DIFFERENTIATED GUIDANCE FOR IN-VEHICLE CROWDING LIMITATION IN URBAN BUS SERVICES
The level of in-vehicle crowding is one of the main indicators of transit services quality estimation. Excessive crowding causes uncomfortable trip conditions and effect on passengers transport fatigue increasing. In this regard, the in-vehicle crowding level is a normalized value. Along with the fact that the negative impact of excessive in-vehicle crowding on the transit services quality is quite understandable and unmistakable, scientific approaches and methods for determining the rational levels of vehicle occupancy are not justified and require further research.
The analysis showed that the existing methods and guidance for in-vehicle crowding limitation do not sufficiently take into consideration the differences in the transit routes, in particular, operational and passenger flow parameters. Instead, both from a scientific and practical point of view, it is more appropriate to use a differentiated approach that will enable individuals to approach the task of choosing the service parameters on each route separately, based on the existing features of its operating.
It was supposed that the differentiation of routes in relation in-vehicle crowding limitation problem solution should be based on the passenger flow characteristics, specifically by using of coefficient of unevenness of the passenger flow along the route in the most loaded direction. The hypothesis test was conducted by an experiment on an optimization model, which suppose to minimize total operator and passengers costs.
Based on the conducted research, the following rational levels of the bus occupancy are allocated: for the magnitude of the coefficient of unevenness of the passenger flow along the route from 1,0 to 1,2 (very low unevenness), it is necessary to plan the service process with the passenger density on the maximum load section of the route 3 - 4 pass/m2; from 1,2 to 1,4 (low unevenness) - passenger density 4 - 5 pass/m2; from 1,4 to 1,6 (average unevenness) - passenger density 5 - 6 pass/m2; from 1,6 to 1,8 (high unevenness) - passenger density 6 - 7 pass/m2; from 1,8 to 2,0 (very high unevenness) - passenger density 7 - 8 pass/m2; more than 2,0 (exceptionally high unevenness) - passenger density 8 pass/m2.
The direction of further research is the development of the proposed approach in terms of adding additional factors and its adaptation for in-vehicle crowding limitation problem solution for other urban transit modes.
Spirin, I. V. (2004) Passenger transportation by public transit: a reference manual, 413.
Dolya, V. K. (2010) Passenger transportation, 504.
Bosnyak, M. G. (2009) Passenger automobile transportation, 272.
Kuzkin, O.F. (2010) Legislative aspects of an estimation of quality of services of the urban public transport. The Journal of Zhytomyr State Technological University, 2 (53), 79-85.
Gulev, N. U. (1993) The choice of a rational number of buses on the urban routes, taking into account the influence of the human factor: dis. ... cand. tech. sciences, 174.
Tirachini, A. et al. (2017) Estimation of crowding discomfort in public transport: Results from Santiago de Chile. Transportation Research Part A: Policy and Practice, 103, 311-326.
Litman, T. (2008) Valuing transit service quality improvements. Journal of Public transportation, 11(2), 43-63.
Li, Z., Hensher, D. A. (2013) Crowding in public transport: a review of objective and subjective measures. Journal of Public Transportation, 16, 2, 107 – 134.
Ceder, A. (2007) Public transit planning and operation: theory, modeling and practice. 0xford: Elsevier, 626.
Batarce, M, Muñoz, J. C., Ortúzar, J. D., Raveau, S. (2015) Valuing crowding in public transport systems using mixed stated/revealed preferences data: the case of Santiago. TRB 94th Annual Meeting Compendium of Papers, Washington DC, 1 – 13.
Batarce, M, Muñoz, J. C., Ortúzar, J. D. (2016) Valuing crowding in public transport: Implications for cost-benefit analysis. Transportation Research Part A: Policy and Practice, 91, 358-378.
Haywood, L., Koning, M., Monchambert, G. Crowding in public transport: Who cares and why? (2017) Transportation Research Part A: Policy and Practice, 100, 215-227.
Hörcher, D., Graham, J., Anderson, R. J. (2017) Crowding cost estimation with large scale smart card and vehicle location data. Transportation Research Part B: Methodological, 95, 105-125.
Tirachini, A., Hensher, D. A., Rose, J. M. (2013) Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transportation research part A: policy and practice, 53, 36-52.
Gorbachev, P. F., Rossolov, A. V. (2012) Modeling demand for passenger route transport services in large cities: monograph, 152.
Dolya, V. K., Ponkratov, D. P. (2016) Objective function of vehicle’s capacity choice for public transit routs. Collection of scientific works of the Ukrainian State University of Railway Transport, 161, 44-52.
Ponkratov, D. P., Davidich, N. V. (2018) Rational areas of using different bus classes of capacity. Automobile transport, 43, 71-77.
The authors who publish in this collection agree with the following terms:
• The authors reserve the right to authorship of their work and give the magazine the right to first publish this work under the terms of license CC BY-NC-ND 4.0 (with the Designation of Authorship - Non-Commercial - Without Derivatives 4.0 International), which allows others to freely distribute the published work with a mandatory reference to the authors of the original work and the first publication of the work in this magazine.
• Authors have the right to make independent extra-exclusive work agreements in the form in which they were published by this magazine (for example, posting work in an electronic repository of an institution or publishing as part of a monograph), provided that the link to the first publication of the work in this journal is maintained. .
• Journal policy allows and encourages the publication of manuscripts on the Internet (for example, in institutions' repositories or on personal websites), both before the publication of this manuscript and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of the citation of the published work (see The Effect of Open Access).