Evaluation of Airport Wayfinding Accessibility with the Use of a Wheelchair Simulator

Abstract

As the number of air passengers with disabilities is expected to increase in the coming decades, the significance of airport wayfinding accessibility has been recognized by airport stakeholders. Emerging assistive technologies have been used to accommodate passengers’ wayfinding needs; however, because of non-standard practices and the complexity of terminal designs, the literature only provides general guidance on improving airport wayfinding accessibility. There is a need for detailed analysis of quantitative traveler performance measures to evaluate airport wayfinding accessibility. This research is the first use of a wheelchair simulator to compare airport wayfinding signage with a mobile wayfinding application. A virtual model of the St. Louis Lambert International Airport main terminal was replicated using as-built computer-aided-design files. A federated simulation architecture was used to integrate the wheelchair simulator with a mobile wayfinding application. Wheelchair simulator experiments were conducted by analyzing twenty-four wheelchair users’ performance measures and eye tracking data. Although the mobile wayfinding application did not significantly reduce total travel time (–23.8 s) and deviation ratio (–3%), it reduced wheelchair users’ reliance on wayfinding signs by decreasing total glance frequency (–23.3 times) and total glance duration (–26.7 s) and helped to reduce travel anxiety in wheelchair users. The potential benefits of a mobile wayfinding application include improving traveler levels of service, reducing airport operating costs, and enhancing non-airline revenue. Overall, this study showed that, with the use of a wheelchair simulator, passenger performance could be captured and analyzed for evaluating the effectiveness of airport wayfinding accessibility and emerging assistive technologies.

Because of the large number of travelers with cognitive, physical, or mobility challenges, the significance of airport wayfinding accessibility has been recognized by Congress (Section 504 and 508 of the Rehabilitation Act, Pub. L. 93-112, Americans with Disabilities Act, Pub. L. 101-336, the Air Carrier Access Act, and the FAA Reauthorization Act) and FAA advisory circulars ( 1 – 4 ), and researched by technical experts ( 5 – 8 ). However, because of non-standard practices and the complexity of airport terminal designs, there is little information available except for general guidelines to enhance airport accessible wayfinding. Therefore, there is a need for the collection and analysis of quantitative user behavior data and performance measures to help provide more detailed guidelines. In this study, wheelchair users’ travel behavior and eye tracking data were captured and analyzed by using a wheelchair simulator. The use of wheelchair simulator-based experiments can investigate the effectiveness of emerging wayfinding assistive technologies. As an example, the effectiveness of a mobile wayfinding application and real-time indoor position-sensing technology can be analyzed using a case study of St. Louis Lambert International Airport (STL).

Literature Review

Wheelchair users in airports are people who use a wheelchair independently or require wheelchair assistance to overcome their reduced mobility. Reduced mobility is the most common disability among adults in the United States, followed by hearing, cognition, and vision disabilities ( 9 ). In April of 2019, there were more than 53,402 passengers who traveled with their wheelchairs/scooters ( 10 ), and the number of requests for wheelchair assistance at large hub airports has been over one million every year ( 6 ). In addition, the Administration of Aging estimates the number of people over 65 will increase to 70 million by 2030 and account for 19% of the population, which is at a faster growth rate than the general population ( 8 ). As a natural result of aging, the number of wheelchair users in airports is expected to increase steadily over the coming decades.

Wayfinding Challenges for Wheelchair Users

People who use a wheelchair in airports share the same wayfinding challenges as passengers without disabilities. The common challenging factors include time pressure and unfamiliarity with a complex airport environment, that is, visual information overload, large numbers of people, and loud ambient noise levels ( 7 ). At some airports, unclear or confusing information and directional signage make the wayfinding task even more difficult. The path of travel in an airport often involves multiple tasks, such as ticketing, safety screening, shopping, and entertainment.

For wheelchair users, one of the extra challenges is that current wayfinding facilities and implemented signage are not optimized for them ( 5 ). For example, because of different eye heights, some wayfinding signs are easily viewed by standing passengers but are not visible to seated travelers. Another additional challenge is to move between floors in terminals. Passengers without disabilities switch floors by using stairs, escalators, and elevators. However, because of reduced mobility, wheelchair users rely on elevators only ( 6 ). If elevators are not adjacent to stairs/escalators and there is no directional signage to show the path between them, the task of switching floors becomes even more challenging for wheelchair users. In addition, wheelchair users have difficulty in using moving walkways, which leads to the challenge of long wheeling distances in the terminal ( 6 ).

Evaluation of Airport Accessible Wayfinding

The evaluation of airport wayfinding systems is important to enhance the airport accessibility and improve wheelchair users’ airport experience, as it helps to identify wayfinding challenges for travelers with disabilities, track improvements, and prioritize resource allocation ( 7 ).

The most common methods used for assessing airport wayfinding accessibility are field visits, surveys, and interviews ( 5 , 6 ). Field visits are usually conducted by designers or managers to ensure compliance with accessibility codes, or by recruiting unfamiliar passengers for task analysis. For example, John Glenn Columbus International Airport conducted a field study of 126 respondents to review its wayfinding system. In addition, different types of surveys and interviews, including compliance reviews and complaint resolutions, were also used to evaluate airport wayfinding accessibility ( 6 ). The assessments based on field visits, surveys, and interviews help airport engineers examine airport accessibility effectively. However, the results are heavily based on participants’ perspectives on the wayfinding system and wayfinding assistive technologies ( 11 ), and they have often been descriptive and policy oriented, making it difficult to use them for comparisons between studies ( 7 ).

In addition to qualitative measures, researchers have used quantitative measures, including visibility index (VI), inter-connection density (ICD), and space syntax ( 11 , 12 ) to evaluate airport wayfinding systems. VI measures the value of available sightlines at decision points to infer ease of wayfinding in an environment ( 13 ). As an objective complexity measure of a floor plan, ICD is calculated as the average number of links or corridors per decision point ( 11 , 14 ). Space syntax uses graph-based techniques (convex/axial map) to describe spatial configurations ( 15 , 16 ). Overall, the quantitative measures indicate the ease of wayfinding systems, and they are used to compare wayfinding systems in different environments. But the qualitative measures are determined by the objective environment without considering any human aspect involved in wayfinding or assistive technologies ( 11 ).

Therefore, there is a need to include both human and environmental aspects to evaluate airport wayfinding systems. The simulator-based approach provides a cost-effective way to allow human subjects to interact with a virtual environment. This study includes the first use of a wheelchair simulator to evaluate airport wayfinding accessibility.

Wayfinding Assistive Technologies

To enhance airport accessibility and help people in wayfinding, new assistive technologies have been implemented by airport operators and managers in recent years ( 3 , 5 – 8 , 17 ). These implementations include accessible websites for virtual tours and pre-trip planning, interactive kiosks which provide traveler interactive directories and maps via touchscreen, visual two-way paging, radio frequency identification (RFID), and mobile (smartphone or tablet) applications.

Among these technologies, the field of accessible wayfinding is centered on the development of wayfinding information via accessible mobile applications ( 6 ). The reasons for the focus on mobile applications are low implementation costs and the capacity to be customizable by passengers. Smartphones and tablets are relatively small, portable, and inexpensive for passengers. Providing a wayfinding application does not require an airport to modify existing buildings, add staff, or disrupt airport operations ( 8 ). In addition, mobile applications provide options to customize the content and the way in which they receive wayfinding support based on their own unique needs and abilities ( 17 ). For example, travelers can use mobile applications not only to find their gates, but also to locate ATMs, restaurants, information desks, or restrooms without additional assistance. Wayfinding mobile applications, such as indoor Google Maps ( 18 ) and LocusMaps of LocusLabs ( 19 ), have been developed for the general public in a limited number of airport terminals. But the wayfinding applications were not optimized for people with disabilities, and the route information (e.g., using an escalator) may not be accessible to wheelchair users.

Meanwhile, the supporting technologies for mobile applications, such as Wi-Fi and Bluetooth digital beacons, have been widely accepted and implemented in airports. A previous study ( 8 ) found that when free Wi-Fi was provided in terminals, the use of mobile wayfinding applications increased significantly. Meanwhile, the use of digital beacons has become popular in airports ( 7 ). MIA, SFO Terminal B, and JFK at New York have installed a network of digital beacons to improve the quality of wayfinding applications.

