Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891363375> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W2891363375 endingPage "1520" @default.
- W2891363375 startingPage "1507" @default.
- W2891363375 abstract "The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners —This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue." @default.
- W2891363375 created "2018-09-27" @default.
- W2891363375 creator A5020651408 @default.
- W2891363375 creator A5030323127 @default.
- W2891363375 creator A5050804608 @default.
- W2891363375 creator A5066290777 @default.
- W2891363375 creator A5070583692 @default.
- W2891363375 creator A5089615958 @default.
- W2891363375 date "2018-10-01" @default.
- W2891363375 modified "2023-10-17" @default.
- W2891363375 title "Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics" @default.
- W2891363375 cites W1650780448 @default.
- W2891363375 cites W167368901 @default.
- W2891363375 cites W1899829068 @default.
- W2891363375 cites W1948184167 @default.
- W2891363375 cites W1952209694 @default.
- W2891363375 cites W1988580225 @default.
- W2891363375 cites W1997070303 @default.
- W2891363375 cites W2018619682 @default.
- W2891363375 cites W2037403365 @default.
- W2891363375 cites W2062363204 @default.
- W2891363375 cites W2075364600 @default.
- W2891363375 cites W2142356756 @default.
- W2891363375 cites W2165991108 @default.
- W2891363375 cites W2176149189 @default.
- W2891363375 cites W2183679353 @default.
- W2891363375 cites W2292548112 @default.
- W2891363375 cites W2342643507 @default.
- W2891363375 cites W2343129607 @default.
- W2891363375 cites W2343567063 @default.
- W2891363375 cites W2461721897 @default.
- W2891363375 cites W2480453913 @default.
- W2891363375 cites W2504266609 @default.
- W2891363375 cites W2510808060 @default.
- W2891363375 cites W2525614189 @default.
- W2891363375 cites W2539121899 @default.
- W2891363375 cites W2585519761 @default.
- W2891363375 cites W2593182953 @default.
- W2891363375 cites W2596628535 @default.
- W2891363375 cites W2597121862 @default.
- W2891363375 cites W2606105273 @default.
- W2891363375 cites W2613403349 @default.
- W2891363375 cites W2743812350 @default.
- W2891363375 cites W2782143633 @default.
- W2891363375 cites W3041188046 @default.
- W2891363375 cites W3099679265 @default.
- W2891363375 cites W3122289956 @default.
- W2891363375 doi "https://doi.org/10.1109/tase.2018.2865494" @default.
- W2891363375 hasPublicationYear "2018" @default.
- W2891363375 type Work @default.
- W2891363375 sameAs 2891363375 @default.
- W2891363375 citedByCount "66" @default.
- W2891363375 countsByYear W28913633752018 @default.
- W2891363375 countsByYear W28913633752019 @default.
- W2891363375 countsByYear W28913633752020 @default.
- W2891363375 countsByYear W28913633752021 @default.
- W2891363375 countsByYear W28913633752022 @default.
- W2891363375 countsByYear W28913633752023 @default.
- W2891363375 crossrefType "journal-article" @default.
- W2891363375 hasAuthorship W2891363375A5020651408 @default.
- W2891363375 hasAuthorship W2891363375A5030323127 @default.
- W2891363375 hasAuthorship W2891363375A5050804608 @default.
- W2891363375 hasAuthorship W2891363375A5066290777 @default.
- W2891363375 hasAuthorship W2891363375A5070583692 @default.
- W2891363375 hasAuthorship W2891363375A5089615958 @default.
- W2891363375 hasConcept C127413603 @default.
- W2891363375 hasConcept C22212356 @default.
- W2891363375 hasConcept C2989549987 @default.
- W2891363375 hasConcept C41008148 @default.
- W2891363375 hasConceptScore W2891363375C127413603 @default.
- W2891363375 hasConceptScore W2891363375C22212356 @default.
- W2891363375 hasConceptScore W2891363375C2989549987 @default.
- W2891363375 hasConceptScore W2891363375C41008148 @default.
- W2891363375 hasFunder F4320321001 @default.
- W2891363375 hasFunder F4320323086 @default.
- W2891363375 hasFunder F4320335787 @default.
- W2891363375 hasIssue "4" @default.
- W2891363375 hasLocation W28913633751 @default.
- W2891363375 hasOpenAccess W2891363375 @default.
- W2891363375 hasPrimaryLocation W28913633751 @default.
- W2891363375 hasRelatedWork W1503285415 @default.
- W2891363375 hasRelatedWork W1516455402 @default.
- W2891363375 hasRelatedWork W1541087157 @default.
- W2891363375 hasRelatedWork W2389528444 @default.
- W2891363375 hasRelatedWork W2899084033 @default.
- W2891363375 hasRelatedWork W300043372 @default.
- W2891363375 hasRelatedWork W3146548853 @default.
- W2891363375 hasRelatedWork W605816536 @default.
- W2891363375 hasRelatedWork W615671618 @default.
- W2891363375 hasRelatedWork W617987602 @default.
- W2891363375 hasVolume "15" @default.
- W2891363375 isParatext "false" @default.
- W2891363375 isRetracted "false" @default.
- W2891363375 magId "2891363375" @default.
- W2891363375 workType "article" @default.