Determinants of Urban Public Transportation Costs in a Rapidly Urbanizing African City: Evidence from Adama, Ethiopia

Determinants of Urban Public Transportation Costs in a Rapidly Urbanizing African City: Evidence from Adama, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Determinants of Urban Public Transportation Costs in a Rapidly Urbanizing African City: Evidence from Adama, Ethiopia Gemechis Sima, Assefa Belda, Ayalneh Demissie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8991578/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Urban transport affordability remains a major policy concern in rapidly urbanizing Sub-Saharan African cities. Despite extensive theoretical work on transport economics, empirical evidence quantifying structural determinants of public transportation cost in secondary African cities remains limited. This study examined the influence of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration on public transportation cost in Adama City, Ethiopia. A cross-sectional study was conducted among transport associations, driver training institutions, and regulatory officials (n = 181; response rate 88%). Data were analysed using multiple linear regression. The regression model was statistically significant (F (4,176) = 63.42, p < 0.001) and explained 59% of the variance in transportation cost (R² = 0.59; adjusted R² = 0.57). Economies of scale showed the strongest inverse association (β = −0.41, p < 0.001), followed by fuel and energy subsidy (β = −0.29, p < 0.01), road maintenance (β = −0.18, p < 0.05), and fare infrastructure integration (β = −0.16, p < 0.05). Structural and policy-level interventions targeting system scale, infrastructure quality, and coordinated fare systems may substantially reduce urban transport costs. The findings contribute empirical evidence from Ethiopia to the broader literature on urban transport economics. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Urban transport cost economies of scale fuel subsidy infrastructure maintenance fare integration Ethiopia Introduction Urban transportation systems are central to economic productivity, labor mobility, and social inclusion [ 1 – 3 ]. Efficient public transport reduces congestion externalities and enhances spatial equity, particularly in rapidly urbanizing regions [ 4 – 6 ]. However, many cities in low-income countries face rising transport costs driven by infrastructure deterioration, fuel price volatility, fragmented service provision, and limited system integration [ 7 – 9 ]. Transport economics theory emphasizes that cost structures in public transportation are strongly influenced by economies of scale, infrastructure condition, energy pricing policies, and operational coordination [ 1 , 8 , 19 ]. As service volume increases and fixed costs are distributed across larger passenger bases, average costs decline, generating scale efficiencies [ 8 , 19 ]. Infrastructure quality also affects operational cost through vehicle depreciation, fuel consumption, and maintenance expenditure [ 9 , 36 ]. Fuel and energy subsidies play a particularly important role in developing economies where transport systems are highly sensitive to global fuel price fluctuations [ 10 , 11 , 38 ]. While subsidies can enhance affordability, poorly designed mechanisms may generate fiscal burdens and inefficiencies [ 10 , 38 ]. Similarly, integrated fare systems reduce transaction costs, enhance revenue stability, and improve operational efficiency [ 12 , 26 , 37 ]. African cities face distinctive structural challenges in urban transport management [ 17 , 20 , 22 ]. Fragmented minibus systems, weak regulatory enforcement, and limited infrastructure financing often constrain efficiency improvements [ 20 , 22 ]. Ethiopia has implemented transport sector reforms and infrastructure expansion programs over the past two decades [ 13 , 14 ]; however, empirical analysis quantifying structural determinants of urban transport cost remains limited. This study addresses this gap by empirically examining the influence of economies of scale, road maintenance, fuel and energy subsidy, and fare infrastructure integration on public transportation cost in Adama City, Ethiopia. By grounding the analysis in established transport economics theory and contemporary empirical literature [ 1 , 8 , 19 , 35 ], the study contributes evidence from a rapidly urbanizing secondary African city. Literature Review Economies of Scale in Public Transport Classical transport economics suggests that public transport exhibits decreasing average cost when operational scale increases [ 1 , 8 ]. Empirical studies in European and Asian systems confirm that high-capacity networks achieve cost efficiency through optimized fleet size and passenger density [ 19 , 35 ]. In developing countries, fragmented and low-capacity systems often prevent realization of scale efficiencies [ 17 , 20 ]. Infrastructure Quality and Operational Cost Infrastructure deterioration increases vehicle operating costs through greater fuel consumption, higher repair frequency, and increased depreciation [ 9 , 36 ]. Road investment therefore functions as both a mobility enhancer and cost-control strategy [ 16 , 36 ]. Evidence from emerging economies demonstrates that poor road quality significantly elevates transport system expenditures [ 9 ]. Fuel Subsidy and Energy Pricing Fuel pricing mechanisms directly influence transport fares and cost structures [ 10 , 11 ]. In African markets, subsidy reforms have had measurable impacts on transport affordability [ 38 ]. While subsidies may reduce short-term costs, long-term fiscal sustainability and efficiency considerations must be balanced [ 10 ]. Fare Integration and Operational Efficiency Integrated fare systems enhance coordination