A multi-stage spatial queueing model with logistic arrivals and departures consistent with the microscopic fundamental diagram and hysteresis

IF 5.8 1区 工程技术 Q1 ECONOMICS Transportation Research Part B-Methodological Pub Date : 2024-06-26 DOI:10.1016/j.trb.2024.103015
Yang Gao, David Levinson
{"title":"A multi-stage spatial queueing model with logistic arrivals and departures consistent with the microscopic fundamental diagram and hysteresis","authors":"Yang Gao,&nbsp;David Levinson","doi":"10.1016/j.trb.2024.103015","DOIUrl":null,"url":null,"abstract":"<div><p>This paper introduces a spatial queueing model for a single bottleneck during morning peak hours. Utilizing the logistic function and after appropriate calibration, it articulates the arrival and departure flows in continuous, differentiable terms. By validating the model across different peak periods and locations, the demand model’s robustness is superior to other commonly used functions. This model also incorporates constant or varying capacity scenarios. It effectively captures key aspects of morning peak traffic, including the emergence of hysteresis loops in fundamental diagrams (FDs) of density and flow. The model’s multi-stage approach recognizes three distinct phases in traffic flow: freeflow, transition, and queued segments, ensuring spatial consistency in flow and density across these stages. It accounts for the growth of the queued segment and vehicle spillback under various bottleneck intensities, with the resulting FDs for speed and density also displaying hysteresis loops. The calibration of model parameters utilizes time-series data of traffic flow and density space–time maps derived from real-world data. The validation results accurately reflect real traffic scenarios, emulating the counterclockwise hysteresis loops observed in density and its heterogeneity, and provide both planar and three-dimensional FDs at different points along the traffic link, each mirroring real-life traffic patterns. Additionally, a comparison with the cell transmission model (CTM) reveals that the proposed model exhibits superior generalization and robustness.</p></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"186 ","pages":"Article 103015"},"PeriodicalIF":5.8000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0191261524001395/pdfft?md5=58a6a1f1daf3c4edbf06e5a6e8783608&pid=1-s2.0-S0191261524001395-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part B-Methodological","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0191261524001395","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper introduces a spatial queueing model for a single bottleneck during morning peak hours. Utilizing the logistic function and after appropriate calibration, it articulates the arrival and departure flows in continuous, differentiable terms. By validating the model across different peak periods and locations, the demand model’s robustness is superior to other commonly used functions. This model also incorporates constant or varying capacity scenarios. It effectively captures key aspects of morning peak traffic, including the emergence of hysteresis loops in fundamental diagrams (FDs) of density and flow. The model’s multi-stage approach recognizes three distinct phases in traffic flow: freeflow, transition, and queued segments, ensuring spatial consistency in flow and density across these stages. It accounts for the growth of the queued segment and vehicle spillback under various bottleneck intensities, with the resulting FDs for speed and density also displaying hysteresis loops. The calibration of model parameters utilizes time-series data of traffic flow and density space–time maps derived from real-world data. The validation results accurately reflect real traffic scenarios, emulating the counterclockwise hysteresis loops observed in density and its heterogeneity, and provide both planar and three-dimensional FDs at different points along the traffic link, each mirroring real-life traffic patterns. Additionally, a comparison with the cell transmission model (CTM) reveals that the proposed model exhibits superior generalization and robustness.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
与微观基本图和滞后一致的逻辑到达和离开的多阶段空间排队模型
本文介绍了早高峰时段单一瓶颈的空间排队模型。该模型利用逻辑函数并经过适当校准,以连续、可微分的方式阐明了到达和离开流量。通过在不同高峰时段和地点对模型进行验证,该需求模型的稳健性优于其他常用函数。该模型还包含恒定或变化的容量方案。它能有效捕捉早高峰交通的关键方面,包括密度和流量的基本图(FD)中出现的滞后循环。该模型的多阶段方法识别了交通流的三个不同阶段:自由流、过渡段和排队段,确保了这些阶段的流量和密度在空间上的一致性。该模型考虑了各种瓶颈强度下排队段和车辆回溢的增长,由此产生的速度和密度的 FD 也显示出滞后环。模型参数的校准利用了交通流量的时间序列数据和来自真实世界数据的密度时空图。验证结果准确地反映了真实的交通场景,模拟了在密度及其异质性中观察到的逆时针滞后环,并提供了交通链路上不同点的平面和三维 FD,每一个都反映了现实生活中的交通模式。此外,通过与小区传输模型(CTM)进行比较,发现所提出的模型具有更好的概括性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Transportation Research Part B-Methodological
Transportation Research Part B-Methodological 工程技术-工程:土木
CiteScore
12.40
自引率
8.80%
发文量
143
审稿时长
14.1 weeks
期刊介绍: Transportation Research: Part B publishes papers on all methodological aspects of the subject, particularly those that require mathematical analysis. The general theme of the journal is the development and solution of problems that are adequately motivated to deal with important aspects of the design and/or analysis of transportation systems. Areas covered include: traffic flow; design and analysis of transportation networks; control and scheduling; optimization; queuing theory; logistics; supply chains; development and application of statistical, econometric and mathematical models to address transportation problems; cost models; pricing and/or investment; traveler or shipper behavior; cost-benefit methodologies.
期刊最新文献
Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market Amachine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making Making the most of your private parking slot: Strategy-proof double auctions-enabled staggered sharing schemes Editorial Board Safety, liability, and insurance markets in the age of automated driving
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1