Demographic and Operational Factors in Public Transport-Based Parcel Locker Crowdshipping: A Mixed-Methods Analysis

Abstract

Background: The rapid rise of e-commerce has intensified last-mile logistics challenges, fueling the need for sustainable, efficient solutions. Parcel locker crowdshipping systems, integrated with public transport networks, show promise in reducing congestion, emissions, and delivery costs. However, operational and physical constraints (e.g., crowded stations) and liability complexities remain significant barriers to broad adoption. This study investigates the demographic and operational factors that influence the adoption and scalability of these systems. Methods: A mixed-methods design was employed, incorporating survey data from 368 participants alongside insights from 20 semi-structured interviews. Quantitative analysis identified demographic trends and operational preferences, while thematic analysis offered in-depth contextual understanding. Results: Younger adults (18–34), particularly gig-experienced males, emerged as the most engaged demographic. Females and older individuals showed meaningful potential if safety and flexibility concerns were addressed. System efficiency depended on locating parcel lockers within 1 km of major origins and destinations, focusing on moderate parcel weights (3–5 kg), and offering incentives for minor route deviations. Interviews emphasized ensuring that lockers avoid station congestion, clearly defining insurance/liability protocols, and allowing task refusals during peak passenger hours. Conclusions: By leveraging public transport infrastructure, parcel locker crowdshipping requires robust policy frameworks, strategic station-space allocation, and transparent incentives to enhance feasibility.

1. Introduction

The rapid expansion of e-commerce has reshaped urban logistics, especially in last-mile deliveries. Global e-commerce sales are projected to climb from USD 5.311 trillion in 2022 to USD 8.034 trillion by 2027 [1], placing immense pressure on logistics providers to fulfill escalating consumer demands for speed and convenience. However, the “last mile” is the most resource-intensive link in the supply chain, often resulting in urban congestion, inefficiencies, and higher carbon emissions [2,3,4]. As cities grapple with sustainability goals, cost constraints, and growing customer expectations, innovative strategies are required to manage the complex dynamics of final-step deliveries.

Parcel locker networks and crowdshipping are two such emerging solutions. Parcel lockers, strategically positioned at urban transport hubs or busy commercial areas, reduce the frequency of failed deliveries and consolidate drop-off points, thereby enhancing overall delivery density [3,5]. Crowdshipping, on the other hand, activates the existing mobility of individuals by transforming public transport users into occasional couriers. Facilitated by user-friendly technology platforms, these non-professional couriers fulfill delivery tasks along their regular commutes [6]. By leveraging the travel already occurring in public transit systems, crowdshipping aims to decrease vehicle kilometers traveled (VKT), thus reducing emissions and urban congestion [7,8].

Public transport-based crowdshipping merges these two concepts, offering an efficient, eco-friendly model that taps into underutilized commuter capacity [9,10]. By situating parcel lockers at bus stops, train stations, or other transit nodes, logistics operators can facilitate convenient parcel handoffs for both senders and recipients [5,11]. Such an approach not only reduces the reliance on dedicated delivery vehicles but also streamlines operations through shared urban infrastructure [1]. Several studies suggest that integrating last-mile logistics with existing public transit can significantly cut carbon emissions—by up to 50% in certain operational models [8,11].

Despite their potential, these systems face noteworthy implementation hurdles. For instance, station spaces can be crowded at peak hours, raising questions about whether people can comfortably carry parcels on congested trains and whether physical room exists to install lockers without impeding foot traffic. One set of challenges involves operational factors, such as synchronizing parcel drops with commuter schedules, determining appropriate locker placement, and managing real-time allocations so parcels reach their destinations on time [12]. Another set relates to demographics and user behavior. Younger adults, driven by the flexibility and supplemental income of crowd-based work, often participate more readily in crowdshipping [13,14]. Yet older populations, or those less accustomed to public transport, may be deterred by physical limitations, safety perceptions, or unfamiliarity with gig platforms [3]. Understanding how these demographic factors intersect with operational constraints is critical for designing inclusive, scalable systems.

Technology adoption plays a pivotal role in addressing these issues. Advanced algorithms, user-friendly mobile apps, and real-time tracking tools can improve route planning and task assignment, mitigating inefficiencies and ensuring smooth handoffs [6,15]. Still, small and medium-sized logistics providers (SMLPs) can struggle to implement such technologies, often requiring supportive policies or collaborations with larger stakeholders [15]. Moreover, public trust and acceptance hinge on transparent processes and robust security measures. As parcel lockers and crowd-based couriers handle goods of varying size and value, reliable mechanisms for tracking deliveries, verifying identities, and compensating couriers fairly become indispensable for long-term viability.