A mobile wayfinding application and real-time indoor position-sensing technology are likely to have the most impact and support the largest number of travelers in the near future ( 7 , 17 ). Therefore, in this study, the effectiveness of a mobile wayfinding application was evaluated by using a wheelchair simulator.

Methodology

A within-subjects experiment was developed to evaluate the accessibility of airport wayfinding designs and the benefits of assistive technologies. The framework of methodologies is shown in Figure 1. This research is the first use of a wheelchair simulator to collect and analyze quantitative wheelchair user behavior data and performance measures. The following section outlines the research steps that were employed in this study.

Figure 1.

Framework of methodologies.

Apparatus

ZouWheel, the University of Missouri wheelchair simulator, was used in the study. As shown in Figure 2, ZouWheel is built around a power wheelchair with triple 120-in. display screens (resolution of 5,760 × 1,080 pixels) where the horizontal field of view is 180° and the vertical field of view 40° with the eye height of 48 in. The running and turning speed of ZouWheel was calibrated based on the performance of a power wheelchair in an indoor environment and video data collected in the STL airport.

Figure 2.

ZouWheel and its control unit.

The active instrumentation of the wheelchair simulator includes a rubber joystick and two buttons. The joystick is used to control the moving and turning speed of the virtual wheelchair. The buttons operate elevators only when a participant can see the corresponding buttons on the screen. As the virtual airport has only two floors (ticketing and concourse), only two buttons (up/down) were implemented. The control unit was installed on a separate portable stand, so the power wheelchair can be moved away in case participants want to use their own wheelchairs.

Mobile Wayfinding Application

To evaluate the effectiveness of a mobile wayfinding application, the ZouMap wayfinding application was developed for the wheelchair simulator. Unlike wayfinding applications relying on Wi-Fi or digital beacons, ZouMap accessed the wheelchair’s real-time position and orientation directly using the High Level Architecture (HLA) of the federated simulation framework. Federated simulation refers to standards and specifications that allow multiple simulators, here ZouWheel and ZouMap, to be integrated into a single virtual environment ( 20 ). ZouMap has three feet or better of position accuracy and five degrees (out of 360 degrees) or better of orientation accuracy.

The ZouMap followed application guidelines and implementation of Airport Cooperative Research Program (ACRP) Research Report 177: Enhancing Airport Wayfinding for Aging Travelers and Persons with Disabilities ( 6 ). The user interface and features of ZouMap are shown in Figure 3. The mobile application allowed participants to select their destination and obtain paths and directions. The interface of ZouMap maximized the contrast between essential foreground information (text, icons, and buttons) and background. It allowed participants using “Prev” and “Next” buttons to review information about where they have been or where they are headed. The “Re-center” button helped the user resume the navigation within the application. Users can zoom in and out by touching buttons (+ and –) in the lower-right corner of the map.

Figure 3.

ZouMap for the wheelchair simulator and its features.

The mobile application was run on a 9.7-in. Windows tablet. As shown in Figure 2, the tablet was held by a flexible stand. Participants can adjust the position of the tablet or hold the tablet based on their preference.

Experiment Design

To evaluate the effectiveness of airport wayfinding systems and mobile applications, a hub airport was built. To enhance simulator fidelity, the airport was designed using STL Terminal 1 building layout and wayfinding signs. The architectural drawing of the terminal was provided by the STL Airport Authority. The research team conducted a site study at STL to gather information necessary for the wayfinding system and interviewed the ADA Coordinator.

The virtual airport terminal was a two-floor building (check-in and concourse level), which had two concourses and more than 40 boarding gates. It provided enough space and architectural complexity to evaluate the wayfinding system and assistive technologies. As this study focuses on wayfinding systems, some parts in the terminal were simplified, such as the airline ticketing and TSA screening processes. Participants received their paper boarding passes with the gate number at the start of the simulator study, so they were not required to check in at the airport. To avoid participants navigating based on their memory of STL, the interior design was changed by replacing the current airlines and stores.

