Article
citation information:
Adeel, M., Khurshid, M.B., Khan, U., Khan,
J.A. Multi-dimensional freight and trade capacity analysis – a case study on
Burhan-Khunjerab route in China-Pakistan economic corridor (CPEC). Scientific Journal of Silesian University of
Technology. Series Transport. 2025, 126,
5-21. ISSN: 0209-3324. DOI:
https://doi.org/10.20858/sjsutst.2025.126.1.
Muhammad ADEEL[1],
Muhammad Bilal KHURSHID[2],
Usama KHAN[3],
Jamal Ahmed KHAN[4]
MULTI-DIMENSIONAL
FREIGHT AND TRADE CAPACITY ANALYSIS – A CASE STUDY ON BURHAN-KHUNJERAB ROUTE IN CHINA-PAKISTAN ECONOMIC CORRIDOR (CPEC)
Summary. Trade corridors are
critical for fostering global economic growth, reducing transportation costs,
and enhancing regional connectivity, yet increasing trade volumes have imposed
significant demands on these infrastructures. This study focuses on the Burhan-Khunjerab route, a pivotal section of the China-Pakistan
Economic Corridor (CPEC), which connects China to
global markets through Pakistan. Despite its strategic importance, the route
faces challenges including mountainous terrain and limited capacity, raising
concerns about its ability to accommodate future freight demands. The study
employs a combination of capacity analysis and statistical modeling
to estimate the freight-handling capacity of the Burhan-Khunjerab
route under the CPEC scenario, specifically for the
horizon year 2035. Using Level of Service (LOS) 'C' as the performance
benchmark, the analysis identifies the Thakot-Raikot
section as the critical bottleneck, capable of handling 9,519 additional trucks
per day. Statistical models further reveal that an annual increase of 1 million
US dollars in Pakistan’s trade corresponds to a 1.3-million-ton-km annual
increase in road freight, while a 1-million-ton rise in trade volume results in
a 1,684-million-ton-km increase. By 2035, the route is estimated to handle
7.93% of China’s total trade in monetary terms and 6.39% in tonnage. The
findings emphasize the importance of targeted infrastructure improvements and
multimodal integration to optimize freight capacity. Policymakers are urged to
address critical bottlenecks and invest in capacity management strategies.
Globally, this study highlights the transformative potential of trade corridors
like CPEC in driving regional integration and global
economic growth while offering a replicable framework for analyzing
similar emerging trade routes.
Keywords: China Pakistan Economic Corridor (CPEC), Burhan-Khunjerab route,
freight capacity modeling, trade corridor analysis,
Level of Service (LOS)
1. INTRODUCTION
Trade
routes have been a cornerstone of global economic growth, connecting regions
and facilitating the efficient movement of goods. From the ancient Silk Road,
which connected China to Europe, to modern maritime corridors such as the
Trans-Pacific Ocean Trade Route and Asia-Europe Route, trade corridors have
transformed economies by reducing transportation costs and boosting
connectivity [1-3]. These routes not only enhance
trade efficiency but also drive regional integration and GDP growth. However,
as global trade volumes continue to rise, the demands on these corridors
intensify, necessitating advanced freight and trade capacity analyses to
optimize their performance and meet future demands [1,
4, 5].
China’s
Belt and Road Initiative (BRI), one of the largest
infrastructure projects globally, aims to create a network of trade corridors
spanning over 60 countries [6-9]. A flagship project under the BRI, the China-Pakistan Economic Corridor (CPEC), connects Gwadar Port in southern Pakistan to Kashgar in China’s Xinjiang province, offering the shortest
trade route for Chinese goods to reach the Middle East, Africa, and Europe [10-13]. By reducing shipment times and
distances, CPEC holds immense potential to transform
regional trade dynamics. Among its alignments, the Burhan-Khunjerab
route, which includes the Karakoram Highway (KKH) and
Hazara Motorway, is critical as it serves as the sole
trade link between China and Pakistan [14,
15]. This section is shared by all
three major CPEC routes – western, central, and
eastern – underscoring its strategic importance.
Despite
its significance, the Burhan-Khunjerab route faces
several challenges. Its mountainous terrain and infrastructural limitations
create a bottleneck that restricts its ability to handle increasing freight
volumes projected under the CPEC scenario [16,
17]. While existing research
extensively focuses on CPEC’s socio-economic impacts,
studies addressing the freight and trade capacity of this crucial section
remain limited. With this background, this study conducts a multidimensional
freight and trade capacity analysis of the Burhan-Khunjerab
route to evaluate the quantum of China’s trade that can be efficiently handled
under the CPEC scenario. Using statistical modeling
techniques, the analysis estimates the route’s freight-handling capacity while
ensuring compliance with Level of Service (LOS) 'C' standards. The findings
address critical logistical challenges and provide actionable insights for
optimizing freight capacity, contributing to the broader understanding of trade
corridor performance in emerging economies.
