A framework for quantifying Hyperloop's socio-economic impact in smart cities using GDP modeling

Vesjolijs, Aleksejs; Stukalina, Yulia; Zervina, Olga Article A framework for quantifying Hyperloop's socio-economic impact in smart cities using GDP modeling Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Vesjolijs, Aleksejs; Stukalina, Yulia; Zervina, Olga (2025) : A framework for quantifying Hyperloop's socio-economic impact in smart cities using GDP modeling, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-20, https://doi.org/10.3390/economies13080228 This Version is available at: https://hdl.handle.net/10419/329508 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Tsutomu Harada Received: 14 June 2025 Revised: 21 July 2025 Accepted: 25 July 2025 Published: 6 August 2025 Citation: Vesjolijs, A., Stukalina, Y., & Zervina, O. (2025). A Framework for Quantifying Hyperloop’s SocioEconomic Impact in Smart Cities Using GDP Modeling. Economies,13(8), 228. https://doi.org/10.3390/economies 13080228 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article A Framework for Quantifying Hyperloop’s Socio-Economic Impact in Smart Cities Using GDP Modeling Aleksejs Vesjolijs 1, Yulia Stukalina 2,* and Olga Zervina 2 1Engineering Faculty, Transport and Telecommunication Institute, 2 Lauvas Street, LV-1019 Riga, Latvia; [email protected] 2Transport and Management Faculty, Transport and Telecommunication Institute, 2 Lauvas Street, LV-1019 Riga, Latvia; [email protected] *Correspondence: [email protected] Abstract Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Keywords: socio-economic impacts; smart city; logistics; urban development; hyperloop 1. Introduction The concept of the smart city has evolved over the past two decades as a strategic framework for improving the efficiency, sustainability, and livability of urban spaces. In 2024, the world witnessed a continued acceleration in urbanization and digital transformation across multiple regions, driven by infrastructure demands, population growth, and environmental imperatives. Recent studies show that urban demands—including mobility infrastructure, housing, energy consumption, and digital services—have rapidly increased from 2023 to the first quarter of 2025 (UN DESA,2023). This growth is driven by ongoing urban population expansion, post-pandemic recovery policies, and the acceleration of digital transformation across cities worldwide. These pressures underscore the need for scalable, sustainable, and high-speed transport infrastructure solutions such as Hyperloop. By 2050, nearly 70% of the global population is expected to live in urban areas, placing immense pressure on existing infrastructure, particularly in the areas of transportation and Economies 2025,13, 228 https://doi.org/10.3390/economies13080228 Economies 2025,13, 228 2 of 20 logistics (World Bank,2020). Among the most relevant advances in high-speed ground transportation is Hyperloop technology—a system that proposes near-supersonic travel in vacuum tubes, combining the benefits of rail and air transport. Initially proposed as a futuristic concept in 2012 by Tesla (Tesla.com,2012), Hyperloop has rapidly evolved into a tangible innovation, reaching Technology Readiness Level 6 (TRL) (Horizon,2020). According to (Planing et al.,2025), Hyperloop acceptance reached 51.4% across Europe, indicating expectations in high speed, comfort, and environmental aspects despite risks associated with it (Kang,2025). In 2025, European Hyperloop projects are being developed across different regions, for example, Zeleros (Spain) (Zeleros.com,2025), Nevomo (Poland) (Nevomo.com,2025), HARDT (the Netherlands) (Hardt.global,2025), TUM (Germany) (Tumhyperloop.com, 2025), Institute of Hyperloop Technology (Germany) (Iht-emden.de,2025), Swisspod (Switzerland) (Swisspod.com,2025), and others. Hyperloop state-of-the-art includes feasibility studies, pilot projects, experimental runs, test tracks (Tumhyperloop.com,2023), and public–private investments also accelerating its potential deployment in various global regions including the USA (TT Hyperloop) (Hyperlooptt.com,2025), India (Avishkar (2023), Hyperlink (2023)), and China (CASIC) (Starr,2024). Hyperloop technology, with its innovative design, ultra-high speeds, and multimodality, has the potential to change existing intercity travel and transform the logistics and freight systems of smart cities (Premsagar, 2022,2023). Further, its sustainable design supports the goals of the European Green Deal (EC,2024b) and green (EC,2023b) and digital transformation (EC,2023a), which is reflected in recent studies covering Hyperloop’s role in smart city logistics (Hansen,2020), sustainability modeling (Barbosa,2020), high-speed transport integration (Noland,2021), and EU-aligned development strategies (Vesjolijs & Skorobogatova,2025). The application of Hyperloop technology to smart city development has become a prominent topic at scientific and industry conferences dedicated to ultra-high-speed transportation. Notably, the Hyperloop Conference 2023, held in Busan, South Korea, brought together a diverse array of stakeholders—including engineers, researchers, industry leaders, and policy experts—from across North America, Asia, and Europe. The conference provided both theoretical insights and empirical data drawn from active pilot implementations and feasibility studies on the integration of Hyperloop mobility within smart city frameworks. The case of Busan’s smart city initiatives, highlighted during the event, showcased the practical viability of Hyperloop implementation in dense urban environments and offered valuable perspectives on logistical optimization, environmental sustainability, and infrastructure planning. Later, Hyperloop projects presented at the European Hyperloop Week events in 2023 and 2024 showcased practical applications across diverse geographies, including Germany, the UK, the Netherlands, Canada, and India. The corresponding studies highlighted use-case innovation (Vesjolijs & Skorobogatova,2025), cross-border pilot collaborations (Hyperloopconferences.com,2025), and region-specific infrastructure adaptation (EHW,2023). Recent advances in legal frameworks for Hyperloop in the EU demonstrate Hyperloop application potential to smart cities from a regulatory perspective. The European Commission adopted sustainable urban mobility and transport integration principles as main areas for smart cities development in the EU (EC,2025) and prioritized the design of Hyperloop promotion and development strategy (EC,2024a). Analysis of scholar literature in the field highlights the lack of research papers dedicated towards legislation and economic development of Hyperloop; currently, more studies are being dedicated to Hyperloop’s system performance (Mitropoulos et al.,2021). The coherence of smart city principles with Hyperloop technology projects presents a unique opportunity to redefine urban logistics, particularly in the context of mobility as a Economies 2025,13, 228 3 of 20 service, multimodal integration, and sustainable economic growth. According to Masrub et al. (2025), Hyperloop also contributes to an “eco-friendly future society energy network”, which corresponds with smart city technological innovation goals. Hyperloop has also received wide coverage in media, technical feasibility reports, and public infrastructure white papers—focusing on projected energy efficiency (Premsagar,2022), socio-economic potential (Hansen,2020), and planning feasibility within urban ecosystems (Barbosa,2020). However, its role in driving economic change—particularly through improved urban logistics and accessibility—remains underexplored in academic research. This study is intended to bridge this gap by investigating how Hyperloop technology can contribute to the logistics ecosystem of smart cities. It addresses a critical gap at the intersection of infrastructure innovation and urban economic modeling. Hyperloop has been widely discussed in the engineering and feasibility literature; however, few academic studies offer a reproducible method to quantify its macroeconomic effects, particularly at the city level in the context of smart city urban development. To bridge this gap, the present research introduces a scalable modeling framework that integrates geo-spatial analysis, transport accessibility, and trade elasticity into a GDP-based estimation tool tailored for smart cities. By doing so, the study provides policymakers with a novel, data-driven mechanism to evaluate the socio-economic impact of Hyperloop deployment and inform infrastructure planning within the smart city agenda. The remainder of this paper is structured as follows. Section 2outlines the research methodology, including the systematic literature review and the development of the gross domestic product (GDP) impact modeling framework. Section 3presents the use-case analysis of Hyperloop applications within smart cities, based on technical readiness and transport functionality. Furthermore, it introduces the proposed GDP assessment strategy and explains its integration with geo-spatial and socio-economic data layers. Section 4 describes the case studies of Glasgow, Berlin, and Busan and demonstrates the application of the model. Section 5discusses the results, comparing GDP impacts across cities and identifying key drivers of economic change. A summary of the findings, limitations, and proposing directions for future research is given in the Conclusions. 