Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage

Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2024-10, Vol.14 (19), p.8836
Main Authors: Zhou, Xuemei, Zhang, Yunbo, Guo, Huanwu
Format: Article
Language:English
Subjects:
Algorithms
ant colony algorithm
bus scheduling
Buses
Case studies
demand-responsive transit
Efficiency
Energy consumption
Genetic algorithms
Heuristic
Integer programming
Local transit
Methods
Operating costs
Passengers
Public transportation
Route optimization
Scheduling
Traffic
Travel
Vehicles
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c291t-8813772aba95c9f9018503445c678766365b707cc28e384ae25e97469cffdc5d3
container_end_page
container_issue 19
container_start_page 8836
container_title Applied sciences
container_volume 14
creator Zhou, Xuemei
Zhang, Yunbo
Guo, Huanwu
description Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency.
doi_str_mv 10.3390/app14198836
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_6ad11e26b037423eb75692e422ad6879</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A813334661</galeid><doaj_id>oai_doaj_org_article_6ad11e26b037423eb75692e422ad6879</doaj_id><sourcerecordid>A813334661</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-8813772aba95c9f9018503445c678766365b707cc28e384ae25e97469cffdc5d3</originalsourceid><addsrcrecordid>eNpNkVtLJDEQhRtZQVGf_AMBH5fW3DqXR3f2oqAI6u5rqEmqZzLMdHqTHsH99WYcWUweckhOfZxKNc05o5dCWHoF48gks8YIddAcc6pVKyTTXz7po-aslBWtyzJhGD1ulk9-iWG7jsOC3OO0TIGknnzHDQyhfcQypqHEFyTPGaqYyDcoWC0DqW-YX2CKVc92poB5B_mDy-jXSJ7iPyQVQu7jGmGBp81hD-uCZx_nSfP754_n2U179_DrdnZ913pu2dQaw4TWHOZgO297S5npqJCy80obrZRQ3VxT7T03KIwE5B1aLZX1fR98F8RJc7vnhgQrN-a4gfzqEkT3fpHywkGedhGdgsAYcjWnQksucK47ZTlKziEoo21lXexZY05_t1gmt0rbPNT4TjCmlDRWmOq63LsWUKFx6NOUwdcdcBN9GrCvP-Cua2NCSKVYLfi6L_A5lZKx_x-TUbcbpfs0SvEGiIWO7g</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3116648938</pqid></control><display><type>article</type><title>Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage</title><source>Publicly Available Content Database</source><creator>Zhou, Xuemei ; Zhang, Yunbo ; Guo, Huanwu</creator><creatorcontrib>Zhou, Xuemei ; Zhang, Yunbo ; Guo, Huanwu</creatorcontrib><description>Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency.</description><identifier>ISSN: 2076-3417</identifier><identifier>EISSN: 2076-3417</identifier><identifier>DOI: 10.3390/app14198836</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; ant colony algorithm ; bus scheduling ; Buses ; Case studies ; demand-responsive transit ; Efficiency ; Energy consumption ; Genetic algorithms ; Heuristic ; Integer programming ; Local transit ; Methods ; Operating costs ; Passengers ; Public transportation ; Route optimization ; Scheduling ; Traffic ; Travel ; Vehicles</subject><ispartof>Applied sciences, 2024-10, Vol.14 (19), p.8836</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c291t-8813772aba95c9f9018503445c678766365b707cc28e384ae25e97469cffdc5d3</cites><orcidid>0000-0001-6472-7557</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3116648938/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3116648938?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25732,27903,27904,36991,44569,74872</link.rule.ids></links><search><creatorcontrib>Zhou, Xuemei</creatorcontrib><creatorcontrib>Zhang, Yunbo</creatorcontrib><creatorcontrib>Guo, Huanwu</creatorcontrib><title>Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage</title><title>Applied sciences</title><description>Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency.</description><subject>Algorithms</subject><subject>ant colony algorithm</subject><subject>bus scheduling</subject><subject>Buses</subject><subject>Case studies</subject><subject>demand-responsive transit</subject><subject>Efficiency</subject><subject>Energy consumption</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Integer programming</subject><subject>Local transit</subject><subject>Methods</subject><subject>Operating costs</subject><subject>Passengers</subject><subject>Public transportation</subject><subject>Route