2. LITERATURE REVIEW
The
analysis of multidimensional freight and trade capacity is critical in
understanding and optimizing global trade networks. Trade corridors form the backbone
of global trade infrastructure, enabling seamless freight movement, reducing
transit times, and fostering economic growth. Existing research has
predominantly focused on enhancing logistics efficiency, modeling trade flow
patterns, and understanding the economic impact of infrastructure on freight
movement. This study builds on global and regional perspectives to address
specific gaps in the existing literature on freight and trade capacity
analysis, utilizing the Burhan-Khunjerab section of
the China-Pakistan Economic Corridor (CPEC) as a case
study.
2.1. Trade corridors and economic growth:
Global perspective
Trade
corridors are key enablers of economic development, directly contributing to
GDP growth and regional trade connectivity. [18] examined trade volume
redistribution across multimodal transport networks in the United States,
highlighting the critical role of efficient freight networks in addressing
bottlenecks and supporting economic growth. Similarly, the Lagos-Kano corridor
in Nigeria emphasized the importance of balancing road and rail infrastructure,
revealing that road transport often dominates time-sensitive freight operations
[19]. These findings demonstrate the
necessity of integrated transport strategies to optimize freight flows. The
Central Asian Trans-Caspian route underscores the economic importance of robust
cross-border trade corridors. Studies by [20] highlighted logistical challenges
such as transit bottlenecks, which are particularly relevant for emerging
corridors like CPEC. The Pantura
Highway in Indonesia exemplifies the use of clustering analysis to manage
highway saturation caused by growing freight demand [21]. These examples illustrate the
economic benefits of addressing logistical inefficiencies in trade corridors
but also reveal gaps in modeling freight capacity specific to developing
regions.
2.2. Freight capacity modeling approaches
Freight capacity modeling has been a
cornerstone of transportation planning, particularly in developed economies.
Researchers have used diverse methodologies to estimate freight load and
forecast demand. [22] developed an input-output model to
analyze transportation value, correlating freight traffic with economic growth.
[23] introduced stepwise regression
models to examine relationships between freight volume and economic indicators
like industrial structure and consumption coefficients. These approaches
provide robust frameworks for understanding the interplay between economic
activities and freight demands. Advancements in predictive modeling have
introduced hybrid approaches that integrate traditional statistical methods
with machine learning. [24] compared prediction methods,
demonstrating the superiority of hybrid models in forecasting freight demand with
high accuracy. [25] used hierarchical modeling to
couple truck traffic data with socio-economic variables, enabling precise
freeway-level freight demand predictions. Similarly, models from the
Netherlands evaluate congestion impacts and suggest traffic management
strategies to maintain service levels [26]. These methodologies form the
foundation for this study’s statistical modeling approach, which incorporates
GDP, trade volume, and truck traffic as key predictors.
2.3. Gaps in freight studies for emerging
economies
While global studies provide
valuable insights, their applicability to emerging economies is often limited
due to contextual differences. Most existing models overlook factors unique to
developing regions, such as policy instability, inadequate infrastructure, and
fluctuating economic growth rates. Studies like those on Indonesia’s Pantura Highway and the Central Asian transport corridor
reveal challenges in adapting advanced models to regions with resource
constraints [27]. These gaps underscore the need for
region-specific models tailored to the dynamic conditions of emerging trade
corridors like CPEC.
2.4. Insights from past CPEC
studies
Research on CPEC
has extensively focused on socio-economic impacts, emphasizing job creation,
industrial growth, and energy security. [28] examined the strategic importance
of CPEC for enhancing bilateral trade between China
and Pakistan. [29] explored the positive correlation
between Pakistan’s export growth and China’s GDP, while [30] analyzed trade facilitation
bottlenecks in cross-border trade. However, most studies overlook the logistical
and freight capacity challenges of the northern alignment, particularly the
Burhan-Khunjerab route. Studies have highlighted
potential benefits, such as reductions in shipping costs and transit times,
with CPEC projected to decrease shipping costs by up
to 68% for Oman and 36% for European ports [31]. [32] highlighted how CPEC
investments improve connectivity and economic integration in key cities but
warns of growing development inequalities between regions. [33] quantified bilateral economic
impacts, projecting GDP and welfare gains for both Pakistan and China, but did
not address logistical constraints on freight capacity. While these findings
validate CPEC’s economic feasibility, they fail to
address its capacity to handle increasing freight loads, particularly under the
constraints of Level of Service (LOS) standards.