2. Materials and Methods To achieve the aim of the study, the authors developed the four-stage methodology presented in Figure 1. While various methods such as cost–benefit analysis, computable general equilibrium (CGE) models, and system dynamics are commonly applied in transport economics, the present study required a scenario-driven, spatially disaggregated, and modular approach aligned with smart city urban development characteristics. Therefore, a four-stage methodology was developed to meet these specific requirements. Step 1 involved a systematic literature review performed by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, by screening 184 journal articles, white papers, and technical reports published between 2016 and 2025. The review had two objectives: (i) to catalogue socio-economic mechanisms that the Hyperloop literature claims could affect urban performance, covering passenger benefits, freight logistics, energy demand, land-use change, and emissions, and (ii) to identify the quantitative proxies most frequently used to measure those mechanisms. This scan yielded 44 distinct indicators and confirmed a methodological gap in linking them coherently to gross-domestic-product outcomes. Abstract and full-text screening were conducted manually by the authors using pre-defined inclusion criteria in accordance with PRISMA guidelines. No automated review tools (e.g., Abstrackr, BIBOT, etc.) were used in the selection process. Economies 2025,13, 228 4 of 20 Figure 1. Research methodology overview. Use case selection source: Waseem et al. (2025) (authors’ elaboration). Literature analysis was supported by primary source data from Hyperloop Conference 2023 in Busan, Hyperloop Conference 2024 in Gdansk (Hyperloopconferences.com,2025), and European Hyperloop Week 2023 (EHW,2023) in Edinburgh, UK (EHW,2023). EHW 2023 brought together over 400 students, researchers, and professionals to compete and collaborate on diverse Hyperloop topics from engineering to socio-economic modeling. Primary source data from EHW 2023 provided the necessary information of Hyperloop technology applicability to smart cities in the UK, Germany, and the Netherlands. These insights informed the development of the study’s classification framework, use-case matrix, and GDP-impact assessment model. During Step 2, the indicators were classified into the European Commission’s smart city frame: ICT, citizen focus, infrastructure, transport, and mobility. The results of the analyses were consolidated into a short list of passenger and freight use-cases, which confirmed the potential application of Hyperloop to smart cities. With the introduction of GenAI to science, research methods are also advancing, allowing for better determination of blind spots in scientific areas and identification of previously unknown phenomena. Recent studies in liquid AI discussed by scientists and academia representatives at the Future of Information and Communication Conference 2025 (Saiconference.com,2025), specifically the method proposed by Waseem et al. (2025), were applied in this study to classify and rank Hyperloop use-cases based on technical readiness and data availability. Each selected use-case was mapped to a Hyperloop contribution area, creating the bridge between raw engineering attributes and urban policy goals, which helps in understanding how to develop a strategy for GDP calculation and what its components should be. This mapping connects Hyperloop’s technical characteristics to urban development goals and enables the formulation of a model to estimate GDP-related impacts of Hyperloop deployment. While GDP as a macroeconomic measure has established methods and components, the proposed framework builds on these foundations to capture city-level changes that may arise. During Step 3, the classification results were operationalized using the GDP-impact calculation framework developed by the authors in the earlier phase of the study. The two-layer strategy combines a 0.33 km 2 geo-spatial grid with a city-specific Poisson pseudomaximum likelihood (PPML) module proposed by authors (Vesjolijs et al.,2025) and a trade-re-balance block; its step-by-step implementation is summarized in a detailed algorithm with specific actions and outputs. Since the solution is focused on cities and not by countries (as in classical international trade gravity models), the model is applied city-by-city, meaning no pooled cross-sectional structure is imposed. Each city’s baseline and Hyperloop-enhanced rows are evaluated independently using city-specific regressions, which helps isolate effects and avoids interdependence across spatial units. This also aligns Economies 2025,13, 228 5 of 20 with our goal of developing a scalable, modular tool applicable to diverse urban settings. Additionally, the PPML specification is particularly suitable for trade flow modeling with heteroskedasticity and zero values, as shown in Silva and