Exploring Benefits and Challenges of 511 Applications in Enhancing Mobility and Safety by Evaluating User Reviews

Abstract

The introduction of 511 systems in the early twenty-first century has significantly enhanced mobility and safety for motorists. Most states across the United States have deployed this mobile application, offering various features for safety and mobility, including real-time updates and trip planning. They are accessible via various platforms, including Google Play Store and the App Store. In these platforms, users can rate and review these 511 applications, providing valuable feedback for improvement. This study analyzes users’ review comments from the Google Play Store and App Store to understand the user perception based on star rating categories to obtain benefits and the challenges faced while interacting with these 511 applications. The study utilizes exploratory analysis and text network analysis to leverage the information from the user reviews. The findings show that the benefits and challenges identified vary from one geographic region to another. However, general user perception indicates that the 511 application disseminates crucial road conditions and road safety during the winter season. However, users report problems such as continuous application failure and advertisement clutter hindering the process of disseminating road safety information. This study offers crucial insights to state departments of transportation, suggesting enhancements to 511 systems to improve user experience and traffic safety.

Keywords

511 systems travel information application Google Play Store App Store real-time incidents

Get full access to this article

View all access options for this article.

References

Johnson

C. M.

Tinklenberg

511-America’s Traveler Information Number. Institute of Transportation Engineers. ITE Journal, Vol. 71, No. 8, 2001, pp. 32–36.

Lindly

J. K.

Stephen

Hill

OVERVIEW STUDY 511 Traveler Information Services for The Alabama Department of Transportation. Alabama Department of Transportation, Montgomery, 2003.

Lawther

W. C.

Berman

E. M.

Second Annual Evaluation of the SmartRoute Systems Advanced Traveler Information Services Contract for Miami-Dade, Broward and Palm Beach Counties. FHWA – ITS Joint Program Office, Florida Department of Transportation, 2002.

Yusuf

J. E.

Wallace

C. Y.

Kreis

S. D.

Evaluation of the Eastern Kentucky Rural Highway Information Project 511 Tourism Service. Kentucky Transportation Cabinet, Frankfort, 2006.

Vasudevan

Wunderlich

K. E.

Burnier

Glassco

Mobility and Commute Disutility Effects of 511 Deployment in Salt Lake City, Utah. Transportation Research Record: Journal of the Transportation Research Board, 2008. 2086: 64–71.

Morris

Ton

Cooper

Edwards

Donath

A Next Generation Non-Distracting In-Vehicle 511 Traveler Information Service. Minnesota Department of Transportation, St. Paul, 2014.

Stanley

L. M.

Kelly

M.J.

Lassacher

Driver Performance While Interacting with the 511 Travel Information System in Urban and Rural Traffic. Proc., Third International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Rockport, ME, 2005.

Kimley-Horn and Associates Inc. ATIS Evaluation for 511 and Internet-Based ATIS Enhancements and Implementation. Task 1: Evaluation of Existing Web and Phone-Based ATIS in Nebraska . Nebraska Department of Roads (NDOR). 2003.

Swan

Baker

Hintz

Trimble

511 Virginia Evaluation. Virginia Department of Transportation, Richmond, 2004.

10.

Jay

Hill

S. E.

Overview Study 511 Traveler Information Services for the Alabama Department of Transportation. Alabama Department of Transportation, Montgomery, 2003.

11.

Hutto

C. J.

Gilbert

VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8, No. 1, 2014, pp. 216–225. https://doi.org/10.1609/icwsm.v8i1.14550.

12.

Swarnkar

. VADER Sentiment Analysis: A Complete Guide, Algo Trading and More. 2020. https://Blog.Quantinsti.Com/Vader-Sentiment/.

13.

Yuliansyah

Mulasari

S. A.

Sulistyawati

Ghozali

F. A.

Sudarsono

Sentiment Analysis of the Waste Problem Based on YouTube Comments Using VADER and Deep Translator. Jurnal Media Informatika Budidarma, Vol. 8, No. 1, 2024, p. 663. https://doi.org/10.30865/mib.v8i1.6918.

14.

Kutela

Oscar

Kidando

Mihayo

Seeing through Eyes of Drivers: Space Consideration in Investigating Visibility of Vulnerable Road Users Involved in Crashes from Driver’s Perspective. Accident Analysis and Prevention, Vol. 192, 2023, p. 107260. https://doi.org/10.1016/j.aap.2023.107260.

15.

Paranyushkin

Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Venture Fiction Practices. Vol. 2, No. 4, 2011.

16.

Kutela

Das

Dadashova

Mining Patterns of Autonomous Vehicle Crashes Involving Vulnerable Road Users to Understand the Associated Factors. Accident Analysis and Prevention, Vol. 165, 2022, p. 106473. https://doi.org/10.1016/j.aap.2021.106473.

17.

Kutela

Novat

Langa

Exploring Geographical Distribution of Transportation Research Themes Related to COVID-19 Using Text Network Approach. Sustainable Cities and Society, Vol. 67, 2021, p. 102729. https://doi.org/10.1016/j.scs.2021.102729.

18.

Kim

Jang

S.-n.

Mapping the Knowledge Structure of Frailty in Journal Articles by Text Network Analysis. PLoS One, Vol. 13, No. 4, 2018, p. e0196104. https://doi.org/10.1371/journal.pone.0196104.

19.

Kutela

Teng

Prediction of Drivers and Pedestrians’ Behaviors at Signalized Mid-Block Danish Offset Crosswalks Using Bayesian Networks. Journal of Safety Research, Vol. 69, 2019, pp. 75–83. https://doi.org/10.1016/j.jsr.2019.02.008.

20.

Kutela

Teng

Evaluating the Influential Factors for Pushbutton Utilization at Signalized Midblock Crosswalks. Safety Science, Vol. 122, 2020, p. 104533. https://doi.org/10.1016/j.ssci.2019.104533.

21.

R Core Team. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/.

22.

Benoit

Watanabe

Wang

Nulty

Obeng

Müller

Matsuo

Quanteda: An R Package for the Quantitative Analysis of Textual Data. Journal of Open Source Software, Vol. 3, No. 30, 2018, p. 774. https://doi.org/10.21105/joss.00774.