Digitalization in Sustainable Transportation Operations: A Systematic Review of AI, IoT, and Blockchain Applications for Future Mobility

Abstract

Despite increasing interest in AI, IoT, and blockchain for sustainable transportation, existing reviews remain fragmented—focusing on single technologies, descriptive benefits, or narrow applications—without providing an integrated synthesis across domains. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines and a bibliometric analysis of 102 peer-reviewed papers to provide the concurrent integrative synthesis of AI, IoT, and blockchain in enabling sustainable transport. Data were drawn from Scopus, Web of Science, PubMed, Semantic Scholar, and Google Scholar, and analyzed using VOSviewer to identify research clusters, emerging themes, and knowledge gaps. The results reveal three thematic clusters: smart traffic systems for congestion management, sustainable logistics and supply chains, and data-driven urban governance. Across these clusters, AI is more mature in predictive modeling, IoT remains fragmented in interoperability, and blockchain is still at a pilot stage with governance and scalability issues. The analysis highlights synergies (e.g., AI–IoT integration for real-time optimization) and persistent challenges (e.g., standardization, data security). This review contributes a strategic research roadmap linking bibliometric hotspots with policy and practice implications. By explicitly identifying gaps in governance, interoperability, and cross-domain integration, the study offers actionable directions for both researchers and policymakers to accelerate digital transitions in transport.

1. Introduction

The modern transportation infrastructure has given rise to complex challenges stemming from rapid urbanization, population growth, and increasing demands for human mobility. The concept of smart transport integrates advanced technologies into transportation applications, as envisioned by urban planners, intending to optimize transportation networks to enhance service delivery [1]. These cutting-edge technologies include improvements in cloud computing, wireless connectivity, computer vision, and location-based services [2]. However, as efficiency takes center stage, there remains a critical need for cost-effective and environmentally sustainable transportation solutions to preserve ecological biodiversity [3]. Otherwise, uncontrolled urbanization results in pollution, traffic congestion, and accidents [4,5], necessitating integrating modern technological developments with clean energy sources to create a greener urban environment [6,7,8].

Indeed, climate change wreaks havoc on transportation systems [9,10]; fossil fuel transport emissions are one of the most significant contributors to urban air quality and warming temperatures [11]. Although the quest for cleaner means of transportation is underway, difficulties arise in recognizing economic obstacles [12] and the reliance on fossil fuels worldwide [13,14]. For example, the 50-year high demand for automobiles has resulted in the generally tumultuous congestion of transportation networks—a trend that is likely to persist with increases in population and technological advances in the near future [15,16,17,18]. Today, 97% of all motor vehicles on the road are combustion-powered and powered by gasoline [19], significantly contributing to environmental pollutants [20]. These issues can be overcome by deploying intelligent transportation systems. This is made possible by promoting the usage of fuel-efficient vehicles, alternative ways to go around, and policies that reduce energy consumption and vehicle miles driven [21]. Moreover, reducing carbon dioxide emissions is probably possible if fossil fuels are replaced with renewable energy sources like solar and wind, which is necessary to construct sustainable urban ecosystems [6,17]. This transition addresses environmental issues and contributes to achieving the broader vision of resilient, environmentally friendly transport networks for the future.

Integrating innovative transportation technologies into urban infrastructure has been one of the most hopeful opportunities for mitigating the environmental impacts of transport systems. Embedding AI algorithms and solutions for IoT devices will further enable cities to optimize transportation networks, enhance safety, and offer better ecological compliance [22,23]. For instance, smart mobility presents an intelligent transportation system that can deliver operational data in real-time, historical traffic data, and congestion reduction [2]. Transportation networks may become much more efficient and emit less carbon emissions by implementing traffic management systems with real-time analytics and high-speed communication networks. These tactics also relieve traffic jams in the cities [24]. While these challenges are well recognized, what remains less clear is how digital technologies can jointly address them systematically and sustainably.

Furthermore, when fundamental transportation technologies are integrated with additional technologies, like blockchain, IoT, and AI, supply chains and logistics management become more efficient and sustainable [25,26]. AI algorithms, for instance, are essential to smart transportation because they use data from sensors and IoT devices to improve safety procedures, optimize traffic flow, and boost system performance overall [22]. Additionally, blockchain provides unmatched advantages, such as record-keeping and decentralized energy management, which are essential for new smart transportation systems [27,28]. The integration of AI, IoT, and blockchain is a revolutionary coalition that is expected to take transport systems a significant leap toward sustainable urban mobility transformation. This approach will optimize resource utilization, enhance operational efficiency, and provide cleaner air [29,30]. The AI-based systems will mitigate congestion, promote safety, and optimize routing [31,32,33]. In addition, other AI-based applications facilitate traffic demand modeling for efficient traffic flow and fewer emissions, dynamic route guidance, and signal control [34,35,36]. Practical applications, such as GPS-enabled systems, can alert drivers in real-time about over-speeding, sharp turns, and other hazardous situations that may lead to an accident, thereby increasing road safety [35].

Even though innovations such as trip optimization, passenger management, and load monitoring are available, they will still face challenges like cybersecurity and privacy issues in transportation systems [37,38,39]. IoT-enabled smart sensors help the company calculate its carbon footprint and inform it of critical areas that need immediate attention [40]. Additionally, the research will thoroughly cover literature reviews, emerging strategies, and technologies aimed at reducing carbon emissions, with a primary focus on developing a predictive framework for intelligent transportation system development in the future. This study systematically reviews pertinent academic and industry publications with rigorous research methodology based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] framework and Systematic Literature Review. By outlining the field’s intellectual framework and providing a sophisticated understanding of how blockchain, artificial intelligence [AI], and the internet of things [IoT] help to lower carbon emissions and promote sustainability in transportation systems, bibliometric studies enhance analysis even more.

Technically, the urgent need to decarbonize transportation and reduce congestion has spurred interest in leveraging digital technologies. Usually, smart cities initiatives increasingly advocate AI, IoT, and Blockchain to transform mobility systems, promising optimized traffic flow, cleaner logistics, and more transparent governance [22]. For instance, IoT sensors and AI algorithms possibly forecast traffic and suggest alternative routes to alleviate congestion, while blockchain works for securing data exchanges in shared mobility or supply chains [22,34]. However, existing reviews tend to treat each technology or application in isolation (e.g., traffic control, logistics) [22,36]. However, there is a lack of integrated analysis addressing how these technologies work together across transportation domains to deliver sustainability outcomes [40,41]. In particular, only a few studies have combined systematic review with bibliometric mapping to chart the research landscape and emergent themes in sustainable mobility (unlike, e.g., tourism or energy sectors). Thus, this study fills that gap by synthesizing interdisciplinary literature on digitalization in transportation, explicitly focusing on the intersection of AI, IoT, and blockchain for sustainability.

Specifically, this study focuses on the collaborative effects of integrating these technologies to optimize transportation networks, improve routing, and monitor vehicle emissions in real-time. This quantifies the estimated impact of these new digital tools and shows their significant contribution to reducing traffic congestion and carbon emissions. This study also highlights their considerable role in substantially reducing carbon emissions and traffic congestion by quantifying the potential impacts of these digital tools. Thus, studying the prospects of evolution due to newly emerging transport technologies, including the hyperloop, subterranean roadways, drone deliveries, driverless autos, aerial taxis, and maglev trains, toward a likely new urban mobility and another step in a march toward sustainability.

This study articulated clear research gaps, novelty, and contributions compared to earlier work. First, this study applied a rigorous PRISMA-based methodology to identify and screen peer-reviewed studies, ensuring completeness and transparency [42,43,44]. Second, this study distinguished the systematic review by combining quantitative bibliometric analysis with qualitative thematic synthesis. Third, this study presented a new conceptual framework that maps how AI, IoT, and blockchain jointly enable key outcomes such as carbon emission reduction, congestion management, and sustainable supply chains. Finally, this study drew strategic implications for city planners and policymakers, and prioritized future research directions.

The remainder of the paper is organized into sections; specifically, the methodology section details the PRISMA-guided search strategy, inclusion/exclusion criteria, time frame, and bibliometric analysis tools. An AI–IoT–Blockchain synergy framework is, however, added textually. Results and Discussion synthesized findings into three thematic domains of AI, Blockchain, and IoT, and their extended roles in lowering carbon emissions and future transportation technologies. Moreover, a dedicated section outlined strategic implications for urban planners, transport authorities, and policymakers, enabling a prioritized agenda for future work. Although reviews exist for individual technologies in transport, to our knowledge, none have provided an integrated synthesis of AI, IoT, and blockchain. This study is distinct in that it combines a systematic PRISMA-based review with bibliometric mapping to chart emerging themes. This approach allows us to identify synergies, highlight overlooked gaps, and propose a strategic research roadmap for sustainable transport.

