English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ubiquitous taxi trajectory data has made it possible to apply it to differenttypes of travel analysis. Of interest is the need to allow someone to monitortravel momentum and associated congestion in any location in space in realtime. However, despite an abundant literature in taxi data visualization andits applicability to travel analysis, no easy method exists. To measure taxitravel momentum at a location, current methods require filtering taxitrajectories that stop at a location at a particular time range, which iscomputationally expensive. We propose an alternative, computationally cheaperway based on pre-processing vector fields from the trajectories. Algorithms areformalized for generating vector kernel density to estimate a travel-model-freevector field-based representation of travel momentum in an urban space. Thealgorithms are shared online as an open source GIS 3D extension calledVectorKD. Using 17 million daily taxi GPS points within Beijing over a four-dayperiod, we demonstrate how to generate in real time a series of projectionsfrom a continuously updated vector field of taxi travel momentum to query apoint of interest anywhere in a city, such as the CBD or the airport. Thismethod allows a policy-maker to automatically identify temporal net influxes oftravel demand to a location. The proposed methodology is shown to be overtwenty times faster than a conventional selection query of trajectories. Wealso demonstrate, using taxi data entering the Beijing Capital InternationalAirport and the CBD, how we can quantify in nearly real time the occurrence andmagnitude of inbound or outbound queueing and congestion periods due to taxiscruising or waiting for passengers, all without having to fit any mathematicalqueueing model to the data.

Online monitoring of local taxi travel momentum and congestion effects using projections of taxi GPS-based vector fields