@article{bibcite_36826,
  author = {Hao Liu and Alex A Kurzhanskiy and Wanshi Hong and Xiao-Yun Lu},
  title = {Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment},
  abstract = {<p>Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles{\textquoteright} followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller{\textquoteright}s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5\% to 7.8\% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5\% to 3.6\% energy savings in a 2\% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.</p>},
  year = {2025},
  booktitle = {Journal of Intelligent Transportation Systems},
  journal = {Journal of Intelligent Transportation Systems},
  series = {Journal of Intelligent Transportation Systems},
  volume = {29},
  pages = {612-625},
  month = {02/11/2025},
  institution = {Informa UK Limited},
  publisher = {Informa UK Limited},
  issn = {1547-2450, 1547-2442},
  url = {https://doi.org/10.1080/15472450.2024.2369988},
  doi = {10.1080/15472450.2024.2369988},
}