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

Connected and automated vehicles (CAVs) are expected to change the way we travel. Before both the vehicles and infrastructures are fully automated, users of CAVs are required to respond appropriately to any adverse on-road conditions or malfunction that may prevent the autonomous driving system from reliably sustaining the dynamic driving task performance. The objective of this study is to construct spatiotemporal driving volatility profiles to help CAVs or drivers identify the potential hazards in the existing transportation network and make proactive driving decisions. The volatility profiles are constructed based on the historical traffic dynamics, varying spatially and temporally in the network. For demonstration, this study exploited the Basic Safety Messages datasets from Safety Pilot Model Development program in Ann Arbor, Michigan. The driving volatility is a measure to reflect the variability of driving performance, which is often used to show a vehicle or driver’s performance on road. This study extends the concept to capture the driving dynamics as a performance of the transportation network. This study also matched the driving volatility to the spatial and temporal occurrence of historical traffic crashes. Modeling results showed the volatility is significantly related to safety outcomes; therefore, the driving volatility profiles can be compiled into the high definition (HD) maps to inform CAVs and drivers of potential on-road hazards and assisting in making proactive driving decisions. Further, the results offer implications for potential upgrades of the transportation infrastructure for full automation in the future.

Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks