Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC
Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous researc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-04-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10095020.2019.1621008 |
| _version_ | 1819049691771305984 |
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| author | Andreas Keler Jukka M. Krisp Linfang Ding |
| author_facet | Andreas Keler Jukka M. Krisp Linfang Ding |
| author_sort | Andreas Keler |
| collection | DOAJ |
| description | Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous research shows that due to service restrictions, boro taxis have typical customer destination locations on selected Saturdays: many drop-off clusters appear near the restricted zone, where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing. Detected crossings imply recent infrastructural modifications. We want to follow up on these results and add one additional group of commuters: Citi Bike users. For selected Saturdays in June 2015, we want to compare the destinations of boro taxi and Citi Bike users. This is challenging due to manifold differences between active mobility and motorized road users, and, due to the fact that station-based bike sharing services are restricted to stations. Start and end points of trips, as well as the volumes in between rely on specific numbers of bike sharing stations. Therefore, we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods. |
| first_indexed | 2024-12-21T11:36:11Z |
| format | Article |
| id | doaj.art-42e76b2480524d4e998b1411602b4145 |
| institution | Directory Open Access Journal |
| issn | 1009-5020 1993-5153 |
| language | English |
| last_indexed | 2024-12-21T11:36:11Z |
| publishDate | 2020-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj.art-42e76b2480524d4e998b1411602b41452022-12-21T19:05:26ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532020-04-0123214115210.1080/10095020.2019.16210081621008Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYCAndreas Keler0Jukka M. Krisp1Linfang Ding2Technical University of MunichUniversity of Augsburg (UniA)Free University of Bozen-BolzanoTaxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics. It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities. Previous research shows that due to service restrictions, boro taxis have typical customer destination locations on selected Saturdays: many drop-off clusters appear near the restricted zone, where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing. Detected crossings imply recent infrastructural modifications. We want to follow up on these results and add one additional group of commuters: Citi Bike users. For selected Saturdays in June 2015, we want to compare the destinations of boro taxi and Citi Bike users. This is challenging due to manifold differences between active mobility and motorized road users, and, due to the fact that station-based bike sharing services are restricted to stations. Start and end points of trips, as well as the volumes in between rely on specific numbers of bike sharing stations. Therefore, we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods.http://dx.doi.org/10.1080/10095020.2019.1621008urban transportationspatial analysismobility patternsboro taxisvehicle fleetsbicycle-sharing service |
| spellingShingle | Andreas Keler Jukka M. Krisp Linfang Ding Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC Geo-spatial Information Science urban transportation spatial analysis mobility patterns boro taxis vehicle fleets bicycle-sharing service |
| title | Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC |
| title_full | Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC |
| title_fullStr | Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC |
| title_full_unstemmed | Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC |
| title_short | Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC |
| title_sort | extracting commuter specific destination hotspots from trip destination data comparing the boro taxi service with citi bike in nyc |
| topic | urban transportation spatial analysis mobility patterns boro taxis vehicle fleets bicycle-sharing service |
| url | http://dx.doi.org/10.1080/10095020.2019.1621008 |
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