International Freight Transportation System Planning to Minimize Backhaul for Sustainable Cross-Border Freight Systems

2.1 Backhaul inefficiency in road freight transportation

Backhaul, or empty return trips, represents a persistent inefficiency in road freight transportation. When vehicles complete outbound deliveries without securing return cargo, transport capacity is underutilized, raising unit costs by spreading fixed expenses—such as vehicle ownership, labor, and compliance—over fewer revenue-generating kilometers [16, 17]. At the system level, empty trips increase fuel consumption, emissions, congestion, and infrastructure wear, thereby imposing social and environmental costs beyond firm-level inefficiency [16, 18].

Early logistics research conceptualized backhaul as a structural imbalance within freight networks rather than a random operational outcome. Planning-oriented studies argue that empty trips arise systematically from information asymmetry, fragmented freight markets, and weak coordination mechanisms [17, 18]. Subsequent empirical work reinforced this interpretation, identifying backhaul as embedded logistics waste that persists even under stable demand conditions, rather than a temporary response to demand fluctuation [18, 19].

In cross-border contexts, backhaul inefficiency is typically more pronounced. Additional uncertainty related to customs clearance, regulatory compliance, and institutional heterogeneity discourages advance commitment to return loads, leading carriers to prioritize outbound contracts and treat return trips as residual movements [20, 21]. Empirical evidence further indicates that small and medium-sized operators face disproportionately higher backhaul rates due to limited access to freight information, weaker bargaining power, and constrained planning resources—conditions commonly observed in emerging economies with fragmented logistics markets [19].

2.2 Cross-border logistics and institutional constraints

Cross-border freight transportation operates within complex institutional environments shaped by national regulations, bilateral agreements, and regional cooperation frameworks. While infrastructure development has been central to regional integration efforts, a growing body of research demonstrates that non-physical barriers—such as customs procedures, documentation requirements, and regulatory misalignment—often generate greater inefficiencies than physical bottlenecks [21, 22].

In the GMS, cooperative initiatives aim to facilitate cross-border flows by harmonizing regulations and streamlining border procedures. However, their effectiveness depends heavily on implementation capacity at national and local levels. Empirical studies show that partial or uneven implementation frequently results in persistent delays, unpredictable clearance times, and operational uncertainty at border crossings [19, 23].

Institutional fragmentation shapes freight planning behavior by increasing uncertainty over transit times, compliance costs, and enforcement practices. This uncertainty discourages long-term planning and reduces incentives for collaborative logistics arrangements, leading carriers to adopt risk-averse strategies and avoid pre-commitment to return loads—even when bilateral trade volumes are substantial [24]. Governance complexity at border crossings, involving multiple agencies with overlapping mandates, further amplifies variability in operational conditions and reinforces conservative planning practices that prioritize reliability over efficiency [22, 25].

2.3 Transport planning systems and cooperative logistics

Transport planning systems determine how freight capacity is allocated across logistics networks. Traditional planning practices emphasize firm-level optimization within individual contracts, which—while rational at the micro level—often generate suboptimal outcomes at the system level under conditions of fragmented demand and institutional uncertainty [17, 26]. As a result, capacity allocation decisions tend to reinforce existing inefficiencies rather than resolve them.

The logistics literature increasingly highlights cooperative planning as a means to reduce inefficiencies such as backhaul. Coordination among carriers, shippers, and intermediaries enables information sharing, schedule alignment, and capacity pooling, with empirical evidence showing reductions in empty trips, transportation costs, and emissions [27, 28]. However, adoption remains uneven in cross-border contexts where trust, data sharing, and regulatory alignment are limited [24, 29].

Digital planning tools—such as freight platforms and data-driven routing systems—can facilitate cooperative logistics, but their effectiveness depends on data availability, institutional support, and user adoption. In emerging economies, barriers such as limited digital infrastructure, technical capacity constraints, and fragmented governance often constrain their practical impact [30]. Consequently, transport planning systems must be adapted to border-specific institutional realities rather than directly transferred from domestic or fully integrated markets [29, 31].

