Fare determination in public transport systems is traditionally grounded in cost-recovery and affordability principles. Cost-based approaches emphasize the need to cover operational and capital expenditures, including fuel, labor, maintenance, and depreciation [3]. However, purely cost-recovery models often conflict with social equity objectives, particularly in low-income contexts [5]. As a result, hybrid models incorporating subsidies and cross-financing mechanisms have emerged [4]. Distance-based and flat-fare systems reflect different policy priorities [22]. In developing countries, fare principles are often shaped by informal practices [1]. Furthermore, pricing strategies are increasingly designed to maintain financial sustainability under changing economic conditions [26]. The findings of [27] on distance-based fare determination shows that travel distance has a consistent diminishing return to fare rate in Accra and Dar es Salaam as indicated in Figure 1Figure 1. The authors implied that longer travel distances are associated with lower fare rates and vice versa. This finding does not provide fairness on traveler’s transit fare computations.

(a) (b)

Source: https://ccsenet.org/journal/index.php/jsd/article/view/0/51479.

Figure 1. Distance-based fare rate based on scatter plot results [27]. (a) Accra - Ghana; (b) Dar es Salaam - Tanzania.

Analytical methods for fare setting have evolved from simple accounting-based calculations to more advanced quantitative and decision-support models. Traditional approaches rely on cost allocation and break-even analysis to determine minimum viable fares [3]. More recent studies incorporate decision-support frameworks and stakeholder-driven approaches to inform fare policy design [25]. Simulation models and scenario analysis are increasingly used to assess the impacts of fare changes under varying conditions [24]. However, these methods often depend on high-quality data, which is limited in many developing countries [2]. Additionally, there is often a disconnect between analytical outputs and actual decision-making due to political and institutional constraints [26]. Thus, while analytical methods have advanced significantly, their practical application remains uneven.

Stakeholder dynamics play a critical role in shaping public transport fare decisions, particularly in contexts characterized by fragmented governance. Key stakeholders typically include government agencies, transport operators, unions, and passengers, each with competing interests [1]. Operators often advocate for higher fares to cover rising costs, while users resist increases due to affordability concerns [5]. Government authorities are frequently tasked with balancing efficiency and equity considerations in fare policy design [4]. In many developing countries, informal transport unions wield significant influence over fare negotiations, often leading to non-transparent outcomes [2]. Power asymmetries among stakeholders can result in decisions that favor dominant groups, undermining equity and accountability [25]. Moreover, limited structured engagement mechanisms contribute to conflicts and delays in fare adjustments [26]. The literature highlights the need for more inclusive and institutionalized stakeholder engagement mechanisms.

Computational approaches to fare setting have gained prominence with advances in data analytics and optimization techniques. Mathematical programming models are used to determine fare structures that balance efficiency and financial sustainability under given constraints [3]. Advanced optimization and data-driven approaches enable more adaptive and responsive pricing strategies [24]. Stakeholder-oriented and decision-support models are also applied to evaluate policy outcomes in complex transport systems [25]. However, the adoption of computational methods is limited in many developing countries due to data scarcity and technical capacity constraints [2]. Additionally, concerns about transparency and interpretability of analytical outputs remain significant [26]. Despite these challenges, computational approaches offer promising avenues for improving evidence-based fare determination.

On the premises of fare settings, [6][7] treated the setting of fare as an optimization problem by maximizing revenue, passenger miles or social welfare which are all subject to budget constraints. While [6] used elasticity-based demand function to compute peak and off-peak prices, [7] used a linear demand function to compute fares for different modes. Though most transport fares are computed on distance-based approach for both inter-city and intra-city trips, transport operators and their unions consider other primary components such as oil price, exchange rate and inflation on spare parts [19]. On cost determination, [30] and [31] show that operating costs (fuel, maintenance, labor), capital costs, and demand levels are central to fare calculation. This is further discussed under section 4.1.3.

In a quest to harness an all-inclusive public transport system, some stakeholders will hold offenses if they feel it does not lure to their benefit. For example, the changing of differentiated-fare structure system to a flat-fare structure system to provide low-cost transportation to lower income earners as well as simplify fare collection sparked transit fiscal crises in the US [20]-[22].

Early and conceptual works such as Fare structures by [32] and [33] establish that fare systems are traditionally guided by principles of cost recovery, efficiency, and equity. These principles are operationalized through institutional arrangements involving regulators and operators, as further emphasized in the [34] report on fare integration, which highlights governance structures and coordination challenges. However, these studies largely reflect global practices, indicating a gap in documenting localized institutional evolution, particularly in contexts like Ghana.

Quantitative indicators such as cost revenue balance, demand elasticity, and ridership levels are frequently used to justify fare adjustments [35]. These studies rely heavily on econometric modeling and cost-based frameworks, suggesting that fare decisions are often grounded in measurable economic variables, though they may insufficiently account for social and equity considerations.

