Cruising taxi transport power scale dynamic adjusting method

Abstract

The invention discloses a cruising taxi transport power scale dynamic adjusting method; the method uses operation data of a taxi information management system as basis, fully considers single car daily working trips, a mileage utilization rate, an earning level, a single car average operation duration, a single trip waiting time duration, and an online car hailing business shared ratio, employs adata mining-decision tree method to construct a cruising taxi transport power scale adjusting regression tree model, and proposes a cruising taxi dynamic adjusting mechanism and thresholds and rankingrelations of key indexes, thus solving the non-unified system problems of the cruising taxi transport power scale adjusting indexes, and providing clear quantitative standards for each key index threshold and importance ranking relations between the indexes. Compared with the prior art, the method can scientifically and reasonably rank the importance of each key index relevant to the taxi transport power scale, thus obtaining more accurate taxi transport power scale predicted values, and further providing theory basis and decision supports for adjusting the city cruising taxi transport powerscale.

Description

technical field [0001] The invention relates to a method for dynamically adjusting the capacity scale of cruise taxis, which belongs to the technical field of road traffic engineering. Background technique [0002] Establishing a reasonable taxi capacity adjustment system is an important means for the government to implement macro adjustments to the structure of the taxi industry, and an important guarantee for alleviating industry contradictions and promoting the healthy development of the taxi industry. Existing forecasts for the scale of taxi capacity usually involve many indicators and thresholds such as the number of people owned by 10,000 people, the ratio of taxis to public transport, mileage utilization rate or empty driving rate, etc. There is no clear quantitative standard for the threshold of key indicators and the lack of importance ranking relationship among indicators, so it is difficult to provide support for the scale adjustment of cruise vehicle capacity. ...

AI Technical Summary

Problems solved by technology

Existing forecasts for the scale of taxi capacity usually involve many indicators and thresholds such as the number of people owned by 10,000 people, the ratio of taxis to public transport, mileage utilization rate or empty driving rate, etc. There is no clear quantitative standard for the threshold of key indicators and the lack of importance ranking relationship between indicators, which makes it difficult to provide support for the scale adjustment of cruise vehicle capacity

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