Tenreyro (2006). A working prototype was developed under a conventional software-development life-cycle (SDLC) (Olorunshola & Ogwueleka,2022) during Step 4. The requirements were formalized in unified modeling language (UML) use-case diagrams (Koç et al.,2021); dataingestion, computation, and visualization layers were coded in Python 3.12 (Python.org, 2025); unit and integration tests were executed with PyTest and Pandas-based fixtures; and validation was performed on the Glasgow, Berlin, and Busan case studies. The SDLC method, together with step-by-step algorithm for prototype development, allows reproducing the proposed solution and modeling tuning (Olorunshola & Ogwueleka,2022). The authors then summarized the use-case analysis, case study, strategy, algorithm, and prototype into an integrated framework proposed for GDP-impact evaluation of Hyperloop deployment in smart cities (Step 5). 3. The Role of Hyperloop Technology for Smart Cities and Urban Development and Examination of Possible Implications for Economic Development In this section, the authors present the results of a systematic literature review, apply use-case classification using Waseem et al.’s (2025) method, and develop the logic of the empirical assessment of the GDP calculation and evaluation of multifaceted factors. 3.1. Role of Hyperloop Technology for Smart Cities Use-case analysis provides an overview of the potential that Hyperloop offers to smart cities, contributing to the development of the solution proposed by the authors (Table 1). 3.2. Hyperloop Impact on GDP in the Context of Smart Cities GDP is a well-established macroeconomic indicator used to measure the total value of goods and services produced within a country or region. Traditional GDP measurement encompasses various methods (expenditure, income, and production approaches) and has also faced critiques regarding its adequacy in capturing environmental, social, and distributional dimensions. This study does not aim to alter the theoretical foundations of GDP calculation. Instead, it introduces a modeling framework that estimates the potential change in GDP at the urban level resulting from the deployment of Hyperloop infrastructure. The model is designed to operate within existing GDP frameworks while accounting for spatial accessibility, trade effects, and time-efficiency gains enabled by the new transport mode. The proposed modeling framework integrates geospatial disaggregation with socioeconomic impact assessment to estimate the macro-level returns that a passengerand freight-Hyperloop corridor could generate for individual smart cities. Analysis of smart cities area maps from Glasgow (UK), Berlin (Germany), Busan (South Korea), Singapore, Oslo (Norway), Amsterdam (the Netherlands), Helsinki (Finland), Zürich (Switzerland), Bridgewater (Canada), Columbus (USA), Brisbane (Australia), Fujisawa (Japan), Shenzhen (China), and other locations revealed that each smart city has unique area, geo-spatial features, and changing boundaries, and they are not homogenous. Another complexity that some cities might have is combined zones. For example, Busan area contains a Free Trade Zone, which, in fact, facilitates geographical advantages or different districts inside one area, which can be considered as separate entities. Economies 2025,13, 228 6 of 20 Table 1. Possible Hyperloop use-cases for smart city integration based on Waseem et al. (2025) methodology. PHS—passenger Hyperloop; FHS—freight Hyperloop (authors’ construction). ID PHS Use-Case Source ID FHS Use-Case Source 1Ultra-high-speed daily commuting between smart city areas (Swissloop.ch,2023;S. Chen et al.,2022;Kumar & Singh,2022)1 Critical parcel delivery (Tumhyperloop.com,2023;Hyperlooptt.com,2025;Barbosa, 2020;Bhuiya et al.,2022) 2Airport to city hyper shuttle connector (Celestin,2023;Kitika & Suwatcharapinun,2024) 2 Fresh produce cold chain logistics (Hansen,2020;Barbosa,2020;Noland,2021) 3 Intercity regional passenger corridors (Hansen,2020;Barbosa,2020;Noland,2021; Vesjolijs et al.,2025;Silva & Tenreyro,2006)3 Pods container shuttle (Tumhyperloop.com,2023;S. Chen et al.,2022) 4 Event-based crowd transport (Celestin,2023) 4 Airport cargo to downtown consolidation hub (Tumhyperloop.com,2023;Hyperlooptt.com,2025;S. Chen et al.,2022;Kitika & Suwatcharapinun,2024) 5 Emergency medical evacuation pods (Premsagar,2022,2023;Noland,2021;Kumar & Singh,2022)5 Manufacturing supply line (EC,2023b;Kumar & Singh,2022;Bhuiya et al.,2022;Singh et al.,2021) 6 University connectivity (Singh et al.,2021;Tudor & Paolone,2019;J. Li et al.,2021;Aitken et al.,2016)6 High-value electronics secure transit (EC,2023b;Hansen,2020;Kitika & Suwatcharapinun,2024) 7 Tourism enhanced access (Cao et al.,2023;Arma˘gan,2020) 7 Waste removal (EC,2023b;Hansen,2020;Arma˘gan,2020) 8 Integrated