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From Demand to System Co-Shaping: A Review of User Roles in Transportation Systems

Fangting Zhou, Balazs Kulcsar, Jelena Andric

arXiv:2609.37295v1eess.SYcs.SY

TL;DR

Transportation models often represent users mainly as demand inputs, while evidence across transportation domains shows that user information is not yet consistently integrated into system decisions. This review organizes user roles and decision integration through a three-role taxonomy, finding progress in behavior measurement but limited translation into actionable decisions and planning feedback.

  • Problem

    Transportation models often represent users through demand inputs, while a cross-domain account of how user roles enter system decisions remains needed.

    Source: §1 · p. 2, §3.2 · p. 7

  • Method

    The review synthesizes transportation research through a taxonomy distinguishing passive demand, operational agents, and system co-shapers.

    Source: §9 · p. 19

  • Results

    User preferences are increasingly measurable, but are still often used for demand prediction or behavioral explanation rather than as decision-relevant inputs.

    Source: §9 · pp. 19–20

  • Takeaways & Limitations

    User-driven design calls for feedback linking participation, operational decisions, and long-term planning while addressing heterogeneity, acceptance, equity, privacy, and deployability.

    Source: §9 · pp. 20–21

    Systematic feedback from user responses and operational outcomes into subsequent planning remains limited.

    Source: §5.3 · p. 11

Abstract

from arXiv · show

Users are central to transportation systems, yet their roles are often simplified in transportation modeling and decision-making. Conventional approaches primarily represent users through demand-related inputs, such as trip flows, delivery requests, and charging loads. However, digital, electrified, and platform-based services create more direct user-system interactions, with users responding to prices, incentives, service availability, and information in ways that can influence operational and planning decisions. This review develops a concept-oriented, taxonomy-based synthesis of how user-related information is represented and used in transportation decision-making. Rather than organizing the literature primarily by transport domain, we distinguish three user roles: passive demand, operational agents, and system co-shapers. Drawing on studies across transportation electrification, urban logistics, shared mobility, multimodal transport, and Mobility-as-a-Service (MaaS), the review examines how user preferences, flexibility, willingness to pay, acceptance, participation, and feedback are incorporated into modeling and decision frameworks. The synthesis shows that progress has been made in measuring and predicting user behavior, but user-related variables are still often used mainly for behavioral analysis rather than as actionable inputs to system decisions. Their integration into operational decision-making remains incomplete, and systematic feedback from operational user responses and outcomes to long-term planning is even more limited. Key challenges include closing the loop between operation and planning, modeling heterogeneous and strategic users, integrating optimization with user acceptance, and ensuring privacy, equity, and deployability. Together, these findings point toward a shift from user-behavior analysis to user-informed and user-driven transportation system design.

1 Introduction

Transportation models often treat users as demand inputs, although users increasingly respond to system conditions in ways that affect operations and planning. The review addresses the need for a cross-domain account of how user roles and information enter system decisions.

1 Introduction

  • Conventional models commonly use trip flows, delivery requests, charging demand, or service requests as planning and optimization inputs, giving only a partial view of user influence.Source: §1 · p. 2
  • Digital, electrified, and platform-based services make users operational agents whose responses to prices, incentives, information, and service availability affect short-term system outcomes.Examples span charging behavior, MaaS bundles, and logistics delivery choices.Source: §1 · p. 2
  • User behavior and preferences can also inform infrastructure and service planning through charging choices, MaaS behavior, mobility-hub preferences, and logistics customer acceptance.Source: §1 · pp. 2–3
  • Existing reviews are fragmented by domain, leaving a need for a unified cross-domain synthesis of user roles and their implications for system-level decisions.Source: §1 · p. 3
  • The review organizes user influence as passive demand, operational agents, and system co-shapers, linking user-related information to decisions from demand estimation through planning and service redesign.It connects electrification, urban logistics, shared mobility, multimodal transport, and MaaS through mechanisms such as flexibility, acceptance, willingness to pay, and feedback.Source: §1 · p. 4

2 Review Scope and Decision-Integration Framework

This review classifies how user-related information enters transportation decisions, from demand representation to operational response and planning feedback. Its framework spans five transportation domains and links these roles to system decision levels.

