Understanding the relationship between complicated crossings and frequently visited locations – a case study with boro taxis in Manhattan

Abstract

Urban mobility has complex patterns and principles. Data of moving entities on the underlying transportation infrastructure can help understanding those complex patterns and principles. Therefore, we need static infrastructural information and knowledge on spatio-temporal movement patterns of public transport services and of various vehicle fleets. We focus on inspecting data partitions of individual taxi movement acquisitions in New York City (NYC), together with OpenStreetMap (OSM) data extracts, for gaining more knowledge about the complex daily mobility patterns in NYC. We select trip information of tracked boro taxi drivers, who are restricted to pick up customers at the airports and the southern part of Manhattan. By computing with taxi customer dropoff positions, we define drop-off clusters as the customer destination hotspots of selected Saturdays in June 2015. These hotspots are then related to the OSM road network, in particular to its derivatives: complicated crossings. By comparing with a previous assumption of detecting 'fast leaving' behaviour within the restricted zone, we receive characteristic matching results: only few destination hotspots appear at complicated crossings. Nearly all the matching intersections have nearby situated pedestrian zones and many are associated with previous construction measures. Finally, we reason on the usefulness of the proposed method.

Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data

“…We show results obtained analyzing hotspot D, since it is characterized by multiple usages [17] allowing the displaying of every activity level behavioral class. Interestingly, this area is also found to be an hotspot by [9]. Clearly, the stigmergy-based characterization of hotspot daily activity allows to cluster days according to their behavioral class which corresponds to the arrangement we assumed.…”

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

“…We use for our approach the method by Keler and Krisp (2016b) for extracting boro taxi drop-off hotspots. The two main components of this technique are applying OPTICS (Ankerst et al 1999) for the density cluster generation, and subsequently using the gift wrapping algorithm (Jarvis 1973) for convex hull generations.…”

“…We select boro taxi data from the four Saturdays in June 2015. Keler and Krisp (2016b) show that it is possible to represent typical destinations of boro taxi users by density-based clusters. These users come in large part from the outer boroughs of NYC, outside of Manhattan.…”

“…Local knowledge of specific mobility services can enrich specific road representations in the way of defining typical operational patterns on parts of the road. The usage of boro taxi destination points, for example, for relating with complicated crossings of a road network can benefit the understanding of specific functions of the urban road network (Keler and Krisp 2016b). In particular, boro taxi operating areas with a restriction in customer pick-ups, as in the southern part of Manhattan, have more densities at the border to the restriction zone.…”

“…From the data partitions, we extract start and destination points and focus on the latter for inferring customer drop-off hotspots. Additionally, there is a possibility of associating boro taxi drop-off hotspots with road segments (as for example from the OpenStreetMap project) or, based on the previous, complicated crossings (Keler and Krisp 2016b).…”