Integrating rail transport with crowd logistics for sustainable urban freight: a systematic literature review Open Access

Urbanization and the rapid expansion of e-commerce have significantly intensified pressure on urban freight transport systems. The increasing demand for fast, flexible, and reliable delivery services has led to a substantial rise in freight volumes, contributing to congestion, environmental pollution, noise and increased energy consumption in densely populated areas (Allen, Browne, & Cherrett, 2012; Bosona, 2020; El Moussaoui, Benbba, Jaegler, & El Andaloussi, 2022). Among the various components of the logistics chain, last-mile delivery is particularly inefficient and costly, often accounting for a disproportionate share of total logistics costs and emissions (Gatta, Marcucci, Nigro, & Serafini, 2019; Elbert & Rentschler, 2021). These challenges highlight the urgent need for innovative and sustainable freight transport solutions that can improve efficiency while reducing environmental impact.

Rail transport is widely recognized as a sustainable alternative for long-haul freight movement due to its high capacity, energy efficiency and significantly lower carbon emissions per ton-kilometer compared to road transport (Alessandrini, Delle Site, Filippi, & Salucci, 2012; Crainic, Ricciardi, & Storchi, 2009; Gholamizadeh, Zarei, & Yazdi, 2022). However, its application in urban logistics remains constrained by limited flexibility, particularly in first- and last-mile operations where demand is fragmented and time-sensitive. In contrast, crowd logistics (CL), also referred to as crowdshipping, has emerged as a flexible and demand-responsive delivery model that leverages the unused capacity of individuals and their mobility resources to perform short-distance deliveries (Carbon, Rouquet, & Roussat, 2021; Buldeo Rai, Verlinde, Merckx, & Macharis, 2017; Le, Stathopoulos, Van Woensel, & Ukkusuri, 2019). By combining the structural efficiency of rail with the adaptability of CL, there is growing potential to develop integrated, multimodal freight systems capable of addressing urban logistics challenges more effectively.

Despite the increasing body of research on rail freight, urban logistics and CL, these domains have largely evolved in isolation. Existing studies predominantly focus either on road-based freight optimization or on last-mile crowdshipping solutions, with limited attention to their systematic integration (Elbert & Rentschler, 2021; Derse & Van Woensel, 2024). More critically, no prior study has provided a comprehensive and structured synthesis of how rail transport can be operationally and conceptually integrated with CL within a unified framework. As a result, current knowledge remains fragmented, particularly regarding integration models, technological coordination mechanisms and system-level sustainability outcomes. This gap is especially pronounced in the context of developing and road and rail-dependent economies, where such integration could offer significant efficiency and sustainability benefits.

To address this gap, the present study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines. The review synthesizes 50 peer-reviewed studies published between 2009 and 2025, with the aim of developing a comprehensive understanding of Rail-CL integration. The study is guided by the following research questions.

This study makes three key contributions to the literature. First, it provides the first structured and comprehensive synthesis explicitly focused on the integration of rail freight and CL, thereby bridging two previously disconnected research streams. Second, it advances theoretical understanding by identifying and consolidating key integration models, system architectures and enabling mechanisms within a unified analytical framework. Third, it offers practical insights for policymakers, railway operators and logistics providers by highlighting critical barriers, infrastructure requirements and policy implications necessary for implementing integrated and sustainable freight systems, particularly in developing economies. The remainder of this article is structured as follows. Section 2 reviews the relevant literature on rail transport and CL and develops the conceptual foundation for integration. Section 3 describes the systematic review methodology. Section 4 presents and discusses the results of the analysis. Finally, Section 5 concludes the article by outlining key findings, contributions, limitations and directions for future research.

Rail transport has long been recognized as a key enabler of sustainable freight distribution, particularly for long-haul and high-volume transport. Its advantages include high carrying capacity, energy efficiency and significantly lower greenhouse gas emissions per ton-kilometer compared to road transport (Alessandrini et al., 2012; Gholamizadeh et al., 2022). These characteristics position rail as an environmentally favorable alternative within the broader context of decarbonizing freight transport systems. In the context of urban logistics, rail has been explored as a means to reduce road congestion and improve the environmental performance of freight distribution. Early and recent studies highlight the potential of urban rail systems, including freight trams and metro-based logistics, to support the movement of goods into city centers (Marinov et al., 2013; Elbert & Rentschler, 2021). Such approaches can facilitate higher levels of consolidation and reduce the number of freight vehicles operating in urban areas.

However, the application of rail in urban freight systems remains limited. A key constraint is its lack of flexibility in handling first and last-mile deliveries. Rail systems typically require fixed infrastructure and schedules, making them less adaptable to dynamic urban delivery demands (Crainic et al., 2009; Ozturk & Patrick, 2018). As a result, rail-based freight solutions often rely on complementary modes to ensure end-to-end service delivery, while rail transport offers significant sustainability benefits for long-distance freight movement. Critical observation from the literature is that rail's role in urban freight is almost exclusively framed as a line-haul backbone, with first/last-mile treated as a separate problem to be solved by other modes. This separation reflects an implicit theoretical assumption that modal boundaries are fixed rather than reconfigurable. Few studies challenge this assumption by exploring how rail infrastructure itself could be adapted for granular, crowd-based distribution. Consequently, the literature perpetuates a “modal silo” perspective that undermines genuine integration.

CL represents an innovative strategy for enabling rapid shipping services by delegating last-mile delivery to ordinary individuals. These “crowd” participants agree to transport goods to customers along their planned routes, using personal vehicles in return for a modest incentive (Carbon et al., 2021; Bortolini, Calabrese, & Galizia, 2022). At its core, CL builds on a network of interconnected users to enhance the efficiency and sustainability of goods movement, storage and delivery worldwide. It depends on individuals linked through mobile technologies and typically emphasizes small-scale operations (Carbon et al., 2021; Le et al., 2019). Essentially, CL fosters connections between those possessing logistics resources and those in need of them (Andrejić & Jeremić, 2019). No universal definition exists for crowdsourcing or its derivative CL. The roots of CL trace back to crowdsourcing, which combines “crowd” a large group of people with “outsourcing” delegating tasks to external parties (Buldeo Rai et al., 2017). Crowdsourced delivery addresses escalating customer demands for quicker, more personalized and cost-effective services. It capitalizes on technological advancements, such as geolocation and mobile applications, alongside societal shifts toward sharing and collaboration (Rougès & Montreuil, 2014).

Rail-CL integration can be defined as: A coordinated logistics system in which rail transport performs long-haul, high-volume freight movement, while CL facilitates first and last-mile distribution through decentralized, digitally coordinated delivery networks. Despite growing interest in rail freight and CL, their integration remains conceptually fragmented, with most studies addressing them independently (Bosona, 2020; Mohri, Ghaderi, Nassir, & Thompson, 2023). To address this gap and in line with RQ1, this study conceptualizes Rail-CL integration as a multimodal socio-technical system that combines rail's line-haul efficiency with flexible, crowd-enabled first and last-mile delivery.

The framework is grounded in socio-technical systems theory (Geels, 2004) and stakeholder theory (Freeman, 1984), emphasizing the interaction between infrastructure, digital technologies and institutional actors. Rail functions as a high-capacity, low-emission backbone for long-distance freight, while CL provides decentralized, demand-responsive distribution via digitally coordinated participants (Carbon et al., 2021; Le et al., 2019). This complementarity addresses inefficiencies in urban freight systems, particularly last-mile challenges (Gatta et al., 2019; Elbert & Rentschler, 2021).

The framework comprises three interconnected layers. The line-haul layer represents rail-based bulk transport between major terminals (Crainic et al., 2009; Gholamizadeh et al., 2022). The node layer includes rail terminals and urban consolidation centers enabling freight transfer across modes (Alessandrini et al., 2012). The distribution layer involves CL-based delivery using flexible modes such as bike and private vehicles, coordinated through digital platforms (Buldeo Rai et al., 2017; Mladenow, Bauer, & Strauss, 2016). This layered structure captures system-wide freight integration. Four enabling components underpin system performance. Physical infrastructure ensures efficient transshipment and connectivity; digital platforms enable real-time matching, routing and tracking (Seng, Ang, Ngharamike, & Peter, 2023; Chen, Zhang, Lin, & Fang, 2019); the stakeholder ecosystem requires aligned incentives, governance and trust (Carbon et al., 2021; Cebeci, Tapia, Kroesen, de Bok, & Tavasszy, 2023); and operational synchronization across consolidation, rail transport and last-mile delivery ensures reliability (Cheng, Fessler, Nielsen, Larsen, & Jiang, 2024; Maleki, Rayburg, & Glackin, 2025).

Aligned with RQ2, the framework supports triple bottom line sustainability outcomes. Environmental benefits include emission reduction and congestion mitigation, while economic gains stem from improved efficiency and asset utilization. Social benefits relate to accessibility and participation in logistics services (Buldeo Rai et al., 2017; Naganawa, Hirata, & Thompson, 2025; Carbon et al., 2021). However, these outcomes are constrained by regulatory uncertainty, infrastructure limitations and coordination challenges (Derse & Van Woensel, 2024; Cebeci et al., 2023). Critical observation: More critically, the existing frameworks in the literature remain predominantly conceptual rather than operationalized, with limited empirical testing and no substantive validation in developing-economy contexts. Moreover, existing frameworks tend to assume seamless and benevolent coordination that rail operators, platform providers, crowd participants and regulators will naturally align incentives. This ignores well-documented principal-agent problems, trust deficits, and power asymmetries. A theoretically robust Rail-CL framework must therefore incorporate governance mechanisms (contract design, reputation systems, dispute resolution) rather than assuming coordination away.

The integration of rail freight with CL can be operationalized through three complementary approaches that structure freight flows, enhance system efficiency and support sustainable urban logistics. These approaches consolidation and deconsolidation, Freight-on-Transit (FOT) and co-modality reflect different but interrelated mechanisms for linking high-capacity rail systems with flexible last-mile delivery networks, directly addressing the study's objective of identifying integration models (RQ1).

The first approach consolidation and deconsolidation approach structures freight flows through strategically located rail hubs. Upstream, consolidation aggregates dispersed shipments into centralized terminals to improve load factors and achieve economies of scale in rail transport (Crainic et al., 2009; Zhao, Zhao, Hu, Li, & Stoeter, 2018). Downstream, deconsolidation enables the fragmentation of bulk shipments into smaller consignments suitable for CL-based last-mile delivery (Alessandrini et al., 2012). This approach enhances first and last-mile connectivity while maintaining rail efficiency. However, its effectiveness depends on terminal capacity, spatial configuration and coordination among logistics actors (Gholamizadeh et al., 2022; Allen et al., 2012).

The second approach, freight-on-transit (FOT), extends rail functionality by integrating freight into existing passenger rail and urban transit systems, thereby utilizing spare capacity and reducing reliance on dedicated freight infrastructure. Empirical and review-based studies demonstrate the feasibility of this model, particularly in dense urban contexts where passenger networks are underutilized during off-peak periods (Elbert & Rentschler, 2021; Bacher, Klopp, Ortbauer, & Lackner, 2024). Applications such as public transport-based crowdshipping and mixed passenger freight systems illustrate how FOT can improve asset utilization and reduce environmental impacts (Gatta et al., 2019; Serafini, Nigro, Gatta, & Marcucci, 2018; Naganawa et al., 2025). Nonetheless, operational challenges including scheduling conflicts, demand uncertainty and economic viability remain critical barriers to large-scale implementation (El Amrani, Fri, Benmoussa, & Rouky, 2024).

The third approach, co-modality, emphasizes the dynamic and optimized integration of multiple transport modes within a unified logistics system (Derse & Van Woensel, 2024). Unlike traditional multimodal transport, co-modality is inherently demand-responsive and enabled by advanced digital platforms and real-time data systems (Seng et al., 2023). Within this approach, rail serves as the high-capacity backbone, while CL provides flexible, decentralized delivery through crowdsourced resources (Carbon et al., 2021; Le et al., 2019). Simulation and empirical studies indicate that integrating freight flows into passenger transport systems such as buses, rail and on-demand mobility can improve system efficiency by reducing empty trips and enhancing capacity utilization (Ronald, Yang, & Thompson, 2016; Yang, Wu, & Huang, 2023). However, co-modality introduces coordination complexity across heterogeneous stakeholders and requires robust digital infrastructure to ensure service reliability and synchronization (Cebeci et al., 2023; Cheng et al., 2024).

Critically, these three approaches are not mutually exclusive, yet the literature treats them as separate research streams. Consolidation studies rarely engage with FOT, and co-modality papers seldom cite consolidation research. This fragmentation is a theoretical weakness. A genuine Rail-CL integration framework should synthesize these approaches: using consolidation for volume aggregation, FOT for urban penetration and co-modality for dynamic mode switching. Furthermore, each approach embeds different assumptions about who controls the platform (rail operator vs. third-party vs. decentralized protocol), who bears risk, and how value is distributed. These questions are almost entirely absent from current theoretical models.

This study adopts an SLR approach to provide a structured and transparent synthesis of existing research on the integration of rail transport and CL. The review follows the PRISMA 2020 guidelines to ensure methodological rigor and reproducibility. The process includes identification, screening, eligibility assessment and final inclusion of studies.

