Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles

Yin, Zhenzhong; Zhang, Bin

doi:10.3390/fi15070222

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…The result is shown in Figures 3-5. Referring to previous research [8,10], through an experimental comparison of clustering results at different levels, the hierarchical clustering results divided by blue lines in the figure are selected as an example. By comparing the subgraphs in Figures 3-5, it can be found that the driving style of the same driver on the same road segment is not the same at different periods.…”

Section: Clustering Results Of Driving Styles For Multiple Routes Dri...mentioning

confidence: 99%

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Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method

Yin,

Wang,

Zhang

et al. 2024

Applied Sciences

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Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes’ driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively.

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Section: Clustering Results Of Driving Styles For Multiple Routes Dri...mentioning

confidence: 99%

“…These uncertain factors increase the uncertainty of bus travel and arrival time. This will cause bus bunching [10] and negative impacts such as increased bus operating costs and longer waiting times for passengers [11,12].…”

Section: Introductionmentioning

confidence: 99%

Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method

Yin,

Wang,

Zhang

et al. 2024

Applied Sciences

Self Cite

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Theoretical basis for development of indicators for improving training and retraining of specialists and passenger road transport personnel

Bebinov,

Trofimova

2024

Vestn. SibADI

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Introduction. The theoretical basis for the development of indicators for improving the training and retraining of road transport personnel is aimed, first of all, at ensuring the results of transportation and technical operation activities corresponding to the output in road transport. The studied indicators should take into account the amount of labor costs of all types of workers ensuring the operation of rolling stock. Training and retraining of personnel largely determine the safety of the transportation process and must comply with the existing operating conditions of rolling stock. The purpose of this article is to theoretically substantiate the indicators for improving the training and retraining of specialists and personnel of road transport, as an element of the “personnel-bus-road-operating environment” (PADS) system to ensure the planned production of rolling stock in accordance with the requirements of the municipal contract for regular passenger transportation and luggage, taking into account operating conditions.Materials and methods. The theoretical basis for the development of indicators for improving the training and retraining of personnel is based on a systematic analysis of professional and qualification requirements, as well as the theory of current planning of the work of motor transport enterprises, taking into account the probabilistic nature of operating conditions under the influence of the external environment. As a result, structural connections between the driver, mechanic and technical condition inspector were identified as elements of the new PADS system.The parameters of the developed mathematical model of personnel activities and the costs of their training and retraining are determined using methods of mathematical statistics and expert assessments. The content of the new approach is to integrate the production of rolling stock with professional and qualification requirements for personnel.Results. This study focuses on fulfilling the municipal contract for regular transportation of passengers and luggage, i.e. to obtain the planned mileage, it is necessary to take into account the joint activities of groups of personnel and in each group of an individual employee.Discussion and conclusion. The scientific novelty and main results of the study consist in the theoretical substantiation of indicators for improving the training and retraining of passenger road transport personnel, as an element of the PADS system, to ensure the planned production of rolling stock, taking into account operating conditions.Based on the results of the study, the indicators of training and retraining of personnel of automobile passenger transport, ensuring the fulfillment of the contract, are grouped in accordance with their levels and periods of formation.A mathematical model that determines the planning of the results of fulfilling the terms of the contract by each employee has been developed. Further research will be aimed at experimentally determining the values of the identified indicators.

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Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles

Cited by 2 publications

References 46 publications

Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method

Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method

Theoretical basis for development of indicators for improving training and retraining of specialists and passenger road transport personnel

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