Scaling public transport-based parcel locker crowdshipping, therefore, demands a nuanced blend of environmental, technological, and social considerations [16]. While some pilot programs have demonstrated the environmental benefits of repurposing commuter journeys, gaps remain in understanding how different user groups (e.g., older adults vs. younger gig workers) respond to various operational designs [17]. There is also a need for empirical data on how distance, parcel weight, and compensation structures shape willingness to participate. Furthermore, recent crowdshipping research in Beijing underscores that uncertainties in delivery distance, courier income, and task volume hinder stable courier participation and consistent delivery coverage. A proposed optimization model enables logistics companies to efficiently manage costs and sustain coverage. Experiments using Beijing data indicate that spending around USD 4.07 million monthly ensures door-to-door delivery to 94% of the city [18].

To situate our study more firmly in the evolving literature, we selected operational elements, such as parcel weight limits, distance willingness, and route deviations, that previous works identify as critical threshold factors for crowdshipper participation [5,6,9]. Similarly, we focused on demographic attributes (age, gender, gig experience) based on prior findings indicating that gig familiarity and generational usage patterns strongly shape acceptance [13,14,19].

This study addresses these gaps by examining both operational and demographic dimensions of parcel locker crowdshipping. Through a mixed-methods design—encompassing a survey and interviews—the research evaluates the system’s functionality and user adoption patterns. Specifically, it explores factors such as weight preferences, maximum travel distances, route deviations, and overall train usage, linking them to diverse demographic profiles. By synthesizing these findings with established literature, this work aims to inform policymakers, transport authorities, and logistics providers about designing effective, inclusive crowdshipping services that can transform the urban last mile.

Ultimately, this paper seeks to deepen the understanding of how public transport infrastructure can be leveraged for more sustainable freight delivery. The subsequent sections outline the study’s methods, present key results, and discuss practical and theoretical implications for advancing urban logistics through integrated crowd-based solutions.

2. Materials and Methods

2.1. Research Design

This study used a mixed-methods approach to explore the demographic and operational factors shaping parcel locker crowdshipping in urban train networks. Mixed-methods research combines quantitative and qualitative approaches to capture both numerical patterns and the contextual nuances underlying complex phenomena [20]. A convergent parallel design was adopted, ensuring that quantitative and qualitative data collection proceeded simultaneously and was later compared to yield integrated insights [21].

2.2. Data Collection

Two principal methods of data collection were employed: an online survey to gather demographic and operational preferences, and semi-structured interviews to capture expert perspectives on system design, feasibility, and challenges.

2.2.1. Online Survey

A structured questionnaire was developed and administered through Qualtrics (www.qualtrics.com, assessed on 1 February 2025). This survey included items on age, gender, gig economy experience, and train usage patterns, as well as questions about acceptable parcel weight, travel distances for parcel tasks, and willingness to deviate from normal routes. Although most items were nominal or ordinal in nature, Likert-scale questions were treated as interval data for analysis, which is a common practice in social science research [22].

We used Prolific (www.prolific.com, assessed on 1 February 2025) to recruit participants, given its demonstrated reliability in published research across social sciences and transport domains, e.g., [23]. Prolific’s prescreening and demographic targeting tools help ensure diverse samples, making it suitable for exploratory crowdshipping studies. We also implemented multiple attention checks to maintain data quality and compared age–gender distributions with national census data. The study invited 405 individuals, requiring that they be Australian residents (≥18 years) who had used urban trains at least once in the previous 12 months. After applying attention checks and exclusion criteria, 368 valid responses remained.

Our approach to sample selection—while not a perfect random sample of all Australian commuters—was designed to capture a broad, initial cross-section of likely users or potential participants. To ensure data quality, we inserted multiple attention checks (e.g., consistency checks, “select option B if you are reading”) and excluded any respondent who failed a check. We also compared our final age and gender breakdowns to general Australian census data to check for major disparities, finding a reasonably balanced distribution for an exploratory study.