knowledge hub (Hansen,2020;Bhuiya et al.,2022;Kitika & Suwatcharapinun,2024;Arma˘gan,2020;Gohar & Nencioni,2021)8Pharmaceutical distribution (Zhang et al.,2007;Rathore et al.,2015;Edoh,2017; Schlingensiepen et al.,2016) 9 Rural accessibility (Tumhyperloop.com,2023;Hyperlooptt.com, 2025;EC,2023b;Vesjolijs et al.,2025;Kitika & Suwatcharapinun,2024;Lingli,2015;Edoh,2017)9 Reverse logistics (Hyperlooptt.com,2025;EC,2023b;Vesjolijs et al.,2025;Silva & Tenreyro,2006;Singh et al.,2021;Rathore et al.,2015) 10 Inclusion services (Rathore et al.,2015;Schlingensiepen et al.,2016) 10 Medical organs transportation (Cao et al.,2023;Rathore et al.,2015;Sheikh et al.,2022; Willems,2021;Sharma et al.,2024) 11 MaaS ticketing (Tumhyperloop.com,2023;Hyperlooptt.com,2025; EC,2023b;Hansen,2020;Silva & Tenreyro,2006)11 Disaster relief supply chain support (EC,2023b;Hansen,2020;Python.org,2025;Kitika & Suwatcharapinun,2024) 12 Nighttime pod service (EC,2023b;Hansen,2020;Nikitas et al.,2017; McGillycuddy,2024)12 Autonomous warehouse-to-warehouse hyper lanes (Kitika & Suwatcharapinun,2024;Sane,2020;Mogaji,2024) 13 Workforce belt expansion (Silva & Tenreyro,2006;Schlingensiepen et al., 2016;Mogaji,2024)13 Integration with urban consolidation centers (Tumhyperloop.com,2023;Hyperlooptt.com,2025;Hansen, 2020;Kitika & Suwatcharapinun,2024;Schlingensiepen et al.,2016) 14 High-frequency business rush-hour shuttles between bottlenecks (McGillycuddy,2024;Sane,2020) 14 High-density energy cell transport (Gohar & Nencioni,2021;Mogaji,2024) 15 Rapid disaster area evacuation (Gavzy & Scalea,2022) 15 Post delivery (Silva & Tenreyro,2006;Gohar & Nencioni,2021; Mogaji,2024) 16 MaaS pods for reduced mobility persons (Cavar et al.,2011) 16 Urban micro fulfilment node connector (Hyperlink,2023;Kitika & Suwatcharapinun,2024;Yavuz & Öztürk,2021;Kale,2019) 17 On-demand autonomous passenger pods (Avishkar,2023;A. Chen et al.,2023;Nikitas et al., 2017;Werner et al.,2016)17 Carbon-neutral freight corridors (Yavuz & Öztürk,2021;Kale,2019;Hyperloop Development Program,2022;Abraham et al.,2024) 18 Integrated park and ride hyperloop hubs (Nikitas et al.,2017;Kale,2019) 18 Private transport relocation (Rocha et al.,2021;Swiftube,2024) 19 Education support for rural–urban connections (Rocha et al.,2021;Legaspi et al.,2020) 19 Urban–rural hubs connectivity (Avishkar,2023;Starr,2024;Hansen,2020;Noland,2021; Kitika & Suwatcharapinun,2024) 20 Carbon-neutral nature parks connections (Brkljaˇci´c et al.,2020;Alawad et al.,2023) 20 Drone relocation (Nath et al.,2023) 21 Cyber parks (Brkljaˇci´c et al.,2020;Alawad et al.,2023) -/- -/- -/- 22 Aging population support (Ghosh,2004) -/- -/- -/- 23 AR/VR MaaS experience (Anthopoulos,2017) -/- -/- -/- Economies 2025,13, 228 7 of 20 Therefore, one of the key requirements for the solution is that GDP calculation should take into account urban area geo-spatial specifics and capture different Hyperloop deployments. For example, a circle-closed-loop Hyperloop can have the same length as a straight-line Hyperloop from point A to point B but at the same time cover a different area. Also, incorporating geo-spatial data has the potential to more accurately represent the transportation network and energy grid if necessary. Further, depending on requirements, users can calculate the application of Hyperloop to a specific district area or zone and to the whole urban area. As a result, the authors proposed a solution that facilitates the above-mentioned requirements. It uses an integrated multi-layer approach, allowing to capture GDP allocation given the geo-spatial specifics of the exact smart city area. A high-level overview is presented in Figure 2. Figure 2. Strategy of GDP impacts assessment that couples a geo-spatial grid with a city-specific PPML socio-economic layer (authors’ construction). (Legend for geo-spatial layer on 1 km 2 sector: squares—1/3 km 2 grid sectors within a smart city and sectors further subdivision to 1/9; black circles—current transport connection nodes, purple circle—node representing start of a Hyperloop route; green diamond—new connection nodes; orange lines—graphs representing node connection by identified Hyperloop route; grey sector—out of smart city transportation network). At the outset, each city is granulated into a grid of 0.33 km 2 sectors using a pathfinding algorithm proposed by Horzyk and Montebello (2025) for drones’ navigation. The method has both empirical and theoretical use-cases. For uncharted areas of smart cities, this can be used to define a grid using the unmanned flying vehicles, and for those areas that already have a detailed grid, it can be applied for mapping. This granular representation allows both the existing urban structure and the planned Hyperloop alignment to be mapped onto a common spatial canvas. Within this