2. Materials and Methods

This research discovers recent technological advancements in transport systems, focusing on reducing carbon emissions and enhancing operational efficiency. It underscores the importance of integrating sustainable practices into transportation planning to address pressing environmental challenges, including traffic congestion and inefficiency. The study emphasizes that innovative transportation solutions are crucial for enhancing instantaneous information systems, optimizing traffic flow, and endorsing sustainability in urban environments. Alongside an in-depth literature review, the research develops a predictive framework based on findings from previous studies. This framework is designed to justify and advance the application of technologies relevant to intelligent transportation systems. The guiding research question for this investigation is as follows:

“What are the implications of integrating Artificial Intelligence, Internet of Things, and Blockchain technologies in smart transportation systems for mitigating carbon emissions and fostering sustainability in urban environments?”

By addressing the above question, the study offers critical insights into the evolving strategies and technological innovations contributing to reducing carbon emissions. It also lays the groundwork for actionable solutions, guiding policymakers, urban planners, and technologists toward designing intelligent, efficient, and sustainable transportation systems This research covers the gaps in the literature reviews of previous studies by analyzing the integration of AI, Blockchain, and IoT for highly advanced delivery mechanisms and provides an overall citation-based framework that presents how these technologies can enable a sustainable and intelligent transport system.

The proposed study will employ an intensive methodology, utilizing the systematic literature review (SLR) approach and the PRISMA framework, to address the research question. It systematically identifies, selects, and evaluates relevant literature from scholarly platforms and academic databases. This is complemented by a bibliometric study that maps the intellectual structure and identifies key themes, trends, and linkages between scholarly contributions through co-citation network analysis and keyword co-occurrence clustering. This research adopts a multidimensional approach that critically examines the convergence of AI, IoT, and blockchain towards sustainable transportation development. It has been extremely helpful in highlighting the use of these technologies to lower carbon emissions and shape the future of transportation networks.

This study adopted a narrative synthesis approach rather than a meta-analysis because the body of literature on AI, IoT, and blockchain applications in sustainable transportation is diverse in scope, methodology, and outcome measurement. Based on the conceptual framework, this study followed the criteria of descriptive synthesis, such as systematically organizing, comparing, and interpreting findings across these diverse approaches, focusing on thematic patterns, technological convergence, and policy implications. However, this approach provided a holistic and context-sensitive understanding of how these emerging technologies collectively impact carbon reduction, congestion management, and sustainable logistics, which is consistent with the goal of informing researchers and policymakers about strategic direction rather than achieving a single integrated quantitative impact.

Specifically, this systematic literature review follows PRISMA 2020 guidelines (Figure 1) by searching five databases, such as Google Scholar, Scopus, PubMed, Semantic Scholar and OpenAlex with search strings combining terms for “smart transportation” And “carbon emission”, “transportation” with AI, Internet of Things, and Blockchain. It also covered most publications from 2008 through 2024 to capture recent advances through inclusion and exclusion criteria. After removing duplicates, two authors independently screened titles/abstracts for relevance, followed by full-text review, resolving discrepancies through discussion.

Figure 1. PRISMA Flow Diagram.

2.1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA]

A qualitative technique, therefore, is used in this research, taking into account technology-driven platforms like Blockchain, IoT, and AI-based adaptations that can aid in lowering carbon emissions in the transportation sector. Ross et al. [42], Moher [43], and Kashem et al. [44] made essential contributions that systematically informed the PRISMA paradigm employed in this research. At an early stage of this research, emphasis was placed on identifying and selecting publications that are potential contributors to this review. Typically, PRISMA categorizes articles into broad sections, including abstracts, introduction, methodology, findings, discussion, and funding, which must align with each other and the research objectives and aims. Abstract screening involves an analysis of the background, goals, data sources, and methodology. The Eligibility criteria analyze the logical flow in introductions, clarity of the research question, and its compatibility with the study’s objectives.

To identify relevant research, specific keywords and criteria related to intelligent transportation, carbon emissions, and technological applications were employed. Of the 177 publications that were initially reviewed, a total of 104 were selected to be used in the study. The PRISMA guidelines were followed, and the IMRAD framework, which includes Introduction, Methods, Results, and Discussion, was employed. The IMRAD framework further facilitated structural organization and clarity for the article [45]. Finally, the methodology was prioritized, using PRISMA and funded studies to ensure a high standard of systematic reviews and scholarly rigor. Figure 1 presents the methodological rigor. Therefore, this level of methodological rigor provides a comprehensive foundation upon which to discover the transformative role of Blockchain, IoT, and AI-based adaptations in enabling sustainable transportation systems.

This study follows the PRISMA framework for the systematic review process, adopting a narrative synthesis approach rather than a meta-analysis. The narrative synthesis is supported by EndNote, which organizes the data and references sources efficiently. Transparency and accountability have been ensured through the registration of the review process in the Open-Science-Forum [OSF] [46] available at https://doi.org/10.17605/OSF.IO/XDVHE. Although this research study does not include a meta-analysis aspect in the PRISMA framework, the narrative review method allows for a broader qualitative consideration of available literature. This is a methodological choice, as it will enable deeper analysis of trends, themes, and emerging technologies in the field of smart transportation and sustainability.

2.1.1. Inclusion Criteria

The inclusion criteria for study selection include the following:

I.

Innovative transportation systems, such as public transportation, vehicle optimization, traffic management, and route planning, were discussed.

II.

The study of how smart transportation technologies affect mitigation or reduction plans for carbon emissions.

III.

The research discussed how blockchain, the Internet of Things, and artificial intelligence (AI) may be used to reduce carbon emissions in smart transportation scenarios.

IV.

Only regarded conference proceedings, peer-reviewed journals, or other respectable academic sources.

V.

Only studies published in English will be included for consistency and analysis.

VI.

Review papers and empirical research relevant to smart transportation, carbon emissions, and emerging technologies will be included.

2.1.2. Exclusion Criteria

The following exclusion criteria will further narrow the scope of this review:

I.

Disregarding research that is not relevant to smart transportation or the use of blockchain, AI, or IoT technologies to lower carbon emissions.

II.

No duplicate datasets or research are included.

III.

Due to a lack of rigor, research outputs that are not subjected to peer review—such as blogs, opinion pieces, and news articles—are not included.

IV.

Studies conducted in languages other than English will not be included to ensure excellent comprehension and uniformity.

V.

The studies provide little information or contribution to developing intelligent transportation and carbon emission reduction plans.

VI.

Research has serious methodological flaws that compromise the reliability or validity of the results.

This structured methodology provides a clear basis for the research objectives, as it selects the studies that align with the intention to analyze the potential role of AI, IoT, and Blockchain in fostering sustainable transportation systems.

2.2. Systematic Literature Search and Citation Metrics

This research mainly draws on scholarly articles from the Google Scholar database, due to its ease of citation and access, and its broad coverage across various disciplines of research studies [47]. However, publications in languages other than English, strictly technical works, and those perceived to be more about fact contributions rather than practical and business-oriented insights were out of the scope of the analysis. The authors created the initial bibliometric dataset by gathering and examining essential information: author names, publication titles, abstracts, keywords, and other metadata. A preliminary search across Google Scholar and PubMed databases identified approximately 2111 papers related to smart transportation. A comprehensive review revealed that research on smart transportation and carbon emissions from 1989 to 2024 is also available in repositories such as Scopus, Semantic Scholar, and OpenAlex. Keywords used for the search included terms such as “transportation,” “smart transportation,” “smart transportation AND carbon,” and “smart transportation AND carbon emission” [Table 1].

Table 1. Systematic Literature Search and Inclusion/Exclusion Criteria.

Keywords/Search String	Search Engine	No. of Papers
“smart transportation”	Google Scholar	980
“smart transportation” And “carbon”	Google Scholar	3
“smart transportation” And “carbon”	PubMed	32
“smart transportation”	Scopus	200
“smart transportation” And “carbon emission”	Semantic Scholar	349
“transportation”	OpenAlex	550

The research study selected 104 articles, after carefully perusing the abstracts, keywords, and manuscripts, that were most relevant to the study’s stated objectives, as presented in Table 2. This ensured the quality of the studies, focusing on the interaction of digitalization, emerging technologies, and sustainability in transport systems.

Table 2. Number of selected papers published from 1989 to 2024.

Year	1989	2001	2008	2009	2011	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025
No. of Papers	1	1	1	2	1	1	2	1	4	10	2	12	12	25	12	9	8

Frequently cited works and their linkages map the intellectual structure of the area. The method helps understand foundational literature and how ideas evolve in the area. More precisely, the findings identified a web of seminal works and influential authors whose contributions have framed the discourse on smart transportation and carbon emission reduction. The co-citation network brings out the intellectual genealogy and how ideas have diffused and connected across the research ecosystem.

An analysis of the citations from 1989 to 2024 [Table 3] reflects an h-index of 254, an hA-index of 60, and a g-index of 404. These indices denote that the research area has a high impact factor and scholarly attention.

Table 3. Citation Metrics.