across modes and reduce transaction inefficiencies [ 12 , 26 ]. Empirical evidence from Latin American and European systems shows that fare integration improves cost recovery and reduces fragmentation [ 26 , 37 ]. Despite this growing global literature, limited quantitative studies have examined how these determinants jointly influence public transportation cost in Sub-Saharan secondary cities. This study responds to this empirical gap. Results Socio-demographic Characteristics of Respondents A total of 181 respondents were included in the final analysis, yielding a response rate of 88%. The majority of participants were male (n = 161, 89%), while females accounted for 11% (n = 20). Regarding age distribution, 44.4% (n = 80) were between 31 and 40 years, followed by 33.4% (n = 60) aged 41–50 years. Only 11% (n = 20) were within the 20–30 age category. This age structure suggests that the urban transport workforce in Adama is predominantly composed of economically active middle-aged individuals with substantial operational experience. In terms of educational attainment, 58% (n = 105) had certificate-level or lower qualifications, 27% (n = 49) held diplomas, and 15% (n = 27) possessed degree-level or higher education. The educational profile reflects the operational and vocational nature of the urban transport sector, where formal academic training beyond diploma level remains limited (Table 1 ). Table 1 Socio-demographic characteristics of respondents involved in urban public transportation in Adama City, Ethiopia (n = 181) Variable Category Frequency (n) Percentage (%) Sex Male 161 89.0 Female 20 11.0 Age (years) 20–30 20 11.0 31–40 80 44.4 41–50 60 33.4 > 50 21 11.6 Education ≤ Certificate 105 58.0 Diploma 49 27.0 Degree and above 27 15.0 Multiple Linear Regression Analysis of Determinants of Public Transportation Cost Multiple linear regression analysis was conducted to examine the independent effects of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration on public transportation cost. The overall model was statistically significant (F(4,176) = 63.42, p < 0.001), indicating that the set of predictors collectively explains a significant proportion of variability in transportation cost. The model demonstrated strong explanatory power, accounting for 59% of the variance in public transportation cost (R² = 0.59; adjusted R² = 0.57). This indicates that structural determinants substantially contribute to cost variation within the urban transport system. Economies of scale emerged as the strongest predictor of transportation cost (β = −0.41, 95% CI: −0.52 to − 0.30, p < 0.001). The negative coefficient indicates that improvements in operational scale—such as higher passenger capacity and optimized fleet management—are associated with significant reductions in perceived transport cost. Fuel and energy subsidy also showed a statistically significant inverse association with transportation cost (β = −0.29, 95% CI: −0.41 to − 0.17, p < 0.01). This finding suggests that stable and supportive energy pricing mechanisms contribute to cost containment within the urban transport sector. Road maintenance and upkeep demonstrated a moderate but statistically significant negative association with transportation cost (β = −0.18, 95% CI: −0.30 to − 0.06, p < 0.05). Improved road conditions appear to reduce vehicle operating expenses and maintenance burdens, thereby lowering overall system costs. Fare infrastructure integration was also significantly associated with reduced transportation cost (β = −0.16, 95% CI: −0.28 to − 0.04, p < 0.05). The findings suggest that integrated fare systems and coordinated service structures may reduce operational inefficiencies and transaction costs. Diagnostic testing confirmed that multicollinearity was not a concern, with VIF values ranging between 1.34 and 2.12. Residual analysis indicated normal distribution (Shapiro–Wilk p = 0.21), and the Breusch–Pagan test showed no evidence of heteroskedasticity (p = 0.32), supporting the robustness of the regression model (Table 2 ). Table 2 Multiple linear regression analysis of structural determinants of public transportation cost in Adama City, Ethiopia (n = 181) Predictor Variable Unstandardized β Standard Error Standardized β 95% CI p-value EOS −0.41 0.05 −0.48 −0.52 to − 0.30 < 0.001 FES −0.29 0.06 −0.33 −0.41 to − 0.17 0.002 RMU −0.18 0.06 −0.19 −0.30 to − 0.06 0.014 FII −0.16 0.06 −0.17 −0.28 to − 0.04 0.021 Model statistics: R² = 0.59; Adjusted R² = 0.57; F (4,176) = 63.42; p < 0.001. Discussion The findings confirm that structural determinants significantly influence urban transport cost, consistent with classical and contemporary transport economics theory [1, 8,19]. The strong effect of economies of scale supports empirical evidence demonstrating that high-capacity systems reduce per-unit operational cost [19, 35]. Fuel subsidy impact aligns with macroeconomic research showing energy pricing reforms directly influence urban mobility affordability [10, 38]. Infrastructure maintenance findings reinforce evidence that deteriorated roads increase operational expenses [9, 36]. Fare integration effects correspond with international case studies demonstrating efficiency gains through coordinated ticketing systems [26, 37]. The 59% explained variance indicates that system-level reforms, rather than incremental operational adjustments, are necessary to achieve meaningful cost reduction. Limitations The cross-sectional design limits causal inference. Self-reported measures may introduce perception bias. The study focused on a single city, limiting generalizability. Objective financial cost data were not directly incorporated. Policy Implications The findings indicate that structural reform rather than incremental operational