Methodologically, the literature demonstrates the use of statistical analysis, simulation models, and cost-based estimation techniques to inform fare adjustments [36][37]. Emerging work such as the [38] further illustrates the role of real-time data and digital tools in enhancing transparency and decision-making, pointing toward computational approaches for fare determination. Details of these literature findings are found in Table 2.

Stakeholder dynamics are implicitly addressed across studies. Government agencies, transport operators, and passengers are identified as key actors, with interactions often involving trade-offs between financial sustainability and affordability [31][39]. However, explicit analysis of consultation processes, perceived fairness, and compliance remains limited. Evidence suggests that fare changes can face resistance when perceived as inequitable or inadequately justified, especially in cases of fare increases or subsidy removal.

Overall, the literature highlights the importance of integrating cost data, demand analysis, and institutional coordination in fare setting while revealing gaps in stakeholder engagement and transparency. These gaps underscore the need for a data-driven, transparent computational framework to support evidence-based negotiations and improve trust in fare adjustment processes.

Literature consistently emphasizes that public transport fare setting is both an economic and institutional process, influenced by pricing principles and governance frameworks. Early conceptual work [32][33] establishes that fare structures are designed to balance cost recovery, efficiency, and equity, while accommodating demand variations. Fare setting is rarely arbitrary; it requires coordination among operators, regulators, and policymakers, highlighting the governance dimension.

Figure 3. Harzing’s publish or perish Google Scholar search interface.

Cost-based analyses, such as [30] and [31], illustrate that operating costs, capital expenditure, and demand projections are central to determining fares. SSRN studies [35][37] demonstrate the use of quantitative economic indicators such as cost revenue balance and elasticity of demand to justify pricing decisions. These approaches are embedded within institutional frameworks that regulate fare increases, subsidies, and integration across modes, as noted in [34] report on Poland’s fare integration.

Table 2.Data extraction matrix for transport fare settings.

Table 3. Data extraction matrix for transport fare collection system.

Table 4.Data extraction matrix for transport fare modeling.

Governance mechanisms are critical for ensuring transparency and coordination. The reviewed studies indicate that integrated fare systems require formal rules and institutional oversight, which reduce fragmentation and enable predictable, fair pricing. Emerging digital platforms like [38] exemplify how real-time data and interoperable systems can enhance governance by enabling evidence-based pricing and providing transparency to both operators and passengers.

However, the literature also highlights gaps. While cost and demand considerations are well documented, the evolution of institutional practices and local governance structures particularly in specific national contexts like Ghana remains underexplored. Likewise, there is limited empirical evidence on how governance arrangements affect the implementation and acceptance of fare policies. In sum, existing research underscores that effective transport pricing depends on robust governance, coordination among stakeholders, and integration of economic indicators, forming a foundation for more transparent and evidence-based fare policies.

Table 5.Data extraction matrix for equity in transport fare.

The evolution of fare collection systems has accelerated significantly over the past decade, shifting from manual and paper-based systems to sophisticated Automatic Fare Collection (AFC) platforms. As shown in Table 5, this body of literature spans multiple technological paradigms, including sensing technologies, digital payment platforms, blockchain, IoT, and machine-learning-driven analytics.

Early and foundational works describe AFC as a sociotechnical infrastructure that automates fare processing, improves revenue security, and reduces transaction time [40]. Subsequent empirical studies expand on this by assessing technological efficiency and feasibility. For example, NFC-based mobile fare systems demonstrate the advantages of smartphone-enabled ticketing [41], while Arduino and fingerprint-based prototypes highlight low-cost local innovations aimed at improving monitoring and reducing fare evasion [42].

A major trend across the literature is the move toward distance-based and sensor-supported AFC, where IoT technologies calculate fares dynamically [43][44]. These systems increase accuracy, reduce manual errors, and offer scalable alternatives to flat-fare structures. Similarly, high-security AFC models integrating encryption demonstrate the growing need for data protection in digital mobility environments [45]. More advanced literature emphasizes systemwide modernization and interoperability, especially in contexts such as Poland [34] and Belarus [46]. Surveys of AFC technologies globally also point to fragmentation across standards and the urgent need to harmonize solutions [47]. Emerging innovations include blockchain-based fare transactions [48] and digital-first fare media for Latin America [49]. Table 3 presents a collection of literature of fare collection systems.

Finally, AFC data has become a major analytical asset. Studies now use AFC datasets to model demand [50] and even infer trip purpose using comparative machine-learning approaches [51]. Overall, the literature demonstrates that AFC systems are transitioning from simple payment tools to data-driven, integrated mobility platforms central to modern public transport operations.