Publication Year	Papers	Citations	h-Index	g-Index	hA-Index
1989–2024	2111	217,336	254	404	60

2.3. Bibliometric Study

For bibliometric analysis, this study used the VOSviewer (Version 1.6.20) for network visualization. Bibliometric data, such as the number of publications per year and keyword co-occurrence, were extracted from the final set of selected papers. Descriptive statistics were also generated, including annual publication trends, country contributions, and author productivity. VOSviewer was employed to create co-authorship, keyword co-occurrence, and citation networks, with default settings (e.g., minimum threshold of 5 co-occurrences for keywords). This dual approach, quantitative mapping plus qualitative synthesis, has been shown to be effective in providing “a complete overview of both theoretical and practical developments” in a research domain. All analysis settings and thresholds are documented for reproducibility.

This bibliometric study systematically reviews and analyzes various academic publications, research articles, and conference papers. Its main objective is to study AI, IoT, and blockchain technologies’ roles in improving smart transportation systems that minimize carbon emissions and promote sustainability [Figure 2 and Figure 3]. According to the findings, traffic management by artificial intelligence, predictive modeling, and route optimization is essential to transport system performance [44,48,49]. The citation analysis portrayed in Figure 2 and Figure 3 further emphasizes the potential for disruption from AI-driven applications [such as machine learning techniques and deep neural networks] in enhancing smart transportation operations and sustainability outcomes [22,28,31,32]. Taken together, these analyses demonstrate how digital technologies have the potential to revolutionize transportation systems, enabling them to meet sustainability goals and reduce environmental impacts.

Figure 2. Keyword Co-occurrence Cluster.

Figure 3. Co-citation Network Analysis.

The IoT, a crucial constituent of this research paradigm, is supported by sensors and connected devices that update real-time data on traffic trends, vehicle performance, and environmental conditions [28,37,39]. By networking the vehicle, infrastructure, and smart devices, the IoT enables data-driven decisions, significantly reducing carbon emissions [36,50,51,52,53]. It also details the creative application of blockchain technology for enhancing security, transparency, and traceability in intelligent transportation networks [25,26,27,28,33,44,53,54,55,56,57,58]. This review aims to discern emerging trends for potential future research and help explore new ideas that enable researchers to design and fabricate advanced, sustainable systems related to transportation through citation analysis across multidisciplinary fields.

Keyword Co-occurrence Cluster Analysis demonstrates the interrelationship among advanced technology for smart mobility, methods for reducing carbon emissions, integration difficulties, and regulatory concerns. Concurrent utilization of AI, IoT, and blockchain, assisted by well-placed policy interventions, charts the path for a low-carbon-emission future and improved functional efficiency in transport systems [28]. The review provides an in-depth examination of the complications introduced by smart transportation, considering carbon emissions, and identifies major thematic clusters that characterize the domain [19]. These determine how such technologies converge, while emphasizing smart transportation, strategies for mitigating carbon emissions, policy and regulation frameworks issues, and integration and interoperability [2,4,9,16,19,24,25,29,34,41]. The thematic clusters identified range from AI-powered solutions and IoT-based systems to blockchain applications, all contributing different approaches to reducing carbon footprints, including electrification and sustainable mobility, as well as more advanced solutions [28]. The clusters also emphasize the challenges of seamless integration and the importance of interoperability, with the influential role that regulatory measures play in steering sustainable transportation development [59].

3. Result

3.1. Artificial Intelligence

Artificial intelligence [AI] is revolutionizing carbon emission reduction strategies in smart transportation through higher technological levels, such as predictive maintenance [1,60]. AI-powered predictive maintenance ensures vehicles operate at peak performance by analyzing vehicle data, identifying patterns, and predicting mechanical failures. Thus, it minimizes unexpected downtime and reduces emissions from poorly performing or faulty automobiles [2,61]. This upbeat maintenance approach enhances vehicle reliability and safety while meaningfully reducing carbon discharges by maintaining peak efficiency [22]. AI also plays a pivotal role in traffic management, facilitating the development of new methods to reduce carbon emissions from transportation systems [1,60]. AI-powered traffic management structures depend on real-time data to augment the timing of traffic signals, thereby streamlining traffic and reducing congestion. Smoother traffic and safety, reduced idling times, and better fuel efficiency decrease the environmental impact of congestion and inefficiencies [2,4,62].

Another application of artificial intelligence in transportation [63] is dynamic route planning, which relies on the latest traffic information and algorithms to steer drivers through the most fuel-efficient routes, thereby avoiding congestion as well as reducing emissions resulting from excessive fuel consumption and idle times [22]. Such algorithms consider traffic flow, road characteristics, and vehicle class to enhance eco-driving routing and reduce carbon footprints [2]. The primary focus of such systems is on minimizing unnecessary acceleration and braking to improve sustainability in transportation [63,64]. Additionally, eco-driving support systems utilize AI to help drivers with immediate feedback and suggestions on minimizing fuel consumption and reducing emissions to the lowest levels possible [2]. These systems assess driving styles, traffic conditions, and environmental factors, offering personalized advice on tactics such as smooth acceleration, efficient braking, and optimal cruising speeds. By fostering sustainable driving behaviors, eco-driving systems enable drivers to save fuel and reduce emissions, contributing to broader environmental sustainability goals [21,60].

Real-Time Vehicle Diagnostics: AI-driven real-time vehicle diagnostics utilize onboard sensors and sophisticated algorithms to monitor a vehicle’s status and performance continuously. The ability to analyze key metrics in real-time, including but not limited to engine efficiency, fuel consumption, and emissions, enables the early detection of impending issues through these systems, ensuring that vehicles always remain at peak performance. Taking this proactive stance with maintenance reduces emissions while minimizing fuel consumption by optimizing engine functionality [6,34]. These diagnostics further enhance the reliability of vehicles, prolonging their life and contributing to a healthier environment through reduced emissions by identifying and removing inefficient or faulty vehicles.

3.2. Blockchain Technology

The tracking, reporting, and control of carbon emissions in intelligent transportation systems might be revolutionized by blockchain technology. In blockchain-based carbon emission tracking, data from vehicles, infrastructure, and industrial processes is recorded on decentralized ledgers in a secure and validated manner to create transparent and tamper-proof records [65,66]. The system ensures accurate measurement and accountability, which are essential for effectively managing and reducing carbon footprints [22]. Another critical innovation that blockchain facilitates is the tokenization of carbon credits. By converting carbon credits into a smaller, tradable unit, blockchain networks can enable all stakeholders to buy, sell, and trade credits easily, thereby offering improved market liquidity. Tokenization incentivizes emission reduction by effectively monetizing organizational efforts [28,67]. Blockchain also enables transparent emissions reporting, ensuring that carbon credits are precisely distributed and imputed to their rightful owners, thereby fostering trust and reliability in the system [25,62].

Blockchain also supports carbon offset mechanisms through the automation of smart contracts. Under the governance of predefined rules, self-executing contracts trigger actions such as issuing financial rewards or allocating carbon credits when predefined targets related to emission reduction are met, thereby achieving sustainable goals. This innovation streamlines processes, simplifies administrative complexity, and ensures transparency in rewarding emission-reduction efforts [40]. Through blockchain, stakeholders gain more reliable, effective, and transparent systems for managing carbon emissions, promoting sustainable behavior, and supporting environmental goals for smart transportation systems.

Smart contracts can transform and automate carbon offsetting mechanisms in smart transportation systems by improving efficiency. When predefined emissions reduction targets are met, blockchain-enabled contracts automatically issue rewards in the form of money or carbon credits [40]. Automating incentive-related processes creates an environment of transparency and trust, as all transactions and actions occur on tamper-proof blockchain networks [68]. By eliminating middlemen from carbon markets and enabling peer-to-peer transactions directly, decentralized carbon offset techniques improve efficiency and transparency. This reduces complexity and accelerates processes, allowing stakeholders to engage in the emission trading systems [28] directly. Similarly, secure data sharing over decentralized networks facilitates the timely monitoring and management of carbon emissions, ensuring that carbon-related data are accurately and effectively transferred in real-time across stakeholders [28].

Blockchain also provides emission data traceability to stakeholders, who can trace the origin and impact of carbon emissions across the supply chain, from production to consumption. This traceability promotes accountability and reduces administrative overhead, thus simplifying carbon offset transactions [25]. By lowering carbon emissions through data-driven optimization, the Internet of Things [IoT] plays a crucial role in creating sustainable and effective transportation networks. Transportation authorities can detect congestion hotspots, track traffic trends, and apply dynamic management techniques to lower urban congestion and related emissions thanks to real-time traffic monitoring made possible by IoT sensors and sophisticated analytics [39,69,70,71]. This system minimizes idling times and enhances traffic flow, significantly cutting carbon emissions.

