Ramp merging areas are a persistent bottleneck of urban expressways: on-ramp vehicles compete with the mainline stream for the same gaps, the resulting deceleration propagates upstream, and stop-and-go behaviour raises both delay and fuel consumption. Connected and automated vehicles (CAVs) can exchange state information with surrounding vehicles and with roadside infrastructure, which makes the merging manoeuvre controllable instead of dependent on individual gap acceptance. This paper develops a two-layer cooperative merging strategy for a single-lane mainline and a single-lane on-ramp on which all vehicles are CAVs. The first layer formulates the merging sequence as a mixed-integer program that minimises the total time at which the controlled vehicles clear the merge point under vehicle dynamics and headway constraints. The second layer plans the longitudinal motion of each vehicle by optimal control with a weighted cost on acceleration and jerk, and the resulting trajectory is available in closed form for real-time execution. In simulations with balanced and unbalanced demand, the coupled strategy reduced average delay and raised mean speed relative to first-in-first-out and zipper merging, and cut fuel consumption by 12.56% and 12.90% at the highest demand, although it used slightly more fuel when demand was low.

Mingyu Jiang. A Traffic Flow Optimization Strategy for Ramp Merging Bottlenecks Based on Cooperative Control of Connected and Automated Vehicles. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 9: 23-30. https://doi.org/10.25236/AJCIS.2026.090904.

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