A Prediction Method of Travel Time Reliability Distribution in Complex Scenarios
A travel time, complex scene technology, used in traffic flow detection, road vehicle traffic control systems, instruments, etc., can solve problems such as analysis and modeling methods that do not consider complex traffic patterns and lack travel time reliability, and achieve perfection Distribution results, the effect of ensuring the reliability of the travel time
Active Publication Date: 2020-12-15
ENJOYOR COMPANY LIMITED
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Problems solved by technology
However, in some travel analysis reports in recent years, there are few studies that use probability distribution and multiple indicators to comprehensively evaluate the reliability of travel time
Moreover, the existing research lacks a unified and general analytical modeling method for travel time reliability, does not consider complex traffic patterns, and does not establish a rich scene library based on information such as weather, abnormal events, and construction.
Most of the existing prediction methods are to predict the specific travel time, and lack the overall prediction and analysis of the reliability of the travel time
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[0032] Example: such as figure 1 As shown, a method for predicting the distribution of travel time reliability in complex scenarios includes the following steps:
[0033] (1) Obtain complex scene data and establish an event model.
[0034] Complex scene data includes, but is not limited to: weather data, road construction data, traffic event data, and road control data.
[0035] The event model is used to extract scene event features from complex scene data, where scene event features include event types and event features; for example figure 2 As shown, among them,
[0036] The types of events include but are not limited to: weather, construction, accidents; the types of accidents include but are not limited to: vehicle collisions, dumping of hazardous chemicals, road collapse, high temperature explosions;
[0037]The event characteristics include but are not limited to: severity (such as the number of affected lanes), impact space, and duration characteristics;
[0038]...
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The invention relates to a travel time reliability distribution prediction method in a complex scene. The method comprises the steps: constructing an event model based on complex scene data and traffic data, building travel time sample libraries in different scenes, extracting a travel time reliability distribution sample feature set, and constructing a training model to predict the future traveltime reliability. According to the invention, complex scene factors are considered; the method is advantaged in that travel time reliability analysis demands under different actual traffic scenes aresatisfied, prediction of the future travel time is realized based on the travel time reliability distribution characteristics, the more complete travel time reliability distribution result can be provided, travel time reliability prediction accuracy and adaptability are improved, and flexible selection of travelers is facilitated.
Description
technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for predicting travel time reliability distribution in complex scenarios. Background technique [0002] Travel time is the travel time required by a vehicle between any origin and destination in the road network. The starting and ending points may correspond to the smallest unit road segment on the road, or correspond to a path formed by connecting multiple road segments. Compared with traffic flow, travel time is an indicator from the perspective of travelers. Because it is closer to the intuitive experience of travelers, it has gradually become one of the key indicators to describe congestion and evaluate road traffic performance in recent years. Travelers usually want the average travel time of a trip to be as short as possible, and at the same time want its uncertainty to be as low as possible. In order to describe the uncertainty of travel time,...
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IPC IPC(8): G08G1/01
CPCG08G1/0125
Inventor 丁楚吟徐甲谢竞成袁鑫良金峻臣李瑶邹开荣
Owner ENJOYOR COMPANY LIMITED