2.4 Sustainability implications of backhaul reduction

Beyond cost considerations, backhaul reduction has significant environmental implications. Empty trips generate fuel consumption and emissions without corresponding economic output, making them a critical source of transport-related greenhouse gas emissions and energy inefficiency [32, 33]. Improving vehicle utilization through planning interventions is therefore recognized as a cost-effective strategy for enhancing environmental performance without requiring large-scale infrastructure investments [27, 28].

Unlike modal shifts or network expansion, which require substantial capital and long implementation periods, transport planning interventions can yield immediate sustainability benefits. This is particularly relevant for developing regions where fiscal constraints limit large-scale investments and where incremental efficiency gains are policy-relevant [33]. However, sustainability gains depend on aligning the incentives of carriers, shippers, and public authorities, whose priorities may differ across cost efficiency, service reliability, and regulatory compliance. Effective planning systems must therefore integrate economic, environmental, and institutional considerations simultaneously [28].

2.5 Analytical framework

Drawing on the reviewed literature, this study conceptualizes backhaul inefficiency as a system-level outcome shaped by three interrelated dimensions: institutional context, transport planning capacity, and operational outcomes. This perspective aligns with freight transport research emphasizing interactions among governance structures, planning mechanisms, and network performance rather than isolated operational decisions [26, 34].

Within the framework, institutional factors—such as regulatory alignment, border governance, and administrative capacity—define the contextual conditions under which planning decisions are made [21, 22]. Transport planning capacity, encompassing information availability, coordination mechanisms, and planning tools, is positioned as a mediating mechanism linking institutional conditions to operational performance [31, 35]. Prior research suggests that planning capacity is critical in translating institutional environments into measurable logistics outcomes, particularly in complex and uncertain settings [25, 35].

Operational outcomes are reflected in indicators such as backhaul frequency, transportation costs, and capacity utilization. From this perspective, backhaul inefficiency is not an exogenous constraint but a modifiable outcome influenced by targeted planning interventions. By examining how planning systems operate within specific institutional contexts at border crossings, the framework provides a robust basis for empirical analysis and guides the development of a transport planning system tailored to cross-border freight operations, bridging planning theory and operational realities in emerging-economy logistics systems [32, 35-38].

4.1 Characteristics of backhaul operations at border crossings

The empirical results indicate that backhaul operations constitute a persistent and economically relevant share of cross-border freight movements at both Chong Mek and Chong Sa-Ngam border crossings. Despite strong outbound freight activity, a considerable proportion of vehicles returned without cargo, reflecting systematic underutilization of transport capacity.

As presented in Table 1, Chong Mek recorded a total of 1,240 observed freight trips, of which 266 return trips (21.5% of total return trips) were conducted without cargo. In contrast, Chong Sa-Ngam exhibited a higher backhaul frequency, with 260 empty return trips out of 1,010 total trips (25.8% of total return trips). This difference suggests a greater imbalance between outbound and inbound freight flows at Chong Sa-Ngam.

Although outbound movements were predominantly loaded—accounting for 78.5% of trips at Chong Mek and 74.2% at Chong Sa-Ngam—the disparity between outbound and inbound cargo flows remained pronounced. The higher backhaul frequency at Chong Sa-Ngam reflects greater fragmentation of inbound cargo and more limited opportunities for systematic cargo aggregation. These findings indicate that backhaul inefficiency arises not from insufficient return demand, but from limitations in coordination and transport planning mechanisms that hinder effective matching of available cargo with returning vehicles.

Overall, the results demonstrate that backhaul inefficiency at both border crossings is structural rather than incidental, providing a quantitative baseline for subsequent analysis of its cost implications and underlying institutional drivers.

Table 1. Distribution of observed freight trips by direction and cargo status

Note: This table focuses exclusively on return movements, where backhaul is defined as empty return trips. Backhaul frequency is calculated as the proportion of empty return trips relative to total return trips at each checkpoint.