2 Review Scope and Decision-Integration Framework

  • The review synthesizes studies connecting user behavior, preferences, responses, participation, or feedback to transportation system outcomes and decisions.It defines user-driven design as integrating these user inputs into operation, planning, service design, and system adaptation.Source: §2 · p. 4

2.1 Review scope, search strategy and study selection

  • The scope covers electrification, urban logistics, shared mobility, multimodal transport, and MaaS, using database and citation searches for studies published mainly from 2010 to 2026.Studies were retained when they linked user-related factors to transportation decisions or outcomes; solely individual-level or supply-side studies without that link were excluded.Source: §2.1 · pp. 4–5

2.2 Role-based taxonomy

  • The review classifies users as passive demand, operational agents, or system co-shapers according to whether information supports demand decisions, short-term operations, or long-term system design.These roles are not mutually exclusive groups or a required progression; the same user may occupy several roles.Source: §2.2 · pp. 5–6

2.3 Decision-integration framework

  • The framework connects user information, role representation, decision use, outcomes, and feedback, with its three layers indicating increasing user influence in decision-making.It traces information from demand inputs through operational responses to planning feedback.Source: §2.3 · p. 6, Figure 1 · p. 6

3 Users as Passive Demand

Passive-demand models represent users chiefly through the travel, charging, delivery, or service demand they generate. These representations support planning but provide limited insight into user adaptation, heterogeneity, and acceptance of system decisions.

3 Users as Passive Demand

  • Demand-based representations simplify or aggregate behavioral mechanisms, providing tractable system inputs but limited representation of how users adapt to and influence transportation systems.Source: §3 · pp. 6–7

3.1 Demand-based representation across transportation domains

  • Across passenger transport, logistics, electrification, and shared mobility, users are represented through trip flows, orders, charging profiles, and service requests for planning and operational models.Source: §3.1 · p. 7
  • More detailed observations can improve demand estimation without necessarily changing users’ passive role in subsequent decision models.Source: §3.1 · p. 7

3.2 Limitations of passive-demand representation

  • Passive-demand models remain essential for large-scale planning, but fixed or weakly responsive demand, aggregation, and limited feedback constrain their representation of user behavior.They may show when and where demand occurs without showing whether users will accept proposed prices, schedules, facilities, or services.Source: §3.2 · p. 7

4 Users as Operational Agents

As operational agents, users respond to system conditions and can provide flexibility that affects short-term transportation performance. A central challenge is incorporating those responses directly into operational decisions.

4.1 Behavioral responses to operational conditions

  • Users respond to prices, time, reliability, accessibility, incentives, information, and service availability, affecting utilization, congestion, waiting, efficiency, energy demand, and revenue.Source: §4 · p. 8, §4.1 · p. 8

4.2 Flexibility and operational participation

  • User flexibility can support charging coordination, relocation, rebalancing, routing, and crowdshipping, but its operational value depends on willingness to participate under offered conditions.Participation depends on factors including compensation, perceived control, trust, charging reliability, mobility requirements, and task characteristics.Source: §4.2 · pp. 8–9

4.3 Integrating user responses into operational decisions

  • Behavioral analysis alone does not ensure that user acceptance or participation affects operator decisions, and optimization models may simplify responses or treat them as exogenous.Source: §4.3 · p. 9
  • Studies increasingly integrate user behavior into operational decisions, but this integration remains uneven and short-term responses rarely feed systematically into later infrastructure or service planning.Examples include EV charging bids in pricing and scheduling, and customer choice in logistics pricing and routing.Source: §4.3 · p. 9

5 Users as System Co-Shapers

System co-shapers are users whose behavior, preferences, participation, and feedback inform long-term infrastructure planning, service redesign, and system evolution. The review traces how operational outcomes and expressed user information can become planning inputs, while noting that systematic feedback loops remain limited.