A comprehensive database search initially identified 587 records from major academic sources, including ScienceDirect, MDPI, IEEE Xplore, Emerald Insight and Springer Nature. Additional mixed sources were identified through Taylor & Francis Online, Wiley Online Library, Frontiers, conference proceedings and institutional repositories to ensure comprehensive coverage. The search was conducted between May 2, 2025 and March 3, 2026, covering publications from 2009 to 2025. The timeframe 2009–2025 was selected because CL and crowdshipping emerged as recognizable concepts in the late 2000s. The final database-specific search strings included.

1. (“crowd logistic” OR “crowdshipping” OR “crowdsourcing” OR “crowdsourced deliver”) AND (“rail*” OR “rail freight” OR “multimodal transport” OR “intermodal”)

2. (“urban freight” OR “first-mile” OR “last-mile”) AND (“rail” OR “freight rail”) AND (“sustainable” OR “barrier” OR “challenge”)

3. (“co-modality” OR “freight-on-transit” OR “consolidation hub”) AND (“crowd logistic” OR “crowdshipping”). Figure 1 shows the distribution of records by source.

Inclusion criteria selected studies explicitly addressing rail transport, CL or their integration in urban or multimodal systems. Only English-language, peer-reviewed articles and reputable conference proceedings from 2009 to 2025 with full-text availability were considered.

Exclusion criteria removed studies on unrelated sectors (e.g., aviation or maritime) or general logistics without specific rail or CL applications. Non-peer-reviewed items, such as editorials, opinion pieces and blogs, were omitted to uphold academic credibility.

The study selection followed a structured PRISMA approach. From the initial 587 records, title and abstract screening reduced the dataset to 82 articles for full-text assessment. After applying inclusion and exclusion criteria, 53 studies were retained for quality appraisal. Using the MMAT evaluation, studies that did not meet the quality threshold were excluded, resulting in a final sample of 50 studies included in the synthesis. The selection process is illustrated in Figure 2.

Data extraction was conducted using a standardized Microsoft Excel framework to capture key variables, including authorship, publication year, study type, geographic focus, transport modes, technologies, sustainability outcomes, integration strategies, barriers, mathematical models and limitations. The final dataset consisted of 50 studies. The analysis combined thematic synthesis for qualitative insights and descriptive analysis for quantitative patterns. Due to heterogeneity in study designs, meta-analysis was not performed.

The methodological quality of the selected studies was evaluated using the Mixed Methods Appraisal Tool (MMAT) (2018), which assesses studies across five criteria with scores ranging from 0 to 5. A total of 53 studies were appraised, and those scoring below 3 were excluded to ensure methodological rigor, resulting in 50 studies retained for analysis. The appraisal was conducted by dual reviewer using predefined criteria, and ambiguous cases were re-evaluated to enhance consistency and minimize bias.

This section provides a complete synthesis of 50 reviewed studies, classified into qualitative 20 papers, quantitative 23 papers and mixed-methods 7 papers. The analysis focuses on study types, objectives, geographic focus, first and last mode of transportation, technology, sustainability, integration focus, integration barriers, mathematical model and study limitations.

A bibliometric analysis was conducted on the chosen corpus using VOSviewer to understand how keywords and topics evolved. A minimum keyword co-occurrence of n = 2 was selected, resulting in the retention of 105 keywords and the creation of Figure 3. The analysis reveals a clear structural evolution in the literature on urban freight and CL. Early research is predominantly anchored in urban freight systems, rail transport and sustainability, reflecting foundational concerns with efficiency and environmental performance. Over time, the field has shifted toward digitally enabled, decentralized logistics models, with increasing emphasis on CL, platform-based coordination, and innovative business models. A critical observation is that rail transport and CL remain weakly interconnected research clusters. While “urban freight” and “last-mile delivery” act as central nodes, rail-related keywords are only loosely connected to emerging CL concepts, indicating that integration is still conceptually underdeveloped. This directly supports RQ4, confirming that Rail-CL integration is an emerging but fragmented research domain. Furthermore, the temporal overlay suggests that post-2020 research is increasingly driven by digitalization and platform economies, yet this shift has not been matched by corresponding advances in multimodal integration frameworks. This imbalance highlights a structural gap, namely that technological innovation is advancing faster than system-level integration.