Other solutions based on IoT involve intelligent traffic signal synchronization. The timings change dynamically in response to changes in traffic flow conditions. Doing so minimizes unnecessary stops and accelerates at crossroads, easing congestion, improving fuel efficiency, and reducing emissions from idling vehicles [19,38]. In turn, traffic would be smoother, and a transport network would be efficient, contributing to sustainability goals [16]. Through such innovations, IoT proves its potential to transform transportation operations, paving the way toward reduced emissions, improved efficiency, and a more sustainable future.

3.3. IoT-Enabled Solutions in Smart Transportation

Smart parking systems powered by IoT technology significantly improve parking efficiency while reducing emissions caused by drivers searching for parking spaces. The systems identify the real-time parking availability and route drivers to empty spaces. These help minimize traffic congestion and emissions related to unnecessary idling and circling for parking [72]. Another IoT-driven innovation is dynamic toll pricing, which uses smart infrastructure and sensors to incentivize off-peak travel and mode shifts. This system reduces congestion during peak travel times by adjusting toll rates in response to demand, thereby contributing to lower carbon emissions [34].

Vehicle-to-infrastructure communication-based solutions will further enhance traffic flow by facilitating smooth interactions between vehicles and transportation systems. Drivers will therefore be able to make more educated judgments, resulting in less fuel consumption and, ultimately, fewer emissions, thanks to real-time information on traffic congestion, road dangers, and the best course to follow [1,66]. A number of dynamic lane management techniques, including HOV and express lanes, that enhance traffic flow and lower emissions are also made possible by using vehicle-to-infrastructure communication [29]. In order to alleviate traffic congestion on roads and highways, automated traffic management systems use IoT data and analytics to modify traffic patterns dynamically. These systems significantly reduce emissions by optimizing traffic flow and minimizing delays [34].

IoT sensors and telematics provide real-time vehicle diagnostics, which guarantee that preventive vehicle maintenance is carried out to attain peak performance. These technologies may identify issues considerably earlier than they could otherwise, ensuring that cars travel with fewer pollutants, thanks to continuous monitoring of indicators including engine health, fuel consumption, and emissions [34,36]. By integrating ride-sharing services with public transportation, multimodal transportation strategies are promoted, which lowers emissions associated with transportation overall and single-occupancy car travel [72,73]. In the same way, IoT sensors for traffic density monitoring help authorities make well-informed judgments about managing traffic, which promotes sustainability in transportation [74]. In these IoT-driven innovations, transportation systems are developing toward more efficiency, lower emissions, and a more sustainable future.

3.4. AI–IoT–Blockchain Synergy Framework for Sustainable Transportation

This framework (Figure 3) is entangled with a synergy that drives sustainable transportation outcomes. In this framework, IoT devices (sensors, connected vehicles, trackers) continuously gather real-time mobility and environmental data (traffic density, vehicle emissions, freight locations, etc.) [34,52]. These data are processed by AI algorithms (machine learning, optimization, predictive analytics) to enable adaptive decision-making, for example, dynamic traffic signal timing, demand forecasting, and route optimization for freight [1,75]. However, blockchain acts as a decentralized ledger and smart-contract platform that ensures data integrity, transparency, and secure transactions among stakeholders (e.g., verifying green credentials of logistics, or managing decentralized energy use for electric vehicles) [76]. Together, this triad leads to multiple sustainability outcomes, such as carbon emission reduction (via optimized routing and eco-driving AI), congestion management (via predictive signal control and ride-sharing coordination), and improved logistics efficiency (through transparent, trustworthy supply chains) [77]. It also supports urban governance goals by providing auditable data streams to policymakers and enabling citizen-centric services (e.g., fair dynamic pricing, accessible mobility apps) [78]. In addition, Figure 4 text placeholder depicts these interactions in a layered architecture, highlighting feedback loops (e.g., AI models retrained on blockchain-validated data). Hence, this integrative view is novel compared to prior reviews that have addressed these technologies separately or in narrow domains.

Figure 4. AI–IoT–Blockchain Synergy Framework for Sustainable Transportation.

This AI–IoT–Blockchain synergy framework provides a well-referenced theoretical foundation with proper academic references. Usually, systems are “more than the sum of their parts” when they express collaboration or emergent behavior, whereas synergistic effects have been the drivers of cooperative relationships at all levels of living systems [75]. In the case of network effects and value creation, typically resulting in positive feedback systems, synergies are generated that allow companies to dominate markets and move into adjacent markets [41]. However, recent research supports the existence of synergy, showing that digital proactiveness, change commitment, and organizational flexibility contribute jointly to digital transformation [76]. Again, transportation logistics is undergoing a transformative shift driven by innovative technologies to overcome human-generated obstacles, with governments and city authorities increasingly keen on these technological advancements because they can reduce traffic and carbon emissions [41].

Technically, the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain technologies in Industry 5.0 has revolutionized supply chain management, offering unprecedented opportunities for efficiency, transparency, and sustainability [79]. Moreover, using a combination of IoT, blockchain, and AI, engineers can use smart city blockchains as a decentralized identity and authorization layer between numerous IoT devices, including smart transportation systems [58]. In addition, digital technologies individually have operation-wise merits, but their combined effect shows potential for enhanced sustainability outcomes [80]. Also, the integration of digital technologies is driving a shift towards dynamic and adaptive network management, with real-time data and algorithms offering potential to continuously optimize flows and configurations within complex networks [34]. Based on the above, this theoretical foundation demonstrated that the framework is not merely a technological proposal but a scientifically grounded approach supported by established theories from multiple disciplines. This comprehensive theoretical base thus provides legitimacy for the synergistic effects and emergent capabilities in sustainable transportation systems.

3.5. Integrated Approaches to Smart Mobility, Sustainable Logistics, and Data-Driven Urban Governance

The reviewed literature clusters into three major thematic domains, each of which is synthesized below rather than describing technologies in isolation.

3.5.1. Smart Traffic Systems and Congestion Management

Urban traffic management is a focal area where AI and IoT coalesce. Numerous studies highlight the use of IoT-enabled sensors, cameras, and vehicle-to-infrastructure (V2I) communication for real-time traffic monitoring [34]. Adaptive Traffic Control Systems (ATCS) then employ AI (e.g., reinforcement learning, neural networks, optimization heuristics) to adjust signal timings and manage flows [1]. For instance, optimizing traffic signals has significantly reduced congestion, travel time, and emissions. Metaheuristic algorithms and machine learning models (e.g., deep RL, fuzzy logic) effectively balance intersection loads and minimize queue lengths [77]. Case studies report that AI-driven control can maintain fluid traffic under varying conditions, far outperforming fixed-timing signals [81].

In practice, smart traffic systems integrate IoT data streams (from loop detectors, cameras, and GPS) with edge/cloud computation [82]. This enables proactive responses to incidents: for example, AI-enabled image analysis detects accidents or congestion, triggering alternative routing and alerts [34]. IoT-based solutions also extend to vehicle-to-everything (V2X) networks, where vehicles communicate with signals and each other [58]. These systems improve efficiency and safety by anticipating bottlenecks and enabling rapid incident response. Importantly, the literature notes that incorporating weather and event data into these models further enhances performance, allowing systems to preemptively mitigate spikes (e.g., dispersing traffic before events) [83].

A complementary role emerges for Blockchain in traffic contexts. While less prevalent than AI/IoT, blockchain can secure shared mobility platforms (e.g., ticketing, toll payments) and preserve the privacy of user data [22]. For example, decentralized ledgers can validate vehicle emissions credits or manage peer-to-peer carsharing transactions without a central authority [84]. Thus, in the smart traffic domain, AI and IoT drive core functionality (predictive control, sensing) while blockchain underpins trust in the data and transactions, although further research is needed on scalable implementations [76]. Ultimately, AI–IoT systems enable real-time adaptive traffic control, leading to reduced vehicle idling and emissions [78]. Emerging results indicate that such systems can cut congestion significantly, supporting carbon reduction targets by smoothing urban flows and minimizing stop-and-go driving.

3.5.2. Sustainable Logistics and Supply Chain Operations

Transport logistics—freight and cargo—is another domain where digitalization promises decarbonization. AI is applied to route planning, demand forecasting, and fleet management, while IoT devices (RFID tags, GPS trackers, smart containers) provide end-to-end visibility [85]. One bibliometric review notes that “dominant themes include the integration of cutting-edge technologies such as AI, big data analytics, blockchain, and sustainable transportation methods,” which collectively enhance logistics quality and reduce environmental impact [77,79]. In practice, AI-driven route optimization helps trucking and delivery fleets minimize miles and fuel use; predictive analytics anticipate maintenance needs, reducing breakdowns; and IoT monitoring ensures that cargo (perishables, hazardous goods) is handled efficiently [36].

Blockchain has been proposed to elevate logistics sustainability further [76]. Immutable ledgers can record every supply chain handoff, improving goods’ traceability and enabling circular-economy practices [76]. For example, smart contracts can enforce that only low-emission carriers are used, or track the provenance of goods [30,40]. Empirical studies (e.g., case analyses of companies like Walmart) suggest blockchain enhances transparency and trust in supply chains [79]. Indeed, systematic reviews find that blockchain “elevates [s] data quality, availability and lineage” in logistics contexts, which is critical for high-confidence analytics and sustainability reporting [22,25].

