公共交通评估中的机器学习应用

Aug 5, 2026

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Public passenger transport plays a central role in transportation systems, making reliable forecasting essential for practical transport planning. Intelligent Transportation Systems (ITS) provide real-time operational data that can improve prediction of traffic flow and support safer, more effective public transit. This research uses heterogeneous inputs to estimate route-specific transport demand and arrival times, promoting real-time passenger demand forecasting for dynamic bus scheduling. DBSCAN clustering combined with SARIMA is applied, and the proposed Prophet model is compared with ARIMA and SARIMA for accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Introduction

Role of public transport and operational planning

ITS data sources (APC, AVL, RFID, GPS, smart cards)

Limitations of manual counts and traditional forecasting

Role of clustering and time-series models

Methodology

Passenger demand forecasting workflow

DBSCAN clustering with SARIMA and Prophet model comparison

Performance of Public Transport Appraisal using Machine Learning R. Thiagarajan 1, Dr. S. Prakash kumar2 1Ph,D Research Scholar/PG and Research Department of Computer Science Maruthu Pandiyar College Vallam Post,Thanjavur-613 403. (Affiliated to Bharathidasan University,Tiruchirappalli,Tamilnadu,India) Email id: [thiagarajan.nvr@gmail.com](thiagarajan.nvr@gmail.com) 2Assistant Professor/ PG and Research Department of Computer Science Maruthu Pandiyar College Vallam Post,Thanjavur-613 403. (Affiliated to Bharathidasan University,Tiruchirappalli,Tamilnadu,India) Email id: [drsp1974@gmail.com](drsp1974@gmail.com) Abstract: Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services. Keywords: Public Transport, Automatic Passenger Counting, Automatic Vehicle Location, Dwell times Introduction: The dependability of public transport (PT) is a significant issue in modern metropolitan cities and achieving a balance between resource usage and revenue while delivering high-quality services requires effective operational planning. Modern technologies like Radio-frequency Identification (RFID) readers, Global Positioning System (GPS) antennas and 3G connection devices have recently been added by major PT operators to their fleets. These technologies enable the collection of real-time data, including Automatic Passenger Counting (APC) and Automatic Vehicle Location (AVL) which is transmitted to a central server. The ITS have gained prominence since their adoption at the World Congress in Paris, 1994. In order to inform travellers and improve the efficiency and safety of public transportation networks, ITS makes use of electronics and communication technology, computer. The capacity of ITS to support efficient and secure road transport movement is one of the technology's key benefits. According to recent studies, smart card data has the potential to be a useful tool for managing and planning transportation. Every passenger's smart card stores important commute information, including trip dates, hours, places of origin, destinations and travel distances. Leveraging this demand information from smart cards can enable transport authorities to optimize the entire transport network. Subsequently, smart card data has only been used sparingly for such purposes in research so far

FAQs

该研究解决的核心问题是什么？

围绕公共交通的预测需求，研究如何利用ITS数据更可靠地预测客流移动、到站时间与特定线路的运输需求，从而支持调度与管理。

研究中使用了哪些关键数据与采集方式？

基于自动客流统计与车辆定位等ITS数据，包括Automatic Passenger Counting（APC）和Automatic Vehicle Location（AVL），并提到RFID、GPS、3G连接以及智能卡等信息来源。

模型与方法如何进行对比与验证？

采用DBSCAN聚类与SARIMA用于实时客流需求预测，并将提出的Prophet模型与传统时间序列模型ARIMA、SARIMA进行准确性比较。

Aug 5, 2026

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