The survey instrument was organized into sections. The first section collected demographic characteristics and classified participants according to prior gig economy engagement. Subsequent sections examined train usage frequency (daily, weekly, monthly), parcel weight preferences, and distance willingness for both home and workplace. Participants could specify how many parcels they would carry, provided the cumulative weight did not exceed their own self-reported capacity. The final questions addressed detours from normal travel routes, allowing for both multiple-choice and open-ended responses. We explicitly asked participants if certain incentives (e.g., discounted fares, per-delivery payments) would change their distance or weight tolerance; however, we did not assign fixed amounts in this preliminary study.

We also briefly surveyed participants’ minimum pay for carrying a <5 kg parcel over approximately 10 train stations: 3% indicated AUD 1–2, 25% indicated AUD 3–4, 35% indicated AUD 5–6, 18% indicated AUD 7–8, and 17% indicated AUD 9+. Additionally, 51% of respondents were employed full-time, with 23% part-time, 13% students, 6% unemployed (looking for work), 2% unemployed (not looking), and 2% retired. Since the current paper centers on age, gender, gig experience, and operational preferences (weight, route deviations), a more detailed economic analysis of these monetary/employment data is deferred to future research.

2.2.2. Semi-Structured Interviews

Qualitative insights were obtained from 20 interviews with experts drawn from academia and the logistics industry. Interviewees were identified through academic publications, specialized crowdshipping forums, and professional referrals. These interviews probed system scalability, user recruitment, insurance and liability matters, technology adoption, and other contextual factors influencing public transport-based crowdshipping.

Potential interviewees were identified through academic publications, professional networks, and industry platforms such as LinkedIn. Snowball sampling [24] expanded the pool when initial respondents suggested additional contacts. To reduce the risk of bias inherent in snowball approaches, the research team actively pursued diverse perspectives, balancing academic and industry voices [25].

Interviews were conducted either remotely via Microsoft Teams or Zoom or in person, depending on feasibility. Each participant received an information sheet outlining the study’s purpose, anticipated benefits, and data handling procedures. Informed consent was obtained digitally or via signed forms, and sessions lasting 30–45 min were recorded for transcription. Field notes were made during and after each interview to document non-verbal cues and immediate reflections.

2.3. Data Analysis

2.3.1. Quantitative Analysis

Survey data were exported from Qualtrics into IBM SPSS Statistics (Version 29.0.2.0 (20)) for cleaning and analysis. Descriptive statistics, such as means and standard deviations, captured the central tendencies of various measures. We employed Cronbach’s alpha [26] to ensure the reliability of any multi-item scales, all of which demonstrated acceptable internal consistency (≥0.70). Bar charts and frequency histograms provided visual representations of demographic distributions and operational preferences. Subgroup comparisons (e.g., gig-experienced vs. non-gig-experienced) assessed differences in parcel weight tolerance or deviations from normal train routes.

2.3.2. Qualitative Analysis

Interview transcripts underwent thematic analysis according to the six-phase framework [27]. Transcripts were read repeatedly to gain familiarity, and codes were generated to capture recurring ideas such as safety perceptions, locker placement feasibility, and insurance protocols. These codes were grouped into broader themes that aligned with the study’s key questions on operational efficiency, demographic barriers, and technology readiness. Thematic categories were then iteratively reviewed and refined, ensuring internal consistency and distinctiveness before drafting a final narrative report.

To integrate the interview findings more closely with our survey outcomes, we interspersed select quotes from experts throughout the “Results” section (Section 3) to highlight how practical considerations aligned or contrasted with participants’ stated preferences.

2.4. Ethical Considerations

Ethical approval was granted by Swinburne University’s Ethics Committee, with all procedures following institutional and national guidelines. Survey participants provided digital informed consent, and interviewees signed written or electronic forms after reviewing detailed information about the study’s purpose, confidentiality measures, and data retention protocols. Identifiers were removed from transcripts and databases to maintain privacy, and data were stored on encrypted drives accessible only to authorized personnel.

Reflexivity was integrated throughout the research, prompting investigators to consider how their personal perspectives and professional backgrounds might shape participant interactions and data interpretation [28,29]. The use of reflexive memos and ongoing team discussions helped ensure that the insights drawn were representative of participants’ viewpoints.

2.5. Methodological Rigor

Several strategies were adopted to strengthen the credibility of the findings. Triangulation was a core feature of the design, allowing survey and interview data to complement and validate each other [20]. Both the survey and interview guides underwent pilot testing, which helped refine ambiguous questions and optimize the data collection workflow [30]. Data collection for both methods proceeded concurrently, so emerging threads from interviews could guide follow-up quantitative queries, and vice versa. Audit trails and logs documented methodological decisions and provided transparency for an external review.

