Measuring the Length of a Two-Lane Curbside Based on Traffic Features

Abstract

The geometric design of airport curbsides is critical for both airport planners and passengers as it affects airport ground traffic capacity and creates airline delays. Rational design of curbsides can yield increased efficiency in the use of road space and time resources. Yet a limited number of studies have reported the impact of traffic features on curbside’s geometric design. This paper provides a two-lane curbside measurement with a simulation model to calculate the length of curbsides, which solves the curbside berth supply and curbside length calculation problems using a refined curbside parking model (RCPM) with constraints of a car-following (CF) model, as well as uniformly distributed speed, dwell time, and parking demand; it thereby unveils the detailed microscopic mechanisms of interaction of traffic features and curbsides’ length. The required lengths of curbsides under various parking demands are obtained after a set of numerical simulations. The result further reveals the significant impacts of car-following and braking behaviors, particularly under the huge parking demands on curbside traffic flow. It also demonstrates that a certain delay is created and a suboptimum level of service (LOS) occurs even though satisfying all parking demands, which indicates that lengthening the curbside and increasing parking supply do not necessarily improve the LOS.

Analyzing the Features of Passenger Drop-Off Behavior at Airport Curbsides: A Case Study From Guangxi Province, China

“…In formula (1), set 𝑂 𝑖 𝑡 represent the information about the vehicle, set 𝑂 𝑘 𝑡 represent the information about the door, and the parameter op left denotes the status of the left car…”

“…Some researchers proposed the use of private car trajectory data to achieve fuzzy logic-based dwell behavior detection and dwell time inference [27]. Some proposed an airport curbside simulation model that calculates the optimal length of an airport curbside based on traffic characteristics, such as speed, dwell time, and parking demand [1]. For the study of passenger queuing behavior in airport terminals, Wi-Fi tracking signals have been employed to extract queuing data, with both Feedforward Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks utilized for passenger behavior analysis and prediction [8].…”

“…Special drop-off lanes are commonly designated for travelers at or near terminals such as airports and train stations [1], [2]. Typically, these special lanes are located on the inner side of the airport curbside, closest to the terminal building.…”