Study of the Influence of Factors on Passenger Service Quality and Efficiency of Rolling Stock Use

The tasks of transport planning are relevant for most countries and comprise implemenation of solutions at regional and local level. The development of transport planning documents in the constituent entities of the Russian Federation is an acute problem and implies the achievement of such goals as improving the quality of passenger transportation and reducing transportation costs. Each of the transport planning documents includes a list of measures, the implementation of which improves the quality of passenger service and the efficiency of the use of rolling stock.
The objective of the article is to determine the significance of the influence of technical and operational factors on the resulting indicators of passenger service quality and efficiency of rolling stock use. The research applied experiment planning method described in the work of Yu. P. Adler.
The experiment planning method used makes it possible to obtain quantitative estimates of the influence of factors with the same reliability as with other methods. The evaluation was carried out for independent or conditionally independent factors. In the researched case, such factors comprise the number of buses on the route, the length of the route, the turnaround time, the downtime at the terminal points, the allowable deviation from the schedule, the work hours of the drivers, the zero mileage of all buses.
Constants in the calculations are independent factors related to the characteristics of the demand for transportation, since when carrying out measures to improve the organisation of the work of buses on routes, they cannot be influenced. These factors comprise the walking distance on the route, the average travel distance of a passenger on the route and the passenger flow on the most loaded haul.
The method is implemented in the article on the example of an operating bus on the route. For all basic factors, the upper, lower, and main levels are set. Based on the analysis of the actual values of technical and operational indicators on the existing routes of Moscow region, the numerical values of the above levels were determined. Next, the variation intervals for each factor were selected. An experiment in which all possible combinations of factor levels are implemented is known as a full factorial experiment. The coefficients calculated from the results of the experiment indicate the strength of the influence of a factor. The value of the coefficient corresponds to the contribution of this factor to the value of the optimisation parameter when the factor moves from the zero level to the upper or lower one.
As a result of the research, it was found that five basic factors influence the time spent by a passenger, travel comfort, and the completeness of revenue collection, and ten factors affect the daily costs of servicing the route for a carrier. The specificity of the problem is that for all four optimisation parameters, one and the same matrix can be constructed. To conduct a full factorial experiment with varying ten factors at two levels, it is necessary to carry out more than a thousand calculation options. However, in accordance with the methodology, in this case, we can limit ourselves to the minimum number of calculations. On this basis, an experiment planning matrix was built, then, based on the results of calculations, the coefficients in the regression equations for each of the optimisation parameters can be determined. The regression coefficients obtained when calculating the experiment design matrix are similar to those that could be obtained as a result of calculations using the least squares method. Based on this, it is possible to calculate all the statistical characteristics of the basic factors necessary to determine the closeness of the relationship between the factors and optimisation parameters, as well as between the factors themselves.
The experiment planning method used in the study made it possible to identify the factors influencing each of the four optimisation parameters. Therefore, the explicit dependence of the optimisation parameters on such a factor as the number of buses on the route was confirmed, but at the same time, the factors were ranked according to the degree of their influence on the result. The correspondence of the obtained results to real and obvious dependencies has allowed to conclude that the chosen method and its implementation are correct.

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