Two sets of travel origin/destination pairs were selected for this study. The selection considerations included travel distance and complexity of wayfinding. The shortest travel paths of the two sets of travel origin and destination are shown in Figure 4. The blue path was from the curb near the airport Entry 5 on the ticketing level to Gate A18; the red path was from the curb near the airport Entry 4 on the ticketing level to Gate C17. Both travel paths involved using elevators to move between floors.

Figure 4.

The shortest paths between two sets of origin and destination.

Table 1 shows the four possible combinations of scenarios in the simulator study involving two origin/destination pairs and two technology scenarios: with and without the mobile application. To avoid sequence bias, also known as order of learning effect, the scenarios were randomly assigned to each participant ( 21 ). For example, row 3 shows a participant asked to travel to Gate C17 using the wayfinding application from the airport Entry 4 on the first trip, and then travel to Gate A17 from the airport Entry 5 without the mobile application on the second trip.

Table 1.

Scenarios for Wheelchair Simulator Study

Scenario	First trip		Second trip
	Travel path	Mobile application	Travel path	Mobile application
1	Blue (Gate A18)	Yes	Red (Gate C17)	No
2	Blue (Gate A18)	No	Red (Gate C17)	Yes
3	Red (Gate C17)	Yes	Blue (Gate A18)	No
4	Red (Gate C17)	No	Blue (Gate A18)	Yes

The concourses were renamed and the boarding gates numbers changed between each trip. For example, if concourse A and B were used in the first trip, then they will be replaced by concourse C and D in the second trip, including all wayfinding signs and gate numbers. In this way, participants could not rely on their memory from the first trip in case they explored the entire airport on the first trip.

Participants

Wheelchair users with long-term or short-term disabilities were recruited through a variety of venues such as the Rusk Rehabilitation Hospital, ThinkFirst Missouri, and the University of Missouri community, including the wheelchair basketball team and the disability center. A recruitment flyer was developed and distributed along with postings on social media and via word of mouth.

Twenty-four human subjects participated in the simulator study. Two participants were not able to complete the experiment because of muscle weakness and were not given the post-simulator survey. Eye tracking data from five participants was excluded because of poor data quality because the eye tracker shifted during the experiment. Participants represented a diverse population with respect to age and gender. The age distribution was 17% for ages 18–25, 38% for ages 26–40, 21% for ages 41–55, and 25% for age 56 and over. The age distribution was skewed slightly toward younger wheelchair users. Approximately 58% of the subjects were male. In relation to travel experience, 96% of the participants have traveled by air and 67% of them have traveled alone by air.

Procedures

The human subject study protocols and measurement tools were evaluated and approved by the campus institutional review board and a standard hosting script was used. First, a participant’s informed consent was obtained after he or she was introduced to the simulator and the experiment’s purpose. Then, each human subject rode the wheelchair simulator through the virtual airport twice for two different destinations. For each trip, various measurements of the wheelchair user behavior through the airport were recorded, including eye tracking data, for example, travel time, travel distance, eye glance duration, and glance frequency. Figure 5 shows the eye glance location as a large green dot on the image taken from the eye tracker’s world camera mimicking a wheelchair user’s field of view.

Figure 5.

Eye tracking data from the simulator study.

Measures of Effectiveness

The following four measures of effectiveness (MOEs) of wheelchair users were used in the simulator study:

MOE 1—travel time (seconds). The time that lapses between when the participant starts to move from the origin and when the participant arrives at the destination. Shorter travel time is desirable.

MOE 2—deviation ratio. The deviation ratio is the ratio of the travel distance to the shortest travel path (i.e., the travel path planned by the mobile application). A lower value is desirable, as it means the passenger did not deviate from the shortest travel path.

MOE 3—total glance frequency on wayfinding signs or the mobile application. It reflects how many times the participant looked at wayfinding signs or the mobile application during the trip.

MOE 4—total glance duration (seconds) on wayfinding signs or the mobile application. It reflects how long the participant looked at wayfinding signs and the mobile application during the trip.