2.5. Bridging the research gaps
This study addresses the critical
gaps identified in the existing literature by focusing on the freight and trade
capacity of the Burhan-Khunjerab route within the CPEC scenario. Unlike previous research, which primarily
assesses socio-economic benefits, this study employs statistical modeling to
estimate freight load capacity using economic indicators as independent
variables, including GDP, trade volume, and truck traffic. The models are
further applied to evaluate the corridor’s ability to accommodate China’s trade
while maintaining LOS 'C' standards. By integrating global best practices with
region-specific insights, this research provides a novel framework for
assessing trade corridor performance in underdeveloped regions. The findings not
only enhance understanding of CPEC’s logistical
potential, but also offer a replicable approach for optimizing freight capacity
in similar emerging trade corridors worldwide.
3. METHODOLOGY
3.1. Data description
Miscellaneous data for the study have been collected from various
government departments of Pakistan (e.g., Planning Commission, Finance
Division, National Highway Authority (NHA)) and the
National University of Sciences and Technology (NUST),
Pakistan (e.g., Chinese Studies Centre (CSC), School of Social Sciences and
Humanities (S3H)). Historic traffic data from NHA were used as input in capacity analysis. Study-related
time series economic data (from the year 2000 to 2022) were extracted from web
sources to perform the statistical modeling. This data included road freight in
million-ton km, number of registered trucks, number of trucks on the road,
trade (import/ export) in million US dollars and million tons, population, GDP,
and length of roads.
3.2. Conceptual framework
The conceptual structure of the
study is presented in Fig. 1. For analysis purposes, the Burhan-Khunjerab route has been divided into five sections [34] and capacity analysis of each section was
carried out with respect to the level of service (LOS) using the HCM (Highway Capacity Manual) method [35]. LOS is a measure of road functional
performance with respect to its traffic capacity and congestion. LOS is based
on categories from A to F. LOS ‘A’ represents the best road functional condition,
while LOS ‘F’ indicates extremely congested traffic conditions (i.e., traffic
jams). In this study, the year 2035 has been considered as the ‘horizon year,’
considering that CPEC would be considerably completed
and fully operational by this year.
Considering that trucks would
primarily constitute the CPEC-induced traffic,
maximum traffic (capacity) has been estimated in terms of the number of
additional trucks per day that could be accommodated on the road in addition to
the projected traffic for a particular year. Capacity analyses have been
carried out for the years 2025, 2030, and 2035, keeping the traffic within the
threshold, which is the maximum limit of LOS ‘C.’ Statistical models have been
developed in this study to estimate the freight traffic/load and percentage of
China’s trade that could be accommodated by the Burhan-Khunjerab
route in the CPEC scenario. Finally, vital
conclusions and recommendations have been proffered.
4. RESULTS
4.1. Capacity analysis of Burhan-Khunjerab route
Burhan-Khunjerab
route has been divided into five sections for capacity analysis, as explained
in Tab. 1 and Fig. 2. The additional number of trucks per day that could be
accommodated by each section under the threshold condition (i.e., maximum limit
of LOS 'C') was calculated using HCM-based Highway
Capacity Software (HCS). The procedure adopted to determine the additional
number of trucks was to add the number of trucks to the projected trucks for a
particular year of analysis, keeping the base non-truck traffic constant until
the LOS 'C' changed to the next lower LOS i.e., LOS 'D'. The number of
additional trucks, beyond which the LOS changed from 'C' to 'D', was designated
as the threshold/ maximum limit of LOS 'C'. These calculations were performed
for the years 2025, 2030 and the horizon year 2035 using a growth factor of 3% [36]. Although LOS ‘D’ is expected to yield higher
capacity for all sections under consideration, LOS 'D' is considered
practically inappropriate, exhibiting considerably congested traffic
conditions. Therefore, LOS 'C' was considered as the threshold condition for
this study.
Fig. 1. Conceptual framework of the
study
Tab. 1
Sections
of Burhan-Khunjerab route
|
Section |
Description of Section |
Distance (Km) |
Number of Lanes |
|
Hazara Motorway (M-15) |
|||
|
1. |
Burhan to Havelian (BH) |
60 |
6 |
|
2. |
Havelian to Mansehra (HM) |
39 |
4 |
|
3. |
Mansehra to Thakot (MT) |
80 |
2 |
|
National Highway N-35 |
|||
|
4. |
Thakot to Raikot (TR) |
270 |
2 |
|
5. |
Raikot to Khunjerab (RK) |
336 |
2 |
|
Total |
785 |
- |
|
|
Note: Letters in parentheses show the abbreviation used to indicate a
particular section of the route. |
|||
Fig. 2. Burhan-Khunjerab
Alignment-Hazara Motorway (M-15) and National Highway
N-35
Results of the capacity analysis are
presented in Tab. 2. Numbers in the table represent the maximum number of
additional trucks per day that could be accommodated by each section of M-15
and N-35 under the threshold condition of LOS 'C'. Since the section
accommodating the lowest number of additional trucks would dominate the
capacity of the whole alignment, therefore such a section has been designated
as 'critical section'. Based on the capacity analysis (Tab. 2), 'Thakot-Raikot (TR)’ section has
been found to be the most critical section of Burhan-Khunjerab
route in the CPEC scenario with critical capacity of
9,519 trucks per day in addition to the projected base traffic volume in the
year 2035 under the threshold condition of LOS 'C'. It is important to
highlight that the Thakot-Khunjerab (TK) section
(which is 77% of the Burhan-Khunjerab route),
especially the Thakot-Raikot (TR)
section of N-35, is located on extremely mountainous terrain which is the most
difficult section with respect to road construction and expansion (lanes
addition).
Tab. 2
Additional number of trucks per day
under the threshold condition of LOS ‘C’
|
Ser. |
Year |
Burhan-Havelian (BH) (No.) |
Havelian-Mansehra (HM) (No.) |
Mansehra-Thakot (MT) (No.) |
Thakot-Raikot (TR) (No.) |
Raikot-Khunjerab (RK) (No.) |
Lowest Trucks/ Day (No.) |
Critical Section |
|
1. |
2025 |
54,045 |
32,508 |
21,574 |
10,366 |
10,584 |
10,366 |
TR |
|
2. |
2030 |
52,708 |
30,732 |
21,303 |
9,974 |
10,022 |
9,974 |
TR |
|
3. |
2035 |
51,156 |
28,673 |
20,991 |
9,519 |
9,671 |
9,519 |
TR |
4.2. Statistical modeling for estimation of freight traffic capacity of
Burhan-Khunjerab route in CPEC
scenario
CPEC is expected to considerably
increase the trade and freight load on the existing road infrastructure along CPEC routes. Therefore, a detailed analysis is required to
estimate the percentage of China's trade that is expected to be routed through
the Burhan-Khunjerab section of CPEC
based on its freight handling capacity. Since the capacity of the Burhan-Khunjerab route is the most vital element affecting the
capacity of the whole corridor, therefore, the trade handling capacity of this
section is essential for analyzing the capacity of the complete corridor. With
this perspective, statistical modeling has been carried out in this research to
estimate freight traffic and load in the CPEC
scenario. Details pertaining to time series data of various economic
indicators, from 2000 to 2022, used in the regression modeling, are mentioned
in Tab. 3. The methodology adopted in this study for statistical modeling to
predict the freight capacity of the Burhan-Khunjerab
route in CPEC scenario is elaborated in Fig. 3. Three
regression models were developed, which were further employed to estimate
China's freight that could be accommodated by the Burhan-Khunjerab
route in the CPEC scenario.
Tab. 3
Economic indicators and model
variables
|
Ser. |
Economic Indicators/ Model Variables |
Abbreviations |
Units |
Model Variable Type |
|
1. |
Annual Pakistan road freight |
RF |
Million Ton Kms |
Continuous |
|
2. |
Annual number of trucks on the road in
Pakistan |
TOR |
Numbers |
Discrete |
|
3. |
Annual Pakistan trade in million US dollars |
PT (USD) |
Million US dollars |
Continuous |
|
4. |
Annual Pakistan trade in million tons |
PT (Tons) |
Million Tons |
Continuous |
Fig. 3. Methodology for statistical
modeling to predict and estimate the freight traffic.
4.2.1. Statistical modeling for the development
of relationships between freight load and economic indicators
To explore the relationships between
freight load and various economic indicators/
variables, three regression models were developed using statistical software,
i.e., Statistical Package for Social Sciences (SPSS). Details of three regression models, with
model statistics exhibiting aptness of these models, are presented in Tab. 4.
74% of the data was used for model development, while 26% of the data was used
for model validation based on the Mean Absolute Percentage Error (MAPE) method as shown in Tab. 4. MAPE
values for the developed models, being less than 30%, exhibit appropriate
predictability of the developed models [37,
38]. Moreover, observed versus predicted plots for
the response variables of three regression models: (a) Regression Model 1 –
Annual Number of Trucks on Road in Pakistan (TOR), (b) Regression Model 2 –
Annual Pakistan Road Freight (RF), and (c) Regression Model 3 – Annual Pakistan
Road Freight (RF), as presented in Fig. 4, also exhibit their very good
predictability and statistical goodness of fit [39-41].
