Selective modeling method of all-day multi-mode trip chain based on Nested Logit model

Abstract

The invention discloses an all-day multi-mode trip chain selection modeling method based on a Nested Logit model. The method comprises six steps of passenger all-day trip data acquisition, passenger family identity information acquisition, all-day trip chain classification, trip mode selection set restriction value setting, passenger selection trip mode influence factor judgment and all-day trip chain generation. According to the method, the influence of an overlarge number of selection sets on individual selection errors is reduced by collecting one-day complete travel data and personal social economic attributes of passengers, analyzing all-day travel features of the passengers, performing discrete selection modeling analysis on all-day travel chain selection preferences of the passengers and adopting an implicit selection set generation method; according to the method, a selection set generation mode is obtained, the selection set generation mode is embedded into a traditional Nested Logit model, influence factors of different passengers during selection of all travel modes in one day are obtained, and personalized travel scheme recommendation is carried out through a calculated travel chain selection model.

Description

technical field [0001] The invention belongs to the technical field of multi-mode traffic travel selection modeling, and more specifically relates to a selection modeling method for all-day multi-mode travel chains based on a Nested Logit model. Background technique [0002] Since the economic development and urbanization, the urban space has expanded rapidly, the urban population has grown rapidly, and social activities have increased sharply. As an important guarantee for urban development, urban transportation is an important prerequisite for sustainable urban development. How to rationally rectify the urban traffic problem has become a proposition that needs to be solved urgently. Building efficient and diverse transportation infrastructure in a limited urban space can improve transportation capacity to a certain extent, but it also stimulates further growth in traffic volume, which is essential for reasonable traffic guidance. At present, the connection between variou...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the existing travel selection model is difficult to solve the selection set caused by the surge of the travel combination index in the travel chain, and cannot effectively reflect the individualized differences of different passengers in the all-day travel chain, the present invention provides a comprehensive model based on Nested Logit The selection modeling method of multi-day multi-mode travel chain, by collecting the complete travel data of passengers for one day and personal socioeconomic attributes, analyzes the characteristics of personal travel throughout the day, and conducts discrete choice modeling analysis on the choice preference of passengers for all-day travel chains. The set generation method can reduce the influence of too large number of selection sets on individual selection errors, and embed the selection set into the Nested Logit model to obtain the influencing factors of all travel modes of different passengers in a day, and carry out individualized selection through the calculated travel chain selection model. Recommended travel plans

Images