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24.02.2021

Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality, gamification, and social components in mobile apps to influence travel decisions

verfasst von: Yuntao Guo, Srinivas Peeta, Shubham Agrawal, Irina Benedyk

Erschienen in: Transportation | Ausgabe 2/2022

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Abstract

This study aims to understand the impacts of Pokémon GO, a popular location-based augmented reality (AR) mobile gaming app, on route and mode choices. Pokémon GO leverages AR to introduce virtual objects at fixed and dynamic locations that translate through the app interface to incentives in the real world that potentially influence users’ route and mode choices. Its gaming nature and social components can possibly enhance long-term user engagement through applying the characteristics of game elements and providing opportunities for competition, collaboration, companionship, and social reinforcement. An online survey is conducted to collect the self-reported behavior of a group of Pokémon GO users to explore its impacts on the following aspects of travel behavior: (1) the frequency of changing the route to interact with virtual objects; (2) the likelihood of carpooling more instead of driving alone for more in-app collaboration; and (3) the likelihood of shifting mode from drive alone to public transit, walking, and cycling if provided with additional incentives. The ordered survey responses including frequency and likelihood are analyzed using random parameters ordered probit models to account for the unobserved heterogeneity across users and identify subpopulations of travelers who are more susceptible to the influence of Pokémon GO. The modeling results identify four types of variables (attitude and perceptions related to Pokémon GO, app engagement, play style, and sociodemographic characteristics) that affect users’ travel behavior. The results illustrate that such apps with integrated AR, gamification, and social components can be used by policymakers to influence various aspects of travel behavior. The study findings and insights can provide valuable feedback to system operators for designing such apps to dynamically manage traffic in real-time and promote long-term sustainable mode shifts.

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Abdel-Aty, M.: Analysis of driver injury severity levels at multiple locations using ordered probit models. J. Saf. Res. 34(5), 597–603 (2003)CrossRef

Abou-Zeid, M., Ben-Akiva, M.: The effect of social comparisons on commute well-being. Transp. Res. Part A Policy Pract. 45(4), 345–361 (2011)CrossRef

Akoglu, H.: User’s guide to correlation coefficients. Turk. J. Emerg. Med. 18(3), 91–93 (2018)CrossRef

Allan, R.: Pokémon GO Usage Statistics Say It’s the Most Popular Mobile Game in U.S. History (2016). https://medium.com/@sm_app_intel/pok%C3%A9mon-go-usage-statistics-say-its-the-most-popular-mobile-game-in-u-s-history-ea09ea2bf6df#:~:text=first%20to%20respond.-,Pok%C3%A9mon%20GO%20usage%20statistics%20say%20it's%20the,mobile%20game%20in%20U.S.%20history&text=As%20of%20yesterday%2C%20Pok%C3%A9mon%20GO,mobile%20game%20in%20US%20history. Accessed 10 Oct 2020

Althoff, T., White, R.W., Horvitz, E.: Influence of Pokémon go on physical activity: study and implications. J. Med. Internet Res. 18(12), e315 (2016)CrossRef

Althubaiti, A.: Information bias in health research: definition, pitfalls, and adjustment methods. J. Multidiscip. Healthc. 9, 211 (2016)CrossRef

Anastasopoulos, P.C.: Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis. Anal. Methods Accid. Res. 11, 17–32 (2016)

Anastasopoulos, P.C., Karlaftis, M.G., Haddock, J.E., Mannering, F.L.: Household automobile and motorcycle ownership analyzed with random parameters bivariate ordered probit model. Transp. Res. Rec. 2279(1), 12–20 (2012)CrossRef

Armstrong, R.A.: When to use the Bonferroni correction. Ophthalmic Physiol. Opt. 34(5), 502–508 (2014)CrossRef

Ayers, J.W., Leas, E.C., Dredze, M., Allem, J.P., Grabowski, J.G., Hill, L.: Pokémon GO—a new distraction for drivers and pedestrians. JAMA Intern. Med. 176(12), 1865–1866 (2016)CrossRef

Azevedo, C.L., Seshadri, R., Gao, S., Atasoy, B., Akkinepally, A.P., Christofa, E., Zhao, F., Trancik, J., Ben-Akiva, M.: Tripod: sustainable travel incentives with prediction, optimization, and personalization. In: The 97th Annual Meeting of Transportation Research Board (2018)