This multifaceted approach furnished a robust empirical base from which to examine how demographic attributes intersect with operational preferences in public transport-based parcel locker crowdshipping. Although the present study did not incorporate actual traffic or emissions data, it builds on existing literature that suggests significant environmental benefits when deliveries piggyback on existing commuter trips [5,9,31]. The next section presents the quantitative and qualitative results, while acknowledging practical constraints raised by interviewees, such as station-space limitations, peak-hour crowding, and insurance coverage.

3. Results

This section presents the findings from the survey and interviews, emphasizing operational factors and their interactions with key demographic variables: gender, gig economy experience, and age. These results form the basis for evaluating the operational scalability and integration potential of the system within existing train networks.

3.1. Demographic Results

This section examines participants’ age, gender, and prior gig-economy experience. While these data do not directly indicate willingness to use parcel locker crowdshipping, they shed light on the prevalence of gig background within different demographic groups, which may inform system design and recruitment strategies. Interviewees repeatedly mentioned that prior familiarity with app-based ‘task work’ can reduce barriers to entry.

Gig Experience Across Gender and Age

As shown in Figure 1, a substantial portion of young participants (18–34) completed the survey, but their distribution of gig experience varied by gender. Among 97 young male respondents, 18 reported having prior gig experience (about 19%), whereas out of 109 young female respondents, 9 had prior gig experience (about 8%). One logistics manager noted the following: “Younger men who’ve done food delivery are already comfortable with on-demand tasks—crowdshipping feels similar”.

In middle-aged categories (35–54), fewer participants overall reported gig experience, and an even smaller fraction was found among older adults (55+). Older participants, in particular, tended to have the least gig experience in this sample. An academic interviewee pointed out the following: “Many seniors may not be comfortable using apps or carrying parcels in crowded trains, but they might do smaller deliveries off-peak”. Figure 1 provides a snapshot of how gig familiarity is distributed rather than a direct measure of interest in new crowdshipping roles.

3.2. Train Usage Results

The frequency of train usage is a key determinant of participants’ potential engagement with the parcel locker crowdshipping system. This analysis explores variations across gender, age groups, and gig experience, as illustrated in Figure 2.

For brevity, only major findings are summarized:

• Gender: Males dominate the frequent train user category (4–5 times or more per week). Females are more evenly distributed across usage frequencies.
• Age Group: Younger participants (18–34) are the most frequent train users, highlighting potential for higher engagement. Middle-aged respondents show moderate usage, while older participants (55+) predominantly use trains rarely or sometimes.
• Gig Experience: Participants with gig experience show higher train usage overall, possibly reflecting a lifestyle conducive to flexible, on-demand work.

3.3. Operational Results

3.3.1. Parcel Handling Preferences

The operational efficiency of the parcel locker crowdshipping system depends heavily on participants’ preferences for handling parcels of varying weights. As illustrated in Figure 3, the 3–5 kg range emerges as the most popular across demographics. Males express slightly higher tolerance for heavier parcels (6–8 kg or more), while females lean toward lighter weights. A gig-experienced participant explained: “If the pay is decent, I’ll carry 8 kg. But if it’s just a tiny bonus, it’s not worth the hassle”.

3.3.2. Maximum Distance Willingness from Home or Business

Participants typically prefer locker locations within 1 km of their home or workplace. However, older individuals and gig-experienced users appear more flexible if incentives are involved. An interviewee with transport-planning experience noted: “If a locker is 400 m from a station exit, crowdshippers won’t see it as a big detour. But if it’s 2 km away, they’ll likely drop out unless there’s additional compensation”.

3.3.3. Deviation from Normal Routes

Participants’ willingness to deviate from normal train routes for parcel delivery is pivotal for operational planning. As it can be seen in Figure 4, roughly half the sample are unwilling to deviate at all; younger and gig-experienced respondents are more open to minor deviations (1–2 stations). Few participants accept major deviations unless well compensated. One industry expert emphasized: “You have to keep route changes minimal, or crowdshippers just say no”.