The geographical scope of sector studies demonstrating Figure 4, Approximately 73% of the studies originate from Europe, indicating a strong regional concentration of research on CL and rail transport. This dominance reflects advanced policy frameworks, infrastructure maturity and early adoption of sustainable urban logistics practices in European contexts. Asia and America contribute 13% and 9 respectively, showing growing interest in these areas. Africa has the least, about 5%. This imbalance directly addresses RQ4, indicating a clear geographic research gap. The dominance of European studies reflects advanced policy frameworks and established urban logistics systems, which may not be transferable to developing regions.

The reviewed studies reveal distinct patterns in transport modes used for first and last-mile operations in CL. First-mile activities predominantly rely on motorized, higher-capacity vehicles, with private cars being the most common (12 studies), followed by vans and trucks (10 studies). Public transport modes such as trains (9 studies) and buses (4 studies) appear less frequently, indicating limited integration of shared mobility in early-stage freight movement. Nonmotorized modes bicycles (3 studies) and motorbikes/scooters (1 study), are rarely utilized, and no study reports on-foot first-mile delivery. Eleven studies do not specify the mode used, reflecting inconsistent reporting (see Figure 5).

In contrast, last-mile delivery shows a strong shift toward flexible, low-cost and urban friendly modes. Motorbikes and scooters feature most prominently (16 studies), followed by bicycles (9 studies). Private cars (7 studies) and on-foot delivery (8 studies) also play a notable role, highlighting the diversity of last-mile participants. Vans and trucks appear in only four studies, while no evidence supports the use of buses or trains, underscoring their limited practicality for door-to-door distribution. Six studies do not specify last-mile modes (See Figure 6).

Overall, the evidence indicates that first-mile tasks depend on larger motorized vehicles suited for consolidation and longer distances, whereas last-mile activities favor agile, low-emission options like walking, cycling and two-wheel motorized modes. The distribution across modes is detailed in Table 1.

This pattern partially addresses RQ1, indicating that Rail and public transport modes are rarely utilized in first or last-mile operations, highlighting weak multimodal coordination. From a sustainability perspective (RQ2), the increasing use of nonmotorized and low-emission modes in last-mile delivery suggests positive environmental outcomes. However, the continued reliance on motorized vehicles in first-mile operations limits overall system sustainability. The findings also reinforce RQ4, as the limited use of rail and public transport in first and last-mile logistics reveals a significant thematic gap. Existing studies focus primarily on road-based CL systems, with minimal attention to Rail-CL integration.

This section synthesizes findings from 20 qualitative studies; the review is organized by main dimensions study including type, objectives, technologies, sustainability, integration aspects, integration barriers and limitations. A detailed classification of studies is presented in Table 2.

The 20 qualitative studies comprise three main designs: case-based studies, conceptual analyses and literature reviews. Four case-based studies (e.g., Frehe, Mehmann, & Teuteberg, 2017) provide practical insights into CL feasibility, operational features and implementation challenges. Seven conceptual studies (e.g., Carbon et al., 2021; Andrejić & Jeremić, 2019) develop theoretical models and frameworks for integrating CL into broader transport and supply chain systems. Nine literature reviews (e.g., Bosona, 2020) synthesize existing knowledge, identify dominant themes and highlight methodological gaps. Overall, this distribution reflects a balance between theoretical development and empirical exploration; however, the limited number of case-based studies suggests a need for more real-world validation of CL applications.

The qualitative literature reveals four interconnected research objectives that collectively shape the trajectory of CL research. Sustainability and freight optimization dominate the literature, with nine studies examining how CL improves resource efficiency, reduces delivery costs and lowers environmental impacts in urban last-mile operations (Samad, Ganguly, & Das, 2023). Conceptual framework development represents the second major objective, with five studies clarifying CL's role within multimodal freight systems and defining its operational principles and integration potential (Bosona, 2020; Elbert & Rentschler, 2021). Value co-creation, addressed by only two studies, explores the collaborative nature of CL, emphasizing digital platforms and stakeholder interactions that generate shared value among logistics providers, crowd participants and end users (Carbon et al., 2021; Rougès & Montreuil, 2014). Three studies focus on business model development, examining CL system design, implementation and scalability across different contexts while highlighting economic viability and incentive structures (Frehe et al., 2017; Odong, 2017). More critically, integration-specific research remains marginal, indicating that Rail-CL interaction is still peripheral rather than central in the literature. This directly supports RQ1, confirming the absence of well-developed integration models.