Despite these advances, challenges persist. High energy costs and interoperability issues with existing systems often limit blockchain integration. Research highlights that more work is needed to standardize platforms and quantify the actual carbon trade-offs of blockchains in logistics [75]. Overall, however, the convergence of IoT data streams, AI analysis, and blockchain trust mechanisms is steadily shaping smart logistics platforms that promise leaner operations and lower emissions [58]. Therefore, advanced analytics and connectivity enable green logistics practices, such as optimized routing, which reduces fuel consumption, and transparency mechanisms (blockchain) support compliance with sustainability standards [30,40]. Early evidence shows that these tools can reduce waste and improve service quality while aligning with sustainable supply chain goals [4].

3.5.3. Data-Driven Urban Governance and Policy

The literature increasingly addresses the role of digital tech in supporting transportation governance and planning. This study labels this theme Urban Governance, encompassing data infrastructure, regulatory frameworks, and participatory decision-making. The studies suggest that integrating AI, IoT, and blockchain can transform governance models from top-down to decentralized, transparent systems [77]. For example, IoT-enabled sensors in urban infrastructure (traffic, environment, utilities) feed data to AI models that can automate policy enforcement (e.g., dynamic congestion pricing, emission zone controls). Blockchain provides the means to audit decisions and ensure accountability (e.g., recording policy criteria met before actions are triggered) [52].

Several experimental architectures exemplify this vision, one decentralized system used Raspberry Pi nodes (IoT) with onboard AI and smart contracts to autonomously manage urban intersections and lighting autonomously, validating each action on-chain for resilience and trust [86]. Such systems “redefine urban governance” by enabling real-time, context-aware interventions without central oversight [87]. The convergence of data platforms also facilitates citizen engagement—for instance, apps can share real-time transit data or allow residents to contribute demand information, all secured by blockchain’s tamper-proof records [75].

However, multiple authors note that policy and institutional factors lag behind technology. There is a governance gap, such as cities may have siloed data and no clear standards for sharing or algorithmic accountability. A bibliometric review of “participatory governance” finds growing interest in blockchain for traceability and AI for decision support, but emphasizes a lack of empirical deployment of integrated governance systems [76]. Furthermore, planning contexts vary widely, so strategies must be tailored to local needs—a gap observed in smart city studies as uneven adoption of IoT/AI due to fragmented efforts [78].

When effectively combined, these technologies can enable data-driven policy—improving urban mobility planning, compliance monitoring, and citizen services. For example, analytics on travel patterns can inform infrastructure investment, and smart contracts can enforce low-carbon zones [84]. Nonetheless, realizing this requires addressing privacy, transparency, and coordination challenges.

4. Extended Roles of AI, Blockchain, and IoT in Lowering Carbon Emissions

4.1. Blockchain’s Role in Advancing Sustainability in Transportation

Blockchain technology, with significant evidence, advance sustainability in transportation by providing secure, tamper-proof records for emissions data, enabling transparent verification and reporting, and supporting carbon credit trading systems (Table 4). These features foster accountability, create economic incentives for adopting low-carbon alternatives, and build trust among stakeholders. By integrating blockchain into transportation systems, the industry can reduce its carbon footprint, enhance transparency, and accelerate the development of environmentally sustainable mobility solutions.

Table 4. Role of Blockchain.

Extended Roles	Ways
Record Keeping	The blockchain will act as a safe, decentralized record of all carbon emissions from all forms of transportation, keeping tabs on each vehicle’s emissions history and recording the gradual decline in emissions [25].
Carbon Credits Trading	Carbon credits, which are tradable certificates that may be traded between market players and trading participants, represent the right to release a specific quantity of greenhouse gases [67].
Verification and Monitoring	These blockchain technologies provide safe, unalterable records of the amount of carbon emissions emitted through all transport means and allow corporate accountability for actual emission reductions to be maintained through decentralized ledgers [7,53,55].
Rewarding Low-Carbon Transport	Such blockchain technology further facilitates low-carbon transportation by incentivizing individuals and firms that tend to use “green” modes of transport [40].
Improving Transparency	This technological benefit may increase transparency and allow for more accurate reporting and evaluation of emissions reduction efforts by offering a safe, unchangeable record of carbon emissions and other crucial data points [56,88].

4.2. Expanded Applications of AI in Intelligent Transportation to Lower Carbon Emissions and Traffic Jams

By increasing the sustainability and efficiency of transportation systems, emissions associated with artificial intelligence in smart transportation may help lower the transportation sector’s carbon footprint (Table 5). The contributions of AI to smart transportation for reducing carbon emissions are listed in Table 5 below:

Table 5. AI Extended Roles on Smart Transportation for Reducing Carbon Emissions and Traffic Congestion.

Extended Roles	Ways
Traffic Management	An AI-driven system may optimize traffic flow by employing real-time traffic data to reduce congestion, reduce carbon emissions, and idle time [28,89].
Smart Routing	Smart routing systems that optimize vehicle routes are being developed using AI algorithms. This might help lower the distance traveled, greatly reducing carbon emissions and fuel use [28,48].
Predictive Maintenance	AI anticipates when cars and other transportation systems will require maintenance, cutting down on unplanned repairs and downtime, which lowers fuel use and carbon emissions [90].
Vehicle Optimization	Vehicle performance is improved using AI algorithms, which lower carbon emissions and fuel consumption [2].
Autonomous Vehicles	Because AI-driven AVs increase the efficiency of transportation networks, their deployment is thought to lower carbon emissions [22].

Artificial intelligence (AI) dramatically lowers carbon emissions in smart transportation by improving traffic flow, route optimization, maintenance demand prediction, and vehicle performance. Applications driven by AI would make the Earth greener for future generations by lowering carbon emissions and improving the sustainability of transportation networks.

4.3. IoT’s Expanded Roles in Smart Transportation to Lower Carbon Emissions and Traffic Jams

The Internet of Things (IoT) can be used to lower carbon emissions in smart transportation [see Table 6]. IoT plays the following roles in smart transportation’s ability to lower carbon emissions:

Table 6. Extended Roles of IoT on Smart Transportation for Reducing Carbon Emissions and Traffic Congestion.

Extended Roles	Ways
Monitoring and Optimizing Fleet Operations	Vehicles can be equipped with Internet of Things sensors to track engine performance, fuel consumption, and driving habits [28]. By analyzing and applying this data, fleet operations may be optimized, lowering emissions and fuel usage [41].
Predictive Maintenance	The Internet of Things increases efficiency and sustainability, lowers emissions and breakdowns [50,90], and forecasts auto maintenance [22].
Intelligent Traffic Management	Using IoT devices to collect real-time data on traffic trends may improve traffic flow and decrease congestion [2].
Encouraging Alternative Modes of Transport	The Internet of Things (IoT) can promote alternative forms of transportation, including walking, bicycling, and public transportation [29].
Smart Parking	IoT sensors can monitor the availability of parking spaces, reducing the time vehicles spend searching for one [91].
Carbon Offsetting	The Internet of Things (IoT) can help reduce carbon emissions by promoting energy-efficient devices and renewable energy sources [17,21].

In addition, fleet optimization, traffic reduction, mobility alternatives, and energy-efficient technology all contribute to the significant decrease in carbon emissions that IoT technologies in smart transportation bring about. Meanwhile, there has been a substantial and satisfying decrease in carbon and greenhouse gas emissions [Table 7].

Table 7. Amount or Percentage of Carbon/GHG Emission Reduction.

Metrics	Artificial Intelligence	Internet of Things	Blockchain
The quantity or proportion of GHG and carbon emission reduction	By 2050, up to 34% [92].	By 2030, there will be 63.5 gigatons [49].	Cut emissions associated with documents by more than 99% [57].

Very briefly, AI could significantly reduce carbon and GHG emissions, IoT is expected to cut emissions by several gigatons, and blockchain can eliminate nearly all emissions from document handling—together highlighting their combined potential to enhance efficiency and sustainability in smart transportation systems.

As shown in Table 8, traffic congestion is a major challenge for cities worldwide, prompting the development of smart city initiatives using IoT and AI. Examples from Barcelona, Singapore, and Los Angeles show how real-time data and AI-driven traffic management systems can optimize traffic flow, reduce signal wait times, and improve overall urban mobility.

Table 8. Amount or Percentage of Traffic Congestion Reduction.

City	Implementation	Key Features	Results	Ref.
Barcelona, Spain	Traffic Management System for CityOS	Data in real time from linked automobiles	Traffic congestion is reduced by 25%, while CO2 emissions are reduced by 21%.	[93]
Singapore, Southeast Asia	Intelligent Transport System [ITS]	AI algorithms for traffic condition prediction	Average travel time has been reduced by 12%, and waiting time at traffic signals has been reduced by 22%.
Los Angeles, USA	Advanced Traffic Management System [ATMS]	Real-time data from connected cars and sensors	There is a 12% reduction in travel time and a 16% reduction in stops at intersections.

