Optimization of Bus Stop Location based on Spatial Analysis and K-Means Clustering Method – A Case Study in Hohhot City

Abstract

Abstract The location of bus stops is crucial for attracting bus passengers. Researchers have explored various methods, such as questionnaire surveys and field investigations, to determine the location of bus stops, but these methods largely rely on experience and intuition. With the introduction of big data, a more scientific approach has emerged for siting bus stops. Based on the location data from cellular base stations in Hohhot, China, this paper uses K-means clustering and spatial analysis to calculate the coordinates of bus stops within 4 kilometers of the central business district (CBD) area. Then the study utilizes the city point of interest (POI) data to compare the new bus stops and existing ones in terms of the POI number, bus stop spacing, and POI average distance. The study concludes that the new bus stops outperform the existing ones in terms of POI number and stops spacing. The Analytic Hierarchy Process (AHP) is applied to evaluate the results that indicate the newly constructed bus stops are superior to the existing ones, with an overall optimization rate of 3.194%.The findings of this study can serve as decision-making references for urban planning departments and public transportation operators, aiming to increase public transport passenger flow and improve the traffic conditions in cities. Additionally, the methods utilized in this research can be applied to other cities, assisting them in site selection of bus stops and optimal planning, thereby promoting the development of public transportation.

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