Bamberg, S., Fujii, S., Friman, M., Gärling, T.: Behaviour theory and soft transport policy measures. Transp. Policy 18(1), 228–235 (2011)CrossRef

Barbieri, S., Vettore, G., Pietrantonio, V., Snenghi, R., Tredese, A., Bergamini, M., Previato, S., Stefanati, A., Gaudio, R., Feltracco, P.: Pedestrian inattention blindness while playing Pokémon Go as an emerging health-risk behavior: a case report. J. Med. Internet Res. 19(4), e86 (2017)CrossRef

Barkley, J.E., Lepp, A., Glickman, E.L.: “Pokémon Go!” may promote walking, discourage sedentary behavior in college students. Games Health J. 6(3), 165–170 (2017)CrossRef

Ben-Elia, E., Shiftan, Y.: Which road do I take? A learning-based model of route-choice behavior with real-time information. Transp. Res. Part A Policy Pract. 44(4), 249–264 (2010)CrossRef

Bhowmik, T., Yasmin, S., Eluru, N.: A multilevel generalized ordered probit fractional split model for analyzing vehicle speed. Anal. Methods Accid. Res. 21, 13–31 (2019)

Bordin, S., Menéndez, M., De Angeli, A.: ViaggiaTrento: an application for collaborative sustainable mobility. ICST Trans. Ambient Syst. 1(4), e5 (2014)CrossRef

Boucher, J.P., Turcotte, R.: A longitudinal analysis of the impact of distance driven on the probability of car accidents. Risks 8(3), 91 (2020)CrossRef

Broll, G., Cao, H., Ebben, P., Holleis, P., Jacobs, K., Koolwaaij, J., Luther, M., Souville, B.: Tripzoom: an app to improve your mobility behavior. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, p. 57. ACM (2012)

Carreras, I., Gabrielli, S., Miorandi, D., Tamilin, A., Cartolano, F., Jakob, M., Marzorati, S.: SUPERHUB: a user-centric perspective on sustainable urban mobility. In: Proceedings of the 6th ACM Workshop on Next Generation Mobile Computing for Dynamic Personalized Travel Planning, pp. 9–10. ACM (2012)

Chen, P.L., Saleh, W., Pai, C.W.: Pokemon gaming causes pedestrians to run a red light: an observational study of crossing behaviours at a signalised intersection in Taipei City. Transp. Res. Part F Traffic Psychol. Behav. 55, 380–388 (2018)CrossRef

Christoforou, Z., Cohen, S., Karlaftis, M.G.: Vehicle occupant injury severity on highways: an empirical investigation. Accid. Anal. Prev. 42(6), 1606–1620 (2010)CrossRef

Daoud, J.I.: Multicollinearity and regression analysis. In: Journal of Physics: Conference Series, Vol. 949, No. 1, p. 012009. IOP Publishing (2017)

Dastjerdi, A.M., Kaplan, S., e Silva, J.D.A., Nielsen, O.A., Pereira, F.C.: Participating in environmental loyalty program with a real-time multimodal travel app: user needs, environmental and privacy motivators. Transp. Res. Part D Transp. Environ. 67, 223–243 (2019a)CrossRef

Dastjerdi, A.M., Kaplan, S., e Silva, J.D.A., Nielsen, O.A., Pereira, F.C.: Use intention of mobility-management travel apps: the role of users goals, technophile attitude and community trust. Transp. Res. Part A Policy Pract. 126, 114–135 (2019b)CrossRef

de Almeida Correia, G.H., Looff, E., van Cranenburgh, S., Snelder, M., van Arem, B.: On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: theoretical insights and results from a stated preference survey. Transp. Res. Part A Policy Pract. 119, 359–382 (2019)CrossRef

Di Dio, S., Rizzo, G., Vinci, I.: How to track behaviours’ changes toward more sustainable mobility habits: the serious game of TrafficO₂. In: Proceedings of the International Conference on Design, Social Media and Technology to Foster Civic Self-Organisation Conference, Hasselt (BE) (2015)

