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Article: A modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem

TitleA modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem
Authors
KeywordsDynamic ride-hailing sharing
Artificial bee colony algorithm
Path relinking
Vantage point tree
Issue Date2021
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description
Citation
Transportation Research Part E: Logistics and Transportation Review, 2021, v. 150, p. article no. 102124 How to Cite?
AbstractRide-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel time compared with the case without ride-hailing sharing.
Persistent Identifierhttp://hdl.handle.net/10722/307845
ISSN
2023 Impact Factor: 8.3
2023 SCImago Journal Rankings: 2.884
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZHAN, X-
dc.contributor.authorSzeto, WY-
dc.contributor.authorShui, CS-
dc.contributor.authorChen, XM-
dc.date.accessioned2021-11-12T13:38:44Z-
dc.date.available2021-11-12T13:38:44Z-
dc.date.issued2021-
dc.identifier.citationTransportation Research Part E: Logistics and Transportation Review, 2021, v. 150, p. article no. 102124-
dc.identifier.issn1366-5545-
dc.identifier.urihttp://hdl.handle.net/10722/307845-
dc.description.abstractRide-hailing sharing involves grouping ride-hailing customers with similar trips and time schedules to share the same ride-hailing vehicle to reduce their total travel cost. With the current information and communication technology, ride-hailing customers and drivers can be matched in real-time via a ride-hailing platform. This paper formulates a dynamic ride-hailing sharing problem that simultaneously maximizes the number of served customers, minimizes the travel cost and travel time ratios, and considers the capacity, time window, and travel cost constraints. The travel cost ratio is the ratio of actual passengers’ fare to the passengers’ fare without ride-hailing sharing, whereas the travel time ratio is defined as the actual travel time (including waiting time) over the maximum allowable travel time. To solve the dynamic problem, it is divided into many small and continuous static subproblems with an equal time interval. Each subproblem is solved by a modified artificial bee colony (MABC) algorithm with path relinking, while the contraction hierarchies and vantage point tree are used to determine the shortest path and accelerate the algorithm, respectively. Problem properties and the performance of the proposed solution method are demonstrated using large-scale real-time data from Didi that is the largest ride-hailing company in China. The proposed method is shown to outperform the benchmark, i.e., greedy randomized adaptive search procedure (GRASP) with path relinking. The proposed method also performs better when the length of each time interval is longer, and the tolerance for the incremental travel time caused by detours is higher. We also demonstrate that (a) considering both travel cost and travel time ratios in the objective can achieve a better sharing percentage, and balance the increase in the travel time ratio and the decrease in the travel cost ratio compared with the objective that misses either travel time or the travel cost ratio; and (b) the passengers can gain a large out-of-pocket cost saving in the case of ride-hailing sharing while enduring a relatively small increase in travel time compared with the case without ride-hailing sharing.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description-
dc.relation.ispartofTransportation Research Part E: Logistics and Transportation Review-
dc.subjectDynamic ride-hailing sharing-
dc.subjectArtificial bee colony algorithm-
dc.subjectPath relinking-
dc.subjectVantage point tree-
dc.titleA modified artificial bee colony algorithm for the dynamic ride-hailing sharing problem-
dc.typeArticle-
dc.identifier.emailSzeto, WY: ceszeto@hku.hk-
dc.identifier.authoritySzeto, WY=rp01377-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.tre.2020.102124-
dc.identifier.scopuseid_2-s2.0-85105724149-
dc.identifier.hkuros329298-
dc.identifier.volume150-
dc.identifier.spagearticle no. 102124-
dc.identifier.epagearticle no. 102124-
dc.identifier.isiWOS:000664354400001-
dc.publisher.placeUnited Kingdom-

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