Table 1 shows that despite high levels of loaded outbound movements at both border crossings, a substantial share of return trips remains empty. Chong Sa-Ngam exhibits a consistently higher backhaul frequency than Chong Mek, indicating greater asymmetry in freight flows and weaker capacity for inbound cargo consolidation.

4.2 Cost implications of backhaul inefficiency

Comparative cost analysis highlights the significant economic burden imposed by backhaul operations on cross-border freight transportation. As shown in Table 2, empty return trips generated substantial operational costs—covering fuel consumption, vehicle depreciation, labor, and border-related expenses—while producing no corresponding freight revenue.

Table 2. Round-trip cost model and effective cost increase due to backhaul

Note: Operational costs on the return leg are identical whether the vehicle is loaded or empty; the difference arises from foregone return-leg revenue under backhaul conditions. The reported percentages therefore represent an effective net-cost increase, not a change in operating expenditure.

At Chong Mek, backhaul movements resulted in an effective net increase of 17.8% in round-trip transportation costs, while Chong Sa-Ngam experienced a comparable effective net increase of 18.3%. These increases translated directly into higher unit transportation costs per delivered shipment, placing considerable pressure on logistics operators, particularly those operating with narrow profit margins.

In addition to direct financial impacts, backhaul inefficiency generated indirect cost effects by extending vehicle turnaround times and reducing fleet availability for subsequent assignments. Operators frequently accepted empty returns to avoid uncertainty associated with securing return cargo, prioritizing schedule reliability over potential cost savings. These results indicate that backhaul inefficiency functions as a structural cost driver rather than a short-term operational anomaly.

The effective cost increase due to backhaul is calculated as follows:

Effective cost increase (%) $=\frac{C_t-C_b}{C_b} \times 100 \%$

Although the monetary cost of a return leg is identical whether the vehicle is loaded or empty, an empty return generates no freight revenue. The reported percentage increase therefore represents the share of round-trip cost attributable to non-revenue-generating backhaul movements, expressed relative to the baseline round-trip cost with a loaded return.

4.3 Structural sources of backhaul persistence

The results indicate that persistent backhaul inefficiency is driven by structural and organizational factors, rather than short-term market fluctuations. A central issue is information asymmetry at border crossings, where freight demand information is fragmented across shippers, brokers, and carriers. The absence of centralized or shared platforms limits operators’ ability to identify and secure compatible return cargo, even when inbound demand exists.

Operator-level patterns further reveal that backhaul inefficiency is unevenly distributed across the logistics sector. As shown in Table 3, small and medium-sized logistics operators exhibit substantially higher backhaul frequencies than large operators. This concentration reflects structural constraints, including limited transport planning capacity, weaker bargaining power in negotiating return loads, and restricted access to freight information networks.

Coordination challenges compound these constraints. Operators reported limited trust and weak incentives to engage in collaborative transport planning, particularly in cross-border contexts characterized by regulatory uncertainty. Institutional variability at border crossings—such as inconsistent customs clearance times, documentation requirements, and enforcement practices—discourages advance commitment to return loads. Consequently, operators frequently adopt conservative planning strategies, prioritizing timely vehicle turnaround over capacity optimization. Together, these findings demonstrate that backhaul inefficiency is deeply embedded in the institutional and organizational structure of cross-border logistics systems.

Table 3. Backhaul frequency by operator size

Note: Backhaul frequency refers to the proportion of return trips conducted without cargo.

Table 3 shows that backhaul inefficiency is disproportionately concentrated among small and medium-sized operators, which together account for the majority of logistics firms and exhibit markedly higher backhaul frequencies than large operators. This pattern highlights structural disparities in planning capability and access to freight information, reinforcing the role of organizational capacity and institutional context in shaping backhaul outcomes.

4.4 Performance of the transport planning system

The implementation of the proposed transport planning system resulted in substantial improvements in operational efficiency and vehicle utilization across the studied border crossings. By systematically matching available return cargo with existing outbound movements, the system reduced the incidence of empty return trips and improved overall transport performance without requiring changes to physical infrastructure.