Public transport fare is a critical component in transit planning hence demands careful consideration from the viewpoints of both transit service providers, users and state-actors [10][22]. [10] indicated that fares are direct and flexible instruments in influencing passenger behavior and therefore cost recovery and setting fares is therefore a basic challenge for public transport operators. Meanwhile, [22] indicated that the major source of revenue for transit agencies is the fare collected from users. Transport fare is a direct and flexible instrument in influencing passenger’s behavioral and cost-minded recovery in the patronage of public transport services [10]. Fare setting is a primary problem for companies that operate public transport services.

According to [10], single/monthly ticket fare system seems to be more operator-friendly while the distance-dependent fare system seems to be more customer-oriented [9][10]. [9] proposed that distance-based fare seems to effectively alleviate the disparate impacts caused by a flat fare on low-income and minority households. [52] stipulated how woefully an automated fare collection system failed in five sub-Saharan African cities due to the changes in business model that will enable collective rather than individualized fleet management. [52] responded to passenger demands with appropriate data-driven service plans that maximize average load factors. This confirms how rationally different stakeholders of the public transport system demonstrate their respective interests and biases in respect to fare adjustments and increments.

Subsidy levels for public transport differ significantly across regions worldwide. In developed countries, these subsidies tend to be substantial, covering approximately 65% of operational costs in the largest 20 cities in the United States, 45% in the main 26 European cities, 60% in the top five Australian cities, and 40% in Toronto [3]. In some instances, cities have extended these policies further by implementing fully free public transport systems [53]. In contrast, subsidies are generally less prevalent in developing regions, particularly in Latin America, although notable exceptions exist. For example, subsidy levels reach about 50% in Buenos Aires, 40% in São Paulo, 40% in Santiago, and between 40% and 50% in Bogotá.

From a theoretical perspective, [54] examine optimal fare-setting strategies from a social welfare standpoint, incorporating externalities such as congestion, pollution, accidents, economies of scale, and adjustments in service provision by transit agencies. Their findings suggest that fare subsidies exceeding 50% of operating costs can enhance welfare in cities such as Washington, D.C., Los Angeles, and London. Building on this framework, [55] apply the same model to Bogotá and demonstrate that optimal subsidy levels can vary considerably, ranging from 20% to 100%, depending on key modeling assumptions such as demand elasticities and the extent of modal shifts from private vehicles to public buses as fares decrease. However, a key limitation of these models is their inability to simultaneously account for price changes across multiple transport modes. As a result, they may permit certain high-value trips to occur while also enabling trips that would not be undertaken if users were charged the full social marginal cost. [3] modelled a densely populated city center as shown in Figure 4Figure 4 using data from London and Santiago while simulating different policy scenarios. The authors found that subsidizing transport fares does not influence mode switch to public transport compared to bus dedicated lanes and congestion pricing.

Fuel costs have historically constituted the most immediate and dominant driver of public transport fares, both globally and within Ghana [56][57]. The sector’s heavy reliance on petroleum products makes fuel price fluctuations highly visible and directly transmissible to fare adjustments [56]. Empirical evidence across Sub-Saharan Africa shows that high fuel prices, inefficient and ageing vehicle fleets, and poor road conditions collectively elevate variable operating costs [57]. In Southern Africa, fuel alone accounts for approximately 40% - 50% of total operating costs [58]. Reflecting this centrality, Ghana’s fare-setting framework explicitly ties fare increases to fuel price movements, with agreed adjustments triggered when fuel prices rise by at least 10% [59].

As transport systems evolved, maintenance-related expenditures emerged as the next critical cost layer. Vehicle maintenance covering spare parts, tyres, and lubricants has become particularly burdensome in Ghana due to its import-dependent structure [60]. Rising inflation and persistently high spare part costs have sustained elevated fares even during periods of fuel price decline [61]. Notably, the weak responsiveness of spare part prices to exchange rate improvements suggests deeper structural inefficiencies in supply chains [62]. Comparative studies further indicate that operational inefficiencies, such as higher mechanical breakdown rates in Accra relative to Dar es Salaam, exacerbate cost pressures and influence fare-setting decisions [27].

Figure 4. Representative network basso Sotz & silva Montalva (2023) [3].

Labor costs, although significant, present a structural paradox within the African transport context. While the sector is labor-intensive, relatively low wages should theoretically reduce total costs. However, this advantage is offset by high variable costs, resulting in fare levels [57] comparable to those in developed economies like China [63]. In Ghana’s largely informal and owner-operated system, labor cost dynamics are further shaped by regulatory constraints and government-controlled fare regimes that prioritize social considerations over strict cost recovery [64].

