No-load taxi path planning method based on discrete randomness dynamic planning

Abstract

The invention discloses a no-load taxi path planning method based on discrete randomness dynamic planning, and the method comprises the steps: carrying out the meshing of a target region, collecting the GPS historical data of taxies in the target region, and screening the grids where the taxies arrive according to the GPS historical data; establishing a grid-based discrete randomness dynamic planning model by taking benefit maximization as a target; and calculating parameters in the discrete randomness dynamic programming model by using a strategy iteration method, substituting the obtained parameters into the model, calculating an optimal value corresponding to an optimal strategy, and obtaining a recommended route of the no-load taxi. According to the invention, path planning is carried out on the no-load taxi based on discrete randomness dynamic planning, a mathematical model is established according to a real road network and taxi GPS data, and the long-term income of the no-load taxi is effectively improved; according to the method, the target area is subjected to grid division, the interference of data missing or abnormal values on the path planning result is effectively avoided depending on the data collection model, and the stability of path planning is improved.

Description

technical field [0001] The invention relates to the field of public transportation in transportation planning and management, in particular to an empty taxi path planning method based on discrete random dynamic planning. Background technique [0002] With the rapid development of the urban economy and the steady improvement of the living standards of the residents, the urban transportation methods are becoming more and more diverse, and taxis play an important role in urban road transportation with their flexible characteristics and point-to-point service forms. However, while improving the convenience of residents' travel, a series of operational problems have also emerged, among which the problem of empty taxis is more prominent. There are the following problems with empty taxis cruising aimlessly on city roads: [0003] (1) Causes environmental pollution and waste of road resources: Take Chinese Taipei and Shenzhen as examples, the empty mileage of taxis accounts for mor...

AI Technical Summary

Problems solved by technology

[0003] (1) Cause environmental pollution and waste of road resources: Taking Chinese Taipei and Shenzhen as examples, the empty driving mileage of taxis accounted for more than 40%, and the empty driving time of Shenzhen taxis accounted for as high as 49.2%, that is Taxis are cruising aimlessly half the time;

[0004] (2) Exacerbating the shortage of road resources during peak hours in key areas: Taking Hong Kong, China as an example, taxis account for about 25% of urban traffic, and as high as 50%-60% in key areas, playing an important role. Unreasonable cruising of empty taxis will affect the overall traffic environment in key areas

There are still many problems in the existing route planning of empty taxis in terms of aggregated display, GPS data and road network connections. Some route planning methods for empty taxis do not consider long-term benefits, and often It requires a lot of computing power, and there are problems such as missing data. At the same time, a large number of model parameters need to be calibrated, and the effect obtained is average.

Images