As reported in Table 4, the planned scenarios reduced overall backhaul frequency from 23.6% under observed operations to 14.9%, representing a reduction of nearly 37%. This improvement indicates a significant enhancement in the utilization of existing transport capacity. At the same time, average vehicle utilization increased from 68.2% to 82.5%, reflecting more efficient deployment of freight vehicles across round trips.

Cost efficiency also improved under the planned scenarios. The average transportation cost per trip declined by approximately 14%, driven primarily by the reduction in empty return movements rather than changes in route length or travel time. In addition, fleet turnaround time decreased from 2.8 days to 2.2 days, enhancing operational flexibility and increasing the availability of vehicles for subsequent assignments.

Importantly, these performance gains were achieved through practical planning rules rather than complex optimization algorithms or advanced digital infrastructure. This highlights the feasibility of the proposed system for small and medium-sized logistics operators operating under institutional and resource constraints typical of cross-border environments.

Table 4. Comparison of observed transport outcomes and planned scenarios

Note: Planned scenario results are generated using the proposed transport planning system based on improved cargo matching and capacity utilization.

Table 4 demonstrates that targeted transport planning interventions can yield simultaneous improvements in efficiency, cost, and operational flexibility. The reduction in backhaul frequency plays a central role in driving these gains, confirming that improved planning—not additional infrastructure—is the primary mechanism underlying performance improvements.

4.5 Comparative outcomes between border crossings

Comparative analysis reveals that the effectiveness of the transport planning system varies across border crossings, reflecting differences in freight demand structure, institutional conditions, and operational feasibility. While both Chong Mek and Chong Sa-Ngam benefited from the planning intervention, the magnitude of improvement differed across performance dimensions.

As shown in Table 5, Chong Mek exhibited a higher backhaul reduction potential (41.2%) compared with Chong Sa-Ngam (32.7%). This difference is largely attributable to more stable inbound demand patterns and greater opportunities for cargo aggregation at Chong Mek. Consequently, improvements in vehicle utilization and cost efficiency were also more pronounced at this crossing.

Specifically, vehicle utilization at Chong Mek improved by 22.8%, exceeding the 18.5% improvement observed at Chong Sa-Ngam. Similarly, the average cost reduction per trip was higher at Chong Mek (15.6%) than at Chong Sa-Ngam (12.3%). Despite these differences, both border crossings demonstrated positive performance gains, indicating that the planning framework is adaptable across heterogeneous cross-border contexts.

The comparative results underscore that local demand characteristics and institutional conditions shape the attainable efficiency gains from transport planning interventions. Rather than applying uniform solutions, effective backhaul reduction strategies require tailoring to the specific operational and governance environment of each border crossing.

Table 5. Comparative planning outcomes between Chong Mek and Chong Sa-Ngam

Note: Differences in outcomes reflect variations in inbound demand stability, cargo aggregation potential, and institutional conditions at each border crossing.

Table 5 illustrates that while the proposed transport planning system delivers efficiency gains at both border crossings, contextual factors significantly influence performance outcomes. Chong Mek’s higher feasibility and more stable demand environment enable greater backhaul reduction, whereas Chong Sa-Ngam’s more fragmented inbound flows constrain the achievable gains.

4.6 Summary of key findings

Overall, the results provide clear evidence that backhaul inefficiency at cross-border checkpoints is economically significant, structurally embedded, and responsive to targeted planning interventions. The empirical analysis confirms that a substantial proportion of freight movements at both Chong Mek and Chong Sa-Ngam consist of empty return trips, indicating persistent underutilization of transport capacity despite strong outbound demand.

The findings demonstrate that backhaul inefficiency is not driven by a lack of return demand, but by coordination failures, information asymmetry, and limited transport planning capacity. Cost analysis shows that empty returns impose a considerable financial burden on logistics operators by increasing unit transportation costs and reducing effective fleet availability, thereby functioning as a structural cost driver rather than a temporary operational issue.

At the organizational level, backhaul inefficiency is disproportionately concentrated among small and medium-sized logistics operators, reflecting unequal access to freight information, bargaining power, and planning resources. These constraints are reinforced by institutional uncertainty at border crossings—such as variability in customs procedures and regulatory enforcement—which discourages advance commitment to return loads and promotes conservative planning behavior.