adjustment is required to meaningfully reduce urban transport costs. Policymakers should prioritize high-capacity coordinated systems that enhance economies of scale, implement fiscally responsible energy pricing mechanisms, expand preventive road maintenance investment, and introduce integrated fare management systems. Such interventions could substantially improve affordability and system sustainability in rapidly urbanizing secondary cities. Promote high-capacity, coordinated transport systems. Implement targeted and fiscally sustainable fuel subsidy reforms. Increase budget allocation for preventive road maintenance. Develop integrated digital fare systems to reduce fragmentation. Conclusion Public transportation cost in Adama City is significantly influenced by structural and policy-level determinants. Economies of scale and energy pricing mechanisms exert the strongest effects, while infrastructure quality and fare integration contribute meaningfully. The findings provide empirical evidence to inform sustainable urban mobility policy in Sub-Saharan African cities. Methods Study Design and Setting A facility-based cross-sectional study was conducted between January and March 2024 in Adama City, Oromia Region, Ethiopia. Adama is a rapidly urbanizing commercial hub strategically located along the Addis Ababa–Djibouti corridor and represents a dynamic urban transport environment characterized by mixed formal and semi-formal transit systems. The city is a major commercial hub located 90 km southeast of Addis Ababa and serves as a transport corridor linking central and eastern Ethiopia [12]. Study Population The study included transportation associations, driver training institutions, and transport regulatory authorities. Variables Dependent Variable Public Transportation Cost (perceived cost burden) Independent Variables Economies of Scale Road Maintenance and Upkeep Fuel and Energy Subsidy Fare Infrastructure and Integration Variables were measured using structured Likert-scale instruments. Sample size and sampling Procedure Sample size was calculated using Yamane’s formula (1967): n = N / (1 + N(e²)) With a 5% margin of error and population frame obtained from the Adama Transport Authority, the calculated sample was 206. Sampling Procedure The target population included registered transport associations, licensed driver training institutions, and regulatory officials. A stratified sampling technique was applied to ensure proportional representation. The computed sample size was 206. Of these, 181 completed questionnaires were retained for analysis after excluding incomplete responses, yielding a response rate of 88%. Data Collection Instrument Data were collected using a structured, self-administered questionnaire developed from established transport economics frameworks and prior empirical studies examining transport economics [1, 8, 19, 26]. The instrument comprised sections assessing demographic characteristics, economies of scale indicators, road maintenance and infrastructure quality, fuel and energy subsidy mechanisms, fare integration systems, and perceived public transportation cost. All analytical constructs were measured using five-point Likert scales ranging from strongly disagree to strongly agree. Example a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Constructs measured economies of scale, road maintenance and upkeep, fuel and energy subsidy, fare infrastructure integration, and perceived public transportation cost using five-point Likert scales. Reliability and Quality Assurance Internal consistency reliability was assessed using Cronbach’s alpha. The coefficient was 0.82 for economies of scale, 0.79 for road maintenance and upkeep, 0.85 for fuel and energy subsidy, 0.77 for fare infrastructure integration, and 0.88 for the transportation cost scale. All values exceeded the acceptable reliability threshold of 0.70, indicating satisfactory internal consistency. Prior to the main survey, the instrument was pre-tested on 5% of the calculated sample in a comparable neighbouring town to ensure clarity and contextual relevance. Data collectors received standardized training on ethical procedures and questionnaire administration. Completed questionnaires were checked for completeness at the point of collection, and data were double-entered into SPSS version 26 to minimize entry errors and ensure consistency. Statistical Analysis and Model Diagnostics Descriptive statistics were computed to summarize respondent characteristics. Pearson correlation coefficients were examined to assess bivariate associations among variables. Multiple linear regression analysis was conducted to evaluate the independent contribution of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration to public transportation cost. Model fitness was assessed using the coefficient of determination (R²) and adjusted R². The overall significance of the regression model was evaluated using the ANOVA F-test. Multicollinearity was assessed using Variance Inflation Factor (VIF), with values below 5 considered acceptable. Residual normality was examined using the Shapiro–Wilk test, and homoscedasticity was evaluated using the Breusch–Pagan test. Statistical significance was established at p < 0.05. Multiple linear regression model specification: PTC = β₀ + β₁ (EOS) + β₂ (RMU) + β₃ (FES) + β₄ (FII) + ε Where: β₀ = the constant, the rest β 1 to β 4 were the changes associated with the IDV PTC= public transportation cost EOS = economies of scale RMU = road maintenance and upkeep FES = fuel and energy subsidy FII = fare infrastructure integration ε = error term Declarations Ethical Approval and Consent to Participate Ethical clearance for this study was granted by the Ethical Review Committee of the faculty of post graduate study, East Africa College, (Protocol/ID: EAC/Ada/1537/2024). The study was conducted in accordance with the Declaration of Helsinki. Prior to data collection, all participants were provided with a comprehensive explanation regarding the study's objectives, the nature of their involvement, and their right to withdraw at any stage without prejudice. Following this briefing, informed written consent was obtained from each participant. To maintain strict confidentiality and privacy, all interviews were conducted in dedicated private rooms within the hospital. Data were anonymized at the point of collection using unique identification codes, and no personal identifiers were recorded in the final dataset. Data availability statement All data generated or analyzed during this study are included in this published article. Any additional datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgements The authors would like to thank the Adama City Transport Authority, participating transport associations, and all respondents for their cooperation and valuable contributions to this study. Authors’ Contributions GS conceptualized the study, designed the methodology, curated the data, performed statistical analysis, interpreted the results, and drafted the manuscript. AD provided expertise in public health and served as the corresponding author. AB and AD performed validation and reviewed the manuscript for critical intellectual content.. All authors reviewed and approved the final manuscript. Consent for Publication Not applicable. The manuscript does not contain any individual person’s identifiable data. Competing Interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Small, K. A. & Verhoef, E. T. The Economics of Urban Transportation . (Routledge, 2007) . Vuchic, V. R. Urban Transit Systems and Technology . (Wiley, 2005) . Rodrigue, J. P. & Notteboom, T. The geography of transport systems . 5th edn. (Routledge, 2020) . Levinson, D. Transportation and Economic Development. J. Transp. Geogr. 68 , 157–165 (2018). Hernández, D. & Titheridge, H. Mobilities and poverty nexus: urban transport barriers. J. Transp. Geogr. 54 , 334–345 (2016). World Bank. Africa Urban Transport Report . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8991578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606527262,"identity":"b8be8a64-2fec-41f6-b9c5-4977b0f74b63","order_by":0,"name":"Gemechis Sima","email":"","orcid":"","institution":"East Africa College, faculty of post graduate studies","correspondingAuthor":false,"prefix":"","firstName":"Gemechis","middleName":"","lastName":"Sima","suffix":""},{"id":606527263,"identity":"99a58274-2c29-4522-bb48-033b4915417e","order_by":1,"name":"Assefa Belda","email":"","orcid":"","institution":"East Africa College, faculty of post graduate studies","correspondingAuthor":false,"prefix":"","firstName":"Assefa","middleName":"","lastName":"Belda","suffix":""},{"id":606527264,"identity":"ad717af8-b6d8-4f06-9cce-4de5656adfc1","order_by":2,"name":"Ayalneh Demissie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYHACxgMMDAcYGxiAiMGAQQ4kdOABAT0oWozBIgnEaYGARDADnxb+GckPDhdU3JHtFzvc/PFHQV36/LDDD4G22MnpNmDXInEjzeDwjDPPjGfOTmyT5jE4nLvxdpoBUEuysdkBHNbcSDA4zNt2OHHD7cQ2ZgaDA7kbZyeAtBxI3IZDi/yN9A+Hef8dTtx/OxHoMIO6dMPZ6R/wajG4kQO0pQFoi3RigwSPAXOCvHQOflsMz7wpOMxz7LDxjNsQvxhukM4pOJBggNsvcsfTNz7mqTks2z87/fHHH3/q5OVnp2/+8KHCTg6n9wUS0J0KVmmAQzkI8KObJd+AR/UoGAWjYBSMSAAAigNwE/rQXSwAAAAASUVORK5CYII=","orcid":"","institution":"Arsi University College of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ayalneh","middleName":"","lastName":"Demissie","suffix":""}],"badges":[],"createdAt":"2026-02-27 21:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8991578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8991578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104790918,"identity":"d116c2d1-3da9-40e6-b692-0afecacc00ad","added_by":"auto","created_at":"2026-03-17 08:35:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":768135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8991578/v1/d3dd00bb-1d20-4b7a-b9ce-0b4dfae30fb1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Urban Public Transportation Costs in a Rapidly Urbanizing African City: Evidence from Adama, Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban transportation systems are central to economic productivity, labor mobility, and social inclusion [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Efficient public transport reduces congestion externalities and enhances spatial equity, particularly in rapidly urbanizing regions [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, many cities in low-income countries face rising transport costs driven by infrastructure deterioration, fuel price volatility, fragmented service provision, and limited system integration [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTransport economics theory emphasizes that cost structures in public transportation are strongly influenced by economies of scale, infrastructure condition, energy pricing policies, and operational coordination [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As service volume increases and fixed costs are distributed across larger passenger bases, average costs decline, generating scale efficiencies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Infrastructure quality also affects operational cost through vehicle depreciation, fuel consumption, and maintenance expenditure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuel and energy subsidies play a particularly important role in developing economies where transport systems are highly sensitive to global fuel price fluctuations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While subsidies can enhance affordability, poorly designed mechanisms may generate fiscal burdens and inefficiencies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, integrated fare systems reduce transaction costs, enhance revenue stability, and improve operational efficiency [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfrican cities face distinctive structural challenges in urban transport management [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Fragmented minibus systems, weak regulatory enforcement, and limited infrastructure financing often constrain efficiency improvements [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Ethiopia has implemented transport sector reforms and infrastructure expansion programs over the past two decades [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; however, empirical analysis quantifying structural determinants of urban transport cost remains limited.\u003c/p\u003e \u003cp\u003eThis study addresses this gap by empirically examining the influence of economies of scale, road maintenance, fuel and energy subsidy, and fare infrastructure integration on public transportation cost in Adama City, Ethiopia. By grounding the analysis in established transport economics theory and contemporary empirical literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the study contributes evidence from a rapidly urbanizing secondary African city.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEconomies of Scale in Public Transport\u003c/h2\u003e \u003cp\u003eClassical transport economics suggests that public transport exhibits decreasing average cost when operational scale increases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Empirical studies in European and Asian systems confirm that high-capacity networks achieve cost efficiency through optimized fleet size and passenger density [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In developing countries, fragmented and low-capacity systems often prevent realization of scale efficiencies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInfrastructure Quality and Operational Cost\u003c/h3\u003e\n\u003cp\u003eInfrastructure deterioration increases vehicle operating costs through greater fuel consumption, higher repair frequency, and increased depreciation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Road investment therefore functions as both a mobility enhancer and cost-control strategy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Evidence from emerging economies demonstrates that poor road quality significantly elevates transport system expenditures [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eFuel Subsidy and Energy Pricing\u003c/h3\u003e\n\u003cp\u003eFuel pricing mechanisms directly influence transport fares and cost structures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In African markets, subsidy reforms have had measurable impacts on transport affordability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While subsidies may reduce short-term costs, long-term fiscal sustainability and efficiency considerations must be balanced [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eFare Integration and Operational Efficiency\u003c/h3\u003e\n\u003cp\u003eIntegrated fare systems enhance coordination across modes and reduce transaction inefficiencies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Empirical evidence from Latin American and European systems shows that fare integration improves cost recovery and reduces fragmentation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this growing global literature, limited quantitative studies have examined how these determinants jointly influence public transportation cost in Sub-Saharan secondary cities. This study responds to this empirical gap.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic Characteristics of Respondents\u003c/h2\u003e \u003cp\u003eA total of 181 respondents were included in the final analysis, yielding a response rate of 88%. The majority of participants were male (n\u0026thinsp;=\u0026thinsp;161, 89%), while females accounted for 11% (n\u0026thinsp;=\u0026thinsp;20). Regarding age distribution, 44.4% (n\u0026thinsp;=\u0026thinsp;80) were between 31 and 40 years, followed by 33.4% (n\u0026thinsp;=\u0026thinsp;60) aged 41\u0026ndash;50 years. Only 11% (n\u0026thinsp;=\u0026thinsp;20) were within the 20\u0026ndash;30 age category. This age structure suggests that the urban transport workforce in Adama is predominantly composed of economically active middle-aged individuals with substantial operational experience.\u003c/p\u003e \u003cp\u003eIn terms of educational attainment, 58% (n\u0026thinsp;=\u0026thinsp;105) had certificate-level or lower qualifications, 27% (n\u0026thinsp;=\u0026thinsp;49) held diplomas, and 15% (n\u0026thinsp;=\u0026thinsp;27) possessed degree-level or higher education. The educational profile reflects the operational and vocational nature of the urban transport sector, where formal academic training beyond diploma level remains limited (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of respondents involved in urban public transportation in Adama City, Ethiopia (n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le; Certificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiple Linear Regression Analysis of Determinants of Public Transportation Cost\u003c/h3\u003e\n\u003cp\u003eMultiple linear regression analysis was conducted to examine the independent effects of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration on public transportation cost. The overall model was statistically significant (F(4,176)\u0026thinsp;=\u0026thinsp;63.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the set of predictors collectively explains a significant proportion of variability in transportation cost.