5.1 From operational feedback to planning signals

  • Repeated congestion, rejected offers, high utilization, and low adoption can signal capacity, pricing, accessibility, or service-design issues for subsequent planning.The shift is from treating operational outcomes as end-point performance measures to using them as inputs to later planning decisions.Source: §5.1 · pp. 9–10

5.2 Translating user responses into infrastructure and service planning

  • Charging location preferences, detour tolerance, travel patterns, demand, and adoption can inform station siting, capacity, and expansion decisions.The review also describes joint charger placement and dynamic pricing that allows price-sensitive users to shift charging or opt out.Source: §5.2 · p. 10
  • Subscription behavior, willingness to pay, accessibility needs, and user choices can shape mobility-hub design and location, pickup-point deployment, and delivery-network planning.Accessibility-based planning can also use commuting patterns, activity destinations, and existing public transport to inform mobility-hub decisions.Source: §5.2 · p. 10

5.3 From implicit feedback to active co-shaping

  • Observed use or non-use is ambiguous: low use may reflect poor access, limited information, exclusion, or unreliable service rather than low demand.Planning feedback therefore requires distinguishing preference, acceptance, access, and constraint.Source: §5.3 · p. 10
  • Explicit preferences and offer acceptance can reveal requirements that utilization alone misses, but few studies show repeated responses updating infrastructure or service decisions.Examples include acceptable charging windows, minimum energy needs, price limits, and willingness to provide flexibility.Source: §5.3 · p. 11

6 Modeling Approaches Across User Roles

The section compares how modeling approaches represent users at different decision levels and considers their integration and evaluation. It emphasizes that no single approach captures every user role or decision level.

Modeling approaches across user roles

  • Passive-demand models estimate where, when, and how much demand occurs; operational-agent models capture endogenous responses; co-shaping connects user feedback with long-term planning and adaptation.Table 2 summarizes representative approaches, applications, strengths, and limitations across these roles.Source: §6 · p. 11, Table 2 · p. 12

Integrating modeling approaches

  • No single approach captures all user roles: demand models scale, behavioral models represent heterogeneous responses, and feedback approaches link behavior to planning and adaptation.Hybrid frameworks may integrate these strengths, but must balance behavioral, computational, and data requirements.Source: §6 · p. 11

Evaluation and comparison

  • Evaluation should pair operational indicators with acceptance, waiting time, willingness to pay, rejection, fairness, and implementation considerations such as scalability and deployability.Direct quantitative comparison across studies is difficult because service designs, behavioral assumptions, pricing, and infrastructure constraints vary; Table 3 instead organizes variables, measurement forms, decision relevance, and interpretation cautions.Source: §6 · pp. 11–12

7 Practical Implementation, Governance, and Industry Requirements

The section considers regulatory, market, societal, data, stakeholder, and operational conditions for deploying user-informed transportation systems. It argues that deployability requires attention to behavioral feasibility alongside technical performance.

7.1 Regulatory, market, and societal considerations

  • Deployment must account for data-protection and automated-decision rules, limits on cross-provider data sharing, fairness and accessibility concerns, and gaps in privacy, transparency, acceptance, and governance practices.Personalized decisions may raise transparency, explainability, consent, and contestation concerns; opaque or discriminatory pricing may also face scrutiny.Source: §7.1 · pp. 13–14

7.2 Industry needs, practical requirements, and cautions

  • A practical transition may start with user-informed decision support, where user information improves planning and operations before fully adaptive user-driven systems are feasible.Table 3 organizes measurable user variables by decision relevance and interpretation cautions.Source: Table 3 · p. 13, §7.2 · p. 14

7.2.1 Available user data and behavioral information gaps

  • Operational traces show what users did but often not why; willingness to pay, flexibility, trust, fairness perceptions, and rejection reasons may require surveys or active feedback.User variables must be decision-relevant and feasible to collect, update, and maintain.Source: §7.2.1 · p. 14

7.2.2 Passenger and commercial transportation requirements

  • Passenger applications emphasize individual travel and accessibility, while commercial settings prioritize fleet and delivery operations and may treat managers or dispatchers as the relevant decision-makers.Commercial applications must reconcile multiple stakeholders' objectives and constraints.Source: §7.2.2 · pp. 14–15

7.2.3 Operational and user-centric performance metrics

  • Performance frameworks should combine operational KPIs with user-centric measures such as acceptance, flexibility participation, rejection, perceived fairness, customer effort, and retention.Operational examples include charger availability, delivery success, and fleet utilization.Source: §7.2.3 · p. 15

7.2.4 Practical considerations and cautions for deployment

  • Deployment requires interpretable, tractable models compatible with existing platforms and data governance; efficiency alone does not ensure behavioral feasibility or user-centered decisions.Solutions may fail if they reduce convenience, reliability, fairness, trust, or perceived control, even when technical metrics improve.Source: §7.2.4 · p. 15, Table 4 · p. 16

8 Research Agenda: Toward User-Driven Transportation System Design

The research agenda calls for moving user-related information from descriptive or predictive measures into operational decisions, planning signals, and adaptive system design. It identifies six directions while stressing behavioral heterogeneity, acceptance, privacy, equity, and deployability.

