Flightpath 2050, the European Commission's vision for aviation, requires that the aviation industry achieves a 75 % reduction in CO2 emissions per passenger mile and airports become emission-free by ...2050. Airport shuttle buses in the airfields are going to be electrified to reduce ground emissions. Simultaneously, the airfield movement space and time schedules are becoming more limited for adopting stationary charging facilities for electrified ground vehicles. Therefore, the dynamic wireless charging technology becomes a promising technology to help improve the stability of electrification of the airfield transport network. This paper proposes a techno-economic assessment of wireless charging, wired charging, and conventional technologies for electrifying airport shuttle buses. A bi-level planning optimisation approach combines the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-III) and mixed integer linear programming (MILP) algorithm to handle a large number of decision variables and constraints generated from the investigated problem. The airport shuttle bus transport is simulated through a multi-agent-based model (MABM) approach. Four case studies are analysed for illustrating the techno-economic feasibility of wireless charging technology for airport electric shuttle buses. The results show that the wireless charging technology enables the electric shuttle buses to carry smaller batteries while conducting the same as tasks conventional diesel/petrol vehicles and the bi-directional wireless charging technology could help mitigate the impact of electrification of shuttle buses on the distribution network.
•A multi-agent-based model for electrified airfield transport simulation is developed.•Bi-directional wireless charging enables the shuttle bus serving as energy storage units.•Techno-economic assessment of four shuttle bus energy supply scenarios•Bidirectional wireless charging technology presents economic benefits.
The airport shuttle bus scheduling problem Öner, Nihat; Gultekin, Hakan; Koç, Çağrı
International journal of production research,
12/2021, Letnik:
59, Številka:
24
Journal Article
Recenzirano
This paper introduces the airport shuttle bus scheduling problem (ASBSP) as a new practical scheduling variant. In this problem, a number of identical vehicles that have a specific number of ...available seats provides transfer service between the airport and the city centre. After making a transfer in one direction, the vehicle can either make a new transfer in the opposite direction depending on the availability and the schedule of the passengers or make an empty return to make a new transfer in the same direction. The vehicles can wait in either location until their next transfer. The passengers have certain time windows for the transfer in relation to their flight times and operational rules to satisfy customer satisfaction. This is a profit-seeking service where transfer requests can also be rejected. The ASBSP aims to prepare a daily schedule for the available vehicles and to assign passengers to these vehicles with the objective of maximising the total profit. This paper presents two alternative mixed integer programming formulations and proposes two valid inequalities to get better bounds. Furthermore, it develops a hybrid metaheuristic that integrates multi-start, simulated annealing and large neighbourhood search for its solution. Extensive computational experiments on real-life benchmark instances have been made to test the performances of the formulations and the hybrid metaheuristic. Furthermore, the impacts of several problem parameters including the number of vehicles, vehicle capacity, transfer fee, transportation time and allowable passenger waiting times on the problem complexity and results have been investigated.
An airport shuttle bus (ASB), as an environmentally friendly mode of green transportation, is an effective way to solve the "first/last mile" of aviation passengers, which can attract a higher ...passenger transfer from private cars to public transport, thereby reducing emissions of carbon dioxide and other polluting gases. This study presents a multi-objective mixed-integer linear programming for ASB services in a dynamic environment. Taking into account time-varying demand and travel time characteristics in different periods, the proposed model provides a comprehensive framework that simultaneously advises passengers to join the bus at the nearest bus stations, designs routes for transporting them from these selected stations through the airport, and computes their departure frequencies in multiple periods. The primary objective is to optimize both the total ride time and waiting time for all passengers. The secondary objective is to optimize the total transfer distance of all passengers simultaneously. Given the Non-Deterministic Polynomial (NP) hardness of this problem, a two-stage multi-objective heuristic approach based on the non-dominated sorting genetic algorithm (NSGA-II) is combined with a dynamic programming search method and further advanced to obtain the Pareto-optimal solutions of the proposed model within a reasonable time. Finally, the proposed model and algorithm feasibility are proved by a test example of designing a shuttle bus route and schedule at Tianjin Airport, China. The results show that the total passenger travel time of the presented model is markedly reduced by 1.21% compared with the conventional model.
With the growing volume of the airport passengers, public transit is needed for healthy and sustainable city development, in which airport shuttle buses play a key role in satisfying the demand. In ...this paper, a two-phase airport shuttle bus stop planning method is proposed based on taxi GPS data. It aims at providing convenient public transit to the airport by identifying optimal airport shuttle bus stop. In our method the first phase focuses on filtering the irrelevant "dirty" records. Then the remained data set is divided into two parts consisting of origin dataset and destination dataset. In the second phase, the k-means clustering algorithm is employed to identify representative points as candidate airport shuttle bus stops based on these two datasets. After that, taking advantage of traffic model and rules defined in traffic engineering, the candidate stops set can be further optimized. Finally, extensive experiments are conducted on a large-scale real-world taxi GPS data set to verify the practicality of our method on an In-memory database platform, HANA.