\u003c/p\u003e \u003cp\u003eThe model demonstrated strong explanatory power, accounting for 59% of the variance in public transportation cost (R\u0026sup2; = 0.59; adjusted R\u0026sup2; = 0.57). This indicates that structural determinants substantially contribute to cost variation within the urban transport system.\u003c/p\u003e \u003cp\u003eEconomies of scale emerged as the strongest predictor of transportation cost (β = \u0026minus;0.41, 95% CI: \u0026minus;0.52 to \u0026minus;\u0026thinsp;0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The negative coefficient indicates that improvements in operational scale\u0026mdash;such as higher passenger capacity and optimized fleet management\u0026mdash;are associated with significant reductions in perceived transport cost.\u003c/p\u003e \u003cp\u003eFuel and energy subsidy also showed a statistically significant inverse association with transportation cost (β = \u0026minus;0.29, 95% CI: \u0026minus;0.41 to \u0026minus;\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding suggests that stable and supportive energy pricing mechanisms contribute to cost containment within the urban transport sector.\u003c/p\u003e \u003cp\u003eRoad maintenance and upkeep demonstrated a moderate but statistically significant negative association with transportation cost (β = \u0026minus;0.18, 95% CI: \u0026minus;0.30 to \u0026minus;\u0026thinsp;0.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Improved road conditions appear to reduce vehicle operating expenses and maintenance burdens, thereby lowering overall system costs.\u003c/p\u003e \u003cp\u003eFare infrastructure integration was also significantly associated with reduced transportation cost (β = \u0026minus;0.16, 95% CI: \u0026minus;0.28 to \u0026minus;\u0026thinsp;0.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The findings suggest that integrated fare systems and coordinated service structures may reduce operational inefficiencies and transaction costs.\u003c/p\u003e \u003cp\u003eDiagnostic testing confirmed that multicollinearity was not a concern, with VIF values ranging between 1.34 and 2.12. Residual analysis indicated normal distribution (Shapiro\u0026ndash;Wilk p\u0026thinsp;=\u0026thinsp;0.21), and the Breusch\u0026ndash;Pagan test showed no evidence of heteroskedasticity (p\u0026thinsp;=\u0026thinsp;0.32), supporting the robustness of the regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis of structural determinants of public transportation cost in Adama City, Ethiopia (n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.52 to \u0026minus;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.41 to \u0026minus;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.30 to \u0026minus;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.28 to \u0026minus;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel statistics: R\u0026sup2; = 0.59; Adjusted R\u0026sup2; = 0.57; F (4,176)\u0026thinsp;=\u0026thinsp;63.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings confirm that structural determinants significantly influence urban transport cost, consistent with classical and contemporary transport economics theory [1, 8,19]. The strong effect of economies of scale supports empirical evidence demonstrating that high-capacity systems reduce per-unit operational cost [19, 35].\u003c/p\u003e\n\u003cp\u003eFuel subsidy impact aligns with macroeconomic research showing energy pricing reforms directly influence urban mobility affordability [10, 38]. Infrastructure maintenance findings reinforce evidence that deteriorated roads increase operational expenses [9, 36]. Fare integration effects correspond with international case studies demonstrating efficiency gains through coordinated ticketing systems [26, 37].\u003c/p\u003e\n\u003cp\u003eThe 59% explained variance indicates that system-level reforms, rather than incremental operational adjustments, are necessary to achieve meaningful cost reduction.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThe cross-sectional design limits causal inference. Self-reported measures may introduce perception bias. The study focused on a single city, limiting generalizability. Objective financial cost data were not directly incorporated.\u003c/p\u003e\n\u003cp\u003ePolicy Implications\u003c/p\u003e\n\u003cp\u003eThe findings indicate that structural reform rather than incremental operational adjustment is required to meaningfully reduce urban transport costs. Policymakers should prioritize high-capacity coordinated systems that enhance economies of scale, implement fiscally responsible energy pricing mechanisms, expand preventive road maintenance investment, and introduce integrated fare management systems. Such interventions could substantially improve affordability and system sustainability in rapidly urbanizing secondary cities.\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003ePromote high-capacity, coordinated transport systems.\u003c/li\u003e\n \u003cli\u003eImplement targeted and fiscally sustainable fuel subsidy reforms.\u003c/li\u003e\n \u003cli\u003eIncrease budget allocation for preventive road maintenance.\u003c/li\u003e\n \u003cli\u003eDevelop integrated digital fare systems to reduce fragmentation.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePublic transportation cost in Adama City is significantly influenced by structural and policy-level determinants. Economies of scale and energy pricing mechanisms exert the strongest effects, while infrastructure quality and fare integration contribute meaningfully. The findings provide empirical evidence to inform sustainable urban mobility policy in Sub-Saharan African cities.