Table 9 and Table 10 show the advantages of improved traffic management, made possible by computer vision and blockchain technologies, to lessen traffic and travel times in residential and city centers. These strategies improve user mobility and the travel experience, as evidenced by decreased congestion indices and trip times. Travel time reductions are comparatively consistent for all trips, with predictability and efficiency derived from real-time traffic flow management modifications.

Table 9. Amount/Percentage of Traffic Congestion Reduction.

Area	Congestion Index [Before]	Congestion Index [After]	Reduction in Congestion Index	Average Reduction in Travel Time [%]	Ref.
City Center	0.75	0.56	25.33%	10%	[94]
Residential Zone	0.62	0.47	24.19%	10%

Table 10. Amount/Percentage of Traffic Congestion Reduction.

Trip Type	Travel Time [Without Optimization]	Travel Time [With Optimization]	Average Reduction in Travel Time [%]	Ref.
Short Trip	25 min	22.5 min	10%	[94]
Long Trip	45 min	40.5 min	10%

These stand for the notable increases in operational effectiveness and mobility brought about by the introduction of cutting-edge traffic management systems.

4.4. Progression of Transportation Technologies

This section focuses on recent technological changes in smart transportation and outlines the evolutionary modifications of transportation systems to address challenges posed by carbon emissions and identify sustainable solutions. Table 11 summarizes the potential impacts of the advancement in reducing carbon emissions on current transportation systems.

Table 11. Evolution of Transportation Technologies.

Years	Changes	Name of the Technology	Impact
1880s–1920s	Introduction of electric trams and trains	Electric Trams and Trains [36]	Less dependence on coal-powered steam engines and horse-drawn carriages results in fewer city emissions.
1970s	Development of fuel-efficient internal combustion engines	Fuel-Efficient ICE Vehicles [21]	Decreased emissions per mile driven and increased fuel efficiency.
1990s	Introduction of hybrid electric vehicles	Hybrid Electric Vehicles [HEVs] [63]	Considerable cutbacks in greenhouse gas emissions and fuel utilization.
2000s	Emergence of battery electric vehicles [BEVs]	Battery Electric Vehicles [BEVs] [8]	Less reliance on fossil fuels and zero tailpipe emissions.
2010s	Advancements in plug-in hybrid electric vehicles	Plug-in Hybrid Electric Vehicles [PHEVs] [8]	Emissions are further decreased by combining the advantages of conventional and electric cars.
2010s	Increased production of electric buses and trucks	Electric Buses and Trucks [21]	Lower emissions in the transportation of goods and the general public.
2010s	Development of hydrogen fuel cell vehicles	Hydrogen Fuel Cell Vehicles [FCVs] Autonomous Electric Vehicles [18]	Zero tailpipe emissions, with water as the only byproduct.
2020s	Advancements in autonomous electric vehicles	Autonomous Electric Vehicles (AEVs) [29]	Potential for optimized driving efficiency and reduced emissions.
2020s	Expansion of electric charging infrastructure	EV Charging Networks [58]	Reduced the total carbon impact by facilitating the broader use of electric cars.
2020s	Introduction of electric vertical takeoff and landing aircraft [eVTOL]	eVTOL Aircraft [21]	Potential to lower carbon emissions from air travel in cities.
2020s	Integration of renewable energy sources in transportation	Renewable Energy-Powered Transport [13]	Uses solar, wind, and other renewable energy sources to lessen the carbon impact further.
2030s	Anticipated developments in high-speed rail systems, including the hyperloop.	High-Speed Rail and the Hyperloop [30]	Long-distance transport with extremely low emissions can partially replace air travel.

Transportation has steadily evolved from coal-powered trains and horse-drawn carriages to more sustainable solutions. Milestones include the introduction of fuel-efficient engines, hybrid and electric vehicles, plug-in hybrids, electric buses, and hydrogen fuel cell vehicles. Recent developments focus on charging infrastructure, renewable energy integration, and urban aerial mobility, while future innovations like hyperloop and advanced high-speed rail aim to enable ultra-low-emission long-distance travel.

4.5. New Developments in Transportation Technology

Transportation technology advancements can improve people’s quality of life by lowering expenses, lowering stress, and improving safety. Table 12 highlights some of the major areas of continuing progress in this subject, such as:

Table 12. Future transportation technology and its impacts.

Technology	Impacts
Maglev Trains [72]	Due to magnetic levitation produced by powerful electromagnets, these high-speed trains operate at higher speeds while making less noise and vibration than traditional trains.
Aerial Taxis [89]	The flying taxis would take passengers above populated regions with small aircraft. These flying taxis are expected to provide convenient, economical transportation without disturbing those on the ground below.
Driverless Cars [28]	Safety and legal issues eliminate inattentive driving as a cause of fatalities throughout the testing period.
Delivery Drones [22]	Free movement with fewer limitations on delivery.
Underground Roads [39]	Reduce traffic and speed up transit because it is more weather-resistant than flying.
Hyperloop [30]	Tubes are used to transport goods or passenger groups through pressurized tracks.

According to Table 12, emerging technologies such as maglev trains, aerial taxis, autonomous vehicles, drone deliveries, subterranean roads, and hyperloop systems promise faster, safer, and more efficient transportation while reducing congestion and transforming urban mobility.

5. Framework of New Transportation Technology

The framework provides a detailed basis for new transportation technologies that should improve efficiency, increase passenger comfort levels, and maintain environmentally friendly criteria. The system illustrated in Figure 5 combines advanced auto-signaling and V2V/V2P techniques with fixed scheduling, solar panel-powered vehicles, and an adaptive passenger shifting mechanism.

Figure 5. Framework for New Transportation Technology.

5.1. Key Components

I.

Auto Signaling and Tracking:

a.

Passenger Information: Vehicles’ auto-signaling systems provide passengers with timely updates on vehicle status, estimated arrival time, seat availability, and route changes.

b.

Real-Time Updates: Vehicle-to-passenger communication lets passengers receive real-time information about the vehicle’s location during their journey.

II.

Dynamic Route Management:

a.

Route Adjustments: Vehicles can regulate their routes using V2V communication based on passenger demand and in emergencies to optimize capacity and improve service efficiency.

b.

Emergency Routing: During emergencies, routes can be quickly changed to provide timely assistance and further enhance safety and response times.

III.

Fixed Scheduling and Demand Management:

a.

Scheduled Commutes: Office workers’ travel is more predictable and organized when they follow set timetables during peak commuting hours.

b.

Demand-Based Adjustments: Schedule and route changes are made possible by real-time data analysis in response to passenger demand, guaranteeing maximum vehicle occupancy and reducing vacancies.

IV.

Service Optimization:

a.

Dual Service Modes: Offering two services: High-capacity service for efficiency and the fastest public transportation for urgent trips. The vehicle will automatically switch between the two modes with real-time demand perception.

b.

Zero Vacancy Mechanism: Monitoring and continuous adjustment to keep occupancy high and waiting time low.

V.

Sustainable Design:

a.

Solar Panel Integration: Solar panels are fitted to every vehicle to reduce fossil fuel consumption and decrease carbon emissions, thereby increasing sustainability and efficiency in operations.

VI.

Passenger Shifting Mechanism:

a.

Vehicle-to-Vehicle Transfers: Automated systems enable smooth passenger transfers between vehicles with slight disruption and a consistent service stream.

b.

Trolley/bogie System: Include a train-style bogie system in which a single engine pulls many passenger units. This system enables passengers to travel between units without stopping, improving both the passenger experience and service efficiency.

5.2. Implementation Strategy

Below are the implementation strategies:

I.

Technology Integration:

-

Infrastructure creation and implementation for V2V and V2P

-

Integration of auto-signaling and real-time vehicle tracking systems across transport units.

II.

Operational Planning:

-

Create set timetables for the busiest travel times and establish operational procedures for real-time modifications.

-

Developing guidelines for operational transition between the fastest service mode and the highest capacity mode for optimized efficiency

III.

Sustainability Measures:

-

Fit existing vehicles with solar panel systems to harness renewable energy.

-

Regular monitoring and optimization practices will be implemented to ensure the energy efficiency of solar-powered systems.

IV.

Improving the Passenger Experience:

-

Teaching staff on new passenger transfer technology and procedures is also essential.

-

Design user-friendly interfaces to keep passengers informed and facilitate seamless commute management.

This all-inclusive framework for new transport technologies seeks to revolutionize urban transportation by integrating enhanced auto-signaling, adaptive route management, set timetables, sustainably constructed vehicles, and streamlined systems that enable smooth passenger flow. Utilizing these state-of-the-art technologies and promoting eco-friendly usage, this system has proven to be highly efficient, trustworthy, and environmentally friendly.

