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A metro accident delay time prediction method based on a maximum likelihood regression tree

A maximum likelihood and delay time technology, applied in the field of transportation, can solve problems such as difficult calculations

Active Publication Date: 2019-03-08
SHANGHAI MARITIME UNIVERSITY
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  • Abstract
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Problems solved by technology

However, it should be pointed out that these non-parametric models are difficult to calculate the marginal effects of influencing factors on accident delays, and these marginal effects are very helpful for the staff to determine the order of various influencing factors to reduce subway accident delays

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  • A metro accident delay time prediction method based on a maximum likelihood regression tree
  • A metro accident delay time prediction method based on a maximum likelihood regression tree
  • A metro accident delay time prediction method based on a maximum likelihood regression tree

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Embodiment Construction

[0050] Below with the accident data of Hong Kong subway from 2005 to 2012, further illustrate the inventive method.

[0051] The subway accident delay time prediction method based on the maximum likelihood regression tree provided by the present invention, the concrete method that is applied to Hong Kong subway accident delay prediction and analysis is as follows:

[0052] S1. According to the data released by the Legislative Council of Hong Kong, collect the accident data of the Hong Kong subway from 2005 to 2012, and classify it according to the date of the subway accident, the subway line, the cause of the accident, and the delay time of the subway accident, and determine Delay distribution of subway operation accidents. Such as figure 1 shown.

[0053] S2. Carry out descriptive statistical analysis on independent variables such as the date of subway accident occurrence, time of occurrence, power supply failure, and car door failure in the 1332 subway operation accident d...

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Abstract

The invention discloses a metro accident delay time prediction method based on a maximum likelihood regression tree. Since there may be interaction among a plurality of variables causing subway delay,the present invention utilizes the constructed bivariate splitting method to establish the maximum likelihood regression tree (MLRT) model to describe and analyze subway accident delay. The MLRT model based on Hong Kong Metro accident data from 2005 to 2012 consists of 13 leaf nodes, each leaf node is assigned a logarithmic logistic distribution accelerated failure model (AFT). The results show that the two-factor split maximum likelihood regression model is better than the traditional AFT model and the single-factor split maximum likelihood regression tree model. On this basis, the method ofthe invention can accurately predict the subway delay accident and the probability that the subway accident delay exceeds the maximum bearing range, as the important basic information to remind passengers to replan the journey.

Description

technical field [0001] The invention relates to the traffic field, in particular to a subway accident delay analysis and prediction method. technical background [0002] Subway accidents caused by various factors such as emergencies and power failures may lead to a temporary decline in the transportation capacity of the subway, causing huge losses to passengers, especially delay losses to commuters. The subway is an important mode of transportation for urban public transportation, and it is very important to mitigate the impact of subway accidents. [0003] The subway management department needs to implement effective management measures to deal with subway accidents as soon as possible. Rapid clearance of subway incidents often requires the efficient allocation of relevant resources to dispatch crews in a timely manner. To achieve this goal, it is necessary to build a model to comprehensively explore the influencing factors of subway accidents and predict the delays cause...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26
CPCG06Q10/04G06Q50/26
Inventor 翁金贤于尧冯琳
Owner SHANGHAI MARITIME UNIVERSITY
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