Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

Abstract

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.

Predicting the Place Visited of Floating Car: A Three-Layer Framework Using Spatiotemporal Probability

“…For example, in recent years, many studies have used social-media data, mobile-phone data, sign-in data, subway-card data, etc., to understand the population distribution and population flow of the city, thereby discovering hot spots in the city [1][2][3][4]. Combining land-use data with GPS data for taxi-demand analysis and hot-spot detection provides a reference for taxi resource allocation [5][6][7]. We use public-transportation-trajectory data and smart-card data to identify major public-transportation corridors in order to increase the utilization rate on limited road resources [8,9].…”

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

“…The study of travel patterns can improve traffic management and facilitate the construction of smart cities [1][2][3]. With the development of Location Base Service, most of the human mobility studies have used big data with location information, such as the Global Positioning System (GPS) records, subway card records, mobile phone Call Detail Records (CDRs), and social media check-in data [4][5][6][7][8][9][10][11][12]. However, a critical issue that has mostly been neglected in the current big data research is the representativeness of the data [13].…”