6. Key Contributions, Research Gaps, and Strategic Future Implications of Emerging Technologies for Smart Transportation

6.1. The Key Contributions of Emerging Technologies Toward Smart Transportation

Smart transportation policymakers can better understand the factors and challenges involved in adopting various sensing technologies by observing how communities respond to new technologies. The following are some ways that the evolution of cutting-edge technologies has changed smart transportation:

i

RFID [Radio Frequency Identification] is one of the key enabling technologies for smart transportation applications, which allows for the identification and transmission of valuable data and intelligence from connected objects.

ii

RFID offers high reading speeds, real-time, automated recognition, and cost-effective operation, making it ideal for smart parking and air quality monitoring applications.

iii

RFID tags serve as a paperless alternative for ticketing, facilitating vehicle navigation to designated parking spaces. Additionally, they are increasingly utilized to track the locations of various items, including vehicles, goods, infrastructure, and power plants.

iv.

In a variety of smart transportation scenarios, supporting businesses, including transportation, logistics, supply chains, construction, and energy, may highlight the safety advantages of these technologies.

v.

The Internet of Things [IoT] is a vast network of embedded sensors, actuators, smart devices, and linked objects that may be used in the transportation industry.

vi.

Smart grids with RFID and IoT integration will automatically adjust to variations in the supply and demand for power, giving the crucial information required to control and lower consumption. Examples include smart power meters, home security systems, lighting control systems, street and indoor lighting, and industrial automation, where Zigbee applications play a crucial role in energy management.

vii.

A facility manager may use IoT devices, including programmable controls and lighting systems, to monitor building energy efficiency and modify consumption patterns to reduce demand during peak hours. Connected technology will help logistics in the future by giving truck drivers access to vital data, such as parking spaces, rest spots, and weather updates.

viii.

Advanced analytics may help choose the best route and method of transportation by predicting fuel usage based on driving distance and road conditions. Thanks to these developments, smart transportation systems are anticipated to outperform traditional ones.

ix.

Accurate carbon emissions may be simulated using sophisticated modeling tools, system dynamics, and soft computing techniques, supporting Net Zero objectives, the required temperature management for cooling areas, and speed reduction.

x.

Smart transportation systems, combined with AI and blockchain developments, can further reduce carbon emissions by adding renewable energy, alternative fuels, and electric power.

xi.

The integration of blockchain, IoT, and intelligent transportation systems can enhance security, reliability, transparency, and data authentication in smart cities.

xii.

In transport, complex carbon emissions demand urgent and more effective policy measures. Due to the fossil fuel-based system, interrelationships between technical and non-technical measures and carbon emissions are usually complex and nonlinear. A suggested integration approach seeks to solve these issues based on recent research. For sustainable smart transportation, it can modify transportation systems to include technology like blockchain, artificial intelligence, and the Internet of Things.

6.2. Key Research Gaps

This study contributed to the following key research gaps that future work should address, more specifically:

i.

End-to-end platforms on live city data that empirically evaluate systems combining AI, IoT, and blockchain in real transportation operations.

ii.

Relatedly, real-world evidence of environmental impact (e.g., measured carbon reduction) from aforementioned technologies by contextualizing actual performance gains.

iii.

The study should identify the obstacles to combining diverse hardware platforms and protocols, such as AI/IoT insights, across domains with a lack of platform interoperability, especially when centralization is avoided.

iv.

Ought to address the lack of clear guidelines for deploying these technologies at scale for the urban planners and regulators in terms of security, data governance, and equitable access, which may lead to inefficiencies and suboptimal sustainability outcomes.

v.

Address socio-technical and equity considerations/judgements for public acceptance, accessibility, or the risk of widening inequality through digital mobility, and on how stakeholders (residents, small businesses) interact with smart systems.

vi.

This study followed strict guidelines by combining bibliometric analysis with a systematic review approach, which in turn strengthened the validity of the findings and provided a comprehensive understanding of the research landscape [95].

These gaps indicate that while the technological promise is high, systemic, social, and operational factors require more attention.

6.3. Strategic Implications for Stakeholders

The findings suggest several strategic implications for urban planners, transport authorities, and policymakers:

i.

Urban Planners should integrate digital infrastructure into city plans, not as an afterthought. For example, IoT sensor networks and data-sharing platforms must be designed into roads and transit from the outset [25,77], whereas planners can leverage traffic and mobility data for evidence-based scheduling and zoning. In this case, investment in interoperable open-data platforms will facilitate the AI-driven analysis needed for congestion management and emission monitoring [66]. Now, coordination across departments (transportation, energy, environment) is proceeding toward avoiding siloed deployments [96].

ii.

Transport Authorities need to adopt and pilot smart systems (adaptive signals, intelligent fleets) while carefully monitoring outcomes. Authorities should encourage public–private partnerships to deploy and test integrated solutions [86]. For instance, pilot programs that combine AI traffic optimization with blockchain-secured data sharing among agencies can demonstrate feasibility [78]. Authorities must also address workforce skills; staff will require training in managing data-centric systems.

iii.

Policymakers should update regulatory frameworks to enable innovation while ensuring accountability. This includes setting data privacy and security standards in transportation technologies, and developing performance-based incentives for emission reductions (which digital systems can verify) [77]. Policies should encourage open platforms and interoperability standards to prevent vendor lock-in. Importantly, as Waqar et al. note, without evidence-based planning, the benefits of smart technologies remain unrealized; thus, policymakers should require rigorous impact evaluation in any new deployment [18].

All stakeholders must prioritize transparency and stakeholder engagement. Technologies like blockchain can build public trust (e.g., by publishing verified sustainability metrics) [79], and multi-stakeholder forums (including citizens) can help define requirements for mobility services [78]. Moreover, efforts should be made to bridge digital divides so that smart mobility benefits are equitable across communities.

6.4. Linkage Between Future Transportation Technologies and Smart Transportation

AI optimizes energy consumption, reduces carbon emissions, and facilitates eco-friendly practices such as efficient fleet management [7], while IoT sensors in autonomous vehicle networks generate gigantic amounts of data that need efficient collection, processing, and authentication to prevent false-positive reporting from malicious entities [37]. Moreover, blockchain technology provides the trust infrastructure necessary for these complex interactions, with transparent, verifiable, and immutable systems for railway maintenance management [53] through NFT tokens [84], IPFS, and IoT integration, ensuring on-chain recording of every event combined with incentive mechanisms [76]. In addition, Vehicle-to-Blockchain (V2B) communication architectures leverage blockchain technology for transparent and decentralized interactions [86], contributing to the integration of blockchain into V2X and IoT for next-generation transportation systems [34].

Blockchain technology ensures data integrity, nonrepudiation, and trustless communication across nodes [84], while large language models provide adaptive decision-making and reduce latency in real-time data exchange, with incentive and reputation mechanisms further enhancing node participation and reliability [22]. These findings demonstrate practical applications such as connected vehicles becoming a promising research area leading to CV as a Service (CVaaS) [97], with blockchain frameworks securing connected and autonomous vehicles to address requirements for secure, seamless and robust information exchange among vehicles. Nonetheless, the blockchain’s potential for enhancing transparency and accountability in transport ecosystems, usually played role on scalability under peak traffic loads, energy consumption, and regulatory barriers that offers a more robust and realistic perspective on blockchain’s role in smart transportation. The inclusion of pilot projects, such as the RideChain smart wallet initiative, adds credibility by demonstrating real-world performance and adoption outcomes [98]. In the RideChain pilot, blockchain was used to enable secure, tamper-proof ride payment transactions and transparent driver payouts, which improved trust among users and reduced transaction disputes, however, accessing distributed applications (Dapps) that support various ridesharing functions such as user-trust evaluation [99].

In practice, the Internet of Vehicles (IoV) plays a crucial role in smart cities, with blockchain solutions for vehicle communication using Ethereum addressing particular challenges in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure communications [1]. Thus, these concrete examples are evidential for proposed synergies as not merely conceptual but reflect actual technological developments.

Although this review highlights how digitalization through AI, IoT, and Blockchain can influence travel behavior and advance sustainable mobility by drawing lessons from studies on gamified applications and personalized accessibility information, evidence from location-based augmented reality games such as Pokémon GO demonstrates that digital incentives, gamification, and social collaboration mechanisms can actively reshape route and mode choices, encouraging users to walk, cycle, carpool, or use public transit more frequently [100]. Similarly, research on personalized neighborhood accessibility information shows that providing travelers with context-aware, data-driven insights before relocation by reducing weekly driving time and increasing walking and transit usage, indicating measurable behavioral shifts toward sustainability [101].

At last, the novelty of this research lies in its integrative perspective as it is among the first systematic reviews to bring together AI, IoT, and Blockchain technologies within a single analytical framework for sustainable transportation operations. So and so, by synthesizing findings through narrative synthesis and bibliometric analysis, this study moves beyond descriptive summaries and provides strategic insights into technological convergence, highlighting where these tools collectively offer the greatest potential for carbon reduction, congestion management, and resilient logistics. Evidentially, these results not only bridge existing knowledge silos but also offer a roadmap for researchers, practitioners, and policymakers to design data-driven, equitable, and future-ready mobility systems.

