Traffic travel behavior regulation and control method based on block chain technology and travel plan sharing

Abstract

The invention discloses a traffic travel behavior regulation and control method based on a block chain technology and travel plan sharing. The method comprises the following steps: firstly, enabling atraveler to share a travel plan of himself / herself before traveling; secondly, predicting traffic network dynamic traffic demands in different time periods in the future according to a travel plan shared by travelers; thirdly, evaluating the operation level of the urban traffic network based on the prediction result of the dynamic traffic demand and the urban traffic supply information; optimizing a travel plan; randomly selecting travelers, and providing corresponding travel suggestions for the travelers; and enabling the traveler to select whether to accept the travel suggestion or not, andperform dynamic traffic demand prediction based on the selection result after completion, and repeating the cycle until the operation level of the urban traffic network reaches a satisfactory level.According to the method, a hash algorithm is adopted for encryption, a travel plan is shared through public key and private key technologies of a block chain, and the privacy of travelers is protected; and travelers are motivated to share the travel plan and accept travel suggestions.

Description

technical field [0001] The invention belongs to the fields of traffic congestion management and intelligent traffic systems, and relates to a traffic travel behavior control method based on block chain technology and travel plan sharing. Background technique [0002] With the development of urbanization, the problem of traffic congestion is becoming more and more serious, and the key to alleviating or even solving the problem of traffic congestion is to reasonably regulate the travel choice behavior of travelers, such as the choice of planned travel mode, planned travel time, and planned travel route selection, etc. . [0003] In order to encourage travelers to use public transport, reduce private car travel, and avoid travel during peak hours, many cities have adopted measures such as number-limited travel, public transport subsidy policies, staggered commute to and from get off work, and congestion charges to regulate travel choices. However, most of these methods are man...

AI Technical Summary

Problems solved by technology

However, most of these methods are mandatory measures, and there are problems such as fairness and low social acceptance.

Many travelers believe that traffic managers are not formulating corresponding strategies to control traffic problems, but are considering other interests such as increasing government revenue. Therefore, they often have resistance to these control strategies, which leads travelers to formulate strategies for traffic managers. schemes have mistrust issues

In addition, the implementation effect of travel regulation strategies often depends on accurate travel demand forecasts. Existing travel demand forecasts are often based on historical travel data or real-time detected traffic travel information, but there are many uncertainties in the travel choices of travelers. , and historical traveler data are difficult to accurately grasp these uncertainties, so there is a large error in the travel demand forecast

The real-time detection of traffic travel information is the result of the traveler's travel choice and implementation. The real-time control scheme cannot fundamentally control the initial choice behavior of the traveler, so the control effect is not ideal.

[0004] To sum up, the existing traffic control methods have the following problems: the commonly used control methods are often mandatory and unfair, there is a problem of distrust between travelers and traffic management departments, and the social acceptance of the control methods is not high; the control methods are based on Accurate travel demand forecasting, while current travel demand is based on historical travel data and real-time detection data, cannot grasp travel uncertainty well, travel demand prediction accuracy is low, and cannot fundamentally control the initial choice behavior of travelers. The implementation effect of the method is low

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