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

Travel time on a route varies substantially by time of day and from day today. It is critical to understand to what extent this variation is correlatedwith various factors, such as weather, incidents, events or travel demand levelin the context of dynamic networks. This helps a better decision making forinfrastructure planning and real-time traffic operation. We propose adata-driven approach to understand and predict highway travel time usingspatio-temporal features of those factors, all of which are acquired frommultiple data sources. The prediction model holistically selects the mostrelated features from a high-dimensional feature space by correlation analysis,principle component analysis and LASSO. We test and compare the performance ofseveral regression models in predicting travel time 30 min in advance via twocase studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a2.3-mile corridor of I-376E in Pittsburgh region. We found that somebottlenecks scattered in the network can imply congestion on those corridors atleast 30 minutes in advance, including those on the alternative route to thecorridors of study. In addition, real-time travel time is statistically relatedto incidents on some specific locations, morning/afternoon travel demand,visibility, precipitation, wind speed/gust and the weather type. All thosespatio-temporal information together help improve prediction accuracy,comparing to using only speed data. In both case studies, random forest showsthe most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%respectively in afternoon peak hours for the entire year of 2014.

Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents