A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Author:

Abdi Asad1,Amrit Chintan2ORCID

Affiliation:

1. Department of Industrial Engineering and Business Information Systems, Behavioural, Management & Social Sciences, University of Twente, University of Twente, Enschede, Netherlands

2. Department of Operations Management, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands

Abstract

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

Funder

NWO

Publisher

PeerJ

Subject

General Computer Science

Reference180 articles.

1. Empirical evaluation of Bluetooth and Wifi scanning for road transport. Australasian Transport Research Forum (ATRF), 36th Edition;Abbott-Jard,2013

2. An integrated feature learning approach using deep learning for travel time prediction;Abdollahi;Expert Systems with Applications,2020

3. Applications of artificial intelligence in transport: an overview;Abduljabbar;Sustainability,2019

4. Bus arrival time prediction: a spatial kalman filter approach;Achar;IEEE Transactions on Intelligent Transportation Systems,2019

5. An adaptive algorithm for public transport arrival time prediction based on hierarhical regression;Agafonov,2015

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights;Journal of Complex Networks;2023-11-07

2. A new approach to COVID-19 data mining: A deep spatial–temporal prediction model based on tree structure for traffic revitalization index;Data & Knowledge Engineering;2023-07

3. Repurposing Open Traffic Data for Estimating the Mobility Performance;Smart Energy for Smart Transport;2023

4. Real-Time Information for Transit Arrivals: A Review;2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE);2022-11-11

5. Traffic Density Based Travel-Time Prediction With GCN-LSTM;2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC);2022-10-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3