Post-Simulator Survey

A post-experiment survey was administered to obtain stated preferences and qualitative feedback. The 17-question survey asked participants about their travel experiences at airports, wayfinding system preferences, reasons for preferences, and simulator fidelity. A 16-question simulator sickness questionnaire (SSQ), which is widely used for assessing simulator sickness severity of participants ( 22 ), was given at the end of the previous survey.

Results

Simulator Results

The simulator results are shown in Table 2. The table columns represent the sample size (n), mean, standard deviation (SD), difference from the baseline, p-value, and effect size (Cohen’s d). Overall, the simulator results showed that the current wayfinding signs of the STL airport were well-designed, as all participants were able to find their destinations by relying on the wayfinding signs only. The mobile wayfinding application resulted in a 24-s reduction in travel time (p = 0.064). It also reduced the deviation ratio by 3%, but the effect was not statistically significant (p = 0.473). The results also showed that with the use of the mobile wayfinding application, participants reduced their reliance on wayfinding signs significantly. When the wayfinding application was not provided, participants looked at wayfinding signs 58 times and spent 46 s on them. The application resulted in a 40% decrease (–23.3 times) in glance frequency on wayfinding signs (p = 0.002), and a 57% decrease (–26.7 s) in glance duration (p = 0.001).

Table 2.

Simulator Results

		MOE 1 travel time (seconds)
		n	Mean	SD	Diff.	p-value	Cohen’s d
Wayfinding signs		22	06:04.500	67.41	Baseline
Mobile application		22	05:40.696	28.15	−00:23.804	0.064	0.46
		MOE 2 deviation ratio
		n	Mean	SD	Diff.	p-value	Cohen’s d
Wayfinding signs		22	1.10	0.13	Baseline
Mobile application		22	1.07	0.04	−0.03	0.473	0.31
		MOE 3 glance frequency
		n	Mean	SD	Diff.	p-value	Cohen’s d
Wayfinding signs	on wayfinding signs	17	58.07	37.11	Baseline
Mobile application	on wayfinding signs	17	34.79	24.67	−23.28	0.002	0.74
	on the application	17	29.86	15.70	na	na	na
	Total	17	64.64	37.04	6.57	0.319	0.18
		MOE 4 glance duration (seconds)
		n	Mean	SD	Diff.	p-value	Cohen’s d
Wayfinding signs	on wayfinding signage	17	00:46.468	28.15	Baseline
Mobile application	on wayfinding signage	17	00:19.782	12.30	−00:26.686	0.001	1.23
	on the application	17	00:47.597	24.64	na	na	na
	Total	17	01:07.378	33.78	00:33.776	0.011	0.67

Note: MOE = measure of effectiveness; SD = standard deviation; na = not applicable; Diff. = difference. Bold values indicate statistically significant difference at 95% confidence level

Post-Simulator Survey Results

In the post-simulator survey, participants rated ease of finding their destination, clarity of wayfinding messages, awareness of surrounding, adequacy of information, and alleviation of the anxiety of trip. For each question, the maximum score is 5. The results in Table 3 showed that the mobile application scored higher than signage in all five categories and the differences were statistically significant, except in the categories of “increase awareness of surrounding” only. Most participants agreed that the mobile wayfinding application was beneficial for them to find their gates. The direction information helped travelers make route-choices at decision points, while the real-time location information and reference points reinforced their confidence in decision making. Therefore, the wayfinding application was able to reduce wheelchair users’ travel anxiety significantly. Previous research found that travelers with lower wayfinding anxiety were more efficient in finding their way to unfamiliar destinations ( 23 ). With regard to simulator fidelity, most participants (90%) agreed or strongly agreed that they felt like they were at an airport and they can control the wheelchair freely.

Table 3.

Post-Simulator Survey Results

	n	Wayfinding signs		Mobile application
		Mean	Median	Mean	Median	Diff.	p-value
Easy to find my destination	22	3.90	4	4.67	5	0.76	0.029
Provide clear wayfinding message	22	3.48	3	4.62	5	1.14	0.001
Increase awareness of surrounding	22	3.90	4	4.19	5	0.29	0.409
Provide adequate information	22	3.67	4	4.52	5	0.86	0.010
Help to reduce anxiety	22	2.95	3	4.33	5	1.38	0.001

Note: Diff. = difference. Bold values indicate statistically significant difference at 95% confidence level.

