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Method for estimating vehicle import behavior on high-grade road bottleneck zone on-ramp

A high-level, vehicle-based technology, applied in the direction of traffic flow detection, etc., can solve problems such as less analysis

Active Publication Date: 2015-08-12
TONGJI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

So far, some scholars have used some classification algorithms to identify the driver's behavior state, however, few scholars have analyzed the decision-making process of the driver's input behavior and the mechanism behind it.

Method used

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  • Method for estimating vehicle import behavior on high-grade road bottleneck zone on-ramp
  • Method for estimating vehicle import behavior on high-grade road bottleneck zone on-ramp
  • Method for estimating vehicle import behavior on high-grade road bottleneck zone on-ramp

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Embodiment 1

[0112] Such as figure 1 As shown, a method for estimating the merging behavior of vehicles in a high-level road bottleneck area of ​​the present invention includes the following steps:

[0113](1) Data preparation stage: The research object is the process of vehicles entering the ramp at the bottleneck point of Yan'an Elevated Hongxu Road in Shanghai. More precisely, it studies the lane-changing decision-making process of a vehicle located in the innermost acceleration lane merging into an adjacent main lane. The main line at the bottleneck point of Hongxu Road has three lanes and two acceleration lanes about 150m long. A long enough acceleration lane is conducive to the driver making multiple decisions to choose the right time to merge into the main line, and then obtain rich sample data. In this stage, the trajectory extraction software is used to extract the vehicle trajectory data first, and then the traffic flow characteristic index set that affects the inflow behavior ...

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Abstract

The invention discloses a method for estimating vehicle import behavior on a high-grade road (highway and city expressway) bottleneck zone on-ramp. The method uses a hidden Markov model to estimate decision conditions of a driver in an import process. The method comprises five steps of a data preparation stage, a data preprocessing stage, a model learning stage, a model decoding stage, and a result processing stage. The method can estimate states of a vehicle at different time before importing to a high-grade road main line. An estimated state time sequence can reflect the decision process of a driver. The method analyzes driver state transition spatial distribution conditions, so as to find out frequent places and zones where the drivers have lane changing intention. Therefore, the method provides basis for designing and developing an efficient on-ramp bottleneck zone lane-changing assistance system, and provides important reference for measure design for preventing early-onset failure of high-grade road on-ramp bottleneck.

Description

technical field [0001] The present invention relates to a method used in the field of high-level road traffic behavior recognition, more specifically, the present invention relates to a method for estimating the merging behavior of vehicles entering ramps in bottleneck areas of high-level roads based on hidden Markov models. Background technique [0002] The on-ramp bottleneck of high-grade roads (expressways and urban expressways) is a common area for traffic jams and safety accidents. The merging behavior of on-ramp vehicles is one of the important reasons for bottleneck failure. Relevant scholars have mostly discussed the effectiveness of improving this phenomenon from the perspectives of speed limit, setting a fixed import section, and signal control. So far, some scholars have adopted some classification algorithms to identify the driver's behavior state, however, few scholars have analyzed the decision-making process of the driver's input behavior and the mechanism be...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
CPCG08G1/01
Inventor 孙剑王尔根李峰陈长李莉
Owner TONGJI UNIV
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