optimization</subject><subject>Scheduling</subject><subject>Traffic</subject><subject>Travel</subject><subject>Vehicles</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkVtLJDEQhRtZQVGf_AMBH5fW3DqXR3f2oqAI6u5rqEmqZzLMdHqTHsH99WYcWUweckhOfZxKNc05o5dCWHoF48gks8YIddAcc6pVKyTTXz7po-aslBWtyzJhGD1ulk9-iWG7jsOC3OO0TIGknnzHDQyhfcQypqHEFyTPGaqYyDcoWC0DqW-YX2CKVc92poB5B_mDy-jXSJ7iPyQVQu7jGmGBp81hD-uCZx_nSfP754_n2U179_DrdnZ913pu2dQaw4TWHOZgO297S5npqJCy80obrZRQ3VxT7T03KIwE5B1aLZX1fR98F8RJc7vnhgQrN-a4gfzqEkT3fpHywkGedhGdgsAYcjWnQksucK47ZTlKziEoo21lXexZY05_t1gmt0rbPNT4TjCmlDRWmOq63LsWUKFx6NOUwdcdcBN9GrCvP-Cua2NCSKVYLfi6L_A5lZKx_x-TUbcbpfs0SvEGiIWO7g</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Zhou, Xuemei</creator><creator>Zhang, Yunbo</creator><creator>Guo, Huanwu</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-6472-7557</orcidid></search><sort><creationdate>20241001</creationdate><title>Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage</title><author>Zhou, Xuemei ; Zhang, Yunbo ; Guo, Huanwu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-8813772aba95c9f9018503445c678766365b707cc28e384ae25e97469cffdc5d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>ant colony algorithm</topic><topic>bus scheduling</topic><topic>Buses</topic><topic>Case studies</topic><topic>demand-responsive transit</topic><topic>Efficiency</topic><topic>Energy consumption</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Integer programming</topic><topic>Local transit</topic><topic>Methods</topic><topic>Operating costs</topic><topic>Passengers</topic><topic>Public transportation</topic><topic>Route optimization</topic><topic>Scheduling</topic><topic>Traffic</topic><topic>Travel</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Xuemei</creatorcontrib><creatorcontrib>Zhang, Yunbo</creatorcontrib><creatorcontrib>Guo, Huanwu</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Xuemei</au><au>Zhang, Yunbo</au><au>Guo, Huanwu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage</atitle><jtitle>Applied sciences</jtitle><date>2024-10-01</date><risdate>2024</risdate><volume>14</volume><issue>19</issue><spage>8836</spage><pages>8836-</pages><issn>2076-3417</issn><eissn>2076-3417</eissn><abstract>Demand-responsive transit (DRT) is a kind of new public transit tailored to passenger needs that can provide passengers with fast, convenient, and diversified travel services. This paper proposes a scheduling model for demand-responsive transit based on reservations applicable to multi-vehicle task dispatching during the time period. It uses an ant colony algorithm for a solution. The model uses vehicle size and mileage as the optimization objectives while considering practical constraints like multi-vehicle operation, maximum pick-up intervals, etc. The feasibility of the model and the algorithm’s effectiveness are verified using the Shanghai Huyi Highway Demonstration Line as a case study. The results indicate that the model can effectively generate the optimal scheduling plan for DRT, significantly reduce the system’s operating cost, and improve resource utilization efficiency.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app14198836</doi><orcidid>https://orcid.org/0000-0001-6472-7557</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2076-3417
ispartof Applied sciences, 2024-10, Vol.14 (19), p.8836
issn 2076-3417
2076-3417
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_6ad11e26b037423eb75692e422ad6879
source Publicly Available Content Database
subjects Algorithms
ant colony algorithm
bus scheduling
Buses
Case studies
demand-responsive transit
Efficiency
Energy consumption
Genetic algorithms
Heuristic
Integer programming
Local transit
Methods
Operating costs
Passengers
Public transportation
Route optimization
Scheduling
Traffic
Travel
Vehicles
title Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T06%3A33%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Scheduling%20Method%20of%20Demand-Responsive%20Transit%20Based%20on%20Reservation%20Considering%20Vehicle%20Size%20and%20Mileage&rft.jtitle=Applied%20sciences&rft.au=Zhou,%20Xuemei&rft.date=2024-10-01&rft.volume=14&rft.issue=19&rft.spage=8836&rft.pages=8836-&rft.issn=2076-3417&rft.eissn=2076-3417&rft_id=info:doi/10.3390/app14198836&rft_dat=%3Cgale_doaj_%3EA813334661%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c291t-8813772aba95c9f9018503445c678766365b707cc28e384ae25e97469cffdc5d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3116648938&rft_id=info:pmid/&rft_galeid=A813334661&rfr_iscdi=true