New pricing theory of intelligent flexible transportation
Abstract
In the paper, possible pricing structures of flexible transport systems have been investigated. After a brief introduction into demand responsive systems, the currently used pricing systems have been analysed. Having reviewed the conventional pricing methodologies – in line with the average cost and marginal cost based methods – the advantages and the disadvantages of particular systems are presented. What is more, that traditional pricing theory enabled to order costs of flexible transportation systems only approximately to passengers in proportion to their demanded transportation performance, thus traditional pricing framework is not able to fully meet the principle of fairness. For reaching the highest level of fairness loops a fictive unit of individual trips is introduced as the base of pricing. When applying individual loops is gives a unique approach to describe unit cost of the operators especially considering that empty runs are taken into account in a fair way. Beside fairness, it is also an essential objective to represent economies of scale and the preference of early bookings in the pricing methodology. Accordingly, the below presented ‘mixed price system’ had good results in the reduction of average fares related to new travellers and also in the improvement of attraction related to ‘early birds’. Therefore, the goal of this research was to define the direction and the aspects of the development process related to the pricing methods of flexible transportation.
First published online 13 July 2015
Keyword : price, transport expenses, sustainable transport, intelligent transport system, public service
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Carotenuto, P.; Monacelli, D.; Raponi, G.; Turco, M. 2012. A dynamic simulation model of a flexible transport services for people in congested area, Procedia – Social and Behavioral Sciences 54: 357–364. http://dx.doi.org/10.1016/j.sbspro.2012.09.755
Chevrier, R.; Liefooghe, A.; Jourdan, L.; Dhaenens, C. 2012. Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: application to demand responsive transport, Applied Soft Computing 12(4): 1247–1258. http://dx.doi.org/10.1016/j.asoc.2011.12.014
Davison, L.; Enoch, M.; Ryley, T.; Quddus, M.; Wang, C. 2012. Identifying potential market niches for Demand Responsive Transport, Research in Transportation Business & Management 3: 50–61. http://dx.doi.org/10.1016/j.rtbm.2012.04.007
Deb, K.; Filippini, M. 2011. Estimating welfare changes from efficient pricing in public bus transit in India, Transport Policy 18(1): 23–31. http://dx.doi.org/10.1016/j.tranpol.2010.05.004
Deflorio, F. P. 2011. Simulation of requests in demand responsive transport systems, IET Intelligent Transport Systems 5(3): 159–167. http://dx.doi.org/10.1049/iet-its.2010.0026
Deng, T.; Nelson, J. D. 2013. Bus rapid transit implementation in Beijing: an evaluation of performance and impacts, Research in Transportation Economics 39(1): 108–113. http://dx.doi.org/10.1016/j.retrec.2012.06.002
Diana, M.; Quadrifoglio, L.; Pronello, C. 2007. Emissions of demand responsive services as an alternative to conventional transit systems, Transportation Research Part D: Transport and Environment 12(3): 183–188. http://dx.doi.org/10.1016/j.trd.2007.01.009
Gavanas, N.; Politis, I.; Dovas, K.; Lianakis, E. 2012. Is a new metro line a mean for sustainable mobility among commuters? The case of Thessaloniki city, International Journal for Traffic and Transport Engineering 2(2): 98–106.
Horvath, B. 2012. A simple method to forecast travel demand in urban public transport, Acta Polytechnica Hungarica 9(4): 165–176.
Horvath, B.; Horvath, R.; Gaal, B. 2013. Estimation of passenger demand in urban public transport, Acta Technica Jaurinensis 6(3): 64–73.
Jakubauskas, G. 2008. Improvement of urban transport accessibility for the passengers with reduced mobility by applying intelligent transport systems and services, Transport and Telecommunication 9(3): 9–15.
Jansson, K.; Angell, T. 2012. Is it possible to achieve both a simple and efficient public transport zone fare structure? Case study Oslo, Transport Policy 20: 150–161. http://dx.doi.org/10.1016/j.tranpol.2011.07.005
Lazauskas, J.; Bureika, G.; Valiūnas, V.; Pečeliūnas, R.; Matijošius, J.; Nagurnas, S. 2012. the research on competitiveness of road transport enterprises: Lithuanian case, Transport and Telecommunication 13(2): 138–147.
Mageean, J.; Nelson, J. D. 2003. The evaluation of demand responsive transport services in Europe, Journal of Transport Geography 11(4): 255–270. http://dx.doi.org/10.1016/S0966-6923(03)00026-7
Milne, D.; Niskanen, E.; Verhoef, E. 2000. Operationalisation of Marginal Cost Pricing within Urban Transport. VATT Research Reports 63. Helsinki, Finland. 124 p. Available from Internet: http://www.vatt.fi/file/vatt_publication_pdf/t63.pdf
Mulley, C.; Nelson, J. D. 2009. Flexible transport services: a new market opportunity for public transport, Research in Transportation Economics 25(1): 39–45. http://dx.doi.org/10.1016/j.retrec.2009.08.008
Mulley, C.; Nelson, J.; Teal, R.; Wright, S.; Daniels, R. 2012. Barriers to implementing flexible transport services: An international comparison of the experiences in Australia, Europe and USA, Research in Transportation Business & Management 3: 3–11. http://dx.doi.org/10.1016/j.rtbm.2012.04.001
Nelson, J. D.; Mulley, C. 2013. The impact of the application of new technology on public transport service provision and the passenger experience: a focus on implementation in Australia, Research in Transportation Economics 39(1): 300–308. http://dx.doi.org/10.1016/j.retrec.2012.06.028
Nelson, J. D.; Phonphitakchai, T. 2012. An evaluation of the user characteristics of an open access DRT service, Research in Transportation Economics 34(1): 54–65. http://dx.doi.org/10.1016/j.retrec.2011.12.008
Palmer, K.; Dessouky, M.; Zhou, Z. 2008. Factors influencing productivity and operating cost of demand responsive transit, Transportation Research Part A: Policy and Practice: 42(3): 503–523. http://dx.doi.org/10.1016/j.tra.2007.12.003
Paulley, N.; Balcombe, R.; Mackett, R.; Titheridge, H.; Preston, J.; Wardman, M.; Shires, J.; White, P. 2006. The demand for public transport: The effects of fares, quality of service, income and car ownership, Transport Policy 13(4): 295–306. http://dx.doi.org/10.1016/j.tranpol.2005.12.004
Szendro, G.; Csete, M.; Torok, A. 2012. Unbridgeable gap between transport policy and practice in Hungary, Journal of Environmental Engineering and Landscape Management 20(2): 104–109. http://dx.doi.org/10.3846/16486897.2012.660881
Tirachini, A.; Hensher, D. A.; Rose, J. M. 2014. Multimodal pricing and optimal design of urban public transport: the interplay between traffic congestion and bus crowding, Transportation Research Part B: Methodological 61: 33–54. http://dx.doi.org/10.1016/j.trb.2014.01.003