3.3.4. Maximum Parcels Willing to Carry

Understanding participants’ preferences for the maximum number of parcels and their weight provides critical insights into task distribution and operational planning for parcel locker systems. Participants were asked to specify the maximum weight they could carry and the maximum number of parcels they could handle in a single trip, provided the total weight did not exceed their stated maximum carriage capacity. They were also asked if they would be willing to be paid based on the weight and number of parcels carried. This integrated analysis combines parcel count and weight preferences, emphasizing their interdependence and providing actionable insights for balancing user comfort and operational efficiency.

As it can be seen in Figure 5, survey data indicate that most participants prefer carrying two to three parcels, provided each is not excessively heavy. Younger, gig-experienced respondents can handle more parcels or heavier loads, albeit with the expectation of higher pay. An academic interviewee commented: “It mirrors typical gig patterns—flexibility, but a strong link between effort and reward”.

4. Discussion

This study examined how key demographic factors—specifically age, gender, and gig work experience—interact with operational requirements to shape the feasibility and scalability of parcel locker crowdshipping in public transport networks. By triangulating survey results with expert interview insights, the analysis provides a holistic view of both the potential and the limitations of public transport-based crowdshipping systems.

Below, we integrate the main quantitative trends with qualitative observations from interviews, highlighting feasibility issues such as peak-hour congestion, space for lockers, and liability considerations.

4.1. Demographic Insights

4.1.1. Younger Participants as Core Adopters

Younger adults (18–34), especially gig-experienced males, are the most likely to adopt new crowdshipping roles. This aligns with earlier crowd-based delivery research [30,32]. Interviewees noted that these users’ familiarity with app-based work lowers entry barriers, and their physical capacity supports tasks such as handling heavier parcels.

4.1.2. Gig-Experienced Males: Familiarity Effects

Another prominent finding is the strong participation among gig-experienced males. Familiarity with on-demand work norms, such as time-flexible tasks, dynamic remuneration, and route flexibility, appears to heighten acceptance of additional parcel-carrying duties [33,34]. Multiple interviewees indicated that men already active in app-based food or goods delivery showed fewer concerns about detours or heavier parcels, often because they have internalized how to balance time, effort, and pay. This highlights how prior gig experience eases the transition into crowdshipping roles.

4.1.3. Untapped Female Potential

Although young females in our sample had less gig experience, they still show moderate interest in carrying parcels if safety and convenience are assured. Clear security protocols (e.g., ID verification, real-time tracking) and user-friendly interfaces can help bring more women on board [35]. One interviewee suggested partial compensation in the form of public transport discounts: “If women see a tangible benefit—like cheaper train fares—they might be more willing to try it”.

4.1.4. Limited Engagement Among Older Adults

Older individuals often use trains less frequently and appear more concerned about physical strain or unfamiliar apps. Still, some interviewees believed seniors could deliver lighter parcels off-peak. Policy measures, such as robust insurance coverage and simple user interfaces, could facilitate occasional involvement [36].

4.2. Train Usage Patterns

Frequent train users, especially younger respondents with gig experience, demonstrate the strongest inclination toward parcel-carrying tasks. Interviewees emphasized that many regular commuters are already accustomed to brief intermediate stops, making short detours to collect or deliver parcels more acceptable [5,16]. In contrast, older participants, who generally use trains less often, tend to prefer minimal involvement. Women, interestingly, are distributed across a wider range of train usage frequencies, hinting at flexibility if tasks do not conflict with personal schedules or introduce safety uncertainties. These findings reinforce the strategy of locating parcel lockers in or near major train stations to harness existing passenger flows and reduce the environmental impact of last-mile deliveries [37].

4.3. Operational Preferences

4.3.1. Parcel Handling Preferences

The results show two main take-away messages: (1) a focus on moderate-weight parcels (3–5 kg) to include as many participants as possible, and (2) a provision for heavier (6–8 kg) tasks targeted at gig-experienced or otherwise capable individuals. These observations align with references indicating that parcel size and weight constraints play a pivotal role in public transport crowdshipping feasibility [6,37].

In more general terms, interviewees consistently emphasized that a “one-size-fits-all” approach is unlikely to succeed. Instead, having standardized weight categories, along with optional heavier-load tasks for those who are comfortable, is beneficial. One interviewee who has researched operational design in multiple pilot projects made the point that an adequate number of crowdshippers must be available to handle different parcel types—particularly heavier ones—so that no single participant is unduly burdened or discouraged.

An academic interviewee with operational experience explicitly noted: “You need a sufficient pool of crowdshippers… so they wouldn’t have to deviate from their originally planned routes”. This underscores that broader participant engagement, including those comfortable with heavier loads, is vital to minimizing the extra travel effort. Additionally, another expert who evaluated decentralized locker networks commented: “Distributed networks clearly provide better matching opportunities between parcel destinations and crowdshippers”. This view suggests that multiple smaller lockers, rather than a single large hub, can facilitate efficient handoffs and reduce the risk that a participant would be forced into a major detour.

4.3.2. Maximum Distance Willingness from Home/Business Destinations

Data indicate that participants generally prefer parcel lockers within a 1 km radius of their home for maximum convenience. Such proximity reduces the likelihood of extra travel time, aligning with broader findings on public transport crowdshipping [34]. Certain subgroups—older individuals, females, and those with gig experience—may travel farther if compensated. Some prefer short walks but rely on trains for longer routes (5 km+). Weather can reduce willingness, underscoring the need for flexible, on-demand task acceptance.

General insights from multiple interviewees confirm that dynamic incentive structures can address varying thresholds for travel distance. For instance, those who are comfortable with a 1–2 km range might receive a slightly higher fee for tasks beyond the 1 km norm. This approach is consistent with the argument in [16] that early-stage crowdshipping platforms benefit from tiered compensation to encourage participation from different demographic segments.

Similarly, participants prefer business destinations that are no more than 1 km from their workplace or main errands. The literature on public transport-based crowdshipping highlights the role of convenience and minimal detours as core drivers of success [31,37]. Some interviewees mentioned that placing lockers outside station ticket barriers can further increase accessibility for both commuters and non-commuters.

One interviewee, who has researched multi-modal transport systems, noted: “If you put it outside [the station], you increase the possibilities of delivery… and enhance accessibility of a parcel locker to other users, not just those with train tickets”. By ensuring lockers are located in publicly accessible zones, the potential user base grows to include individuals who might not hold valid train tickets or may only pass near the station.

4.3.3. Deviation from Normal Routes

The data on route deviations highlight that participants prefer tasks requiring little to no disruption to their daily routines, especially older and non-gig-experienced individuals. Younger and gig-experienced demographics can handle moderate deviations for an appropriate reward. This resonates with numerous studies indicating that the acceptance rate for crowdshipping tasks decreases sharply with longer or more complex detours [33,38].

Interviewees generally agreed that designing tasks around participants’ existing routes is pivotal. This ensures minimal friction and a higher likelihood of consistent participation. Some also noted that flexible route planning—where participants can opt in or out of certain deliveries on short notice—can help the system absorb fluctuations in supply and demand without overburdening any single courier.

4.3.4. Maximum Parcels Willing to Carry

Finally, the results regarding maximum parcel counts confirm a nuanced interplay between count and weight. Younger participants can often handle 3–5 parcels and weights up to 20 kg. Meanwhile, older participants or those new to gig work prefer fewer, lighter parcels (under 5 kg). Females tend to be comfortable with multiple parcels if they are light, whereas gig-experienced participants are more accepting of both heavier weights and higher counts if properly incentivized.

In general, interviewees indicated that carefully structured “default tasks” (e.g., 2–3 parcels totaling around 5–8 kg) appeal to the broadest range of participants. Optional tasks for heavier loads or more parcels can then be offered at an increased rate to younger or more experienced users. This aligns with references suggesting that segmenting parcels by weight category and complexity leads to better matching in crowdshipping platforms [5,37].

One interviewee who analyzed the economic sustainability of crowdshipping explained: “To attract crowdshippers, you need volume, but to generate volume, you need crowdshippers”. This statement underscores the “chicken-and-egg” dynamic. Providing a variety of tasks—ranging from light, one-parcel deliveries to multi-parcel tasks—helps cultivate a healthy user base, which in turn can handle larger overall delivery volumes.

4.4. Synthesizing Findings and Alignment with the Literature

Overall, the study highlights a demographic composition led by younger, gig-experienced males, yet it identifies considerable potential for middle-aged females. Older individuals may still contribute occasionally if tasks are designed to fit their comfort levels. Train usage patterns corroborate that frequent commuters are optimal crowdshippers, and operational preferences illustrate that moderate parcel weights (3–5 kg) and short travel distances (up to 1 km) are the sweet spot for broad adoption. These conclusions are consistent with prior research on public transport-based crowdshipping, which underscores how leveraging commuter trips can significantly curtail traffic congestion and emissions [6,37]. At the same time, the literature also warns that heavier parcels require proportionate incentives [38,39], and successful early-stage deployment demands recruiting sufficient couriers and generating enough parcel volume [31].