Discussion

The wheelchair simulator-based experiments showed that the wayfinding application not only reduced wheelchair users’ reliance on wayfinding signs but also helped relieve their travel anxiety. Besides, the results of this study suggested that the implementation of a mobile wayfinding application may benefit airports in the following ways.

First, offering mobile wayfinding services in airports may increase non-airline revenue. A previous study showed that when passengers have an effortless wayfinding experience they prefer to spend more time in food courts or retail areas, rather than waiting at gates ( 5 ). In this study, 90 % of the participants agreed that the mobile application helped them prepare for the trip and made it easy to find their destinations. In addition, the eye tracking data showed that the glance frequency on commercial signs (i.e., store and restaurant advertisements) increased by 10% during the trip with the mobile application compared with relying on wayfinding signs only, which can lead to increased spending.

Second, the wayfinding application can help airports reduce operational costs by replacing part of the wheelchair assistants with electric wheelchairs and motorized scooters ( 8 ). In the post-simulator survey, 60% of participants expressed their interest in using an unattended wheelchair or a mobility scooter if the equipment was available in airports. With a well-designed mobile wayfinding application, people with disabilities can use electric wheelchairs or motorized scooters to travel independently and reduce the need for wheelchair assistance.

Last but not least, a mobile wayfinding application can be used as a platform to benefit all passengers and enhance the level of services of airports. For example, with the added function of audio messages, the mobile application can also help travelers who are blind or visually impaired ( 6 ). The mobile application working with other services (e.g., two-way communication with the passenger help center) would improve the level of service and make the airport experience smoother and less stressful.

Conclusion

The simulator and survey results showed that a mobile wayfinding application could help to improve accessibility at airports. Although the mobile wayfinding application did not reduce passenger travel time and deviation ratio significantly, it reduced passenger reliance on wayfinding signs by decreasing total glance frequency and total glance duration on wayfinding signs and also helped participants reduce their anxiety during the trip. In addition, the benefits of the mobile wayfinding application suggest enhancing non-airline revenue, reducing airport operating costs, and improving the level of service. Overall, this study showed that, with the use of a wheelchair simulator, passengers’ performance could be captured and analyzed for evaluating the effectiveness of airport wayfinding accessibility and emerging assistive technologies.

This research could be expanded in many ways in the future. A logical next step would be to conduct a follow-up field study at another medium-hub airport to validate the wheelchair simulator approach and examine the readiness of existing indoor location technologies. Besides, the simulator study could be extended to the entire process of traveling in airports and could include other travel challenges such as curbside unloading, baggage check-in, security check, and boarding, or even before the trip. The issue of unassisted versus assisted wheelchair travel can be compared, including the issues of personal freedom, assistance cost, and overall traveler satisfaction. A walking simulator can be used to investigate the efficiency of prospective/retrospective wayfinding signage and wayfinding technologies for passengers without disabilities.

Footnotes

Acknowledgements

The authors are thankful for the advice and support provided by TRB ACRP panel members Lawrence Goldstein of TRB, Dr. Dave Byers of Quadrex Aviation, Robert Samis and Lillian Miller of FAA; and for the assistance provided by Angel Ramos and St. Louis Lambert International Airport, Dr. Jane Emerson of Rusk Rehabilitation Hospital, Ron Lykins of Mizzou Wheelchair Basketball, and Dr. Michelle Gibler of ThinkFirst Missouri; and for the administration of the ACRP Graduate Research Award Program on Public-Sector Aviation Issues, including Larry Goldstein of TRB, Mary Sandy, Sarah Pauls, and Kelly Wilson of the Virginia Space Grant Consortium. Finally, the authors would like to thank all the study participants.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: Z. Qing, C. Sun, J. Reneker; data collection: Z. Qing, J. Reneker; analysis and interpretation of results: Z. Qing, C. Sun, J. Reneker; draft manuscript preparation: Z. Qing, C. Sun, J. Reneker. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Airport Cooperative Research Program (ACRP) Graduate Research Awards Program.

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