Technology plays a central role in enabling CL systems. Digital platforms and mobile applications are reported in 3 of the 20 studies, including Mohri et al. (2023), enabling real-time matching and tracking of delivery tasks. A majority (13 studies), such as Allen et al. (2012), employ mixed technology approaches without specifying particular systems. Algorithmic optimization appears in one study, Bosona (2020), while 3 studies do not specify the type of technology used. These results indicate that although technology is widely acknowledged as a key enabler, the limited use of advanced technologies such as artificial intelligence, blockchain and IoT suggests a gap in innovation and scalability within current CL systems.

Sustainability is a prominent theme, with 15 of the 20 studies assessing it through the triple bottom line (TBL) framework. These studies show that CL enhances environmental performance, reduces emissions and improves resource efficiency in urban freight systems. Four studies focus mainly on environmental outcomes, such as de Oliveira Leite Nascimento et al. (2023), while one study, Allen et al. (2012), emphasizes economic sustainability. Overall, these findings confirm that CL contributes significantly to sustainable urban logistics; however, the limited focus on social and economic dimensions highlights an important research gap, particularly in areas such as labor conditions, equity and long-term economic viability.

Among the 20 qualitative studies, seven examine CL integration in last-mile delivery, emphasizing its feasibility through flexible modes such as personal vehicles, bicycles, scooters and walking. Nine studies focus on sustainability, while two studies explore integration with traditional freight systems and two analyze multimodal configurations.

Regarding barriers, 13 studies describe general challenges without specifying detailed integration constraints. Four highlight infrastructure limitations and coordination gaps, two identify regulatory uncertainty and one underscores trust and liability concerns. One study does not specify barrier types (Gholamizadeh et al., 2022). Overall, findings show that CL integration is hindered by multifaceted operational, regulatory and infrastructural barriers, pointing to the need for supportive policies and technological enablers. This reinforces the argument that Rail-CL integration is not merely a technical challenge but a socio-technical transformation, requiring alignment across institutional, technological and operational dimensions.

The 20 qualitative studies reviewed exhibit several common limitations. Seven studies rely heavily on conceptual models with limited empirical validation, restricting insights into real-world logistics performance. Most research is concentrated in developed countries, with only two studies addressing developing contexts, limiting broader applicability. The scope is often narrow, focusing primarily on last-mile delivery while overlooking first-mile operations, reverse logistics and longer-distance integration. Additionally, multimodal coordination is insufficiently explored, with only two studies (e.g., Elbert & Rentschler, 2021) examining transport integration, highlighting a significant gap in the qualitative literature. Overall, these limitations reduce the generalizability of findings and highlight critical research gaps, particularly in developing regions and in comprehensive CL-Rail integration frameworks that address entire logistics chains.

This section synthesizes insights from 30 studies (23 quantitative and 7 mixed-methods) examining various dimensions of Rail-CL integration. The analysis covers study type, objectives, technologies, sustainability, integration focus, barriers, modeling approaches and limitations. A summary is provided in Table 3.

Among the 30 quantitative and mixed-methods studies reviewed, case-based research dominates (18 studies), reflecting a strong emphasis on practical, application-oriented investigations of Rail-CL integration. Eleven studies adopt conceptual or modeling approaches, offering valuable theoretical frameworks but with limited empirical validation. Only one study is a mixed-method literature review, indicating a significant gap in comprehensive knowledge synthesis. Overall, the distribution suggests that while practice-focused studies are prevalent, theoretical consolidation and systematic review efforts remain insufficient to support broader, scalable implementation of CL within rail systems.

Quantitative and mixed-methods studies cluster around four main objectives, revealing a strong emphasis on operational and sustainability outcomes with limited attention to technological innovation. Urban last-mile solutions dominate the literature, with 15 of the 30 studies focusing on improving last-mile delivery through CL by addressing congestion, service reliability, and operational efficiency in dense urban areas (e.g., Maleki et al., 2025; Gatta et al., 2019). Sustainability and freight optimization represent the second major objective, with eleven studies examining the environmental and economic benefits of CL and demonstrating its potential to reduce emissions while supporting greener, more efficient freight distribution (e.g., Naganawa et al., 2025). Business model development receives comparatively less attention, with only three studies exploring CL platform business models, thereby highlighting limited research on strategic design and commercial feasibility (e.g., Yang et al., 2023). Technology and algorithm development remains the least explored objective, with just one study centering on technological or algorithmic innovation, underscoring a significant gap in advanced system design for CL-Rail integration (e.g., Seng et al., 2023).