Di Dio, S., Lissandrello, E., Schillaci, D., Caroleo, B., Vesco, A., D’Hespeel, I.: MUV: A game to encourage sustainable mobility habits. In: International Conference on Games and Learning Alliance, pp. 60–70. Springer, Cham (2019)

Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M., Hankey, J.: Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. 113(10), 2636–2641 (2016)CrossRef

Dorward, L.J., Mittermeier, J.C., Sandbrook, C., Spooner, F.: Pokémon Go: benefits, costs, and lessons for the conservation movement. Conserv. Lett. 10(1), 160–165 (2017)CrossRef

Faccio, M., McConnell, J.J.: Death by Pokémon GO: the economic and human cost of using apps while driving. J. Risk Insur. 87, 815–849 (2017)CrossRef

Federal Highway Administration (FHWA): Average Annual Miles per Driver by Age Group (2016). https://www.fhwa.dot.gov/ohim/onh00/bar8.htm. Accessed 27 Dec 2016

Fountas, G., Anastasopoulos, P.C.: A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities. Anal. Methods Accid. Res. 15, 1–16 (2017)

Fountas, G., Anastasopoulos, P.C.: Analysis of accident injury-severity outcomes: the zero-inflated hierarchical ordered probit model with correlated disturbances. Anal. Methods Accid. Res. 20, 30–45 (2018)

Fountas, G., Anastasopoulos, P.C., Abdel-Aty, M.: Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates. Anal. Methods Accid. Res. 18, 57–68 (2018)

Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J., Consolvo, S., Harrison, B., Landay, J.A.: UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits. In: Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1043–1052. ACM (2009)

Frommer, D.: Millions of Americans Still Play Pokémon GO Every Day. https://www.vox.com/2017/3/23/15039626/pokemon-go-daily-users-chart. Accessed 10 Oct 2020 (2020)

Gerlich, R.N., Drumheller, K., Babb, J., De’Armond, D.A.: App consumption: an exploratory analysis of the uses and gamification of mobile apps. Acad. Mark. Stud. J. 19(1), 69 (2015)

Greene, W.H.: Econometric Analysis, 4th edn. Prentice Hall, New Jersey (2000)

Guo, Y., Peeta, S.: Rail-truck multimodal freight collaboration: truck freight carrier perspectives in the United States. J. Transp. Eng. 141(11), 1–11 (2015)CrossRef

Guo, Y., Peeta, S.: Impacts of interactive accessibility information intervention strategy on residential location choice and travel-related behavior. In: Compendium of Transportation Research Board 96th Annual Meeting, Washington D.C., U.S. (2017)

Guo, Y., Peeta, S.: Impacts of personalized accessibility information on residential location choice and travel behavior. Travel Behav. Soc. 19, 99–111 (2020)CrossRef

Guo, Y., Agrawal, S., Peeta, S., Somenahalli, S.: Impacts of property accessibility and neighborhood built environment on single-unit and multiunit residential property values. Transp. Res. Rec. J. Transp. Res. Board 2568, 103–112 (2016a)CrossRef

Guo, Y., Wang, J., Peeta, S.: The impacts of household registration and head of household travel behavior on travel mode choice in China. In: Compendium of Transportation Research Board 95th Annual Meeting, Washington D.C., U.S. (2016b)

Guo, Y., Wang, J., Peeta, S., Anastasopoulos, P.C.: Impacts of internal migration, household registration system, and family planning policy on travel mode choice in China. Travel Behav. Soc. 13, 128–143 (2018)CrossRef

Guo, Y., Peeta, S., Agrawal, S., Benedyk, I.: Understanding Safety implications of location-based augmented reality mobile apps: perception, attitude, and app usage behavior. Presented in 99th Transportation Research Board Annual Meeting, January 15th, 2020, Washington D. C., the U.S.A. (2020a)

Guo, Y., Wang, J., Peeta, S., Anastasopoulos, P.C.: Personal and societal impacts of motorcycle ban policy on motorcyclists’ home-to-work morning commute in China. Travel Behav. Soc. 19, 137–150 (2020)CrossRef

Hamari, J., Koivisto, J.: Social motivations to use gamification: an empirical study of gamifying exercise. In: ECIS 2013 Proceedings, paper 122 (2013)

Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work? A literature review of empirical studies on gamification. In: System Sciences (HICSS), In Proceedings of 2014 47th Hawaii International Conference, pp. 3025–3034 (2014)

Hasegawa, H.: Analyzing tourists’ satisfaction: a multivariate ordered probit approach. Tourism Manag. 31(1), 86–97 (2010)CrossRef

Hessing, D.J., Elffers, H., Weigel, R.H.: Exploring the limits of self-reports and reasoned action: an investigation of the psychology of tax evasion behavior. J. Person. Soc. Psychol. 54(3), 405 (1988)CrossRef

Hopkins, D.: Can environmental awareness explain declining preference for car-based mobility amongst generation Y? A qualitative examination of learn to drive behaviours. Transp. Res. Part A Policy Pract. 94, 149–163 (2016)CrossRef

Hopkins, D., Stephenson, J.: Generation Y mobilities through the lens of energy cultures: a preliminary exploration of mobility cultures. J. Transp. Geogr. 38, 88–91 (2014)CrossRef

Hsiao, K., Chen, C.: What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty. Electron. Commer. Res. Appl. 16, 18–29 (2016)CrossRef

Hsu, C.L., Lin, J.C.C.: What drives purchase intention for paid mobile apps?—An expectation confirmation model with perceived value. Electron. Commer. Res. Appl. 14(1), 46–57 (2015)CrossRef

Hu, X., Chiu, Y.C., Zhu, L.: Behavior insights for an incentive-based active demand management platform. Int. J. Transp. Sci. Technol. 4(2), 119–133 (2015)CrossRef

Jalayer, M., Shabanpour, R., Pour-Rouholamin, M., Golshani, N., Zhou, H.: Wrong-way driving crashes: a random-parameters ordered probit analysis of injury severity. Accid. Anal. Prev. 117, 128–135 (2018)CrossRef

Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sengupta, R., Walker, J.: Quantified Traveler: Travel Feedback Meets the Cloud to Change Travel Behavior (No. UCTC-FR-2013–06) (2013)

Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sengupta, R., Walker, J.L.: Quantified traveler: Travel feedback meets the cloud to change behavior. J. Intell. Transp. Syst. 19(2), 109–124 (2015)CrossRef

Jin, S.T., Kong, H., Wu, R., Sui, D.Z.: Ridesourcing, the sharing economy, and the future of cities. Cities 76, 96–104 (2018)CrossRef

Juhász, L., Novack, T., Hochmair, H.H., Qiao, S.: Cartographic vandalism in the era of location-based games—the case of OpenStreetMap and Pokémon GO. ISPRS Int. J. Geo-Inf. 9(4), 197 (2020)CrossRef

Jylhä, A., Nurmi, P., Sirén, M., Hemminki, S., Jacucci, G.: Matkahupi: a persuasive mobile application for sustainable mobility. In: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 227–230. ACM (2013)

Kaczmarek, L.D., Misiak, M., Behnke, M., Dziekan, M., Guzik, P.: The Pikachu effect: social and health gaming motivations lead to greater benefits of Pokémon GO use. Comput. Hum. Behav. 75, 356–363 (2017)CrossRef

Kato, T.A., Teo, A.R., Tateno, M., Watabe, M., Kubo, H., Kanba, S.: Can Pokémon GO rescue shut-ins (hikikomori) from their isolated world. Psychiatry Clin. Neurosci. 71(1), 75–76 (2017)CrossRef

Kazhamiakin, R., Marconi, A., Perillo, M., Pistore, M., Valetto, G., Piras, L., Avesani, F., Perri, N.: Using gamification to incentivize sustainable urban mobility. In: 2015 IEEE First International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2015)

Kenworthy, J.R., Laube, F.B.: Patterns of automobile dependence in cities: an international overview of key physical and economic dimensions with some implications for urban policy. Transp. Res. Part A Policy Pract. 33(7–8), 691–723 (1999)CrossRef

Khoo, H.L., Asitha, K.S.: User requirements and route choice response to smart phone traffic applications (apps). Travel Behav. Soc. 3, 59–70 (2016)CrossRef

Klein, N.J., Smart, M.J.: Millennials and car ownership: less money, fewer cars. Transp. Policy 53, 20–29 (2017)CrossRef