Tab. 4
Regression models with R-squared and
MAPE values
|
Regression Model |
Regression Models Equation |
R-Squared (%) |
MSE |
MSPE |
MAPE (%) |
|
Model 1 |
TOR = -16476.32 + 1.45 x RF |
0.98 |
34080119.69 |
24602182.16 |
27.81 |
|
Model 2 |
RF = 75852.15 + 1.3 x [PT (USD)] |
0.90 |
65001361.21 |
61679798.04 |
5.11 |
|
Model 3 |
RF = 42609.2 + 1683.97 x [PT (Tons)] |
0.94 |
39288998.00 |
46756398.74 |
19.01 |
Explanation of abbreviations:
RF = Annual Pakistan Road Freight
(Million Ton Kms);
TOR = Annual Number of Trucks on Road
in Pakistan (Numbers);
PT (USD) = Annual Pakistan Trade in
million US Dollars (Million USD);
PT (Tons) = Annual Pakistan Trade in
Million Tons (Million Tons)
4.2.2. Percentage of trucks on
Burhan-Khunjerab route
To predict the freight load in the CPEC scenario, the annual average daily number of trucks on
the Burhan-Khunjerab route (i.e., 1,842 trucks) is
compared to the total annual number of trucks on the roads in Pakistan (i.e.,
305,371 trucks) in the year 2022 [42]. It was revealed that the annual average daily
number of trucks on the Burhan-Khunjerab route is
0.603% of the total annual number of trucks on the road in Pakistan in the year
2022.
4.3. Estimation of China’s freight to be accommodated by the Burhan-Khunjerab route in CPEC scenario
in the year 2035
To estimate China's freight load to
be accommodated by the Burhan-Khunjerab route in the CPEC scenario, two methods were adopted as shown in Fig. 3
and as explained in ensuing sections.
Fig. 4. Observed versus predicted
plots of regression models
4.3.1. Method 1 – Using Model-1 and Model-2
The
annual number of trucks on road in Pakistan (TOR) (i.e., CPEC
induced additional trucks) in the year 2035 has been estimated to be 1,578,084
trucks by considering the following two parameters:
a.
Estimated percentage of trucks on the
Burhan-Khunjerab route compared to the annual number of trucks on road in
Pakistan (i.e., 0.603 %; as already explained in Section 6.2) and
b.
Estimated CPEC induced additional trucks
per day on this route under the threshold condition of LOS 'C' in the year 2035
(i.e., 9,519 trucks per day).
Trade
capacity of the Burhan-Khunjerab route in CPEC scenario (under
the threshold condition of LOS ‘C’) in the year 2035, in terms of Pakistan trade [PT (USD)], was
estimated to be 786,362.76 million US Dollars by following two steps:
Step-1: Using the CPEC-induced annual number of trucks on road in Pakistan
(TOR) (in year 2035) in Model-1, Pakistan road freight (RF) in year 2035 was estimated
to be 1,099,696.46 million ton-kms.
Step-2: Pakistan
trade [PT (USD)] in year 2035 was estimated to be 786,362.76 million US Dollars
by employing estimated Pakistan road freight (RF) in year 2035 in Model-2.
China's
total trade for the year 2035 was estimated to be 9,920,173 million US Dollars,
employing trend analysis based on the time series data from the year 2000 to
2022 [36, 43],
as shown in Fig. 5. Comparing the estimated trade
capacity of the Burhan-Khunjerab route (i.e.
786,362.76 million US Dollars) and projected China's total trade (i.e.
9,920,173 million US Dollars), it was revealed that 7.93% of China's trade (in
million US Dollars) could be accommodated by the Burhan-Khunjerab
route in CPEC scenario in the year 2035.
Fig. 5. Trend of
China’s total trade (million tons) from the year 2000 to 2035
4.3.2. Method 2 – Using Model-1 and Model-3
Trade
capacity of the Burhan-Khunjerab route in CPEC scenario (under
the threshold condition of LOS ‘C’) in the year 2035, in terms of Pakistan trade [PT (Tons)], was
estimated to be 627.73 million tons by following two steps:
Step-1: Pakistan road freight (RF)
in year 2035 was estimated to be 1,099,696.46
million ton-kms by using CPEC-induced
annual number of trucks on road in Pakistan (TOR) in year 2035 (i.e., 1,578,084
trucks) in Model-1.
Step-2: The estimated Pakistan road freight (RF) in year 2035 (i.e. 1,099,696.46 million ton-kms)
was used in Model-3 to determine Pakistan
trade [PT (Tons)] in year 2035 (i.e., 627.73 million tons).
China's total trade for the year 2035 was
estimated to be 9,828 million tons, employing trend analysis based on the time
series data from the year 2000 to 2022 [36, 43, 44], as shown in Fig. 6. Comparing the estimated
trade capacity of the Burhan-Khunjerab route (i.e.,
627.73 million tons) and projected China's total trade (i.e., 9,828 million
tons), it was revealed that 6.39 % of China's trade (in million tons) could be
accommodated by the Burhan-Khunjerab route in CPEC scenario in the year 2035.
Fig. 6. Trend of China’s total trade (million tons)
from the year 2000 to 2035
5. DISCUSSION
The study
highlights significant findings regarding the freight and trade capacity of the
Burhan-Khunjerab route in the CPEC
scenario, addressing critical gaps in understanding and optimizing global trade
corridors. The analysis identifies the Thakot-Raikot
section as the most critical bottleneck due to its mountainous terrain and
limited potential for expansion. This section's capacity of 9,519 trucks/day
under LOS 'C' by 2035 emphasizes the physical constraints on infrastructure
development and its impact on overall route performance. The regression models
developed in this study establish strong relationships between economic
indicators and freight loads, demonstrating that an annual increase of 1
million US dollars in trade contributes an additional 1.3-million-ton
kilometers to Pakistan's road freight, while a 1-million-ton increase in trade
volume adds 1,684-million-ton kilometers. These results affirm the direct link
between economic growth and freight demands, highlighting the importance of
aligning infrastructure development with projected trade expansions.
Under the
projected 2035 trade volumes, the Burhan-Khunjerab
route is estimated to handle 7.93% of China’s trade by value and 6.39% by
volume. These findings underscore the strategic importance of this route in
facilitating trade and promoting regional connectivity. However, the data also
point to the limitations of relying solely on this corridor for China’s growing
trade needs, suggesting that supplementary infrastructure investments are
necessary to mitigate capacity constraints. Globally, the study’s findings
contribute to the broader understanding of trade corridor performance,
particularly in emerging economies. The statistical modeling approach employed
provides a replicable framework for analyzing freight capacity in constrained
terrains, offering valuable insights for policymakers worldwide.
6. CONCLUSIONS
This study focused on analyzing the
freight and trade capacity of the Burhan-Khunjerab
route under the CPEC scenario, a crucial trade
corridor with significant regional and global importance. By examining its
capacity to handle increasing trade volumes, the research provides critical
insights into optimizing freight flow and addressing logistical challenges
along this strategic alignment. A review of the literature revealed limited
analyzes on the capacity and logistical challenges of CPEC’s
northern alignment, particularly in the context of projected freight volumes
and infrastructural constraints. Employing statistical modeling and a robust
methodological framework, this study assessed the capacity limitations and
trade-handling potential of the Burhan-Khunjerab
route, focusing on its ability to maintain Level of Service (LOS) 'C'
standards.
The findings underscore the
criticality of the Thakot-Raikot section, which
presents the lowest capacity along the route due to its challenging mountainous
terrain and limited scope for expansion. By the horizon year 2035, based on the
most critical section, the route is estimated to accommodate an additional
9,519 trucks per day, beyond the projected 5,047 daily vehicles, resulting in a
total traffic volume of 14,566 vehicles per day (including all types of
traffic). Statistical models further revealed significant correlations between
economic indicators and freight capacity. The findings suggest that an annual
increase of 1 million US dollars in Pakistan's trade would result in a
corresponding annual increase of 1.3-million-ton-kms
in road freight per year. Similarly, a rise of 1 million tons in Pakistan's
trade volume annually would enhance the country’s road freight capacity by
1,684 million ton-kms per year. These projections
underscore the profound impact of trade growth on freight infrastructure,
emphasizing the need for targeted planning to accommodate escalating demands.
The Burhan-Khunjerab route holds significant
strategic importance within the CPEC framework, with
its freight capacity estimated to accommodate 7.93% of China’s total trade in
monetary terms (million US Dollars) and 6.39% in trade tonnage (million tons)
by 2035. These figures underscore the route’s pivotal role in facilitating
regional and global trade. To fully utilize this potential, targeted
investments in infrastructure development and capacity enhancement along this
corridor are required.
From a global perspective, this
study contributes to the understanding of the role of economic corridors in
facilitating trade and optimizing freight flows. The methodological approach
and findings offer valuable insights for policymakers and planners involved in
the development and management of emerging trade corridors worldwide. The
emphasis on maintaining service-level standards while accommodating growing
freight volumes provides a replicable framework for addressing logistical
challenges in similar contexts. Policymakers are encouraged to prioritize both
infrastructural enhancements and strategic capacity management, alongside
multimodal integration, to improve the resilience and operational efficiency of
trade corridors. This study highlights the transformative impact of economic
corridors like CPEC in fostering regional
connectivity and advancing global economic growth while effectively addressing
key logistical challenges.