Infrastructure conditions introduce an additional structural dimension by indirectly amplifying operating costs. Poor road networks increase fuel consumption, accelerate vehicle wear and tears, reduce tyre lifespan, and lower operational efficiency [65]. In Ghana, studies consistently link high transport costs to deteriorating road infrastructure, alongside factors such as informal charges, fuel price volatility, and rising input costs [1][2]. Policy responses, including internationally supported road improvement programs, have aimed to reduce vehicle operating costs and travel time as mechanisms for moderating transport fares. However, the effectiveness of these interventions remains constrained by institutional and market inefficiencies.

Finally, long-term structural pressures such as fleet ageing, capital replacement constraints, and rising overhead costs reinforce the persistence of high transport fares. Ageing vehicles increase fuel consumption and maintenance requirements, while limited access to finance constrains fleet renewal in many developing transport systems [1][2]. Concurrently, insurance costs, licensing fees, and statutory levies contribute to the cumulative cost burden. Evidence from Ghana indicates that fare adjustments are typically the outcome of negotiations between transport unions and government authorities, reflecting multiple interrelated cost components, particularly fuel and operational expenses [59].

This section examines analytical models used to support fare adjustment decisions in public transport systems. It highlights how quantitative approaches such as optimization, econometric, and simulation models capture cost dynamics, demand responses, and policy constraints. The section further evaluates their effectiveness in guiding systematic and evidence-based fare revisions.

4.4.1. Inflation-Based Adjustment

Direct inflation-indexed fare models do not appear in the reviewed literature. However, several studies incorporate cost-based mathematical modeling that implicitly reflects inflationary pressures.

[66] use a cost estimation function that computes operational costs C = f ( Q , p i ) , where Q is service quantity and p i includes cost parameters such as fuel and labor variables typically affected by inflation. While not explicitly modeling inflation, the structure allows fares to be recalibrated as costs rise. Similarly, [67] employ economic cost modeling, where operator cost functions and marginal cost equations capture long-run cost escalation, providing a computational foundation for inflation-adjusted fare calculations over time. [68] use an optimization function balancing operating cost, demand, and fare, expressed as:

where F is fare and R ( F ) represent revenue as a function of fare. Cost terms can be inflation-adjusted, offering indirect application to inflationary settings. None of the studies presents a dedicated inflation indexation rule, CPI-linked model, or long-run inflation forecasting equation. Inflation is treated only implicitly as part of generic operating costs, leaving a methodological gap for systems requiring formal inflation-indexed fare formulas.

4.4.2. Indexation Models (Fuel Price, CPI)

A review of the selected studies shows that none of the authors propose a formal CPI-indexed or fuel-indexed fare adjustment formula. However, several works present cost-based mathematical structures that could support indexation, even if they do not explicitly implement it.

[66] estimate bus operating costs using a cost function that depends on multiple cost drivers, including fuel and labor. Although not expressed as an indexing formula, their model can be extended into a fuel-linked indexation framework. Their cost structure can be represented as:

where:

Q = service quantity p i = vector of cost parameters (fuel, labor, maintenance), typically inflation-sensitive

[69] use a dynamic optimization model for taxi operations. While their work focuses on fleet size and fare optimization rather than indexation, the underlying cost component often dominated by fuel could be indexed. Their objective function is:

where:

F = fare n = fleet size C ( n ) = operational cost (fuel-dependent) R ( F , n ) = revenue

[67] model long-run automated transit costs. While no CPI or fuel index is applied, their economic cost structure allows indexing by embedding CPI or fuel price components into input prices. Hence, existing literature lacks any empirical evaluation of index-based fare adjustment mechanisms, despite the presence of models capable of supporting them.

4.4.3. Econometric and Forecasting Models

Econometric and forecasting approaches appear in several studies that examine fare impacts on demand or user behavior. [70] utilize mathematical modeling to estimate how fare changes influence transport demand, effectively serving as an econometric elasticity-based framework. By modeling demand relationships, their work functions as a forecasting tool for evaluating future ridership under different fare levels such as:

Which forecast ridership at alternative fare levels. In this model, Q denotes passenger demand, F represents the fare level, a is the baseline demand intercept, and b is the fare-sensitivity parameter. The negative sign indicates that demand decreases as fares increase, reflecting the price elasticity effect on public transport use. [71] models fare evasion using transaction-level probabilistic analysis, effectively functioning as a predictive model for noncompliance. [72], apply psychometric statistical modeling using structural comparisons of perceived fairness vs. unfairness, while [73] use stated-preference discrete choice models, typically written as:

where V i includes variables such as fare and crowdedness and P ( i ) is the probability of choosing alternative i . These models forecast passenger behavior under alternative pricing schemes but do not integrate systemwide econometric forecasting across operations, revenue, and ridership simultaneously. None of the studies presents a comprehensive econometric fare-demand-revenue forecasting model, nor do they integrate long-run macroeconomic variables (CPI, fuel index, GDP). Literature lacks a unified forecast model for holistic fare policy evaluation.

4.4.4. Multi-Objective Optimization

[74] propose a fuzzy bi-level programming model for dynamic subway pricing, optimizing operator revenue at the upper level while meeting passenger equilibrium conditions at the lower level. The bi-level structure inherently reflects multi-objective tradeoffs among service efficiency, fairness, and congestion with fuzzy parameters to handle uncertainty in passenger heterogeneity.

Upper level:

Lower level (passenger equilibrium):

where F . is the fare R ( F ) is the revenue as a function of fare, x is passenger decision and Z ( x , F ) is passenger equilibrium objective function.

[68] use an optimization decision model incorporating both operator cost and demand effects. [69] apply dynamic optimization, jointly optimizing taxi fare and fleet size. [75] employ multi-criteria decision-making (MCDM), mathematically eressed as:

where w j are weights and r i j criterion scores.

[76] used a collaborative game-theoretic multi-objective optimization model, maximizing joint utility across passengers, operators, and authorities. Most models assume perfect rationality, complete information, and lack real-world calibration. Multi-objective models rarely incorporate behavioral unpredictability, inflation, or macroeconomic constraints, leaving a gap for more realistic hybrid optimization frameworks.

4.4.5. Qualitative vs Quantitative vs Computational Modeling

Table 4 presents Literature that demonstrates a strong dominance of quantitative and computational modeling approaches in public transport fare studies, while qualitative analyses appear less frequently and typically serve supportive roles. Qualitative work is exemplified by [77], who use interviews and contextual assessments to evaluate fare structure challenges in Koh Chang. [72] also apply qualitative reasoning within a behavioral pricing framework, examining psychological perceptions of fairness and unfairness through survey-based subjective responses. These studies contribute valuable user-centric insights but do not develop mathematical fare-setting formulas.

In contrast, most studies employ quantitative, optimization, or computational models. [74] use a fuzzy bi-level mathematical model, integrating operator revenue optimization with passenger equilibrium behavior. [68] apply structured optimization models balancing cost recovery and demand. Multi-objective frameworks appear in [76] through collaborative game-theoretic optimization, while [75] utilize multi-criteria decision-making (MCDM) to assess fare system alternatives. Forecasting and demand modeling approaches include [70] mathematical elasticity model and [73] discrete choice stated-preference models. Computational simulations also feature prominently in [78], who evaluates fare collection area performance through simulation modeling. [79] applied a regression model with ARIMA errors to analyze transport fares in the Kumasi metropolitan area. Their findings indicate that both median and mean trip distances (5.3 km and 5.7 km, respectively) significantly influence fare determination. This result aligns with existing literature suggesting that fare structures in Ghana are generally distance-sensitive, although they may vary across routes and operating conditions. Additionally, the study provides a forecast of transport costs in Kumasi, as illustrated in Figure 5Figure 5. Quantitative models dominate fare studies, but lack mixed methods integration and real-world validation, highlighting gaps in combining optimization with qualitative insights. This model explains the exponential rate at which transport fares grow and the potential effect on public transport users.

Recent research demonstrates a clear transition from traditional cost-plus or flat-fare strategies toward computational, data-driven, and algorithmic models that allow transit agencies to incorporate demand patterns, equity considerations, and multi-stakeholder trade-offs into fare decision-making. A major advance is the adoption of machine learning and real-time data analytics for predicting passenger demand and evaluating sentiment toward fare changes. For instance, the [38] integrates real-time fare comparison across modes using interoperable data streams, highlighting the emergence of ML-powered mobility decision-support systems. Similarly, [35] leverage large-scale transit datasets to model fare elasticity and behavioral responses, demonstrating ML’s potential for forecasting demand under alternative fare structures.

Source: http://article.sapub.org/10.5923.j.ijtte.20261501.01.html

Figure 5. Forecast for regression with ARIMA (5, 1, 0) [79].

Simulation models also play a prominent role in exploring fare adjustments and passenger behavior. [36] use simulation to evaluate route-choice changes when transitioning from zonal to distance-based fares, revealing how computational network models capture behavioral nuances better than static approaches. In the same vein, estimation methods for sightseeing rail fares [30] apply cost-simulation techniques to compute unit fares, reflecting a growing reliance on computational pricing tools.

Game theory especially bi-level and cooperative formulations, is increasingly used to resolve conflicts among stakeholders. [74] apply fuzzy bi-level programming, where operators optimize fares while passengers respond in the lower level. Emerging work in the broader fare-policy field includes the use of Shapley-value-based allocation methods for equitable revenue sharing in integrated networks, although not directly implemented in the included studies, the methodological trajectory points toward cooperative game-theoretic negotiation frameworks.

Sub-topics such as multi-stakeholder optimization and decision-support systems are also gaining prominence. Studies on fare differentiation in Rome [80], fare integration barriers [34], and fare discounts in Central Europe [39] emphasize the need for computational tools that simultaneously optimize affordability, cost recovery, and equity. These models increasingly incorporate multi-criteria evaluation (e.g., [81]) and system-level optimization to support evidence-based fare setting.