4.5. Practical Policy Implications

Drawing on both survey data and interview remarks, key implications include the following:

• Siting Lockers Sensibly: Place lockers near main entrances but away from peak foot-traffic bottlenecks.
• Tiered Compensation: Offer higher pay for heavier loads or slight route deviations to attract gig-experienced couriers.
• Align with Transport Authorities: Collaborate with train/bus operators for integrated ticketing or discounted fares for verified crowdshippers.
• Liability and Insurance: Formalize coverage for lost or damaged goods to reduce participant hesitancy.
• In contrast to ride-hailing services, which typically add extra vehicles to urban roads in response to delivery demands, public transport-based crowdshipping leverages commuter trips that are already taking place. By integrating parcel pickups and drop-offs into existing travel patterns, this approach can potentially reduce overall vehicle kilometers traveled (VKT) and cut emissions. Therefore, while ride-hailing offers on-demand convenience, it does not always yield the environmental benefits associated with public transport-based crowdshipping.

4.6. Limitations and Future Directions

4.6.1. Study Limitations

• Sampling: Our use of Prolific was efficient for a broad, initial sample but may not perfectly represent the general commuter population.
• Self-Reported Behavior: Survey responses regarding weight tolerance or distance willingness may overstate actual behavior.
• Environmental Metrics: While the literature suggests potential emission reductions, we did not collect real-world traffic or pollution data.
• Compensation Unspecified: We discussed incentives qualitatively but did not quantify them. Actual uptake could differ with specific monetary or non-monetary rewards.

4.6.2. Future Research Directions

• Multi-City Comparisons: Investigate whether crowdshipping adoption varies in smaller or differently structured cities, emphasizing the need for comparative analyses across diverse geographical settings (e.g., suburban vs. central business districts).
• Pilot Experiments: Test real-time logistics apps in collaboration with public transport operators, measuring actual route deviations, drop-off reliability, and user satisfaction.
• Regulatory Models: Explore how insurance companies and train operators can formalize coverage.
• Pandemic-Era Shifts: Building on [40], examine post-pandemic travel patterns and changes in e-commerce demand to see if public transport crowdshipping remains viable under shifting conditions.
• Economic Incentives and Labor Segments: While this study primarily examined factors such as parcel weight tolerance, distance willingness, and demographics (age, gender, gig experience), we also collected data on participants’ expected compensation and employment status. A separate, more detailed analysis of these monetary preferences and labor categories (e.g., unemployed vs. employed) will be undertaken in subsequent research. Such inquiries are crucial for determining how compensation structures could affect participation rates and whether certain groups, like unemployed or part-time workers, might be more receptive to flexible crowdshipping tasks.

5. Conclusions

This study demonstrates that public transport-based parcel locker crowdshipping can potentially reduce urban congestion and emissions by tapping into commuters’ existing travel. Younger adults, especially gig-experienced males, emerge as the most flexible demographic, willingly accommodating heavier parcels and minor route deviations. Yet substantial potential exists among younger and middle-aged females, provided the system prioritizes safety, convenience, and fair incentives. Older participants may still contribute to lighter, off-peak deliveries, albeit in smaller numbers.

From an operational standpoint, offering tasks in the 3–5 kg weight range, placing lockers within 1 km of major train stations or residences, and establishing tiered incentives appear crucial. However, real-world feasibility also hinges on station-space allocation, robust insurance frameworks, and commuter willingness to deviate their routes, especially during crowded periods. The success of this approach ultimately requires collaborative involvement of policymakers, transport authorities, insurers, and local communities.

While we did not capture real-time traffic or emissions metrics, multiple studies suggest that integrating parcels into existing passenger trips can reduce net vehicle kilometers traveled. Future pilots would benefit from measuring route-specific emissions data, as recommended in Section 4.6.

Despite its promise, public transport-based crowdshipping depends on reliable schedules and sufficient courier participation. Future research should explore multi-modal expansions (e.g., integrating bus or micro-mobility options) and deeper cost–benefit analyses. Empirical pilots comparing incentive schemes, locker placements, and security features would help validate the potential for large-scale adoption. By designing inclusive systems aligned with demographic patterns and addressing operational barriers head-on, parcel locker crowdshipping could evolve into a sustainable, innovative solution for the urban last mile.