Across the 30 quantitative and mixed-methods studies, three main technology categories emerge. Mobile apps and digital platforms are most common (9 studies), reflecting their central role in real-time communication, routing and coordination in CL systems (e.g., Le et al., 2019; Seng et al., 2023). Mixed-technology approaches appear in 7 studies, combining digital platforms with network-based coordination to support more complex, hybrid solutions (e.g., Lin et al., 2020; Sárdi, 2022). Algorithmic optimization and social network tools are used in 6 studies, underscoring the importance of computational methods for routing, forecasting and participant matching (e.g., Chen et al., 2019; Masson et al., 2017). Notably, 8 studies do not explicitly specify the technologies applied (e.g., Gatta et al., 2019; Mohri et al., 2025).

Sustainability assessments across the 30 studies show a strong alignment with the Triple Bottom Line (TBL) framework. Fifteen studies address all three dimensions environmental, economic and social indicating a growing shift toward holistic sustainability evaluation in Rail-CL integration research. Nine studies focus primarily on environmental benefits, particularly emission reduction and lower urban pollution through measures such as consolidation and integration with public transport systems (e.g., Naganawa et al., 2025; Bakioglu, 2025a). Four studies examine only economic benefits, while one focuses solely on social outcomes, highlighting the persistence of single-dimension assessments. One study does not clearly specify sustainability benefits (Bajec & Tuljak-Suban, 2024). Overall, the prominence of TBL-based analysis reflects increasing recognition of multi-dimensional sustainability in the field.

The 30 quantitative and mixed-methods studies cluster around four main integration focuses: urban first/last-mile integration, freight optimization, technological integration and multimodal integration. Urban last-mile integration dominates (16 studies), reaffirming its central role in improving delivery efficiency in congested cities (e.g., Gatta et al., 2019). Four studies examine broader freight optimization, while six explore technological integration involving digital platforms, mobile apps and algorithmic tools. Multimodal integration appears in three studies, and just one study investigates first-mile integration.

Key barriers commonly reported include trust and reliability issues, infrastructure constraints and stakeholder coordination challenges. Fifteen studies discuss multiple barrier types, underscoring the complex obstacles to Rail-CL integration. Infrastructure and collaboration issues are highlighted in eight studies (e.g., Cheng et al., 2024), while trust and liability concerns appear in three (e.g., Yang et al., 2023). Four studies do not specify barriers. Overall, the findings point to a need for more context-specific analyses that differentiate technical, regulatory, institutional and social challenges.

Across the 30 quantitative and mixed-methods studies, mathematical modeling plays a central role in CL research. Deterministic models are used in 12 studies, providing fixed-input, predictable solutions for routing, scheduling and operational planning (e.g., Cheng et al., 2024). Stochastic models appear in 11 studies and address uncertainty related to travel times, traffic variability and dynamic demand (e.g., Gdowska et al., 2018), offering more flexible and resilient system designs. Five studies do not specify their modeling approach, indicating gaps in methodological transparency. Only two studies focus on the fuzzy MCDM model (e.g. Bakioglu, 2025a, b). Overall, both deterministic and stochastic models contribute substantially to understanding and optimizing crowd-based delivery systems.

Study limitations fall into four main categories: theoretical focus, narrow scope, geographic bias and limited multimodal integration. Many studies adopt a narrow lens, concentrating primarily on urban or last-mile delivery and overlooking wider logistics networks and multimodal connections, particularly with rail freight. Several papers are heavily theoretical and lack validation through real-world cases or pilot implementations. Geographic bias is also evident, with most studies conducted in developed countries and only a few addressing developing contexts. Finally, multimodal integration remains underexplored, as most research centers on road-based CL systems with minimal attention to coordination with rail or other transport modes. Overall, these limitations highlight the need for broader, more context-sensitive research that expands multimodal perspectives and includes underrepresented regions.

The synthesis of findings across Sections 4.1 - 4.5 reveals that research on rail transport and CL remains fragmented across conceptual, operational, and geographic dimensions. While individual studies provide valuable insights, a coherent system-level understanding of Rail-CL integration is largely absent.

First, the literature demonstrates a structural separation between rail freight and CL systems. Rail is predominantly examined in terms of line-haul efficiency, whereas CL is positioned as a last-mile solution. This results in a lack of integrated models addressing intermodal coordination and operational synchronization, confirming that current integration approaches remain limited and largely conceptual (RQ1).

Second, a clear modal imbalance is observed, with strong emphasis on last-mile optimization and limited attention to first-mile logistics and rail coordination. Although CL contributes to environmental improvements, the absence of upstream integration constrains system-wide efficiency, indicating that sustainability outcomes are partial and uneven (RQ2).

