The Effect of User Comments on Traffic Application Usage

Authors: I. Gokasar, G. Bakioglu

Abstract:

With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: Traffic App, real–time information, traffic congestion, regression analysis, dummy variables.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127310

References:

[1] Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., and Morris, R. Smarter cities and their innovation challenges. Computer, 44(6):32–39, June 2011.
[2] Tarasewich, P. (2003) Designing mobile commerce applications, Communications of the ACM, 46, 12, 57-60.
[3] Being mobile (Smart Phone Revolution), Engineering & Technology, (5) 15: 64-65, October 2010.
[4] Golledge, R., and Stimson, R. (1987) Analytical Behavioral Geography. Croom Helm, New York.
[5] Khattak, J. A. and Khattak, A. J. (1998). A comperative analysis of spatial knowledge and en route diversion behavior in Chicago and San Fransisco: Implications for advanced traveler information systems. Transportation Research Record, 1621, 27–35.
[6] Ramming. S. M. (2002) “Network Knowledge and Route Choice”, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. Ph.D. Dissertation.
[7] Wenger, M., Spyridakis, J., Haselkom, M. D., Bartield, W., and Conquest, L. (1990) “Motorist behavior and the design of motorist information systems.” Transportation Research Record, 1281, 159-167.
[8] Khattak, Asad., Driver response to Unexpected Travel Conditions: Effect of Traffic Information and Other Factors, PhD Dissertation, Civil Engineering Department, Northwestern University, Evanston, Illinois, 1991
[9] Polydoropoulou, A., Ben-Akiva, M., Khattak, A., and Lauprete,G. (1996). Modeling revealed and stated en-route travel response to ATIS. Transportation Research Record, 1537, 38–45.
[10] Walpole, R., E. Raymond, H. Myers, S.L. Myers, K. Ye, 2012, Probability and Statistics for Engineers and Scientists, Boston, MA: Pearson.
[11] Ingram, H. (1996). "Classification and grading of smaller hotels, guesthouses and bed and breakfast accommodation." International Journal of Contemporary Hospitality Management 8(5): 30-34.
[12] Kozak, M. & Rimmington, M. (1998). "Benchmarking: destination attractiveness and small hospitality business performance." International Journal of Contemporary Hospitality Management 10(5): 184-188.