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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Toll optimization in a large-scale dynamic traffic network is typicallycharacterized by an expensive-to-evaluate objective function. In this paper, wepropose two toll level problems (TLPs) integrated with a large-scalesimulation-based dynamic traffic assignment (DTA) model of Melbourne,Australia. The first TLP aims to control the pricing zone (PZ) through atime-varying joint distance and delay toll (JDDT) such that the networkfundamental diagram (NFD) of the PZ does not enter the congested regime. Thesecond TLP is built upon the first TLP by further considering the minimizationof the heterogeneity of congestion distribution in the PZ. To solve the twoTLPs, a computationally efficient surrogate-based optimization method, i.e.,regressing kriging (RK) with expected improvement (EI) sampling, is applied toapproximate the simulation input-output mapping, which can balance well betweenlocal exploitation and global exploration. Results show that the two optimalTLP solutions reduce the average travel time in the PZ (entire network) by29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity ofcongestion distribution achieves higher network flows in the PZ and a loweraverage travel time or a larger total travel time saving in the entire network.

Surrogate-based toll optimization in a large-scale heterogeneously congested network