8.1 From behavioral prediction to decision integration

  • Preferences such as charging flexibility, willingness to pay, and service acceptance are often used to explain or predict choices; future models could make them constraints, objectives, thresholds, or feedback signals.Examples include embedding charging windows, delivery flexibility, pickup-point acceptance, and MaaS subscription patterns into operational and planning models.Source: §8.1 · p. 17

8.2 From implicit feedback to active user participation

  • Because observed use can have multiple explanations, systems should let users explicitly express preferences, constraints, and willingness to participate in cooperative services.Examples include smart charging, vehicle-to-grid, self-pickup delivery, crowdshipping, and user-based relocation.Source: §8.2 · p. 17

8.3 Closing the loop between operation and planning

  • Repeated operational outcomes could become planning signals, linking user responses to long-term planning and service adaptation through continuous feedback.Examples include charging congestion, low smart-charging acceptance, low parcel-locker adoption, and MaaS non-renewal.Source: §8.3 · pp. 17–18

8.4 Modeling heterogeneous and strategic users

  • Models should represent differences in time value, price sensitivity, flexibility, access, trust, and expectations, while accounting for strategic responses to incentives and dynamic pricing.The review identifies differentiated user models and incentive-compatible, fairness-aware mechanisms as possible approaches.Source: §8.4 · p. 18

8.5 Integrating optimization with user acceptance

  • Optimization should treat acceptance as part of feasibility and performance, since technically attractive solutions may be inconvenient, unreliable, unfair, or insufficiently beneficial to users.Acceptance can enter models through constraints, objectives, response functions, or participation probabilities.Source: §8.5 · p. 18

8.6 Toward adaptive, responsible, and privacy-preserving user-driven systems

  • User-driven systems should integrate privacy protection, transparent data rules, equity, and public value, while examining how platform recommendations may reshape demand and system externalities.Data-driven systems may overlook users with limited digital access, lower willingness to pay, or constrained mobility options.Source: §8.6 · pp. 18–19

9 Conclusion

The review proposes a three-role taxonomy that reframes users from passive demand toward operational agents and system co-shapers. It finds that user behavior is increasingly measured, but converting those measures into decision inputs and feedback for system design remains a challenge.

9 Conclusion

  • The taxonomy distinguishes users as passive demand, operational agents affecting short-term performance, and system co-shapers informing long-term design.It covers transportation electrification, urban logistics, shared mobility, multimodal transport, and MaaS.Source: §9 · p. 19
  • The reviewed literature has advanced in measuring and modeling user behavior, but user-related variables still need translation into decision-relevant inputs and behavioral KPIs.These inputs could support pricing, scheduling, resource allocation, infrastructure planning, and long-term adaptation.Source: §9 · pp. 19–20
  • Future systems need stronger feedback between users and design, while integrating participation, operational decisions, long-term planning, user heterogeneity, acceptance, equity, and privacy.The review also identifies responsible data use, interoperability, regulatory compliance, operational KPIs, and industry usability as deployability concerns.Source: §9 · p. 20
  • The taxonomy offers a conceptual foundation for treating users as contributors to operational performance, infrastructure planning, and system evolution.The review states that this shift can help build systems that are efficient, adaptive, inclusive, responsible, and responsive.Source: §9 · pp. 20–21

Declarations

The declarations report the work’s funding, author contributions, and competing-interests statement.

Funding.

  • The work was supported by the European Commission, the Swedish Energy Agency, and the Swedish Electromobility Centre.The statement lists Grant No. F-ENUAC-2022-0003 and Grant No. 11129.Source: Funding · p. 21

Authors’ contributions.

  • All three authors are credited with conceptualization, visualization, and writing—review and editing.Their other listed contributions include formal analysis, funding acquisition, investigation, methodology, and supervision.Source: Authors’ contributions · p. 21

Competing interests.

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