ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Abstract

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. The simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2 s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

Keywords

operations traffic simulation mesoscopic traffic simulation sustainability and resilience transportation and sustainability transportation energy transportation network modeling and simulation

Get full access to this article

View all access options for this article.

References

Balakrishna

Antoniou

Ben-Akiva

Koutsopoulos

H. N.

Wen

Calibration of Microscopic Traffic Simulation Models: Methods and Application. Transportation Research Record: Journal of the Transportation Research Board, 2007. 1999: 198–207.

Sigler

Wang

Liu

Garikapati

Kotz

Kelly

K. J.

Lunacek

Phillips

Route Optimization for Energy Efficient Airport Shuttle Operations–A Case Study from Dallas Fort Worth International Airport. Journal of Air Transport Management, Vol. 94, 2021, p. 102077.

Ugirumurera

Severino

Ficenec

Wang

Williams

Chae

Lunacek

Phillips

A Modeling Framework for Designing and Evaluating Curbside Traffic Management Policies at Dallas-Fort Worth International Airport. Transportation Research Part A: Policy and Practice, Vol. 153, 2021, pp. 130–150.

Lopez

P. A.

Behrisch

Bieker-Walz

Erdmann

Flötteröd

Y.-P.

Hilbrich

Lücken

Rummel

Wagner

Wießner

Microscopic Traffic Simulation Using SUMO. Proc., 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, IEEE, New York, 2018, pp. 2575–2582.

Auld

Hope

Ley

Sokolov

Zhang

POLARIS: Agent-Based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations. Transportation Research Part C: Emerging Technologies, Vol. 64, 2016, pp. 101–116. https://doi.org/10.1016/j.trc.2015.07.017.

Chan

Wang

Bachan

Macfarlane

Mobiliti: Scalable Transportation Simulation Using High-Performance Parallel Computing. Proc., 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, IEEE, New York, 2018, pp. 634–641. https://doi.org/10.1109/ITSC.2018.8569397. 7.

Liu

Parallel Discrete-Event Simulation. In Wiley Encyclopedia of Operations Research and Management Science ( Cochran

J. J.

Cox

L. A.

Jr. Keskinocak

Kharoufeh

J. P.

Smith

J. C.

, eds.), Vol. 8, 2011.

Burghout

Koutsopoulos

H. N.

Andreasson

A Discrete-Event Mesoscopic Traffic Simulation Model for Hybrid Traffic Simulation. Proc., IEEE Intelligent Transportation Systems Conference, Toronto, ON, Canada, IEEE, New York, 2006, pp. 1102–1107.

Soh

A. C.

Marhaban

M. H.

Khalid

Yusof

A Discrete-Event Traffic Simulation Model for Multilane-Multiple Intersection. Proc., 9th Asian Control Conference (ASCC), Istanbul, Turkey, IEEE, New York, 2013, pp. 1–7.

10.

Zhang

Cassandras

C. G.

Mosterman

P. J.

A Discrete-Event and Hybrid Traffic Simulation Model Based on SimEvents for Intelligent Transportation System Analysis in Mcity. Discrete Event Dynamic Systems, Vol. 29, No. 3, 2019, pp. 265–295.

11.

Casas

Ferrer

J. L.

Garcia

Perarnau

Torday

Traffic Simulation With Aimsun. Springer, New York, NY, 2010, pp. 173–232. https://doi.org/10.1007/978-1-4419-6142-6_5.

12.

Matloff

Introduction to Discrete-Event Simulation and the Simpy Language, Vol. 2. Department of Computer Science, University of California, Davis. August 2008, pp. 1–33.

13.

Y. D.

Jang

Y. J.

The Optimal System Design of the Online Electric Vehicle Utilizing Wireless Power Transmission Technology. IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 3, 2013, pp. 1255–1265. https://doi.org/10.1109/TITS.2013.2259159.

14.

Liu

Yang

Xie

Feng

Lithium-Ion Battery Charging Management Considering Economic Costs of Electrical Energy Loss and Battery Degradation. Energy Conversion and Management, Vol. 195, 2019, pp. 167–179. https://doi.org/10.1016/j.enconman.2019.04.065.