Third, the field is dominated by conceptual and simulation-based studies, with limited empirical validation. Quantitative research primarily focuses on isolated optimization problems, while qualitative studies emphasize frameworks without sufficient real-world testing. This highlights a methodological gap and limits the practical applicability of proposed solutions (RQ4).

Fourth, the literature exhibits a strong geographic bias toward European and developed nation contexts, raising concerns about the transferability of findings to developing regions. Given differing infrastructural and institutional conditions, existing models may not be directly applicable, underscoring the need for context-sensitive approaches (RQ4).

Overall, the findings indicate that Rail-CL integration should be understood as a socio-technical system challenge, requiring alignment between infrastructure, digital technologies, stakeholders and regulatory frameworks. Future research should therefore move beyond isolated analyses toward integrated, empirically grounded models that capture the complexity of multimodal urban freight systems.

This SLR examined the emerging interface between rail transport and CL by synthesis of 50 peer-reviewed studies published between 2009 and 2025. While rail freight and CL have developed as distinct research streams, their integration remains conceptually fragmented and empirically underexplored. The evidence confirms that Rail-CL integration represents a promising but strategically significant domain within sustainable freight research.

The review shows that current theoretical foundations are primarily derived from collaborative logistics, sharing-economy models and synchro-modal transport frameworks. Rail is generally conceptualized as a high-capacity trunk mode supporting long-distance consolidation, whereas CL provides flexible first and last-mile connectivity through digitally coordinated CL delivery. However, explicitly articulated and empirically validated Rail-CL integration models are very few. Existing studies tend to propose conceptual architectures rather than operationally tested systems. In terms of sustainability, the literature aligns strongly with triple bottom line objectives, reported environmental benefits include emission reduction, congestion mitigation and improved resource efficiency through modal shift and consolidation strategies. Economic gains relate to cost optimization, asset utilization and enhanced delivery flexibility. Social impacts, although less frequently quantified, include improved service accessibility and employment opportunities within crowd networks. Nevertheless, despite these positive indications, much of the evidence remains model-based and highlighting the need for empirical testing through pilot applications.

The review further identifies structural barriers limiting implementation, these are regulatory uncertainty regarding liability and labor classification, insufficient rail-terminal interfaces for first and last-mile integration, digital interoperability challenges and stakeholder coordination complexity emerge as critical constraints. These findings suggest that Rail-CL integration is a socio-technical transformation requiring institutional alignment alongside technological development.

Significant research gaps continue; that is, empirical evidence from developing economies is limited, despite strong relevance for rail-dependent corridors. Methodologically, the field is dominated by conceptual and simulation-based research, with a shortage of longitudinal and large-scale experimental studies. Thematically, attention remains concentrated on last-mile delivery, leaving first-mile logistics and platform governance mechanisms comparatively underdeveloped.

This review makes three principal contributions. First, it provides the first structured synthesis explicitly focused on the integration of rail freight and CL, bridging two previously disconnected research streams. By consolidating theoretical foundations, operational strategies, sustainability outcomes and implementation barriers within a single analytical framework, the study advances conceptual clarity in an emerging field.

Second, the review identifies a critical gap between conceptual ambition and empirical validation. By highlighting the absence of tested Rail-CL operational models, it establishes a clear agenda for future research centered on pilot implementation, interoperability design and performance-based sustainability assessment.

Third, the study offers practical implications for policymakers and industry actors. Successful integration requires coordinated regulatory reform, targeted infrastructure investment at rail hubs and interoperable digital platforms capable of supporting multimodal coordination. For developing economies in particular, Rail-CL integration presents a potentially scalable pathway toward low-carbon and inclusive urban freight systems.

Overall, the findings position Rail-CL integration as a promising yet underdeveloped strategy for enhancing freight efficiency, sustainability and system resilience. The study contributes original insight by clarifying current knowledge boundaries and defining priority directions for research, policy and practice.

This review is subject to some limitations. The analysis was restricted to English-language, peer-reviewed publications from 2009 to 2025, potentially excluding relevant grey and non-English literature. The dominance of conceptual and simulation-based studies limits empirical generalizability, while the geographic concentration on developed regions constrains applicability to developing countries' contexts. Although the MMAT-based appraisal followed a structured dual-review process, some degree of subjectivity remains.

Future research should prioritize empirical validation through pilot implementations and real-world case studies, particularly in developing economies. Longitudinal and large-scale experimental designs are needed to assess operational feasibility and sustainability performance. Further work is also required on first-mile logistics integration, governance of digital CL platforms and regulatory frameworks to support multimodal coordination.