A real-time Active Routing approach via a database for airport surface movement

Abstract

Airports face challenges due to the increasing volume of air traffic and tighter environmental restrictions which result in a need to actively integrate speed profiles into conventional routing and scheduling procedure. However, only until very recently, the research on airport ground movement has started to take into account such a speed profile optimisation problem actively so that not only time efficiency but also fuel saving and decrease in airport emissions can be achieved at the same time. It is envisioned that the realism of planning could also be improved through speed profiles. However, due to the multi-objective nature of the problem and complexity of the investigated models (objective functions), the existing speed profile optimisation approach features high computational demand and is not suitable for an on-line application. In order to make this approach more competitive for real-world application and to meet limits imposed by International Civil Aviation Organization for on-line decision time, this paper introduces a pre-computed database acting as a middleware to effectively separate the planning (routing and scheduling) module and the speed profile generation module. Employing a database not only circumvents duplicative optimisation for the same taxiway segments, but also completely avoids the computation of speed profiles during the on-line decision support owing a great deal to newly proposed database initialization procedures. Moreover, the added layer of database facilitates, in the future, more complex and realistic models to be considered in the speed profile generation module, without sacrificing on-line decision time. The experimental results carried out using data from a major European hub show that the proposed approach is promising in speeding up the search process.

An online speed profile generation approach for efficient airport ground movement

“…It is worth noting that not all the three phases are necessarily required for each edge, as will be determined by the solution algorithm. Comparing with the previous studies (Chen et al, 2016a;Chen et al, 2016b;Weiszer et al, 2014;Weiszer et al, 2015b) in which the speed profile model is applied to the entire segment between two adjacent critical points (e.g., k cp and 1 k cp  in Fig. (1)), the proposed method provides a means to adjust the speed profile for each edge between intermediate control points (see Fig.…”

“…Active Routing proactively considers the taxiing speed profile along with routing and scheduling. To improve the solution efficiency, a database of optimised speed profiles is developed beforehand (Weiszer et al, 2015b). The database consists of desirable speed profiles for every possible segment of the taxiway.…”

“…Considering the issues of feasibility, fuel-efficiency and flexibility, an improved online speed profile generation approach is proposed in this paper. The main contributions can be summarised as follows: 1) As the time window constraints are imposed upon edges between control points, speed profiles are also defined upon edges in the proposed approach, instead of the entire segment between critical points as in existing approaches (Cheng and Sweriduk, 2009;Weiszer et al, 2015b). This not only improves the flexibility to generate feasible speed profiles, but also enlarges the search space to find more desirable solutions.…”

“…2) The arrival times at both the critical and control points can be selected from given ranges, instead of using fixed time points (Cheng and Sweriduk, 2009). And the speeds at both the critical and control points can be specifically allocated according to the arrival time requirements, instead of assuming fixed values beforehand at the critical points (see e.g., Weiszer et al (2015b)). These also make the proposed approach more flexible and facilitate the search for more desirable speed profiles.…”

“…The maximum taxiing speed is set to 10 knots (5.14 m/s) for turning segments and 30 knots (15.43 m/s) for straight segments, respectively. The maximum acceleration or deceleration rate is set to 1 m/s 2 , similar to the earlier work (Weiszer et al, 2015b). The parameters for the particle swarm optimisation algorithm (see Algorithm 1) are set according to Shi and Eberhart (1998) and Zhou et al (2011).…”