AI-based neural network models for bus passenger demand forecasting using smart card data

IF 3.9 2区 社会学 Q1 URBAN STUDIES Journal of Urban Management Pub Date : 2022-09-01 DOI:10.1016/j.jum.2022.05.002
Sohani Liyanage , Rusul Abduljabbar , Hussein Dia , Pei-Wei Tsai
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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.

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基于人工智能的公交乘客需求预测神经网络模型
准确的短期公共交通需求预测对按需公共交通的运行至关重要。了解未来出行需求的预期地点和时间,使运营商能够快速调整时刻表,这有助于提高服务质量和可靠性,并吸引更多乘客乘坐公共交通工具。本研究通过开发基于人工智能的深度学习模型来解决这一需求,该模型基于从墨尔本智能卡票务系统获得的实际乘客数据来预测公交车乘客需求。这些模型考虑了墨尔本一些最重公交线路的旅行需求的时间特征,使用了来自18条公交线路和1781个公交站点的真实数据。使用相同的数据集,对LSTM和BiLSTM深度学习模型与五种传统深度学习模型进行了评估和比较。还对过去十年文献中报道的一些已建立的需求预测模型进行了桌面比较。对比评估结果表明,BiLSTM模型优于其他测试模型,能够以90%以上的准确率预测乘客需求。
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来源期刊
CiteScore
9.50
自引率
4.90%
发文量
45
审稿时长
65 days
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.
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