layer, the model derives several baseline descriptors: the total number of sectors (city grid size), the share of those cells that lie within a 60 min surface-commute catchment, and the stock of multimodal connection nodes, expressed as density per square kilometer. The Hyperloop right-of-way is then superimposed, and additional passenger-station and freight-terminal nodes are allocated along its footprint, creating a modified network topology without altering the economic core of the city. Recent studies have also explored advanced traffic modeling techniques using Lagrange-coordinate-based cellular automata frameworks, such as the multi-lane model proposed by X. Li et al. (2021). While the Lagrange-coordinate-based traffic modeling approach proposed by X. Li et al. (2021) offers valuable insights into multi-lane flow dynamics under empirical traffic conditions, the graph-based pathfinding method by Horzyk and Montebello (2025) is more suitable for our study. The Horzyk and Montebello method enables sector-level spatial disaggregation, node-to-node routing, and adaptation to new Economies 2025,13, 228 8 of 20 or hypothetical transport networks such as the proposed Hyperloop grid, which lacks historical flow data and operates under a fundamentally different infrastructure model. Building upon this geo-spatial grid, the framework processes physical accessibility changes into high-impact GDP effects. Accessibility is measured by the number of grid sectors that a representative worker can reach within the designated commute time; the Hyperloop scenarios enlarge this opportunity set in proportion to their higher travel speeds. Because commuting occupies only a fraction of the 24 h day, the raw reach expansion is attenuated by a time-budget factor that recognizes practical limits on human mobility. The model further adds a modest line-alignment effect, reflecting property-value uplift and station-area activity that tend to accrue along high-capacity corridors. These steps are carried out independently since every city record is scalable, and it can be applied to the specific districts. To convert accessibility impacts into GDP levels, the algorithm relies on a Poisson pseudo-maximum-likelihood specification that is calibrated separately for each city using only its own baseline and Hyperloop scenario rows. This city-specific regression links observed gross product to changes in reachable distance, time savings, and the presence of the Hyperloop dummy, thereby yielding internally consistent predictions that remain free from cross-city contamination. The PPML stage acts as a structural bridge between the spatial layer and the economic layer, regularizing random sector-level variations while preserving relative differences generated by the transport intervention. The PPML model proposed by the authors (Vesjolijs et al.,2025) was chosen to calculate trade within the smart city. A high-level equation of evaluating trade between two economic actors is presented below (Mitropoulos et al.,2021). lnTradeij,t=α0+α1ln (GPDi)+α2ln GPDj+α3lnDistancei,j+∈ij,t(1) where Tradeij,t is the export or import flow from point ito jin year t; ∈ij,t is the error term. For the effect evaluation of Hyperloop on trade, the following form is used (Barbosa, 2020): Tradeij,t=exp(β0+β1ln (GPDi,t) + β2ln(GPDj,t) + β3 ln (Distancei,j) + β4HLij,t+β3ln (TimeReductionij,t+1) + . . .) + ∈ij,t (2) where Tradeij,t is the export or import flow from country ito jin year t; β4 and β5 capture effect of potential Hyperloop connectivity on trade flows; exp (. . .) means the exponential link for PPML is used; ∈ij,t is the error term, according to (Vesjolijs et al.,2025). The calculation approach adjusted for smart cities answers the following questions: •How will the workforce mass? •What is a reduction in transportation time? •What is the effect on trade? •What is the effect on transportation costs? •What is the effect on CO2emissions? •How does GDP change? The framework is extensible and has therefore been configured to include GDP-impact methodology advanced by TransPod (A. Chen et al.,2023;Delas et al.,2019), whereby the welfare effects of a very-high-speed system are decomposed into (i) consumer surplus from lower ticket prices, (ii) monetized environmental externalities, and (iii) productivity gains attributable to travel-time savings. In practice, the present model embeds a tariff module that first benchmarks Hyperloop fares against prevailing rail/air prices on the same OD pairs and then feeds the differential into a demand-elasticity routine to obtain a static consumer-surplus estimate. A parallel emissions block converts mode-shift tonnage into avoided CO 2 using life-cycle emission factors consistent with EU taxonomy guidelines. Economies 2025,13, 228 15 of 20 6. Conclusions An end-to-end