve operational energy performance and promote smoother, more predictable vehicle behavior, indirectly lowering fuel use and carbon output.

Other volumes further broaden the DMRB’s energy-related implications. CD 195 (Designing for Cycle Traffic) promotes compact, continuous, and safe active-travel networks that encourage a modal shift away from higher-energy private-car travel [24]. By integrating cycling infrastructure with public transport and supporting safer, low-carbon mobility, the standard contributes to reduced transport-energy intensity and aligns with the UK’s Net Zero 2050 commitments [8]. CD 224 (Traffic Assessment) complements this by providing methods for accurate forecasting of commercial vehicle flows and axle loads, preventing the over- or under-design of pavements. This reduces material waste, embodied emissions, and maintenance frequency, while improved surface characteristics with lower rolling resistance enhance vehicle energy performance [25].

At the network scale, GD 300 (Enhanced All-Purpose Dual Carriageways) and GD 301 (Smart Motorways) demonstrate the DMRB’s move toward digitally managed and energy-responsive infrastructure. Features such as variable mandatory speed limits (VMSL), automated detection systems, and real-time traffic control generate more stable flow conditions, reduce congestion, and diminish stop–start inefficiencies. Integrated whole-life planning, low-carbon materials, remote diagnostics, and off-network maintenance strategies collectively reduce both embodied and operational energy demands while maintaining reliability and safety [26].

Overall, the DMRB functions as a comprehensive framework that aligns technical precision with environmental and energy-efficiency objectives. Through the integration of digital systems, predictive maintenance, circular-material practices, and electrification readiness, it increasingly positions the UK’s road network as a long-life, low-energy system that supports national decarbonization, operational resilience, and sustainable economic productivity.

2.4. Energy or Other Policy Criteria

To compare how the three manuals embed energy-aware practices, a common set of policy-oriented criteria was developed to translate sustainability goals into the design and operational logic of transport systems. These criteria provide a consistent analytical lens across the three manuals, linking technical requirements with measurable energy, carbon, and environmental outcomes over the full asset life cycle, as summarized in

Table 1. Comparative evaluation of energy and environmental policy criteria in Australia’s Austroads, Hong Kong’s TPDM, and the UK’s DMRB.

Criterion	Australia: Austroads	Hong Kong: TPDM	UK: DMRB	Comparative Insight
Energy-efficiency integration	Strong emphasis on geometric optimization, multimodal connectivity, and reduced idling at intersections. Whole-of-life considerations present but not formalized as mandatory requirements.	Most comprehensive operational-efficiency framework, driven by adaptive traffic control (SCOOT/SCAT/TRANSYT) and compact urban form. Energy performance indicators embedded in updates.	Strong geometric-efficiency guidance; smart-motorway technologies support speed harmonization and flow stability. Pavement and rolling-resistance considerations better developed than in Austroads/TPDM.	TPDM is the strongest among the three manuals reviewed in urban operational efficiency (signal coordination/ATC, transit priority, circulation management); DMRB is the most prescriptive on strategic-road geometry and managed operations that support flow stability, with additional pavement/asset-management co-benefits (often implicit rather than framed as energy measures); Austroads provides explicit active-travel and context-sensitive design guidance that can support mode shift, although system-wide multimodal integration is more fully developed in TPDM.
Renewable and alternative-energy utilization	Supports solar-powered ITS/VMS and emerging renewable microgrid pilots. Guidance encouraging but largely non-mandatory.	Broadest adoption of renewable systems, including Public Transport Interchange (PTI) microgrids, electrified fleets, and energy-efficient facility design. AI-linked load management emerging.	Integrates renewable-powered lighting, solar pilots, and carbon-reduction materials. Strong LCA orientation but infrastructure-scale renewable deployment still limited.	Hong Kong’s framework (TPDM alongside supplementary policies/standards) most clearly emphasizes electrification readiness and facility-level energy provisions; Austroads most clearly reflects off-grid and regional renewable applications (e.g., solar-powered roadside ITS); DMRB ties low-energy and renewable options to whole-life carbon governance and project-level environmental assessment requirements.
Economic and policy alignment	Highly aligned with Net Zero 2050 and circular-economy frameworks; life-cycle cost analysis (LCCA) routinely considered. Focus on freight and productivity benefits.	Integrates land-use/transport policy with congestion-cost reduction; compact development strategy strongly supports energy-economy co-benefits.	Most formalized LCCA and carbon-assessment process via DSRs. Strong resilience and predictive-maintenance orientation.	DMRB most formally embeds whole-life carbon/environmental assessment processes; TPDM most explicitly operationalizes land-use–transport integration as a demand-shaping mechanism; Austroads emphasizes whole-of-life performance and productivity (including freight efficiency), with energy alignment often expressed through guidance and jurisdictional practice rather than uniform mandates.
Technological and AI advancement	Expanding ITS, variable speed limits, and pilot AI-based monitoring. Digital systems growing but unevenly implemented across jurisdictions.	Most advanced in AI-enabled operations: adaptive signal control, predictive modeling, real-time optimization. ITS central to citywide efficiency goals.	Most mature in national-scale digital motorway operations (MIDAS, VMS, automated enforcement, digital twins (DT)). Strong data governance structure.	TPDM is strongest in codified urban-network digital operations (adaptive signals/ATC), while AI applications are largely emergent in supplementary initiatives; DMRB is most mature in motorway-scale digital operations (managed motorways, variable mandatory speeds, detection and control systems); Austroads includes ITS provisions, with implementation varying across jurisdictions and AI-related applications mainly appearing in pilots.

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2.4.1. Energy Efficiency Integration

This criterion evaluates the extent to which planning and design provisions reduce vehicle-level and network-level energy consumption across the entire system life cycle from infrastructure conception through construction, operation, maintenance, and renewal. Energy efficiency in transport systems is shaped by a combination of geometric, operational, modal, and material factors that influence both traffic performance and long-term infrastructure behavior [21].