References
3. Sengupta A. 2024. "Introduction: Emerging Connectivity
Narratives in Eurasia." India’s Eurasian Alternatives in an Era of
Connectivity: Historic Connects and New Corridors. Singapore: Springer
Nature. ISBN: 978-981-97-0235-0. DOI: https://doi.org/10.1007/978-981-97-0236-7_1.
4. Closs D.J., Y.A. Bolumole. 2015. "Transportation’s Role in
Economic Development and Regional Supply Chain Hubs." Transportation Journal 54(1): 33-54. DOI: https://doi.org/10.5325/transportationj.54.1.0033.
5. Profillidis V.A., G.N. Botzoris. 2018. Modeling of Transport
Demand: Analyzing, Calculating, and Forecasting Transport Demand. Elsevier. ISBN: 978-0-12-811513-8.
7. Rana P.B., X. Ji. 2020. China's Belt and Road
Initiative-Impacts on Asia and Policy Agenda. Palgrave Macmillan, Singapore. ISBN: 978-981-15-5170-3.
8. Weber S. 2024. "China’s Belt and Road Initiative:
Implications for Global Trade and Diplomacy." International Journal of
Research and Review Techniques 3(2): 1-9. ISSN: 3006-1075.
9. Lyu Y., Y. Wei, Q. Wang, J. Yan. 2024. "The Belt and Road
Initiative and the Deepening of Global Trade Networks." Journal of WTO & China 14: 40-41.
10. Wolf S.O. 2020. "The
China-Pakistan Economic Corridor of the Belt and Road Initiative (CSAS)." Contemporary South Asian Studies. Springer. ISBN: 978-3-030-16200-9.
DOI: https://doi.org/10.1007/978-3-030-16198-9.
11. Hussain H., A. Bogheiry, T. Alam. 2023. "China Pakistan
Economic Corridor (CPEC): Opportunities and Challenges for
Implementation." Pakistan Journal of International Affairs 6(4): 37-52. DOI: https://doi.org/10.52337/pjia.v6i4.927.
12. Kanwal M. 2023. "China Pakistan Economic Corridor (CPEC) 10
Years Celebrations." Social Science Research Network (SSRN) Electronic
Journal. DOI: https://doi.org/10.2139/ssrn.4667601.
13. Majeed T., Z. Anwar, F. Bashir. 2023. "CPEC: Pakistan and China’s Joint Step towards a Shared Destiny."
Asian Journal of Comparative Politics 8(2): 547-557. DOI: https://doi.org/10.1177/20578911231156102.
14. Zada M.U., S. Khan, M.Z. Nasir. 2023. "Geographical Location of Pakistan: Strategic and Economic Opportunities
for China." Pakistan Research Journal of Social Sciences 2(3):
51-64. Available
at: https://prjss.com/index.php/prjss/article/view/23.
15. Khan A. 2021. "The
Geopolitics of China-Pakistan Historical Connectivity and its Economic and
Strategic Significance." China Quarterly of International Strategic
Studies 7(3): 289-303. DOI:
https://doi.org/10.1142/S2377740021500123.
16. Mustafa T., F. Amjad. 2020. "Analyzing Trade Facilitation Bottlenecks across China-Pakistan Border
and Recommendations for their Improvement under CPEC Framework." In: Proceedings of the 13th Asia Pacific Transportation
Development Conference: 1-9. American Society of Civil
Engineers,
27-30 June, Shanghai, China. DOI: https://ascelibrary.org/doi/10.1061/9780784482902.001.
18. Kim J., M. Anderson, M.D. Sardar, C. Miller. 2015. "Multimodal
Freight Distribution and Economic Development due to International Capacity Expansion."
International Journal of Traffic and Transportation Engineering 4(2): 60-74.
DOI: https://doi.org/10.5923/j.ijtte.20150402.03.
19. Daramola A. 2022. "A Comparative Analysis of Road and Rail
Performance in Freight Transport: An Example from Nigeria." Urban,
Planning and Transport Research 10(1): 58-81. DOI:
https://doi.org/10.1080/21650020.2022.2033134.
20. Kegenbekov J., A. Zhumanov. 2023. "Analysis of Transport and Storage Capacities on the
Trans-Caspian Railway Route." Scientific Journal-Bulletin of KazATC 124(1):153160.
ISSN: 1609-1817. DOI: https://doi.org/10.52167/1609-1817-2023-124-1-153-160.
21. Arifin M. Z., Z. Yulfadli, M. Nafis. 2022. "Characteristics and Patterns of Commodities
Transport Drivers on the Pantura Highway, Java Island with Clustering
Analysis." The Open Transportation Journal 16(1). DOI: https://doi.org/10.2174/18744478-v16-e220623-2021-42.