6.5. Empirical Evidence from Barcelona, Singapore and Los Angeles

Here, in Table 13, the superblock model improved air quality and public health outcomes by reducing urban pollution. On-demand bus systems enhanced travel efficiency, increased vehicle occupancy, and reduced emissions compared to conventional bus services. Scenario analyses further indicated that project implementation would lead to significant reductions in greenhouse gas emissions, contributing to a more sustainable urban transport system.

Table 13. Empirical Evidence from Barcelona, Singapore and Los Angeles.

City/Project	Technology Implemented	Key Outcomes (Empirical Evidence)	Critical Evaluation/Lessons Learned
Barcelona—Smart Mobility & Superblocks Program	AI-powered traffic management, IoT sensors for air quality and mobility data	In case of air quality, NO2 reduced by 25% and PM10 by 17% in the Superblock areas [102].	Successfully reduced car dependence, but scalability remains a challenge due to political resistance and need for citizen engagement. Long-term monitoring is required to assess displacement of traffic to surrounding areas.
Singapore—Smart Nation Mobility Initiatives	AI for predictive traffic modeling, IoT-enabled public transport systems, real-time traveler information	For On-Demand Buses, cut passenger trip time by 30%, boosted occupancy from 8% to >50%, and lowered emissions per passenger by 70% [103].	Singapore’s integrated approach shows strong results, but high infrastructure costs may limit replication in lower-income cities. Data privacy regulations and interoperability standards remain critical.
Los Angeles Traffic Signal Synchronization	IoT-based adaptive traffic signals, AI-based digital twin for simulation	In GHG emissions, project scenario shows 7 million metric tons/year lower emissions (38% reduction) vs. existing conditions and 22,000 metric tons/year lower (<1%) vs. no-project scenario [104].	Promising results for corridor management, but benefits depend on maintenance of IoT infrastructure and continuous data calibration. Scalability across the entire city network is resource-intensive.

7. Theoretical and Practical Implications

These are all high-impact transformational technologies, spanning a rapidly increasing range of applications, affecting businesses, governments, and society as a whole. Despite the rising importance of smart urban development, comprehensive and focused studies exploring the integration of these technologies remain limited. Evaluating the current research status and identifying gaps can stimulate further exploration and develop global academic contributions in AI, blockchain, IoT, and smart transportation. For instance, AI algorithms have the potential to forecast traffic flow, optimize traffic signal timings, reduce waiting times at intersections, smooth delivery truck routes, and minimize both travel distance and fuel consumption. Artificial intelligence also helps electric cars run better, have a longer range, and require less frequent charging.

Contrarily, IoT makes it possible to monitor traffic patterns, control congestion, enhance safety, analyze traffic flow, and modify traffic signals appropriately. It lowers the chance of crashes and breakdowns, monitors vehicle conditions, and identifies maintenance needs. Blockchain can reduce pollution by making it possible to measure carbon emissions accurately and transparently, and make it easier to manage and monitor carbon credits. Blockchain technology can also serve as a means to ensure that the sourcing of material inputs for electric vehicle manufacturing is done ethically and sustainably, thereby reducing on-road vehicle counts and lowering carbon emissions. The insights gained from considering scale, system structure, and technology integration in transportation contribute significantly to valuable knowledge with high policy implications for the development of smart transportation and carbon emission reduction across industries. Integrating such advanced technologies will likely accelerate the shift toward carbon-free, sustainable urban mobility.

8. Future Research Agenda

Building upon the foundation of AI, IoT, and Blockchain, future research in digitalized transportation should prioritize the following areas in Table 14:

Table 14. Futuristic Research Priority Based on Various Categories.

Category	Research Focus Areas	Tentative Research Ideas	Priority Level	Rationale for Urgency
1. Ethical and Societal Implications	Ethical Issues	- Develop robust cybersecurity frameworks for transportation data.	Top Priority	Rising cybersecurity threats make this urgent for policy compliance (GDPR, data protection laws). Foundational for public trust and scalability of all digital solutions.
		- Explore privacy-preserving data analytics methods.
	Social Equity	- Study digitization’s effects on underserved populations.	High Priority	Policy-relevant for inclusive urban planning, but data on underserved populations may be limited in some regions.
		- Propose equitable access strategies for transit services.
2. Human-Centered Design & User Experience	User Interface Design	- Examine how digital technologies can reduce inequalities.	High Priority	Directly supports social adoption and usability; moderately urgent as it improves effectiveness of already-deployed systems.
	Behavioral Insights	- Design intuitive interfaces that accommodate diverse user needs.	Long-Term	Lower policy impact than core infrastructure issues; can be phased in after system maturity.
3. Advanced AI and Machine Learning	Traffic Forecasting & Demand Prediction	- Improve accuracy of demand prediction and traffic forecasting models.	Top Priority	Directly impacts congestion mitigation policies. Data availability is high due to existing traffic sensors and GPS datasets, making implementation feasible.
	Reinforcement Learning	- Build AI-driven early warning systems for accidents and disruptions.	High Priority	Important for resilience but needs robust real-time data pipelines first (dependent on IoT maturity).
4. IoT and Edge Computing	Real-time Monitoring and Control	- Deploy cutting-edge sensors to gather real-time data on infrastructure, transport, and weather.	Top Priority	Foundational for all smart transportation initiatives; critical for real-time decision-making. High data availability from sensors makes it scalable and immediately impactful on policy for congestion and safety management.
		- Reduce latency by using edge computing to process data locally.
	Smart Infrastructure	- Develop self-healing infrastructure systems that detect and fix damage autonomously.	High Priority	Strong long-term scalability potential but requires substantial investment. Digital twins can inform policy on infrastructure planning and lifecycle cost reduction.
		- Use digital twins to model and optimize infrastructure design and maintenance.
5. Blockchain & Decentralized Systems	Secure & Transparent Supply Chains	- Use blockchain for product traceability and disruption management.	High Priority	Strong potential for resilience and compliance but adoption still limited; requires stakeholder collaboration and regulatory alignment.
		- Implement smart contracts for compliance automation.
	Decentralized Energy Management	- Build blockchain-based decentralized EV charging networks.	Long-Term	Strategic for future energy grids but limited current data availability and infrastructure readiness.
		- Enable peer-to-peer energy trading.
6. Integration of Multiple Technologies	Multimodal Transportation	- Develop seamless multimodal networks integrating walking, cycling, public transport, and carpooling.	High Priority	Direct policy impact on urban mobility and emissions reduction, but requires prior IoT data integration and infrastructure upgrades.
		- Use AI and IoT to optimize transfers and minimize travel time.
	Autonomous Vehicles & Drones	- Explore AVs and drones for transport/logistics.	Long-Term	Transformational but requires significant regulatory changes, infrastructure adaptation, and public acceptance before deployment.
		- Develop safety standards and regulatory frameworks.

Thus, by focusing on these areas, researchers can contribute to the development of more sustainable, efficient, and equitable transportation systems.

9. Conclusions

This review provides a comprehensive, integrated assessment of AI, IoT, and Blockchain in sustainable transportation, highlighting how these technologies jointly shape future mobility. The unique contributions of this work include (1) clarifying distinct research gaps (such as the integration and empirical validation gaps listed above), (2) providing actionable insights for practitioners through thematic synthesis, and (3) articulating a prioritized agenda for future research. The key priorities include piloting integrated systems in cities to generate empirical evidence, developing data sharing and interoperability standards, and expanding the socio-technical study of user acceptance and equity. In sum, these findings emphasize that achieving sustainable transportation through digitalization will require advanced algorithms and devices, coordinated planning, robust policy support, and targeted research on unresolved issues. By addressing the identified gaps and following the strategic implications, stakeholders can accelerate the transition to a more efficient, low-carbon, and resilient mobility ecosystem. Moreover, the review represents an in-depth, systematic literature review and a critical analysis of foundational studies to understand the technology in question comprehensively. The idea of sustainable smart transportation, the difficulties caused by environmental issues, and the upcoming technology involved are all covered in this article. For instance, collaboration among urban residents—whether as drivers or passengers—can support the creation and upkeep of sustainable smart cities. The main contribution of this paper is to analyze the intersections of environmental sustainability, smart technology, and transportation innovation in light of traffic congestion, carbon emissions, and related logistical and regulatory issues. Device incompatibility, poor network architecture, constrained coverage and capacity, security and privacy concerns, and ambiguous legislation might all make it challenging to use these cutting-edge technologies for smart mobility. Future research could also be conducted on digitized energy systems to optimize the cost-efficient timing and placement of energy distribution, particularly concerning electric vehicles and the integration of renewable energy sources. While the future may hold flying cars and more, it is time to start strategic planning to ensure that the transport sector’s evolution is safe, environmentally friendly, and economically sustainable, meeting user needs and supporting sustainable transport objectives.