Evaluation of Travel Lane Narrow Configurations and Inflow Restrictions on Congested Arterials

Huang, Yan; Zhou, Baojian; Li, Zongzhi; Zhang, Lei

doi:10.1061/9780784483565.174

Cited by 1 publication

(1 citation statement)

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“…None of these strategies apply to balancing and diverting inflow between two facilities, each with their unique traffic characteristics. Some recent research exist on the traffic control community considers our flavor of problem, e.g., a theoretical model of flow exchanges in multi-reservoir systems [16] and dynamically reorganizing and rearranging inflow on congested lanes and roadways [17,18]. But they do not consider the complex time-varying nature of airport traffic and do not develop and evaluate their approach on real traffic and intervention data.…”

Section: A Model Predictive Control For Traffic Managementmentioning

confidence: 99%

Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Nazir¹,

Vasisht²,

Choudhury³

et al. 2022

Preprint

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We present a data-driven control framework for adaptively managing landside congestion at airports. Ground traffic significantly impacts airport operations and critical efficiency, environmental, and safety metrics. Our framework models a real-world traffic intervention currently deployed at Seattle-Tacoma International Airport (SEA), where a digital signboard recommends drivers to divert to Departures or Arrivals depending on current congestion. We use measured vehicle flow/speed and passenger volume data, as well as time-stamped records of diversionary messages, to build a macroscopic system dynamics model. We then design a model predictive controller that uses our estimated dynamics to recommend diversions that optimize for overall congestion. Finally, we evaluate our approach on 50 real-world historical scenarios at SEA where no diversions were deployed despite significant congestion. Our results suggest that our framework would have improved speed in the congested roadway by up to three times and saved between 20 and 80 vehicle-hours of cumulative travel time in every hour of deployment. Overall, our work emphasizes the opportunity of algorithmic decisionmaking to augment operator judgment and intuition and yield better real-world outcomes.

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Section: A Model Predictive Control For Traffic Managementmentioning

confidence: 99%