At the geometric level, provisions related to horizontal and vertical alignment, gradient optimization, cross-section configuration, and sight-distance design exert significant influence on fuel consumption and traction energy. Designs that minimize unnecessary acceleration and deceleration cycles, limit sharp curvature, and maintain moderate gradients enhance speed stability and reduce engine load. In parallel, junction design and corridor operations, including signal coordination, adaptive timing, speed harmonization, access control, and lane-use management, are central to reducing queuing, idling, stop-and-go behavior, and excess braking. These operational mechanisms are widely recognized as some of the most cost-effective system-level energy-efficiency interventions.

Beyond vehicle flow, multimodal network design plays a key role in reducing total system demand by shifting movement toward public transport, walking, and cycling. Integrating priority lanes, continuous cycling networks, seamless interchanges, and transit-oriented design reduces dependence on private vehicles, thereby avoiding structural energy demand. Such multimodal integration is consistent with international frameworks that emphasize efficiency-first mobility planning.

Material and asset-management provisions also contribute to energy performance [27]. Pavement design, structural capacity, surface characteristics, and maintenance strategies directly affect rolling resistance and therefore fuel or traction-energy requirements. Smooth, durable surfaces reduce vehicle energy use and extend maintenance cycles, mitigating embodied energy associated with resurfacing. Provisions that maintain whole-of-life performance, including timely rehabilitation, drainage upkeep, and deformation control, ensure stable operating speeds and preserve network reliability conditions that are essential for low-energy mobility [28].

Empirical evidence reinforces these relationships. A life-cycle study of the Xiamen Bus Rapid Transit (BRT) system in China demonstrates how integrated planning can influence energy outcomes across multiple stages of the infrastructure life cycle. Using a unified database of material, energy, and labor inputs, the study compares the performance of BRT with Normal Bus Transit (NBT) from material production through operation and end-of-life processes. Findings show that BRT delivers substantially higher energy and environmental efficiency due to smoother operations on dedicated right-of-way, higher occupancy, reduced congestion, and improved suitability for medium- and long-distance urban travel [29]. Although initial construction energy is higher, long-term operational advantages outweigh embodied impacts. The dedicated infrastructure attracts users who might otherwise rely on private vehicles, reducing system-wide energy demand and cumulative emissions.

Together, these insights highlight that energy efficiency is not a single design feature but a multi-layered property emerging from geometry, operations, multimodal integration, and life-cycle asset stewardship. Evidence from Xiamen supports the adoption of integrated, multi-criteria assessment frameworks, as also recommended in international transport-energy transition literature [12,29], to ensure that design decisions systematically enhance energy performance across the full life cycle of road and public-transport systems.

2.4.2. Renewable and Alternative Energy Utilization

This criterion evaluates how transport infrastructure integrates renewable-energy generation and supports low-carbon operation. Key applications include adaptive LED lighting, solar-/wind-powered roadside systems, and microgrids for depots, service areas, and PTIs. A central component is electrification readiness: charging-infrastructure provision, grid-capacity planning, intelligent load management, and the safe accommodation of alternative fuels such as hydrogen. Collectively, these initiatives represent a transition from isolated efficiency measures toward integrated renewable-supply and energy-demand management across transport networks.

In Australia, significant investments are being made to expand hydrogen production for domestic use and export under the national hydrogen strategy. The Australian Capital Territory’s Zero Emissions Industry and Transport Plan further highlights transport’s role in regional decarbonization, noting that Canberra’s high private-vehicle ownership will require substantial electrification to achieve net-zero goals. Hydrogen vehicles, despite higher costs, offer advantages such as longer driving range and faster refueling, which are strategically important for heavy vehicles and inter-city travel. These developments illustrate how renewable-energy integration, fuel diversification, and electrification readiness collectively support the transition toward net-zero transport futures [15,19,30,31].

2.4.3. Economic and Policy Alignment

This criterion assesses how each manual links design decisions with national energy-transition and climate-policy objectives. It includes the application of LCCA and life-cycle assessment (LCA) in option appraisal; the incorporation of carbon accounting into design strategy records; and procurement mechanisms that favor durable, low-carbon materials and long-life pavements. It also considers the alignment of design guidance with wider planning frameworks, such as compact development, transit-oriented growth, and efficient freight corridors, demonstrating how energy-efficient design enhances long-term economic value, asset resilience, and system reliability.

Insights from the trade and manufacturing sectors further reinforce the value of this approach. As Porro and Gia [32] argue, effective sustainability assessment requires indicators that measure resource inputs, economic outputs, and environmental impacts. Evaluating transport-system alignment with sustainability goals therefore provides a clearer picture of both existing performance and improvement needs. Comparative studies show that combining conventional and innovative logistics strategies, supported by heuristic evaluation tools, can strengthen economic viability while meeting carbon-reduction targets. Such cross-sector benchmarking, although analytically demanding, offers substantial long-term advantages by enhancing transport-system efficiency and sustainability [33].

2.4.4. Technological and AI Advancement

This criterion evaluates how intelligent and digital technologies are integrated to enhance energy efficiency, operational performance, and safety. Key components include ITS deployment, adaptive signal control, connected-vehicle communication, variable speed limits, and condition-based maintenance enabled by sensors and DT. AI further enhances system performance by improving incident prediction, flow optimization, corridor management, and asset monitoring. These technologies rely on strong data-governance frameworks, ensuring the availability of high-quality detection, traffic, and energy-use data for continuous benchmarking and system optimization.

For each of the three jurisdictions, evidence is classified as explicit (directly codified in standards), implicit (embedded through design practices with energy co-benefits), or emergent (reflected in pilots, research initiatives, or supplementary guidance). This structured categorization underpins the comparative assessment in Section 3 and clarifies how digital and AI-enabled systems shape modern transport-energy transitions.

Recent empirical work demonstrates the transformative potential of AI. Dikshit et al. [34] show that real-time, AI-optimized vehicle-routing algorithms, integrating roadway characteristics, dynamic traffic conditions, and behavioral patterns, can reduce congestion, travel time, fuel consumption, and emissions. Such findings stress the scalability of AI-based tools and provide practical insights for policymakers seeking to implement data-driven, energy-efficient mobility frameworks.

2.5. Document Analysis

We applied a compact, rubric-based document analysis to selected relevant volumes of the three manuals. Relevant clauses were extracted using keyword anchors (e.g., energy, efficiency, flow, delay, speed, ITS, pavement, maintenance, electrification). Each item was coded for mechanism class:

• Direct (flow/operations/geometry with clear energy link);
• Indirect (safety/durability/accessibility with energy co-benefits);
• Emergent (pilots/supplementary, not yet codified); life-cycle stage (planning/design/construction/operations/maintenance); and the evaluation criterion (the four in Section 2.4).

Ordinal scoring captured codification strength—Mandatory, Guidance, or external policy/pilot, and energy relevance, High, Medium, or Low, based on expected influence on operational-energy proxies (delay, speed variance, stop rate) and/or whole-life energy proxies (materials intensity, maintenance frequency). A gap matrix (convergence/omission/inconsistency) structured the cross-manual comparison reported in Section 3.

3. Results

In this section, evidence from the three manuals is synthesized using the explicit–implicit–emergent classification described in Section 2.5. The results clarify how geometric design, multimodal planning, renewable-energy readiness, and digital systems influence transport-energy performance through direct operational mechanisms, indirect structural effects, and emerging innovation pathways.

3.1. Geometric Design

Geometric design provisions across Austroads, TPDM, and DMRB play a central role in shaping traffic flow stability, which has direct implications for transport energy use. However, the degree to which these effects are explicitly codified varies across the three manuals.

In Austroads, geometric controls governing alignment, sight distance, and intersection layout, particularly in Part 3 and Part 4A, are framed as performance-based design requirements [9]. These provisions are primarily justified on safety and operational grounds, but they implicitly reduce stop–start driving, idling, and inefficient acceleration–deceleration cycles by promoting predictable operating conditions. The Safe System philosophy further reinforces speed consistency and forgiving road environments, yielding indirect energy benefits over the operational life cycle [8,9].

In TPDM, geometric design is more closely integrated with operational control. Junction layout, lane configuration, and corridor design are coordinated with signal-control strategies specified in Volume 4 (Road Traffic Signals) and related volumes [10]. This integration supports coordinated and adaptive signal operation within dense, highly signalized networks, reducing delay and flow disruption and thereby improving energy efficiency, even though energy efficiency is not articulated as an explicit design objective.

In DMRB, geometric standards are the most prescriptive. CD 109 specifies horizontal and vertical alignment, gradient control, and sight-distance requirements for strategic roads, explicitly aiming to maintain speed consistency and reduce flow breakdown [22]. These measures directly lower operational energy use by minimizing braking and acceleration events. Although pavement and asset-condition standards are framed mainly around safety and durability, they further support energy efficiency through smoother surfaces and more reliable operating conditions [21].

Across the three manuals, TPDM and DMRB embed flow-stabilizing effects more explicitly through operationally linked or prescriptive standards, while Austroads relies on context-sensitive, performance-based guidance that delivers energy benefits implicitly.

3.2. Multimodal Planning and Structural Energy Demand

The manuals differ substantially in how multimodal planning shapes structural energy demand through transport mode choice and trip patterns.

TPDM provides the most comprehensive system-level framework. Volume 1 (Transport Planning) establishes a rail-anchored, transit-oriented development approach that aligns land use and transport planning to shorten trip distances and prioritize high-occupancy public transport [35]. Subsequent volumes translate this strategy into facility- and corridor-level guidance for public-transport interchanges, pedestrian environments, and traffic management [10,36,37]. Although energy efficiency is not framed as an explicit planning objective, Hong Kong’s compact urban form and very high public-transport mode share result in substantial implicit reductions in transport energy demand [7,27,38].

Austroads addresses multimodality primarily at the facility and corridor scale. Part 6A provides explicit design guidance for walking and cycling infrastructure, supporting active travel and associated energy savings, particularly in suburban contexts [39]. However, metropolitan land-use integration and broader demand management are governed largely through state and local planning frameworks rather than Austroads itself, limiting its influence on structural energy demand.

DMRB adopts a more limited approach to multimodality, reflecting its focus on the strategic road network. CD 195 establishes explicit requirements for cycle traffic on and across trunk roads and motorways, improving safety and continuity for active modes and generating indirect energy benefits through modal shift [24,40]. Broader public-transport planning and land-use integration lie outside the scope of DMRB.

Overall, TPDM most strongly shapes structural energy demand at the system level, while Austroads and DMRB contribute primarily through indirect, facility-scale mechanisms.

3.3. Renewable-Energy Integration and Electrification

Renewable-energy integration and electrification readiness appear across all three jurisdictions, but with differing levels of codification and emphasis.

In Austroads, explicit references to renewable energy are largely limited to applications such as solar-powered ITS and off-grid roadside systems [41,42]. Other energy-relevant considerations, such as efficient lighting or reduced maintenance travel, are embedded implicitly through asset-management and operational guidance [13]. Broader electrification and hydrogen-fuel readiness are driven by external policy initiatives rather than specified within the Austroads Guide itself [19].

In TPDM, energy-related provisions are most visible at the facility scale. Volume 9 (Public Transport Interchanges) includes guidance on energy-efficient lighting, ventilation, and power provision, supporting the operation of electrified public-transport fleets [43]. Large-scale electrification targets and renewable-energy deployment are articulated primarily in supplementary government policies rather than in TPDM itself [38]. Nevertheless, the manual provides a design framework compatible with electrification transitions, yielding indirect operational energy benefits.

In DMRB, renewable-energy considerations are embedded primarily through environmental governance. LA 104 requires whole-life carbon assessment for National Highways projects, explicitly capturing both embodied and operational energy within environmental appraisal [44]. While specific renewable technologies are often optional rather than mandatory, the governance framework institutionalizes energy and carbon considerations across planning, design, and maintenance. Emerging renewable-energy pilots and circular-materials initiatives are documented through National Highways programs [45].

Thus, TPDM and other related policies in Hong Kong clearly support electrification readiness at public-transport facilities; DMRB embeds energy considerations through whole-life carbon governance; and Austroads reflects renewable integration mainly through practical guidance aligned with external policy.

3.4. ITS, Digital Systems and AI-Enabled Operations

All three manuals recognize the role of digital systems and ITS in improving operational efficiency along with their respective national or regional policies [8,9,31,38,40], though their maturity and scope differ.

Austroads explicitly addresses ITS components such as Variable Message Signs, CCTV, and variable speed management within its traffic-management guidance [42]. These systems support smoother traffic flow and reduced congestion, yielding indirect energy benefits. However, implementation varies across jurisdictions, and AI-based applications largely remain at the pilot or research stage [46].

TPDM places digital operations at the core of urban traffic management. Volume 4 specifies coordinated and adaptive signal control systems, including SCOOT, SCAT, and TRANSYT, as standard practice [37]. These systems directly reduce delay, idling, and stop–start operation in dense urban networks, delivering high energy relevance even though energy efficiency is not stated explicitly as an objective. More advanced AI applications appear mainly in agency-level projects and research initiatives [16,17,18].

DMRB provides the most comprehensive motorway-scale digital framework. Managed-motorway standards, including VMSL, MIDAS incident detection, and centralized traffic control, are mandatory and explicitly aimed at stabilizing flow and reducing congestion [26]. Emerging tools such as digital twins and predictive diagnostics are increasingly applied in practice but remain supplementary to formal DMRB standards [24,25].

Collectively, TPDM leads in urban network digital control, DMRB in strategic-road digital operations, and Austroads shows progressive but uneven adoption of ITS and AI-enabled tools.

3.5. Direct, Indirect and Emerging Mechanisms

Synthesizing Section 3.1, Section 3.2, Section 3.3 and Section 3.4 reveals three pathways through which the manuals influence transport energy performance.

• Direct mechanisms include explicitly codified geometric and operational controls that shape traffic flow and reduce delay, such as coordinated signal systems in TPDM and managed-motorway operations in DMRB [22,26,37].
• Indirect mechanisms arise from provisions framed around safety, accessibility, or durability, including Austroads’ Safe System philosophy, TPDM’s land-use–transport integration, and DMRB’s pavement and asset-management standards. Although not presented as energy measures, these provisions generate substantial long-term energy and emissions co-benefits, as demonstrated in previous studies on safety-driven road design, compact land-use integration, and asset durability and maintenance strategies [7,21,27,29].
• Emergent mechanisms encompass pilots and supplementary initiatives, such as electrification readiness, renewable-energy applications, hydrogen freight concepts, and AI-enabled predictive control, which are increasingly supported by empirical studies and policy-oriented research on digital transport systems and low-carbon infrastructure transitions [16,17,18,19,34,45]. These initiatives are not yet consistently codified but indicate the likely direction of future manual development.

Across all three jurisdictions, energy efficiency is therefore embedded through a combination of direct, indirect, and emergent mechanisms rather than as a single, explicit design objective.

3.6. Evidence-Derived Basis for the Benchmarking Framework

The proposed AI-enabled benchmarking framework is grounded in established research on transport-system benchmarking, infrastructure performance management, and digital governance [47,48,49,50,51]. Existing studies consistently highlight the importance of standardized indicators, life-cycle assessment, and comparative performance evaluation for improving the efficiency and sustainability of transport systems [48,49]. Recent advances in AI, DT, and ITS further indicate that traditionally static design standards can be complemented by data-driven approaches to performance monitoring and operational optimization [34,52]. Drawing on this body of literature, the framework presented in this study adapts established benchmarking principles to the context of road-design manuals, enabling systematic energy-efficiency assessment while remaining compatible with prevailing regulatory and technical structures.

Building on the comparative evidence summarized in Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.5, this review proposes an AI-enabled benchmarking system to support the evaluation and future enhancement of road-system design and operations, with a focus on energy performance. The framework operationalizes benchmarking through a structured process in which manual provisions are identified and standardized, their energy relevance is classified, and comparable indicators are derived to inform policy decisions, revision priorities, and cross-jurisdictional learning, while preserving consistency with the application logic of Austroads, TPDM, and DMRB.

3.6.1. Benchmarking Purpose and Scope

The benchmarking system is designed to support two simultaneous objectives:

• Maintain compliance and usability under existing manuals (i.e., designers and operators can continue to work within Austroads/TPDM/DMRB structures, terminology, and mandatory requirements).
• Make energy saving measurable, comparable, and improvable by introducing a consistent set of energy-efficiency indicators, evidence ratings, and AI-assisted analytics that work along with the manuals.

Accordingly, benchmarking is applied across the road-system life cycle, planning, design, construction, operations, and maintenance, so that both operational energy (traffic flow, delay, stop–start behavior, electrified operations) and whole-life energy/carbon (materials, maintenance frequency, asset condition) are addressed [47].

3.6.2. Evidence Mapping from the Three Manuals: Direct, Indirect, and Emergent Mechanisms

To balance “current manual application” with “future energy saving,” provisions from Austroads, TPDM, and DMRB are mapped into three mechanism classes:

• Direct mechanisms: provisions that shape traffic flow and operating conditions with clear energy links (e.g., geometric consistency, junction layout, signal coordination, variable speed management). These typically yield near-term energy savings by reducing idling, braking/acceleration cycles, and flow breakdown.
• Indirect mechanisms: provisions primarily justified by safety, accessibility, reliability, or asset durability but producing energy co-benefits (e.g., Safe System design, compact land-use integration, pavement/asset quality that reduces rolling resistance and maintenance disruption).
• Emergent mechanisms: pilots and supplementary initiatives that are not consistently codified but signal future direction (e.g., electrification readiness at facilities, microgrids, hydrogen freight corridors, AI-enabled predictive control, DT, circular-materials programs).

This classification ensures the benchmarking system respects what the manuals already regulate (direct and many indirect items), while still capturing innovation trajectories (emergent items) for future revisions.

3.6.3. Benchmarking Workflow and Outputs (AI-Enabled, Manual-Compatible)

The benchmarking system follows five steps and produces standardized outputs that are comparable across jurisdictions:

• Extract (document capture and structuring)

Manual clauses, guidance notes, and related project records are digitized and structured using NLP (Natural Language Processing)/DLOCR (Deep Learning Optical Character Recognition) to create a clause-level repository (including citations and version control).