Kogan, L., Hellyer, P., Duncan, C., Schoenfeld-Tacher, R.: A pilot investigation of the physical and psychological benefits of playing Pokémon GO for dog owners. Comput. Hum. Behav. 76, 431–437 (2017)CrossRef

Kwon, O.H., Yoon, Y., Jang, K.: Evaluating the effectiveness of the law banning handheld cellphone use while driving. Saf. Sci. 70, 50–57 (2014)CrossRef

Langenbucher, J., Merrill, J.: The validity of self-reported cost events by substance abusers: limits, liabilities, and future directions. Eval. Rev. 25(2), 184–210 (2001)CrossRef

Lee, Y.K., Chang, C.T., Lin, Y., Cheng, Z.H.: The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 31, 373–383 (2014)CrossRef

Lehto, T., Oinas-Kukkonen, H.: Persuasive features in six weight loss websites: a qualitative evaluation. In: International Conference on Persuasive Technology, pp. 162–173. Springer, Berlin, Heidelberg (2010)

Li, Y., Landay, J.A.: Into the wild: low-cost ubicomp prototype testing. Computer 41(6), 94–97 (2008)CrossRef

Li, Y., Guo, Y., Lu, J., Peeta, S.: Impacts of congestion pricing and reward strategies on automobile travelers’ morning commute mode shift decisions. Transp. Res. Part A Policy Pract. 125, 72–88 (2019)CrossRef

Liu, C.Z., Au, Y.A., Choi, H.S.: Effects of freemium strategy in the mobile app market: an empirical study of google play. J. Manag. Inf. Syst. 31(3), 326–354 (2014)CrossRef

Liu, J., Kauffman, R.J., Ma, D.: Competition, cooperation, and regulation: understanding the evolution of the mobile payments technology ecosystem. Electron. Commer. Res. Appl. 14(5), 372–391 (2015)CrossRef

Long, J.S., Freese, J.: Regression Models for Categorical Dependent Variables Using Stata, 3rd edn. Stata Press, College Station (2014)

Magliocchetti, D., Gielow, M., De Vigili, F., Conti, G., De Amicis, R.: A personal mobility assistant based on ambient intelligence to promote sustainable travel choices. Procedia Comput. Sci. 5, 892–899 (2011)CrossRef

Mannering, F.L., Shankar, V., Bhat, C.R.: Unobserved heterogeneity and the statistical analysis of highway accident data. Anal. Methods Accid. Res. 11, 1–16 (2016)

Marczewski, A.: Gamification: A Simple Introduction and a Bit More, (Self-Published on Amazon Digital Services, 2013). Kindle edition, Loc, 1405 (2013)

McFadden, D.: Econometric models of probabilistic choice. In: Structural Analysis of Discrete Data with Econometric Applications, pp. 198–270 (1981)

McFadden, D., Train, K.: Mixed MNL models for discrete response. J. Appl. Econom. 15(5), 447–470 (2000)CrossRef

Mehra, A., Paul, J., Kaurav, R.P.S.: Determinants of mobile apps adoption among young adults: theoretical extension and analysis. J. Mark. Commun. pp. 1–29 (2020)

Meloni, I., di Teulada, B.S.: I-Pet individual persuasive eco-travel technology: a tool for VTBC program implementation. Transp. Res. Procedia 11, 422–433 (2015)CrossRef

Meloni, I., Sanjust, B., Delogu, G., Sottile, E.: Development of a technological platform for implementing VTBC programs. Transp. Res. Procedia 3, 129–138 (2014)CrossRef

Mobile App Daily: Pokemon GO Revenue, Statistics And All Figures Revealed (2019). https://www.mobileappdaily.com/2018/07/27/pokemon-go-revenue-statistics#:~:text=Pokemon%20GO%20Retention%20Rate&text=According%20to%20the%20reports%2C%20almost,is%2015%25%20after%203%20days. Accessed 17 Oct 2020

Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., Boda, P.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68. ACM (2009)

Nakashima, R., Sato, T., Maruyama, T.: Gamification approach to smartphone-app-based mobility management. Transp. Res. Procedia 25, 2344–2355 (2017)CrossRef