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design and Setting\u003c/h3\u003e\n\u003cp\u003eA facility-based cross-sectional study was conducted between January and March 2024 in Adama City, Oromia Region, Ethiopia. Adama is a rapidly urbanizing commercial hub strategically located along the Addis Ababa\u0026ndash;Djibouti corridor and represents a dynamic urban transport environment characterized by mixed formal and semi-formal transit systems. The city is a major commercial hub located 90 km southeast of Addis Ababa and serves as a transport corridor linking central and eastern Ethiopia [12].\u003c/p\u003e\n\u003ch3\u003eStudy Population\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe study included transportation associations, driver training institutions, and transport regulatory authorities.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003ch3\u003eDependent Variable\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003ePublic Transportation Cost (perceived cost burden)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cul\u003e\n \u003cli\u003eEconomies of Scale\u003c/li\u003e\n \u003cli\u003eRoad Maintenance and Upkeep\u003c/li\u003e\n \u003cli\u003eFuel and Energy Subsidy\u003c/li\u003e\n \u003cli\u003eFare Infrastructure and Integration\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVariables were measured using structured Likert-scale instruments.\u003c/p\u003e\n\u003ch3\u003eSample size and sampling Procedure\u003c/h3\u003e\n\u003cp\u003eSample size was calculated using Yamane\u0026rsquo;s formula (1967):\u003c/p\u003e\n\u003cp\u003en = N / (1 + N(e\u0026sup2;))\u003c/p\u003e\n\u003cp\u003eWith a 5% margin of error and population frame obtained from the Adama Transport Authority, the calculated sample was 206.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSampling Procedure\u003c/h3\u003e\n\u003cp\u003eThe target population included registered transport associations, licensed driver training institutions, and regulatory officials. A stratified sampling technique was applied to ensure proportional representation. The computed sample size was 206. Of these, 181 completed questionnaires were retained for analysis after excluding incomplete responses, yielding a response rate of 88%.\u003c/p\u003e\n\u003ch3\u003eData Collection Instrument\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured, self-administered questionnaire developed from established transport economics frameworks and prior empirical studies examining transport economics [1, 8, 19, 26]. The instrument comprised sections assessing demographic characteristics, economies of scale indicators, road maintenance and infrastructure quality, fuel and energy subsidy mechanisms, fare integration systems, and perceived public transportation cost. All analytical constructs were measured using five-point Likert scales ranging from strongly disagree to strongly agree. Example a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Constructs measured economies of scale, road maintenance and upkeep, fuel and energy subsidy, fare infrastructure integration, and perceived public transportation cost using five-point Likert scales.\u003c/p\u003e\n\u003ch3\u003eReliability and Quality Assurance\u003c/h3\u003e\n\u003cp\u003eInternal consistency reliability was assessed using Cronbach\u0026rsquo;s alpha. The coefficient was 0.82 for economies of scale, 0.79 for road maintenance and upkeep, 0.85 for fuel and energy subsidy, 0.77 for fare infrastructure integration, and 0.88 for the transportation cost scale. All values exceeded the acceptable reliability threshold of 0.70, indicating satisfactory internal consistency. Prior to the main survey, the instrument was pre-tested on 5% of the calculated sample in a comparable neighbouring town to ensure clarity and contextual relevance. Data collectors received standardized training on ethical procedures and questionnaire administration. Completed questionnaires were checked for completeness at the point of collection, and data were double-entered into SPSS version 26 to minimize entry errors and ensure consistency.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis and Model Diagnostics\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics were computed to summarize respondent characteristics. Pearson correlation coefficients were examined to assess bivariate associations among variables. Multiple linear regression analysis was conducted to evaluate the independent contribution of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration to public transportation cost. Model fitness was assessed using the coefficient of determination (R\u0026sup2;) and adjusted R\u0026sup2;. The overall significance of the regression model was evaluated using the ANOVA F-test. Multicollinearity was assessed using Variance Inflation Factor (VIF), with values below 5 considered acceptable. Residual normality was examined using the Shapiro\u0026ndash;Wilk test, and homoscedasticity was evaluated using the Breusch\u0026ndash;Pagan test. Statistical significance was established at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eMultiple linear regression model specification:\u003c/p\u003e\n\u003cp\u003ePTC = \u0026beta;₀ + \u0026beta;₁ (EOS) + \u0026beta;₂ (RMU) + \u0026beta;₃ (FES) + \u0026beta;₄ (FII) + \u0026epsilon;\u003c/p\u003e\n\u003cp\u003eWhere:\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u0026beta;₀ \u0026nbsp;= the constant, the rest \u0026beta;\u003csub\u003e1\u003c/sub\u003e to \u0026beta;\u003csub\u003e4\u003c/sub\u003e were the changes associated with the IDV\u003c/li\u003e\n \u003cli\u003ePTC= public transportation cost\u003c/li\u003e\n \u003cli\u003eEOS = economies of scale\u003c/li\u003e\n \u003cli\u003eRMU = road maintenance and upkeep\u003c/li\u003e\n \u003cli\u003eFES = fuel and energy subsidy\u003c/li\u003e\n \u003cli\u003eFII = fare infrastructure integration\u003c/li\u003e\n \u003cli\u003e\u0026epsilon; = error term\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthical Approval and Consent to Participate\u003c/h3\u003e\n\u003cp\u003eEthical clearance for this study was granted by the Ethical Review Committee of the faculty of post graduate study, East Africa College, (Protocol/ID: EAC/Ada/1537/2024). The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003ePrior to data collection, all participants were provided with a comprehensive explanation regarding the study's objectives, the nature of their involvement, and their right to withdraw at any stage without prejudice. Following this briefing, informed written consent was obtained from each participant.