2.

Map (taxonomy and traceability)

Each extracted item is mapped to:

• Mechanism class (direct/indirect/emergent);
• Life-cycle stage;
• The study’s evaluation criteria (energy efficiency; renewables/alternative energy; economic/policy alignment; technological/AI advancement).

This mapping enables traceable cross-manual comparison rather than narrative-only assessment.

3.

Score (strength of codification and energy relevance)

Each mapped item is scored on two axes:

• Codification strength: mandatory requirement vs. guidance vs. external policy/pilot.
• Energy relevance: expected influence on operational energy (flow efficiency, mode shift, electrification readiness) and/or whole-life energy/carbon (materials, maintenance, resilience).

This produces a defensible distinction between what is already enforceable and what is currently aspirational.

4.

Compare (cross-manual benchmarking and gap identification)

A gap matrix is generated to identify:

• Convergence (similar provisions across manuals);
• Omission (energy-relevant topics missing or weakly treated in a manual);
• Inconsistency (contradictory thresholds or incompatible approaches).

The result supports targeted recommendations without forcing uniformity where contexts differ.

5.

Improve (feedback to future road systems and manual updates)

Benchmarking outputs inform:

• Revision priorities (what to formalize next);
• Implementation guidance (how to apply energy-saving practices under current standards);
• Monitoring plans (what data to collect to prove performance and refine thresholds).

Key outputs include comparable KPIs, an evidence-rated clause inventory, a gap matrix, and a maturity rating for emergent mechanisms.

3.6.4. Applying Benchmarking to Future Road Systems: Balancing Today’s Standards with Energy Saving

Using this framework, future road systems can be benchmarked and improved through three linked application modes:

• Mode A—“Comply + quantify” (short term): apply existing manual requirements as normal, but require benchmarking to quantify energy outcomes using operational data (e.g., delay, speed variability, stop rate) and asset data (e.g., pavement condition, maintenance frequency). This strengthens accountability without changing the manuals.
• Mode B—“Optimize within the manual” (medium term): use AI analytics and DT to select among compliant design/operation options the ones with superior energy performance (e.g., alternative junction forms, signal plans, speed harmonization strategies, maintenance timing).
• Mode C—“Formalize emergent practice” (long term): use maturity ratings and pilot evidence to propose which emergent mechanisms should become codified (e.g., electrification-ready provisions, energy-performance indicators for facilities, interoperable ITS data standards, whole-life carbon thresholds).

This approach recognizes that energy savings in road transport arise from technology adoption and from consistent design logic that stabilizes flow, supports mode shift where relevant, and reduces life-cycle energy burdens.

3.6.5. Enabling Technologies and Data Layers Supporting Benchmarking

Table 2. AI Tools and Their Applications for Enhancing Energy Efficiency in Transport Systems.

Tool/Technology	Main Role or Transport System Component	Key Application and Challenge Reference	Expected Impact on Energy Efficiency and System Performance
1. BIM Repository with AI-enhanced Data Structuring	Design & Infrastructure Layer	Records integration, cross-disciplinary collaboration	Provides a unified digital foundation for energy-aware design and life-cycle data sharing; reduces duplication and rework, enabling better energy and material optimization across design phases.
2. ML Models for Predictive Analytics	Technological & Operational Layer	Scheduling, risk forecasting, cost and maintenance planning	Anticipates congestion, deterioration trends, and maintenance energy loads; optimizes traffic scheduling and fleet deployment to minimize idle energy consumption and downtime.
3. IoT sensors and Smart Cameras	Monitoring & Maintenance Layer	Environmental and asset condition monitoring	Enables continuous tracking of pavement moisture, structural health, and flow conditions; supports condition-based maintenance that minimizes embodied and operational energy use.
4. DTs	System Coordination & Scenario Modeling Layer	Coordination, scenario planning, and simulation	Creates virtual replicas for analyzing alternative infrastructure configurations and operational scenarios; enables energy-efficient decision-making through real-time optimization and predictive impact assessment.
5. NLP/DLOCR	Governance & Documentation Layer	Digitalization of manuals and records	Streamlines data retrieval and compliance management; supports automated integration of historical performance data into new energy-efficiency benchmarks.
6. CV	Safety & Performance Monitoring Layer	Automated safety and regulation checking	Uses CV to detect non-compliant operations (e.g., lighting inefficiency, vehicle violations) and automatically recommends corrective actions, enhancing both safety and energy performance.
7. CV for Smart Surveillance	Operations & Security Layer	Real-time security and traffic surveillance	Identifies abnormal traffic patterns or safety risks that lead to congestion; supports adaptive control strategies that stabilize flow and reduce unnecessary fuel consumption.

provides the toolset required to operationalize this benchmarking system:

• Building Information Modeling (BIM) repository with AI-enhanced data structuring enables consistent cross-project and cross-manual records for life-cycle benchmarking.
• Machine Learning (ML) predictive analytics supports forecasting of congestion, maintenance energy loads, and intervention timing to avoid reactive, high-energy responses.
• Internet of Things (IoT) sensors and smart cameras supply continuous operational and asset-condition data essential for AI prediction and real-time monitoring capabilities.
• DT supports the testing of physical scenarios within a digital framework, enabling the identification of energy-optimizing solutions within compliant design parameters. The synchronization capability allows for the retrieval of real-time status of the physical environment, thereby enhancing efficient management.
• NLP/DLOCR enables scalable clause extraction and traceable linkage between manual text and performance evidence.
• Computer Vision (CV) strengthens enforcement and operational efficiency (e.g., detecting non-compliant operations that increase congestion or energy waste).
• CV and IoT for smart surveillance support real-time response to abnormal patterns that trigger energy-intensive delay and stop–start conditions.

Altogether, these tools convert the manuals from static references into inputs for a learning system: one that measures, compares, and continuously improves energy performance while protecting the safety and reliability objectives embedded in Austroads, TPDM, and DMRB.

