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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Vehicle trajectories can offer the most precise and detailed depiction oftraffic flow and serve as a critical component in traffic management andcontrol applications. Various technologies have been applied to reconstructvehicle trajectories from sparse fixed and mobile detection data. However,existing methods predominantly concentrate on single-lane scenarios and neglectlane-changing (LC) behaviors that occur across multiple lanes, which limittheir applicability in practical traffic systems. To address this research gap,we propose a macro-micro approach for reconstructing complete vehicletrajectories on multi-lane freeways, wherein the macro traffic stateinformation and micro driving models are integrated to overcome therestrictions imposed by lane boundary. Particularly, the macroscopic velocitycontour maps are established for each lane to regulate the movement of vehicleplatoons, meanwhile the velocity difference between adjacent lanes providevaluable criteria for guiding LC behaviors. Simultaneously, the car-followingmodels are extended from micro perspective to supply lane-based candidatetrajectories and define the plausible range for LC positions. Later, atwo-stage trajectory fusion algorithm is proposed to jointly infer both thecar-following and LC behaviors, in which the optimal LC positions is identifiedand candidate trajectories are adjusted according to their weights. Theproposed framework was evaluated using NGSIM dataset, and the results indicateda remarkable enhancement in both the accuracy and smoothness of reconstructedtrajectories, with performance indicators reduced by over 30% compared to tworepresentative reconstruction methods. Furthermore, the reconstruction processeffectively reproduced LC behaviors across contiguous lanes, adding to theframework's comprehensiveness and realism.

A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing