Abdel-Aty, M. A. (2001). Using ordered probit modeling to study the effect of ATIS on transit ridership. Transportation Research Part C: Emerging Technologies 9(4): 265-277.
- Ben-Akiva, M. E., S. R. Lerman and S. R. Lerman (1985). Discrete choice analysis: theory and application to travel demand, MIT Press.
-Berkson, J. (1944). Application of the logistic function to bio-assay. Journal of the American Statistical Association 39(227): 357-365.
- Brakewood, C., G. S. Macfarlane and K. Watkins (2015). The impact of real-time information on bus ridership in New York City. Transportation Research Part C: Emerging Technologies 53: 59-75.
- Chowdhury, S. and A. A. Ceder (2016). Users’ willingness to ride an integrated public-transport service: A literature review. Transport Policy 48: 183-195.
- de Dios Ortuzar, W. L. (2012). Modeling Transport, (de Dios Ortuzar, J. and Willumsen, LG; 2011 [Book Review]. IEEE Intelligent Transportation Systems Magazine 4(1): 40-41.
- El‐Geneidy, A. M., J. Horning and K. J. Krizek (2011). Analyzing transit service reliability using detailed data from automatic vehicular locator systems. Journal of Advanced Transportation 45(1): 66-79.
- Ge, Y., P. Jabbari, D. MacKenzie and J. Tao (2017). Effects of a public real-time multi-modal transportation information display on travel behavior and attitudes. Journal of Public Transportation 20(2): 3-4.
- Hickman, M. D. and N. H. Wilson (1995). Passenger travel time and path choice implications of real-time transit information. Transportation Research Part C: Emerging Technologies 3(4): 211-226.
-Jeong, R. and L. R. Rilett (2005). Prediction model of bus arrival time for real-time applications. Transportation Research Record 1927(1): 195-204.
- Khattak, A., A. Polydoropoulou and M. Ben-Akiva (1996). Modeling revealed and stated pretrip travel response to advanced traveler information systems. Transportation Research Record 1537(1): 46-54.
-Lai, W. T. and C.-F. Chen (2011). Behavioral intentions of public transit passengers—The roles of service quality, perceived value, Satisfaction and Involvement.Transport Policy 18(2): 318-325.
- McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior.
-Nassiri, H. and A. Rezaei (2012). Air itinerary choice in a low-frequency market: A decision rule approach. Journal of Air Transport Management 18(1): 34-37.
-Nuzzolo, A. and A. Comi (2016). Advanced public transport and intelligent transport systems: new modelling challenges. Transportmetrica A: Transport Science 12(8): 674-699.
- Rahman, M. M., S. Wirasinghe and L. Kattan (2013). Users' views on current and future real‐time bus information systems. Journal of Advanced Transportation 47(3): 336-354.
-Singh, B. and A. Gupta (2015). Recent trends in intelligent transportation systems: a review. Journal of Transport Literature 9(2): 30-34.
-Tang, L. and P. V. Thakuriah (2012). Ridership effects of real-time bus information system: A case study in the City of Chicago. Transportation Research Part C: Emerging Technologies 22: 146-161.
-Tilocca, P., S. Farris, S. Angius, R. Argiolas, A. Obino, S. Secchi, S. Mozzoni and B. Barabino (2017). Managing data and rethinking applications in an innovative mid-sized bus fleet. Transportation Research Procedia 25: 1899-1919.
-Train, K. E. (2009). Discrete choice methods with simulation, Cambridge University Press.
- Zito, P., G. Amato, S. Amoroso and M. Berrittella (2011). The effect of Advanced Traveller Information Systems on public transport demand and its uncertainty. Transportmetrica 7(1): 31-43.