The evidence base from the three manuals supports an AI-enabled benchmarking framework that (i) preserves the practical and regulatory strengths of current standards, (ii) clarifies direct and indirect energy mechanisms already embedded in design guidance, and (iii) systematically tracks emergent innovations toward codification. This evidence-driven benchmarking approach provides a structured pathway for future road systems to achieve measurable energy savings through improved geometry and operations, multimodal integration where applicable, renewable/electrification readiness, and digital/AI-enabled governance across the asset life cycle [49].

3.7. Document-Analysis Results

This section consolidates the rubric-based document analysis described in Section 2.5 and synthesizes how energy-relevant provisions are embedded across Austroads, TPDM, and DMRB. While Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.5 present thematic findings on geometric design, multimodal planning, renewable-energy readiness, and digital systems,

Table 3. Comparative Results of Document Analysis.

Dimension	Australia: Austroads	Hong Kong: TPDM	UK: DMRB	Comparative Insight
Geometric design & corridor flow	Mechanism class: Direct and Indirect; Codification strength: Mandatory; Energy relevance: Medium and High. Performance-based geometry stabilizes speeds; energy benefits largely implicit.	Mechanism class: Direct; Codification strength: Mandatory; Energy relevance: High. Geometry tightly linked to coordinated signal control in dense networks.	Mechanism class: Direct; Codification strength: Mandatory; Energy relevance: High. Prescriptive alignment and gradient control stabilize flow on strategic roads.	All manuals support flow stability; TPDM and DMRB are more explicitly operational, Austroads more context-flexible.
Multimodal planning & structural energy demand	Mechanism class: Indirect; Codification strength: Mandatory; Energy relevance: Medium. Walking and cycling standards support mode shift; land-use integration external.	Mechanism class: Indirect; Codification strength: Mandatory; Energy relevance: High. Rail-anchored land-use integration and public-transport priority reduce vehicle-kilometers.	Mechanism class: Indirect; Codification strength: Mandatory; Energy relevance: Medium. Cycling provision on strategic roads; public-transport planning outside scope.	TPDM shows the strongest system-level energy-demand reduction.
Renewable energy & electrification readiness	Mechanism class: Emergent; Codification strength: Policy/Pilot; Energy relevance: Medium. Solar-powered ITS and off-grid systems; EV/hydrogen readiness policy-driven.	Mechanism class: Direct and Emergent; Codification strength: Guidance to Mandatory; Energy relevance: High. Public-transport facilities designed for electrified fleets and efficient energy systems.	Mechanism class: Direct (governance-based); Codification strength: Mandatory; Energy relevance: Medium and High. Whole-life carbon assessment embeds energy considerations.	TPDM leads in facility-level electrification; DMRB leads in whole-life governance.
ITS, digital systems & AI-enabled operations	Mechanism class: Direct and Emergent; Codification strength: Guidance and Policy/Pilot; Energy relevance: Medium. ITS widely referenced; AI uneven across jurisdictions.	Mechanism class: Direct; Codification strength: Mandatory; Energy relevance: High. Adaptive and coordinated signal control central to operations.	Mechanism class: Direct and Emergent; Codification strength: Mandatory and Policy/Pilot; Energy relevance: High. Managed motorways and variable speed control; AI emerging.	TPDM strongest in urban network control; DMRB strongest at motorway scale.
Overall energy mechanisms (synthesis)	Predominantly indirect, with emergent innovation via pilots and policy.	Largely direct or system-structural, especially in operations and land-use integration.	Institutionalized through whole-life governance, with emergent digital innovation.	Energy efficiency is embedded through different pathways rather than explicit objectives.

provides a condensed comparative summary aligned with those sections. The present section interprets these results through the coding dimensions of mechanism class, life-cycle stage, codification strength, and energy relevance.

4. Discussion

The results of this study report the coded evidence; this section interprets the results against the three research questions within the relevant empirical and theoretical literature on transport-energy efficiency, governance of technical standards, and benchmarking.

4.1. Interpretation of Findings Against the Research Questions

RQ1: How do Austroads, TPDM, and DMRB incorporate provisions that directly or indirectly influence transport energy efficiency and related environmental performance?

Across all three manuals, the analysis confirms that energy efficiency is rarely framed as a standalone design objective, yet it is nonetheless embedded through multiple pathways that closely align with established energy-efficiency mechanisms identified in the broader transport literature: (i) flow stabilization (reducing delay, idling, and speed variance), (ii) structural demand reduction (mode shift and trip-length reduction), and (iii) whole-life performance (materials, maintenance frequency, and asset condition) [12,21,29].

The document coding clarifies that these pathways are expressed with different degrees of explicitness and enforceability:

Direct mechanisms appear most strongly where manuals codify traffic-control and network-management practices that reduce stop–start conditions, e.g., TPDM’s coordinated signal control (Volume 4) and corridor management in a dense urban network, and DMRB’s managed motorway operations (e.g., VMSL, detection and control) that aim to prevent flow breakdown on the strategic network [26,37]. These findings are consistent with evidence that operational strategies which reduce congestion and unstable flow are among the most cost-effective energy-saving interventions in road networks [34].

Indirect mechanisms are pervasive in all manuals. Notably, Austroads’ Safe System orientation emphasizes predictable operating speeds and forgiving design, which plausibly reduces energy waste from harsh acceleration and braking cycles over time [8,9,41]. Similarly, DMRB’s pavement/asset standards and assessment practices indirectly support energy efficiency through smoother surfaces and reduced disruption from maintenance [21,25]. This research therefore reinforces a key point in the literature: energy outcomes often emerge from “non-energy” provisions, especially those related to safety and asset management, and should be interpreted through a system lens rather than by keyword presence alone [21,29].

Emergent mechanisms are visible through pilots or external policy linkages (electrification readiness, renewable applications, AI-enabled prediction and optimization). The manuals differ in how far these are codified. TPDM contains comparatively strong facility-level provisions compatible with electrification (e.g., PTI power and efficiency features), whereas DMRB institutionalizes carbon/energy considerations via whole-life assessment requirements (LA-series governance) rather than mandating specific renewable technologies [43,44]. This reflects the broader empirical observation that the “energy transition” in transport infrastructure often proceeds first via governance, appraisal, and pilots, and only later becomes fully standardized [12,45].