O’Brien, H.L., Toms, E.G.: What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci. Technol. 59(6), 938–955 (2008)CrossRef

Oleksinski, J.: Here’s How to Mater Pokémon GO (2016). https://nypost.com/2016/07/11/heres-how-to-master-pokemon-go/. Accessed 3 Nov 20

Owens, J.M., Antin, J.F., Doerzaph, Z., Willis, S.: Cross-generational acceptance of and interest in advanced vehicle technologies: a nationwide survey. Transp. Res. Part F Traffic Psychol. Behav. 35, 139–151 (2015)CrossRef

Pew Research Center: Mobile Fact Sheet (2019). https://www.pewresearch.org/internet/fact-sheet/mobile/#:~:text=mobile%20revolution%20below.-,Mobile%20phone%20ownership%20over%20time,smartphone%20ownership%20conducted%20in%202011. Accessed 2 Nov 2020

Piras, F., Sottile, E., Calli, D., Meloni, I.: Automatic data collection for detecting travel behavior: the IPET platform. Procedia Comput. Sci. 134, 421–426 (2018)CrossRef

Ploderer, B., Reitberger, W., Oinas-Kukkonen, H., Gemert-Pijnen, J.: Social interaction and reflection for behaviour change. Person. Ubiquitous Comput. 18(7), 1667–1676 (2014)CrossRef

Pokémon, G.O.: Pokémon GO Updates (2020). https://pokemongolive.com/en/post/. Accessed 10 Oct 2020.

Quddus, M.A., Noland, R.B., Chin, H.C.: An analysis of motorcycle injury and vehicle damage severity using ordered probit models. J. Saf. Res. 33(4), 445–462 (2002)CrossRef

Rasche, P., Schlomann, A., Mertens, A.: Who is still playing Pokemon Go? A Web-based survey. JMIR Ser. Games 5(2), e7 (2017)CrossRef

Rauschnabel, P.A., Rossmann, A., tom Dieck, M.C.: An adoption framework for mobile augmented reality games: the case of Pokémon Go. Comput. Hum. Behav. 76, 276–286 (2017)CrossRef

Roider, O., Wegener, S., Stark, J., Judmaier, P., Michelberger, F., Barberi, A.: Merging virtual world with real-life behavior: a concept for a smartphone app to influence young people’s travel behavior. Transp. Res. Rec. 2673, 241–250 (2019)CrossRef

Ruiz-Ariza, A., Casuso, R.A., Suarez-Manzano, S., Martínez-López, E.J.: Effect of augmented reality game Pokémon GO on cognitive performance and emotional intelligence in adolescent young. Comput. Educ. 116, 49–63 (2018)CrossRef

Russo, B.J., Savolainen, P.T., Schneider, W.H., IV., Anastasopoulos, P.C.: Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model. Anal. Methods Accid. Res. 2, 21–29 (2014)

Sawano, T., Ozaki, A., Shimada, Y., Leppold, C., Tsubokura, M., Tsukada, M., Kami, M., Ohira, H.: Pokémon GO & driving. QJM Int. J. Med. 110(5), 311–312 (2017)CrossRef

Schrammel, J., Busch, M., Tscheligi, M.: Peacox-Persuasive advisor for CO₂-reducing cross-modal trip planning. In: PERSUASIVE (Adjunct Proceedings) (2013)

Serino, M., Cordrey, K., McLaughlin, L., Milanaik, R.L.: Pokémon Go and augmented virtual reality games: a cautionary commentary for parents and pediatricians. Curr. Opin. Pediatr. 28(5), 673–677 (2016)CrossRef

Shaheen, S., Cohen, A., Zohdy, I., Kock, B.: Smartphone Applications to Influence Travel Choices: Practices and Policies. U.S. Department of Transportation (2016). https://ops.fhwa.dot.gov/publications/fhwahop16023/fhwahop16023.pdf

Statista: Most Popular Mobile Gaming Related Apps in the United States as of September 2019, by monthly users (2020). https://www.statista.com/statistics/580150/most-popular-us-gaming-apps-ranked-by-audience/. Accessed 13 Oct 2020

Stopher, P.R.: Reducing road congestion: a reality check. Transp. Policy 11(2), 117–131 (2004)CrossRef