method has been proposed to estimate how Hyperloop deployment may alter the macroeconomic dynamics of data-driven smart cities. The study’s main objective was achieved by introducing a framework designed to support GDP-impact calculation for Hyperloop implementation in the context of smart city development. The conducted research confirmed the benefits offered by the Hyperloop technology by applying it to a smart city context and mapping a catalogue of passenger and freight use-cases using the liquid AI method, demonstrating multidisciplinary and multifaceted aspects of ultra-high-speed technology deployment. A structured GDP-impact assessment strategy that couples a geo-spatial grid with a city-specific PPML socio-economic layer (Figure 2) was developed, bridging the gap between empirical geo-spatial pathfinding algorithms and socio-economic aspects of Hyperloop. The strategy was formalized into a reproducible 17-step algorithm (Table 2), beginning with sector-level reach calculations and concluding with the adjustment of gross product based on net trade effects. The algorithm is scalable and can be developed as a digital application, which is confirmed by the applied software development lifecycle method. The algorithm was executed for three contrasting yet density-comparable smart cities—Glasgow, Berlin, and Busan—demonstrating internal consistency and revealing distinct benefit pathways: commuter-reach expansion for Glasgow, corridor-led value capture and export uplift for Berlin, and freight-dominated gains for Busan. The numerical results confirm that Hyperloop can deliver significant GDP increases (more than 30%) under current HL TRL-6 and TRL-9 performance assumptions. The GDP effect increase is achieved by simultaneously densifying multimodal connection nodes, expanding the commute zone, and decreasing effective travel times inside the EU’s 60 min smart city benchmark. The final solution presents a framework (Figure 4) that positions Hyperloop within the European Commission’s climate-neutral and innovationaccess agendas, and it is ready for direct use by planners and investors for smart cities applications. The study provides the first scalable, policy-aligned toolkit for quantifying Hyperloop’s economic leverage across diverse urban contexts and therefore fulfils the research aim. Limitations. Calculations were conducted for a 1-year period. While the current study relies on 2023 static input data for illustrative purposes, the framework was designed to be data-agnostic and can be applied using higher-frequency, real-time, or longitudinal datasets as they become available. Results of the calculations heavily depend on Hyperloop specification in the corresponding TRL. In case of Hyperloop technical specification changes, the result might change dramatically, even to negative values. Research did not evaluate system dynamics of Hyperloop projects overtime. Future study. The next step is to apply the solution to the most relevant data from 2025 when they are published by the corresponding smart cities. Future research will focus on integrating dynamic urban data streams to refine the model’s temporal sensitivity and better support predictive planning tools for smart cities. Further, the authors aim to propose a development strategy to the European Commission for Hyperloop implementation and an appropriate framework for the existing Hyperloop industry projects. Further, it is recommended to conduct a case study for more nuanced implementation with specific components, for example, with or without vacuum or Maglev, and to consider quantifying social factors and use-cases’ impact on smart cities development. Supplementary Materials: The following supporting information can be downloaded at https:// github.com/pirrencode/hpl _ smart _ cities, accessed on 14 June 2025. smart_city_gdp_model.py: Python Program for GDP calculation of Hyperloop on smart cities; /data/smc_input.csv: Input data Economies 2025,13, 228 16 of 20 for case studies; /data/smart_city_metrics_output_ppml.csv: Results of GDP calculation for case study scenarios. Author Contributions: Conceptualization, A.V. and Y.S.; methodology, A.V.; software, A.V.; validation, A.V.; formal analysis, A.V.; investigation, A.V.; resources, A.V.; data curation, A.V.; writing— original draft preparation, A.V. and Y.S.; writing—review and editing, A.V., Y.S., and O.Z.; visualization, A.V.; supervision, Y.S.; project administration, O.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). Conflicts of Interest: The authors declare no conflict of interest. References Abraham, A., Zhang, Y., & Prasad, S. (2024). Evacuation management framework towards smart city-wide intelligent emergency interactive response system. arXiv, arXiv:2403.07003. 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