RQ2: In what ways do similarities and differences across these manuals reflect policy orientations, institutional contexts, and stages of transport-energy transition?

The comparative patterns align with each jurisdiction’s network role and governance structure:

Hong Kong (TPDM): The manual’s strongest energy linkage appears in urban operational control and structural demand management, consistent with a high-density, transit-oriented context where coordinated signals, public-transport priority, and circulation management can produce large network-wide efficiency gains [35,36,37]. The manual’s integration of land use, modeling, and operational control also reflects a governance setting in which strategic demand shaping is central to urban mobility performance.

United Kingdom (DMRB): Energy relevance is most institutionalized through strategic-network reliability and whole-life governance, consistent with DMRB’s role as a national technical standard for the strategic road network. Flow stability is advanced through managed motorway concepts, while whole-life carbon is embedded through formal assessment and record-keeping requirements, which is consistent with the literature emphasizing governance and appraisal as key levers for decarbonizing long-lived infrastructure assets [26,44,45].

Australia (Austroads): Austroads provides performance-based guidance across a multi-state setting, producing energy co-benefits that are often implicit and variably implemented. This helps explain why energy-relevant content appears dispersed across parts (e.g., intersections) rather than consolidated into mandatory energy/carbon governance. This is consistent with the broader observation that performance-based guidance can support innovation and context fit, but may yield uneven adoption without a harmonized benchmarking and accountability layer [42,46,48].

RQ3: How can insights from this comparative analysis inform a conceptual, AI-driven framework for benchmarking and enhancing energy performance?

The key implication is that an overlay benchmarking approach is a pragmatic bridge between (a) the manuals’ safety and operational priorities and (b) the need to make energy performance measurable and improvable. This directly corresponds to established benchmarking frameworks that stress comparable indicators, traceability, and governance (e.g., World Bank benchmarking principles), while adapting them to the institutional reality that road design standards are not primarily written as energy documents. By mapping clauses to mechanism class, life-cycle stage, and codification strength, the proposed framework operationalizes what the literature often treats separately: technical standards as de facto energy-governance instruments, and AI/ITS as enablers of continuous performance management rather than one-off compliance [34,48,49,50].

4.2. Comparison with Prior Research: Convergences and Divergences

Convergence 1—Operational control as a high-impact energy lever: This study’s strongest direct-energy findings (adaptive/coordination signals in TPDM; managed motorways in DMRB) converge with empirical research showing that congestion management, dynamic control, and routing/optimization can reduce delay, fuel use, and emissions [2,34]. The present contribution is to show where and how such mechanisms are embedded (or not) within authoritative manuals, including whether they appear as mandatory standards or as guidance or pilots.

Convergence 2—Mode shift and compact urban form reduce structural energy demand: TPDM’s rail-anchored, transit-oriented planning orientation aligns with established evidence that compact development and high-quality public transport reduce per capita transport energy consumption and emissions [7,10,12]. The analysis further shows that the energy relevance is largely implicit in manual language (e.g., planning, accessibility, interchange efficiency), reinforcing the literature’s point that structural demand reduction is often governed through planning logic and system design rather than explicit “energy” clauses [12].

Convergence 3—Whole-life asset management matters for energy/carbon: DMRB’s stronger formalization of whole-life assessment aligns with infrastructure governance research emphasizing life-cycle methods and durable asset strategies as pathways to reducing embodied and maintenance-related emissions [21,44]. Likewise, evidence that durable systems can offset higher upfront impacts is consistent with life-cycle findings such as the BRT comparison in Xiamen, where operational advantages can dominate over the life cycle despite higher initial construction energy [29].

Divergence 1—Different “locations” of energy governance (operations vs. appraisal): A key divergence across the manuals is where energy governance resides. In Hong Kong, the dominant energy-relevant mechanisms are operational and network-wide (signals, PT priority). In the UK, energy considerations are more strongly routed through project governance and whole-life assessment requirements. This difference can be explained by institutional scope: TPDM governs an intensely signalized urban environment where marginal operational gains scale quickly, whereas DMRB governs a strategic network where appraisal, durability, and reliability are central, and environmental requirements are institutionalized via formal assessment processes [10,21,26,44].

Divergence 2—Variation in codification strength for emergent transition measures: Electrification readiness and renewable integration appear unevenly codified. This divergence is consistent with the transition literature, where electrification and alternative fuels often advance via external policy roadmaps and pilots before becoming fully standardized practice [3,38,45]. This study adds value by distinguishing codified versus policy/pilot status in a traceable manner (Section 2.5).

4.3. Added Value and Original Contribution of This Study

Relative to prior research that typically models individual interventions or evaluates specific projects [2,29,34], this review contributes by:

• Reframing road-design manuals as energy-relevant governance artifacts: It shows that standards written for safety, capacity, and durability can have substantial energy implications through direct and indirect mechanisms, and that these mechanisms differ systematically across jurisdictions.
• Providing a structured, clause-level comparative method: The rubric-based coding (direct/indirect/emergent; life-cycle stage; codification strength; energy relevance) extends conventional narrative reviews by making cross-manual comparison auditable and repeatable.
• Proposing a manual-compatible, AI-enabled benchmarking overlay: Building on established benchmarking principles and recent AI/ITS evidence, the paper proposes a pathway that does not require immediate wholesale rewriting of manuals but instead enables measurable energy-performance management aligned with existing compliance structures.