Importantly, the results indicate that significant efficiency gains can be achieved through context-sensitive transport planning. The proposed planning system substantially reduced backhaul frequency, improved vehicle utilization, and lowered transportation costs without reliance on complex optimization algorithms or large-scale infrastructure investment. While the magnitude of improvement varies across border crossings, positive outcomes are observed in all cases, underscoring the adaptability of the planning framework to heterogeneous cross-border environments.

Taken together, the findings establish a strong empirical basis for the subsequent discussion by demonstrating that backhaul inefficiency is both a systemic problem and a solvable one. The results highlight the central role of transport planning mechanisms in improving cross-border logistics performance and provide clear justification for shifting policy and managerial attention toward planning-based interventions.

This study explored the persistent problem of backhaul inefficiency in cross-border road freight transportation, focusing on two major Thai border crossings—Chong Mek and Chong Sa-Ngam. By weaving together operational freight data, institutional analysis, and transport planning perspectives, the research shows that backhaul inefficiency is not simply the result of demand imbalance. Instead, it emerges as a structural consequence of fragmented information, limited planning capacity, and institutional uncertainty embedded within cross-border logistics systems.

The empirical findings reveal that empty return trips account for a significant share of freight movements, imposing heavy economic costs on logistics operators. These costs extend beyond direct financial losses, reducing fleet availability and constraining operational flexibility. Importantly, the persistence of backhaul despite existing inbound demand highlights that coordination failures—rather than market scarcity—are the primary source of inefficiency.

A key contribution of this study lies in demonstrating the power of planning-based interventions. The proposed transport planning system reduced backhaul frequency, improved vehicle utilization, and lowered transportation costs—without requiring complex optimization algorithms or large-scale infrastructure investments. These outcomes underscore the practical feasibility of incremental, context-sensitive planning solutions, particularly for small and medium-sized logistics operators working under institutional and resource constraints. This emphasis on implementable planning logic is consistent with recent sustainability-oriented planning research that highlights feasibility, inclusiveness, and institutional alignment as critical determinants of effective system improvement [81, 82].

From a theoretical perspective, the study reframes backhaul inefficiency as a planning and governance challenge rather than a purely operational or market-driven issue. By explicitly linking institutional conditions, organizational capacity, and transport planning mechanisms within a unified empirical framework, the research contributes to a more nuanced understanding of cross-border logistics performance in emerging-economy contexts. This integrative view resonates with contemporary sustainable development research that emphasizes the interaction between organizational capabilities, governance structures, and system-level outcomes [83].

The findings carry important implications for both policy and practice. Policymakers should complement infrastructure development with initiatives that strengthen planning capacity, enhance information sharing, and support cooperative logistics arrangements at border crossings. For practitioners—especially small and medium-sized operators—the results highlight the value of systematic planning approaches for improving capacity utilization and cost efficiency without heavy capital investment. Such planning-centered approaches align with broader sustainability and development perspectives that prioritize soft infrastructure and coordination mechanisms alongside physical investment [84].

Several limitations point to future research directions. The analysis focuses on two border crossings, limiting statistical generalization. Expanding the framework to other border contexts or regional corridors could test broader applicability. The evaluation emphasizes short-term operational outcomes; longitudinal studies are needed to assess behavioral adaptation and the durability of efficiency gains. Finally, while this study prioritizes practical planning logic, future research could explore advanced data-driven tools—such as real-time freight platforms, machine learning–based demand forecasting, or cooperative digital marketplaces—to enhance planning effectiveness under diverse institutional conditions, as increasingly discussed in recent sustainable development and planning literature [85].

In conclusion, this research demonstrates that backhaul inefficiency in cross-border logistics is both systemic and solvable. By shifting attention from infrastructure expansion toward planning capacity and coordination mechanisms, the study offers a viable pathway for improving logistics performance and sustainability. The findings encourage a reorientation of future research and policy toward planning-centered solutions that align economic efficiency with institutional realities [80-85].