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

The increase of population has intensified everyday rush. Traffic congestions are still a problem in cities and are one of the main cause for public transport delays. City residents and visitors have experienced time loss by using public transport buses, because of waiting at the bus stops and not knowing if the bus is delayed or already serviced the stop. Therefore it is valuable for people to know at what time the bus should arrive (or is it already missed) at specific bus stop. Real-time public bus tracking and management system development has been the focus of many researchers, and many studies have been done in this area. This paper focuses on bus travel time prediction comparison between linear regression and support vector regression models (SVR), when using limited data set. Data were limited in a way that only historical GPS (Global Positioning System) coordinates of bus location (recorded each 30 seconds) and driven distance were used, there were no information about arrival/departure times, delays or dwell times. Distance between stops and delay (assumed values based on route observations by authors) were used as inputs for both models. It was concluded that SVR algorithm showed better results, but the difference was not significantly large.

Bus Arrival Time Prediction with Limited Data Set using Regression Models