\u003c/p\u003e\n\u003cp\u003eTo maintain strict confidentiality and privacy, all interviews were conducted in dedicated private rooms within the hospital. Data were anonymized at the point of collection using unique identification codes, and no personal identifiers were recorded in the final dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. Any additional datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Adama City Transport Authority, participating transport associations, and all respondents for their cooperation and valuable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGS\u0026nbsp;\u003c/strong\u003econceptualized the study, designed the methodology, curated the data, performed statistical analysis, interpreted the results, and drafted the manuscript. \u003cstrong\u003eAD\u003c/strong\u003e provided expertise in public health and served as the corresponding author. \u003cstrong\u003eAB\u003c/strong\u003e and \u003cstrong\u003eAD\u0026nbsp;\u003c/strong\u003eperformed validation and reviewed the manuscript for critical intellectual content.. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain any individual person\u0026rsquo;s identifiable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSmall, K. A. \u0026amp; Verhoef, E. T. \u003cem\u003eThe Economics of Urban Transportation\u003c/em\u003e. (Routledge, 2007) .\u003c/li\u003e\n \u003cli\u003eVuchic, V. R. \u003cem\u003eUrban Transit Systems and Technology\u003c/em\u003e. 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(Washington DC, 2022) \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban transport cost, economies of scale, fuel subsidy, infrastructure maintenance, fare integration, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8991578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8991578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban transport affordability remains a major policy concern in rapidly urbanizing Sub-Saharan African cities. Despite extensive theoretical work on transport economics, empirical evidence quantifying structural determinants of public transportation cost in secondary African cities remains limited. This study examined the influence of economies of scale, road maintenance and upkeep, fuel and energy subsidy, and fare infrastructure integration on public transportation cost in Adama City, Ethiopia. A cross-sectional study was conducted among transport associations, driver training institutions, and regulatory officials (n = 181; response rate 88%). Data were analysed using multiple linear regression. The regression model was statistically significant (F (4,176) = 63.42, p \u0026lt; 0.001) and explained 59% of the variance in transportation cost (R² = 0.59; adjusted R² = 0.57). Economies of scale showed the strongest inverse association (β = −0.41, p \u0026lt; 0.001), followed by fuel and energy subsidy (β = −0.29, p \u0026lt; 0.01), road maintenance (β = −0.18, p \u0026lt; 0.05), and fare infrastructure integration (β = −0.16, p \u0026lt; 0.05). Structural and policy-level interventions targeting system scale, infrastructure quality, and coordinated fare systems may substantially reduce urban transport costs. The findings contribute empirical evidence from Ethiopia to the broader literature on urban transport economics.\u003c/p\u003e","manuscriptTitle":"Determinants of Urban Public Transportation Costs in a Rapidly Urbanizing African City: Evidence from Adama, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 08:34:52","doi":"10.21203/rs.3.rs-8991578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T08:22:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T04:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190021307864055935087740324662280837192","date":"2026-03-31T01:48:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219901885498002349263665924464071457933","date":"2026-03-28T04:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206108103280092047705961207893708462122","date":"2026-03-28T04:20:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T02:14:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T10:58:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T09:39:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T09:39:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-27T21:04:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7546bc53-331b-49b8-a7f9-7505b3720bcd","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64539586,"name":"Business and commerce/Economics"},{"id":64539587,"name":"Social science/Economics"},{"id":64539588,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":64539589,"name":"Scientific community and society/Geography"},{"id":64539590,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-05-15T21:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 08:34:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8991578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8991578","identity":"rs-8991578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}