4.4. Practical Relevance for Policymakers, Economic Practice, and Stakeholders

The results are useful to agencies and industry because we identify actionable measures that can be implemented under existing standards and investment cycles:

• Policy and investment prioritization: The gap matrix logic (convergence/omission/inconsistency) can support targeted updates, e.g., where a jurisdiction has strong geometric standards but weaker operational digital control, or vice versa—thereby improving cost-effectiveness of decarbonization spending by focusing on high-leverage mechanisms (signal coordination, managed operations, and maintenance planning) [48].
• Operational agencies (traffic management and public transport operators): The results indicate that near-term energy savings can be achieved through operational strategies already consistent with safety-first governance: adaptive signal control, transit priority, speed harmonization, and incident management, without requiring major geometric reconstruction [26,37].
• Asset managers and contractors: By highlighting the energy relevance of pavement condition, maintenance disruption, and whole-life decision processes, the study supports procurement and maintenance strategies that reduce embodied and operational energy, consistent with whole-life governance requirements and durability-focused standards [25,44].
• Economic practice and productivity: Reduced congestion, improved reliability, and optimized maintenance timing have well-known economic co-benefits (time savings, freight efficiency, and reduced disruption). The benchmarking overlay proposed here is intended to help translate these co-benefits into comparable indicators and decision records that support transparent prioritization and accountability, including alignment with net-zero commitments and sustainability frameworks [12,48].

Importantly, because the three manuals differ in scope and context, the study suggests that harmonization should occur through comparable benchmarking rather than uniform standards. This preserves local applicability while enabling cross-jurisdictional learning about which mechanisms deliver measurable energy gains under different urban forms and governance arrangements [48,49,50,51].

4.5. Implications for Strengthening Standards and Future Revisions

The comparative results suggest three practical directions for improving the manuals (or their supporting guidance) while remaining consistent with current design philosophies:

• Make energy performance auditable by linking commonly used operational proxies (delay, speed variability, stop rate) and whole-life proxies (materials intensity, maintenance frequency) to explicit reporting requirements in design records and operational reviews, consistent with life-cycle governance trends [21,44,48].
• Scale digital operations within a safety-first framework, extending the proven logic of adaptive control and managed operations into a continuous improvement cycle supported by AI analytics and data governance [16,17,18,26,34].
• Create a maturation pathway for emergent mechanisms (electrification readiness, microgrids, digital twins, AI-enabled predictive control), using evidence ratings from pilots to determine what should become guidance and eventually mandatory provisions [45,52].

Overall, it is indicated that upgrading road systems for net-zero is less about replacing established design philosophies and more about making energy outcomes explicit, measurable, and governable: An objective that the proposed AI-enabled benchmarking overlay is designed to enable under the practical constraints of existing manuals and institutional processes.

5. Conclusions

Safety remains the foundational principle of road design in Australia, Hong Kong, and the UK. However, the comparative evidence from Austroads, TPDM, and DMRB shows that many provisions developed for safety, capacity, and durability also deliver substantial energy and emissions co-benefits. Measures that stabilize traffic flow, reduce delay and stop–start operation, support multimodal connectivity, and maintain high asset condition consistently lower energy intensity, indicating that safety and energy efficiency are often complementary rather than competing objectives.

Across the three jurisdictions, the dominant pathways to improved energy performance differ by context. TPDM most strongly links compact urban form, public-transport priority, and network-level signal coordination to lower system energy demand. DMRB most clearly institutionalizes whole-life environmental governance and motorway-scale managed operations that improve speed stability and reduce flow breakdown. Austroads provides flexible, performance-based guidance that supports context-sensitive intersection design, active-travel provision, and emerging treatments that smooth corridor operation, although the degree of energy-related codification and consistency varies across jurisdictions.

To strengthen the alignment between standards and net-zero transition goals, three directions follow from the synthesis. First, energy and carbon should be treated as core evaluation dimensions alongside safety, mobility, and cost by embedding operational-energy indicators and LCA/LCCA into option selection, design strategy records, and maintenance planning. Second, digital and AI-enabled operations, such as adaptive control, speed harmonization, and predictive maintenance, should be expanded within a safety-first governance framework to reduce congestion, prevent reactive high-energy interventions, and improve system reliability [52]. Third, a common benchmarking overlay should be adopted to translate manual provisions into comparable performance indicators, enabling cross-jurisdictional learning, transparent gap identification, and evidence-based prioritization of future manual updates.

This study is limited by its document-based comparison of only three manuals, so findings are not globally representative. The manuals also differ in scope, regulatory force, and update cycles, limiting strict like-for-like comparison. Because explicit energy metrics are often absent, energy implications are inferred from operational and asset proxies (e.g., delay, speed variability, stop–start conditions, pavement condition). The review does not quantify marginal energy savings from specific provisions, and real-world outcomes depend on local implementation capacity, enforcement, and data availability/interoperability.

Future research should therefore: (i) quantify the energy impacts of specific interventions (e.g., signal-coordination strategies, speed-harmonization bands, junction forms, pavement texture/rolling-resistance classes, and adaptive-lighting regimes); (ii) evaluate AI-enabled traffic management and predictive-maintenance tools through field pilots using combined safety and energy metrics; (iii) assess how electrification and alternative fuels (e.g., hydrogen) affect infrastructure design, asset loads, operations, and whole-life energy/carbon; and (iv) apply the proposed benchmarking framework across multiple corridors and contexts to test sensitivity, transferability, and policy relevance. In addition, future studies should examine corridor concepts that enable dynamic reallocation of road space and operating parameters—including time-of-day lane assignment, reversible or shoulder running, variable speed and lane-use control, curb-space reallocation, and connected-vehicle coordination—to reduce stop–start conditions and improve corridor energy performance while maintaining Safe System outcomes. A further priority is “road retro-commissioning”: a structured, periodic process of diagnosing and re-optimizing existing corridor assets and control settings (e.g., signal timings and offsets, ramp-metering parameters, VMS/VMSL strategies, lighting dimming profiles, detection calibration, and incident-response plans) to restore or improve as-operated performance relative to design intent and current demand [53]. Trials that combine traffic/energy monitoring with digital-twin scenario testing can quantify energy and carbon benefits, verify safety impacts, identify governance and enforcement requirements, and establish practical triggers and checklists for when retro-commissioning outperforms capital-intensive geometric upgrades [54].

Overall, the findings suggest that upgrading road systems for net-zero does not require abandoning established design philosophies. Instead, it requires making energy performance explicit, measurable, and governable through whole-life assessment and AI-enabled benchmarking that builds on the strengths of existing manuals while guiding the transition toward efficient, resilient, and low-carbon transport networks.