An Optimization Model for the Demand-Responsive Transit with Non-Fixed Stops and Multi-Vehicle Type
As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-o...
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description | As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies in scheduling problems of demand responsive transit which only focus on stop mode such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling methods, the operating costs of multi- occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode of fixed stops, the average full load ratio of mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm. |
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In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies in scheduling problems of demand responsive transit which only focus on stop mode such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling methods, the operating costs of multi- occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode of fixed stops, the average full load ratio of mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3309872</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Carbon dioxide ; Convergence ; Costs ; Demand ; Demand responsive transit ; Efficiency ; Emissions ; Genetic algorithm ; Genetic algorithms ; Genetics ; Indexes ; Multi-vehicle type ; Non-fixed stop ; Occupancy ; Operating costs ; Optimization ; Optimization models ; Passengers ; Public transportation ; Roads ; Scheduling ; Traffic congestion ; Transportation networks ; Transportation planning ; Travel time ; Urban areas ; Urban traffic ; Vehicle driving ; Vehicle scheduling</subject><ispartof>IEEE access, 2023-01, Vol.11, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-f2fe6d8cb4ba8e7895dbc32bf6a44511ec5777eccd3590b572fe38046794970a3</cites><orcidid>0009-0007-0498-9054 ; 0009-0000-2671-7083</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10233852$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,27633,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Song, Cuiying</creatorcontrib><creatorcontrib>Chen, Shiwei</creatorcontrib><creatorcontrib>Wang, Heling</creatorcontrib><creatorcontrib>Chen, Yujun</creatorcontrib><title>An Optimization Model for the Demand-Responsive Transit with Non-Fixed Stops and Multi-Vehicle Type</title><title>IEEE access</title><addtitle>Access</addtitle><description>As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies in scheduling problems of demand responsive transit which only focus on stop mode such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling methods, the operating costs of multi- occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode of fixed stops, the average full load ratio of mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.</description><subject>Carbon dioxide</subject><subject>Convergence</subject><subject>Costs</subject><subject>Demand</subject><subject>Demand responsive transit</subject><subject>Efficiency</subject><subject>Emissions</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Genetics</subject><subject>Indexes</subject><subject>Multi-vehicle type</subject><subject>Non-fixed stop</subject><subject>Occupancy</subject><subject>Operating costs</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Passengers</subject><subject>Public transportation</subject><subject>Roads</subject><subject>Scheduling</subject><subject>Traffic congestion</subject><subject>Transportation networks</subject><subject>Transportation planning</subject><subject>Travel time</subject><subject>Urban areas</subject><subject>Urban traffic</subject><subject>Vehicle driving</subject><subject>Vehicle scheduling</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV1LHDEUhodioaL-AnsR6PVs8zGZJJfLVq3gB7jW25BJznSz7E7GJKu1v96sI-KBcA6H531z4K2qU4JnhGD1c75YnC2XM4opmzGGlRT0S3VISatqxll78Gn-Vp2ktMalZFlxcVjZ-YBux-y3_r_JPgzoOjjYoD5ElFeAfsHWDK6-gzSGIfknQPfRlCGjZ59X6CYM9bn_Bw4tcxgTKiy63m2yrx9g5e2m4C8jHFdfe7NJcPLej6o_52f3i9_11e3F5WJ-VVvGVa572kPrpO2azkgQUnHXWUa7vjVNwwkBy4UQYK0rOO64KDyTuGmFapTAhh1Vl5OvC2atx-i3Jr7oYLx-W4T4V5uY92dpZYoOeta0XJXnFLdSWgCHiZBGdsXrx-Q1xvC4g5T1OuziUM7XVHIlSCOVLBSbKBtDShH6j18J1vtw9BSO3oej38Mpqu-TygPAJwVlTHLKXgEm8Iqn</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Song, Cuiying</creator><creator>Chen, Shiwei</creator><creator>Wang, Heling</creator><creator>Chen, Yujun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0007-0498-9054</orcidid><orcidid>https://orcid.org/0009-0000-2671-7083</orcidid></search><sort><creationdate>20230101</creationdate><title>An Optimization Model for the Demand-Responsive Transit with Non-Fixed Stops and Multi-Vehicle Type</title><author>Song, Cuiying ; Chen, Shiwei ; Wang, Heling ; Chen, Yujun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-f2fe6d8cb4ba8e7895dbc32bf6a44511ec5777eccd3590b572fe38046794970a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Carbon dioxide</topic><topic>Convergence</topic><topic>Costs</topic><topic>Demand</topic><topic>Demand responsive transit</topic><topic>Efficiency</topic><topic>Emissions</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Genetics</topic><topic>Indexes</topic><topic>Multi-vehicle type</topic><topic>Non-fixed stop</topic><topic>Occupancy</topic><topic>Operating costs</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Passengers</topic><topic>Public transportation</topic><topic>Roads</topic><topic>Scheduling</topic><topic>Traffic congestion</topic><topic>Transportation networks</topic><topic>Transportation planning</topic><topic>Travel time</topic><topic>Urban areas</topic><topic>Urban traffic</topic><topic>Vehicle driving</topic><topic>Vehicle scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Cuiying</creatorcontrib><creatorcontrib>Chen, Shiwei</creatorcontrib><creatorcontrib>Wang, Heling</creatorcontrib><creatorcontrib>Chen, Yujun</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Explore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Cuiying</au><au>Chen, Shiwei</au><au>Wang, Heling</au><au>Chen, Yujun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Optimization Model for the Demand-Responsive Transit with Non-Fixed Stops and Multi-Vehicle Type</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023-01-01</date><risdate>2023</risdate><volume>11</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies in scheduling problems of demand responsive transit which only focus on stop mode such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling methods, the operating costs of multi- occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode of fixed stops, the average full load ratio of mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3309872</doi><tpages>1</tpages><orcidid>https://orcid.org/0009-0007-0498-9054</orcidid><orcidid>https://orcid.org/0009-0000-2671-7083</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Carbon dioxide Convergence Costs Demand Demand responsive transit Efficiency Emissions Genetic algorithm Genetic algorithms Genetics Indexes Multi-vehicle type Non-fixed stop Occupancy Operating costs Optimization Optimization models Passengers Public transportation Roads Scheduling Traffic congestion Transportation networks Transportation planning Travel time Urban areas Urban traffic Vehicle driving Vehicle scheduling |
title | An Optimization Model for the Demand-Responsive Transit with Non-Fixed Stops and Multi-Vehicle Type |
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