Transport unions, particularly the Ghana Private Road Transport Union (GPRTU), occupy a central and historically dominant position in Ghana’s fare-setting process. Formed from colonial-era driver associations dating to the 1930s, the GPRTU evolved to define routes and set standardized fares across the country [83]. Today, the GPRTU holds de facto gatekeeping power over fare adjustments; a position reinforced by its capacity to mobilize drivers in industrial action. The Ghana Road Transport Coordinating Council (GRTCC) serves as a coordinating body for commercial road transport unions in Ghana, facilitating dialogue and acting as a liaison between transport operators and the Ministry of Transport. [84] observe that transport unions in developing countries derive negotiation leverage not only from formal institutional membership but from their capacity to coordinate collective action, including strikes and service withdrawals, as tools of pressure in fare negotiations. This dynamic is clearly evidenced in Ghana, where the GPRTU has repeatedly threatened nationwide strikes to compel government concessions on fuel levies and fare approval timelines [85]. Inter-union rivalry including disputes between the GPRTU and the Concerned Drivers Association of Ghana further complicates the negotiation landscape, undermining the coherence of the union bloc and reducing its bargaining effectiveness [86].

The Ministry of Transport (MoT) plays a central regulatory and coordination role in the fare-setting process, facilitating stakeholder negotiations and representing public interest within the transport governance framework. [87] in his analytical model of negotiation symmetry and asymmetry, notes that the stronger party in a negotiation typically seeks to impose its preferred outcome, while the weaker party pursues symmetry through institutional channels or collective action. In Ghana, government agencies nominally hold regulatory authority but are constrained by political economy considerations including the electoral sensitivity of fare increases which frequently result in delayed approvals and incomplete enforcement of approved fare structures [88]. [82] identifies weak institutional capacity as a defining challenge for transport regulators across Sub-Saharan Africa, noting that effective fare governance requires not only legal authority but technical capacity, continuity, and genuine stakeholder engagement qualities that Ghana’s regulatory architecture has inconsistently demonstrated.

Commuters represent the most diffuse and least institutionally organized actor in Ghana’s fare negotiations. Despite being the primary users of public transport, their interests are structurally marginalized in the tripartite process, which formally includes only operators and government [89]. In practice, commuter influence is exercised indirectly through public protest, media pressure, and advocacy by civil society groups rather than through formal representation. [90] documents how commuters in peri-urban areas such as Ajamasu and Buoho face artificial vehicle scarcity and inflated fares with virtually no formal recourse, revealing a fundamental equity gap in the fare governance structure. [91] noted that without explicit mechanisms for commuter representation in fare-setting processes, outcomes tend to systematically favor operator cost recovery over affordability and social equity.

Power asymmetry is a defining feature of Ghana’s transport fare negotiations. [87] establishes that asymmetric power relations shape both the process and outcomes of negotiations, with stronger parties able to steer outcomes in their preferred direction. In Ghana’s context, the GPRTU’s dominance over vehicle supply gives it considerable coercive leverage, enabling it to implement unauthorized fare increases or withhold services when negotiations stall. Government agencies, while formally superior in regulatory authority, are constrained by their dependence on private operators to deliver most urban transport services; a structural dependency that limits their enforcement capacity. [92] documents how the government was compelled to summon union officials and deploy state operators following fare manipulation incidents, reflecting the limits of regulatory authority in a sector dominated by private, union-organized provision. Commuters, lacking organizational representation, occupy the weakest position in this asymmetric arrangement.

Conflict in Ghana’s transport fare governance manifests in several recurring patterns. First, inter-union disputes, such as the disagreement between the GPRTU and its sister unions over the timing and magnitude of fare adjustments, fragment the operator block and create confusion among commuters [86]. Second, government-operator conflicts emerge when unions perceive regulatory decisions as undermining their financial viability as illustrated by the GPRTU’s accusation in 2025 that the government’s imposition of a fuel levy immediately following an agreed fare reduction constituted a deliberate betrayal [85]. Third, operator-commuter conflicts arise through practices such as artificial vehicle scarcity, route abandonment, and fare overcharging, which commuters are largely powerless to contest within the existing institutional framework [93]. Collectively, these conflict patterns point to a governance deficit in which the absence of transparent, binding, and equitably enforced fare-setting rules creates persistent instability for all stakeholders. [87] notes that asymmetric negotiations tend toward instability unless mechanisms are developed to equalize procedural access a reform that Ghana’s transport governance has yet to fully achieve.

Transparency is fundamental to building trust and legitimacy in fare-setting, as opaque or poorly justified decisions often lead to public resistance and non-compliance [5]. Clear communication and perceived fairness enhance acceptance, yet these are frequently constrained by limited data availability and weak institutional capacity [2][5]. Trust is further shaped by past experiences and the consistency of previous fare decisions [25]. While advanced analytical tools can support more accountable decision-making [24], transparency alone is insufficient without meaningful public participation and feedback mechanisms [26]. More broadly, the legitimacy of fare policies depends not only on technical soundness but also on fairness, openness, and effective communication [94]. This is particularly critical in contexts such as Ghana, where commuters are often excluded from formal negotiations and discrepancies persist between announced and implemented fares. In Figure 6Figure 6, transport unions expressed been betrayed for lack for fairness in government re-introduction of fuel levy after their reduction in fare rates due to reduction in fuel prices by government.

5.6.1. Perceived Fairness in Pricing

Perceived fairness in transport pricing is a multi-dimensional construct that encompasses distributional equity, procedural justice, and the proportionality of cost burdens relative to service quality. [94] demonstrate that transport pricing policies are evaluated as fairer and more acceptable when outcomes are perceived to affect all users equally and to serve collective rather than individual interests a finding with direct implications for Ghana, where fare increases disproportionately burden low-income urban and peri-urban commuters. [95] further distinguishes between horizontal equity equal treatment of users with similar needs and vertical equity, which requires that disadvantaged groups receive a more significant share of transport resources. Ghana’s fare structure, driven primarily by operator cost recovery rather than affordability thresholds, has historically failed to meet standard. [96] articulated this perception gap explicitly, arguing that commuters struggling with the high cost of living have a legitimate fairness expectation that reductions in fuel prices be passed on directly as fare reductions an expectation that was widely violated following the 2025 fare reduction announcement. [89] affirms that a fair fare policy is one that maximizes access to public transport for as many people as possible, with affordability as a non-negotiable design criterion rather than an afterthought.

5.6.2. Public Trust in Fare Decision

Figure 6. GPRTU accusing government of betrayal [85].

Public trust in transport fare decisions is a function of both the outcome and the process through which decisions are reached. Ghana’s Minister of State for Government Communications explicitly acknowledged that non-compliant operators were “undermining trust in the public transport system” by charging unapproved fares and creating artificial vehicle scarcity [97]. This observation reflects a growing recognition in policy discourse that fare governance is not merely a technical pricing exercise but a trust-building exercise with significant political and social consequences. [98] establishes that transit fare affordability is deeply intertwined with perceived institutional reliability when commuters cannot predict or verify whether announced fares will be honored, and their trust in the entire fare governance apparatus erodes. [99] in a study of transit user perceptions across several cities found that service reliability and fare predictability are the two strongest predictors of trust in public transport institutions. Ghana’s recurring pattern of unilateral fare increases, mid-period revisions, and operator non-compliance with approved reductions directly undermines both dimensions, creating a chronic trust deficit between commuters and the tripartite fare-setting system.

5.6.3. Communication and Consultation

Effective communication and genuine stakeholder consultation are prerequisites for the social legitimacy of fare decisions. [82] identifies real consultation and collaboration with key stakeholders, especially operators and affected communities, as a non-negotiable element of effective transport regulatory frameworks. In Ghana, however, the communication of fare decisions to the public is typically unidirectional and reactive: announcements are made after negotiations conclude, without prior public engagement or impact assessment. The GPRTU’s Deputy General Secretary acknowledged in 2025 that the union communicated the 15% fare reduction by informing its registered members but conceded that drivers outside formal union structures remained beyond the reach of its communication channels, requiring law enforcement intervention to achieve compliance [85]. [89] recommends that fare-setting authorities adopt principles of transparency, inclusivity, and flexibility in their communication strategies, combining quantitative fare data with qualitative explanation of the cost drivers behind adjustments. Public transport policies often emphasize multi-stakeholder engagement in decision-making; however, this commitment is not always consistently reflected in actual fare-setting practices.

Behavioral responses to fare decisions range from passive acceptance through active resistance, and are shaped by perceived fairness, trust levels, and the availability of alternatives. [94] shows that the acceptability of transport pricing policies is significantly higher when respondents perceive that policy outcomes protect collective interests and are equitably distributed and conversely, that policies perceived as serving operator or government interests at the public’s expense generate systematic resistance. In Ghana, commuter resistance to fare increases has historically been expressed through public protest, media outcry, and political pressure rather than formal institutional channels, reflecting the absence of structured commuter representation in the fare-setting process [89]. Conversely, where fare reductions are announced, acceptance is tempered by skepticism about actual implementation. [90] documents widespread commuter confusion and frustration following the May 2025 fare reduction, with many passengers unsure of the correct fares and unable to enforce compliance against overcharging drivers. This behavioral ambiguity, neither full acceptance nor organized resistance, reflects a governance environment in which commuters lack both information and institutional recourse.

The compliance dimension of fare governance in Ghana reveals a structural enforcement gap that directly undermines transparency and trust [100][101]. Following the May 2025 tripartite-approved 15% fare reduction, the GPRTU reported that while its registered members were largely compliant, “a few members of unregistered unions have decided not to comply”, calling on law enforcement agencies to sanction non-compliant drivers [85]. [95] notes that fare evasion and non-compliance in public transport systems produce not only economic losses but social inequity and heightened insecurity, eroding the foundations of a legitimate fare system. In Ghana’s context, non-compliance is compounded by the large proportion of “floating vehicles” operators outside formal union structures who are structurally beyond the reach of the tripartite governance arrangement [101]. [96] proposed a three-pronged response: empowering Metropolitan, Municipal, and District Assemblies to revoke operating licenses of non-compliant drivers; issuing compliance stickers to facilitate police identification; and deploying public university buses as temporary alternatives during periods of operator non-compliance. These proposals highlight the extent to which fare transparency and compliance are ultimately governance challenges requiring institutional reform rather than simply better communication.

Public acceptance of fare policies is closely tied to affordability, equity, and broader social welfare outcomes as shown in the literature collection in Table 5. Across the literature, affordability emerges as a central determinant of acceptance, especially for low-income groups who experience disproportionate cost burdens under flat or distance-based fare regimes. Studies in Costa Rica show that uniform flat fares result in regressive impacts, where lower-income riders pay a higher share of income for mobility, undermining public support for the fare system [102]. Similarly, fare increases in Beijing disproportionately affected low-income users, reducing affordability and generating negative public response [103]. These findings align with broader debates on fare negotiations where tensions between cost recovery and user affordability shape political and social acceptance [104].

Equity impacts are another major driver of social acceptance [102]. Evidence from Lisbon’s fare reform demonstrates that adjusting fare structures toward integrated or zone-based models can mitigate inequality and improve access for underserved communities [105]. In contrast, shifting to a flat fare often disadvantages short-distance, lower-income riders, creating perceptions of unfairness and lowering acceptance [4]. Research on integrated systems shows that equitable fare structures must consider multiple dimensions income, accessibility, and urban form to ensure socially acceptable outcomes [106][107]

The broader social welfare implications of fare adjustments indicate that well-designed policies can enhance mobility, reduce disparities, and support social inclusion. Optimization-based evaluations of fare-free or reduced-fare periods show improvements in social equity, particularly when aligned with peak needs [108]. Likewise, dynamic or data-driven models can balance operational revenue with societal benefits, increasing public trust and policy acceptability [74].

Known literature underscores that fare acceptance is strongest when policies enhance affordability, demonstrate distributive fairness, and contribute meaningfully to social welfare objectives. The key overarching gap is the lack of a holistic, multi-dimensional fare-setting framework that integrates affordability, equity impacts, public acceptance, operational sustainability, and social welfare into a single decision-support model.

Transport fare negotiation has evolved from ad hoc decision-making to more structured analytical frameworks incorporating economic, behavioral, and institutional perspectives. In public transport systems, fare determination reflects a multi-stakeholder negotiation problem, where regulators, operators, and passengers pursue competing objectives such as cost recovery, affordability, and service quality. Recent studies apply game-theoretic and multi-objective optimization models to formalize these interactions, demonstrating how equilibrium fares emerge from strategic interdependence among stakeholders [74][76].

However, compared to other domains, transport fare negotiation remains less theoretically mature. In international relations, negotiation models explicitly incorporate multilateral bargaining and ratification constraints, showing how outcomes depend on coalition formation and institutional constraints [109]. Similarly, project management literature conceptualizes negotiation as a systematic conflict-resolution process, emphasizing structured decision-making, stakeholder alignment, and iterative bargaining [110]. These approaches highlight the importance of clearly defined objectives, constraints, and negotiation protocols elements often underdeveloped in transport contexts. Behavioral insights further enrich negotiation theory. Studies on negotiation styles demonstrate how competitive, cooperative, and compromise strategies influence agreement outcomes and the risk of deadlock as cited in the work of Calum Coburn of the Harvard Medical School.

(https://hms.harvard.edu/sites/default/files/assets/Sites/Ombuds/files/NegotiationStyles.Understanding%20the%20Five%20Negotiation%20Styles.by%20Calum%20Coburn.pdf).

Foundational negotiation models also emphasize the tension between individual interests and mutual dependence, suggesting that effective agreements require balancing efficiency with fairness [111]. Despite these advances, a gap remains in integrating formal negotiation theory with the practical realities of transport systems, particularly in developing countries where institutional fragmentation and informal practices dominate. Existing literature suggests the need for hybrid models that combine game-theoretic optimization, behavioral considerations, and governance structures to support transparent and sustainable fare negotiation processes.