I. Introduction

Transportation plays a central role in driving development by facilitating tourism, strengthening social connections, boosting economic growth, and promoting cultural exchange. Efficient transportation systems increase tourism activities at local, regional, national, and international levels. In today's global economy, tourism has become a vital pillar of economic development, especially for developing countries where multi-sectoral progress poses significant challenges. These countries often prioritize strengthening key sectors such as construction, healthcare, tourism, and heavy industry.

Nigeria is at the center of its region. It is surrounded by Niger, Chad, Cameroon, Benin, Mali, Ghana, Burkina Faso, Togo, Equatorial Guinea, and Gabon. Most of these countries have populations ranging from 20 to 35 million, with Ghana and Cameroon being the largest. Niger, Chad, and Mali, on the other hand, cover vast areas. Nigeria has a very high population density and is a cornerstone of West Africa. The region in question, based in Nigeria, is a socio-economic summary of West Africa. The integration of this region is of great importance for the political, social, infrastructural, cultural, and geographical future of West Africa. In the future, this region will be populated with a focus on Nigeria. This region also includes the Berber-Sub-Saharan transition regime in the north. In this study, high-speed transportation and urbanization trends were attempted to be measured for the region's integration. Within this framework, major and important cities in Nigeria and other regional countries are listed according to population and livability index. Subsequently, they were subjected to classification analysis in the R Studio program according to various statistical analysis methods. Subsequently, the most suitable class numbers and distributions that emerged were selected. Then, classes were determined based on the appropriate number of classrooms, and the priority class was identified. The cities included in this class have been listed, and high-speed mobility and metropolitan trends have been analyzed and interpreted, taking distance into account. In this context, the results and recommendations have been presented.

Figure 1 illustrates the major urban centers of Nigeria, providing a spatial reference for understanding the country's infrastructural and demographic distribution, which is pertinent to planning high-speed rail networks.

Fig. 1. Nigeria Centered Region (World Atlas Nigeria, 2025)

Due to its location, Nigeria is among the largest and most important countries in Africa and the world. With a population of 238,424,674 (Worldometer, 2025), high population density and growth rate, rich underground resources, industrialization trends, a large young population, sporting achievements, and a land area of 923,768 km2, the country is a significant player in the region. It is the largest country in Africa by population and the 6th largest country in the world. It is also the 14th largest country in Africa and the 31st largest in the world by land area.

The big urbans of Nigeria centered region are outlined below on Fig 2 (Nations Online, 2025), while Fig 3 provides a geographical map illustrating the region's topographic features (Uydu Harita, 2025). Table 1 displays population data for region’s major cities and their livability indexes (OECD, 2020).

Fig. 2. Nigeria Centered Region Big Urbans (Nations Online, 2025)

Fig. 3. Nigeria Centered Region Geographical Map (Uydu Harita, 2025)

TABLE 1: Nigerian Centered Region’s Cities Populations And Liveabilities (Oecd, 2020)

II. Literature Review

In the 21st century, three dominant global trends – urbanization, digitalization, and sustainability – are redefining the framework within which transportation systems are planned and developed. Transportation serves as a central hub for integrating these movements, enabling efficient connectivity while supporting broader policy goals related to climate, economic resilience, and social inclusion.

Simultaneously, digitalization, driven by rapid technological advancements and continuous innovation, has introduced new paradigms in the functioning of cities and transportation systems. Digital transformation has led to the emergence of real-time data integration, automation, and smart mobility solutions, all of which have changed traditional transportation planning frameworks.

In this context, rail systems, especially high-speed trains, offer a viable and effective solution by providing long-term benefits in terms of capacity, efficiency, and sustainability (Givoni, 2006).

III. Methodology

Cluster Analysis

To identify potential high-speed rail corridors and regional connectivity patterns across Nigeria, a multivariate cluster analysis was performed using several established statistical clustering techniques. Specifically, the K-Means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA) methods were applied to a dataset comprising demographic and economic indicators such as population and gross domestic product (GDP).

Euclidean distance metric was used and there is no normalization process except R Studio program’s random process.

K means is a general and useful clustering method for nearly all data types. PAM is a very specific spatial statistical limited data method. Clara is used for big data and understanding the clustering limitations for related data with a precise way. At this analysis, data has a general amount so K means gives the cluster number as 4. Because of all of these, cluster number of 4 is a good selection for a good and robust analysis.

Figure 4: Correlation Matrice of Nigeria Centered Region Cities

1. K-Means Clustering

K-Means clustering, a centroid-based iterative partitioning method, was applied to determine optimal groupings of Nigerian cities based on their economic and demographic characteristics. The analysis aimed to minimize intra-cluster variance. The results for the optimal number of clusters and their visual distribution are shown in:

Figure 5: Optimal Number of Clusters (K-Means)

Figure 6: Cluster Visualization using K-Means

2. Partitioning Around Medoids (PAM)

PAM clustering, known for its robustness to outliers, was employed as a comparative method to validate the stability and interpretability of the K-Means results. This technique choices representative data points (medoids) to minimize dissimilarities within the clusterings. The outputs are presented below:

Figure 7: Optimal Number of Clusters (PAM)

Figure 8: Cluster Visualization using PAM

3. Clustering Large Applications (CLARA)

CLARA, an extension of PAM suitable for large datasets, was used to handle the computational demands of extensive city-level data.

Figure 9: Optimal Number of Clusters (CLARA)

Figure 10: Cluster Visualization using CLARA

Table 2 presents the final clustering results, listing the assignment of each city to its respective cluster group.

TABLE 2. Cluster List Of Nigeria Centered Region’s Cities

IV. Results

Following statistical analysis, urban centers were grouped into four distinct clusters, as presented in Table 2. The selected cities and their respective cluster assignments are listed below that contains Cluster 3, Cluster 1 and over 500000 populated cities of Cluster 2.

TABLE 3. Selected Cities Via Clustering Analyse