{"title":"AI-based neural network models for bus passenger demand forecasting using smart card data","authors":"Sohani Liyanage , Rusul Abduljabbar , Hussein Dia , Pei-Wei Tsai","doi":"10.1016/j.jum.2022.05.002","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.</p></div>","PeriodicalId":45131,"journal":{"name":"Journal of Urban Management","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2226585622000280/pdfft?md5=915133685c1ad571ec129eaae38ae321&pid=1-s2.0-S2226585622000280-main.pdf","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Urban Management","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2226585622000280","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"URBAN STUDIES","Score":null,"Total":0}
引用次数: 7
Abstract
Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.
期刊介绍:
Journal of Urban Management (JUM) is the Official Journal of Zhejiang University and the Chinese Association of Urban Management, an international, peer-reviewed open access journal covering planning, administering, regulating, and governing urban complexity.
JUM has its two-fold aims set to integrate the studies across fields in urban planning and management, as well as to provide a more holistic perspective on problem solving.
1) Explore innovative management skills for taming thorny problems that arise with global urbanization
2) Provide a platform to deal with urban affairs whose solutions must be looked at from an interdisciplinary perspective.