Sunio, V., Schmöcker, J.D.: Can we promote sustainable travel behavior through mobile apps? Evaluation and review of evidence. Int. J. Sustain. Transp. 11(8), 553–566 (2017)CrossRef

Tang, B.J., Li, X.Y., Yu, B., Wei, Y.M.: How app-based ride-hailing services influence travel behavior: an empirical study from China. Int. J. Sustain. Transp. 14, 1–15 (2019)

Tate, D.F., Lyons, E.J., Valle, C.G.: High-tech tools for exercise motivation: use and role of technologies such as the internet, mobile applications, social media, and video games. Diabetes Spectr. 28(1), 45–54 (2015)CrossRef

Tateno, M., Skokauskas, N., Kato, T.A., Teo, A.R., Guerrero, A.P.: New game software (Pokémon Go) may help youth with severe social withdrawal, hikikomori. Psychiatry Res. 246, 848 (2016)CrossRef

Van Grove, J.: Gamification: How competition is reinventing business, marketing and everyday life. Mashable. Accessed 13 Oct 2020 (2011)

Vasserman, S., Feldman, M., Hassidim, A.: Implementing the wisdom of Waze. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

Wagner-Greene, V.R., Wotring, A.J., Castor, T., Kruger, J., Mortemore, S., Dake, J.A.: Pokémon GO: Healthy or harmful? Am. J. Public Health 107(1), 35–36 (2017)CrossRef

Washington, S.P., Karlaftis, M.G., Mannering, F.L.: Statistical and Econometric Methods for Transportation Data Analysis, 2nd edn. Chapman & Hall/CRC, Boca Raton (2010)

Wells, S., Forbes, P., Masthoff, J., Gabrielli, S., Jyllha, A.: SUPERHUB: integrating digital behaviour management into a novel sustainable urban mobility system. In: Proceedings of the 27th International BCS Human Computer Interaction Conference, p. 62. British Computer Society (2013)

Williams, R.: Understanding and interpreting generalized ordered logit models. J. Math. Sociol. 40(1), 7–20 (2016)CrossRef

Williamson, J.W.: Will the ‘Pokémon’ be heroes in the battle against physical inactivity. Sport Exercise Med. 2(1), 13–14 (2016)CrossRef

Wong, F.Y.: Influence of Pokémon Go on physical activity levels of university players: a cross-sectional study. Int. J. Health Geogr. 16(1), 8 (2017)CrossRef

Xian, Y., Xu, H., Xu, H., Liang, L., Hernandez, A.F., Wang, T.Y., Peterson, E.D.: An initial evaluation of the impact of Pokémon GO on physical activity. J. Am. Heart Assoc. 6(5), e005341 (2017)CrossRef

Yamamoto, T., Shankar, V.N.: Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects. Accid. Anal. Prev. 36(5), 869–876 (2004)CrossRef

YouGov: Pokémon Go: 34m Downloads, 31m Current Players, and 10m Paying Users Since U.S. Launch (2016). URL: https://today.yougov.com/topics/entertainment/articles-reports/2016/08/12/pokemon-go-34m-downloads-31m-current-players-and-1. Accessed 25 Oct 2020

Zhang, D., Magalhães, D.J.A.V., Wang, X.: Prioritizing bicycle paths in Belo Horizonte City, Brazil: analysis based on user preferences and willingness considering individual heterogeneity. Transp. Res. Part A Policy Pract. 67, 268–278 (2014)CrossRef

Zheng, L., Guo, Y., Peeta, S., Wu, B.: Impacts of information from various sources on the evacuation decision-making process during no-notice evacuations in campus environment. J. Transp. Saf. Secur. 12(7), 892–923 (2020)

Zichermann, G., Cunningham, C.: Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps. O’Reilly Media Inc., Newton (2011)

Titel: Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality, gamification, and social components in mobile apps to influence travel decisions
verfasst von: Yuntao Guo
Srinivas Peeta
Shubham Agrawal
Irina Benedyk
Publikationsdatum: 24.02.2021
Verlag: Springer US
Erschienen in: Transportation / Ausgabe 2/2022
Print ISSN: 0049-4488
Elektronische ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-021-10181-9

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