{"title":"时空空间中出租车热点的识别与推荐","authors":"Saurabh Mishra, Sonia Khetarpaul","doi":"10.1007/s10707-024-00524-w","DOIUrl":null,"url":null,"abstract":"<p>The GPS-driven mobile application-based ride-hailing systems, e.g., Uber and Ola, have become integral to daily life and natural transport choices for urban commuters. However, there is an imbalance between demand or pick-up requests and supply or drop-off requests in any area. The city planners and the researchers are working hard to balance this gap in demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering the spatial regions to identify the hotspots, which refer to the locations with a high demand for pick-up requests. This study determined that if the hotspots focus on clustering high demand for pick-up requests, most of the hotspots pivot near the city center or in the two-three spatial regions, ignoring the other parts of the city. This paper (An earlier version of this paper was presented at the Australasian Database Conference and was published in its Proceedings: https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10) presents a hotspot detection method that uses a dominating set problem-based solution in spatial-temporal space, which covers high-density taxi pick-up demand regions and covers those parts of the city with a moderate density of taxi pick-up demands during different hours of the day. The paper proposes algorithms based on <i>k</i>-hop dominating set; their performance is evaluated using real-world datasets and proves the edge over the existing state-of-the-art methods. It will also reduce the waiting time for customers and drivers looking for their subsequent pick-up requests. Therefore, this would maximize their profit and help improve their services.</p>","PeriodicalId":55109,"journal":{"name":"Geoinformatica","volume":"27 3 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying and recommending taxi hotspots in spatio-temporal space\",\"authors\":\"Saurabh Mishra, Sonia Khetarpaul\",\"doi\":\"10.1007/s10707-024-00524-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The GPS-driven mobile application-based ride-hailing systems, e.g., Uber and Ola, have become integral to daily life and natural transport choices for urban commuters. However, there is an imbalance between demand or pick-up requests and supply or drop-off requests in any area. The city planners and the researchers are working hard to balance this gap in demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering the spatial regions to identify the hotspots, which refer to the locations with a high demand for pick-up requests. This study determined that if the hotspots focus on clustering high demand for pick-up requests, most of the hotspots pivot near the city center or in the two-three spatial regions, ignoring the other parts of the city. This paper (An earlier version of this paper was presented at the Australasian Database Conference and was published in its Proceedings: https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10) presents a hotspot detection method that uses a dominating set problem-based solution in spatial-temporal space, which covers high-density taxi pick-up demand regions and covers those parts of the city with a moderate density of taxi pick-up demands during different hours of the day. The paper proposes algorithms based on <i>k</i>-hop dominating set; their performance is evaluated using real-world datasets and proves the edge over the existing state-of-the-art methods. It will also reduce the waiting time for customers and drivers looking for their subsequent pick-up requests. 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引用次数: 0
摘要
基于全球定位系统(GPS)的移动应用叫车系统,如 Uber 和 Ola,已成为城市通勤者日常生活中不可或缺的自然交通选择。然而,任何地区的需求或接送请求与供给或送客请求之间都存在着不平衡。城市规划者和研究人员正在努力平衡出租车需求和供给之间的差距。现有的方法主要集中在对空间区域进行聚类,以识别热点区域,即对接送请求需求较高的地点。本研究认为,如果将热点集中在接客需求高的聚类上,则大部分热点都集中在市中心附近或二三空间区域,而忽略了城市的其他部分。本文(本文的早期版本曾在澳大拉西亚数据库会议上发表,并发表在其论文集中:https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10)提出了一种热点检测方法,该方法在时空空间中使用基于支配集问题的解决方案,覆盖了高密度出租车接客需求区域,并覆盖了一天中不同时段出租车接客需求密度适中的城市部分。本文提出了基于 k 跳占优集的算法,并利用实际数据集对其性能进行了评估,证明其优于现有的先进方法。它还将减少客户和司机寻找后续接送请求的等待时间。因此,这将使他们的利润最大化,并有助于改善他们的服务。
Identifying and recommending taxi hotspots in spatio-temporal space
The GPS-driven mobile application-based ride-hailing systems, e.g., Uber and Ola, have become integral to daily life and natural transport choices for urban commuters. However, there is an imbalance between demand or pick-up requests and supply or drop-off requests in any area. The city planners and the researchers are working hard to balance this gap in demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering the spatial regions to identify the hotspots, which refer to the locations with a high demand for pick-up requests. This study determined that if the hotspots focus on clustering high demand for pick-up requests, most of the hotspots pivot near the city center or in the two-three spatial regions, ignoring the other parts of the city. This paper (An earlier version of this paper was presented at the Australasian Database Conference and was published in its Proceedings: https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10) presents a hotspot detection method that uses a dominating set problem-based solution in spatial-temporal space, which covers high-density taxi pick-up demand regions and covers those parts of the city with a moderate density of taxi pick-up demands during different hours of the day. The paper proposes algorithms based on k-hop dominating set; their performance is evaluated using real-world datasets and proves the edge over the existing state-of-the-art methods. It will also reduce the waiting time for customers and drivers looking for their subsequent pick-up requests. Therefore, this would maximize their profit and help improve their services.
期刊介绍:
GeoInformatica is located at the confluence of two rapidly advancing domains: Computer Science and Geographic Information Science; nowadays, Earth studies use more and more sophisticated computing theory and tools, and computer processing of Earth observations through Geographic Information Systems (GIS) attracts a great deal of attention from governmental, industrial and research worlds.
This journal aims to promote the most innovative results coming from the research in the field of computer science applied to geographic information systems. Thus, GeoInformatica provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of the use of computer science for spatial studies.