22. Lu C., S. Fu, J. Fang, J. Haung, Y. Ye. 2021. "Analysis of Factors Affecting Freight Demand
Based on Input‐Output
Model." Mathematical
Problems in Engineering 2021: 1-19. DOI: https://doi.org/10.1155/2021/5581742.
23. Fite J.T., G. D. Taylor, J.S. Usher, J.R. English, J.N. Roberts.
2002. "Forecasting Freight Demand Using Economic Indices." International
Journal of Physical Distribution & Logistics Management 32(4): 299-308. ISSN: 0960-0035. DOI: https://doi.org/10.1108/09600030210430660.
24. Salais-Fierro T.E., J.A.S. Martínez. 2022. "Demand Forecasting for Freight Transport
Applying Machine Learning into the Logistic Distribution." Mobile
Networks and Applications 27(5): 2172-2181. DOI: https://doi.org/10.1007/s11036-021-01854-x.
26. Van Toorenburg J.A. 2021. "Performance of Motorways and Trunk
Routes at High Traffic Volumes." Highway
Capacity and Level of Service: 413-418. Routledge. ISBN: 90-5410011-7.
27. Kotenko A.G., S.B. Sattorov, V.P. Nehoroshkov, K.M. Timuhin. 2021.
"Model for Forecasting the Dynamics and Growth of the Throughput
of the Central Asian Transport Corridor Lines." Journal of Physics: Conference Series
2131(3): 032102. DOI: https://doi.org/10.1088/1742-6596/2131/3/032102.
28. Kumar S. 2007. "The
China – Pakistan Strategic Relationship: Trade,
Investment, Energy and Infrastructure." Strategic Analysis 31(5): 757-790. DOI: https://doi.org/10.1080/09700160701662278.
29. Irshad M.S. 2017. "SWOT Analysis of Pakistan-China Free Trade
Agreement: Pros and Cons." International Journal of Asian Social
Science 7(1): 45-53. Available at:
https://ssrn.com/abstract=2803990.
30. Javed H.M., M. Ismail. 2021. "CPEC and Pakistan: Its Economic Benefits, Energy Security and Regional
Trade and Economic Integration." Chinese Political Science
Review 6(2): 207-227. DOI:
https://doi.org/10.1007/s41111-020-00172-z.
31. Ashraf M., Z. Shiyuan. 2022. "Transportation Costs and Time Impact of the China-Pakistan Economic
Corridor." International Journal of Regional Development 9(1): 1-22. DOI: https://doi.org/10.5296/ijrd.v9i1.18942.
32. Zhao J., G. Sun, C. Webster. 2022. "Does China-Pakistan Economic Corridor Improve Connectivity in Pakistan?
A Protocol Assessing the Planned Transport Network Infrastructure."
Journal of Transport Geography 100:
103327.
DOI: https://doi.org/10.1016/j.jtrangeo.2022.103327.
33. Ali T., J. Huang, W. Xie. 2022. "Bilateral Economic Impacts of China–Pakistan Economic Corridor."
Agriculture 12(2): 143. DOI: https://doi.org/10.3390/agriculture12020143.
34. National Highway Authority
(NHA) Pakistan. "CPEC Routes." Available at: https://nha.gov.pk/.
35. Elefteriadou L. 2016. "The
Highway Capacity Manual 6th Edition: A Guide for Multimodal Mobility
Analysis." ITE Journal 86(4): 14-18. ISSN: 0162-8178.
37. Tofallis C. 2015. "A Better Measure of Relative Prediction
Accuracy for Model Selection and Model Estimation." Journal of the Operational
Research Society 66(8) :1352-1362. DOI:
https://doi.org/10.1057/jors.2014.103.
38. Myttenaere A.D., B. Golden, B.L. Grand, F. Rossi. 2016. "Mean
Absolute Percentage Error for Regression Models." Neurocomputing 192: 38-48. DOI: https://doi.org/10.1016/j.neucom.2015.12.114.
40. Gupta S.C., V.K. Kapoor. 2020. Fundamentals
of Mathematical Statistics. Delhi, India: Sultan Chand & Sons. ISBN: 978-93-5161-173-8.
41. James G., G. Witten, T. Hastie, R. Tibshirani, J. Taylor. 2023. An
Introduction to Statistical Learning:
With Applications in Python. Springer Texts in Statistics. ISBN: 978-3031387463.
42. National
Highway Authority (NHA) Pakistan. "Freight Trucks." Available at: https://nha.gov.pk/.
43. The Observatory of Economic Complexity (OEC).
"China Trade." Available at: https://atlas.media.mit.edu/en/.
Received 07.09.2024; accepted in revised form 30.11.2024
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