The Model And Algorithm Of Activity-based Low-carbon Trip For Urban Residents

With the development of society,the energy consumption and the pollutants caused thereby have been on the rise year by year,which has had a great impact on global ecological environment and people's living condition.Both the national energy strategy and the national plan to combat climate change have put forward some constraint requirements for energy consumption and carbon emission.The transportation,which takes up enormous energy and generates lots of pollutants,is the main cause for the pollution and the main field for energy conservation and emission reduction.The energy consumption and pollutants emission also vary greatly for means of transportation,so optimizing the travel structure is of great significance to reduce energy consumption and carbon emission.If the traffic mode in Beijing shifts mainly to rail transit,the energy consumption and pollutants emission can be reduced by 10%-50%,which would improve our environment.Therefore,analyzing the traffic conduct and optimizing the travel structure is crucial to actively respond to government's requirements and satisfy citizens' need for a blue sky and it's also a great step for the research technique of optimizing the travel structure.This paper studies the theory and measures for optimization and adjustment of the travel structure and travel behavior from three aspects:travel structure optimization,travel activity adjustment and traffic mode selection.For the purpose of constructing the model,the paper also takes a detailed study on the survey methods of travel behavior and multisource data analysis.The paper mainly includes the following four parts:(1)Researches on the travel behavior survey method and data analysis technique based on multi-source heterogeneous data.In this part,the author analyzes the pros and cons of the travel behavior survey method.A comprehensive survey system for travel behavior is established based mainly on household survey and after checking by road traffic volume,GPS position and mobile APP,complemented by mobile phone positioning data,public transportation survey and taxi survey in combination with aspirations survey.Then the author analyzes the characteristics of data collected from the survey and provides the data analysis technique of weighted sampled data expansion and comprehensive check for multisource data.After that,by taking comprehensive traffic survey in Beijing in 2010 as example,the author makes a research on survey items setting and multisource dada analysis.(2)The travel structure optimization model based on low carbon target.From the aspect of macroscopic travel structure adjustment,the author establishes a travel structure optimization model for the purpose of reducing carbon emissions and raising the input-output ratio of government's fund for transportation construction.With citizens' traveling social benefit as the objective function and the energy consumption and emissions reduction level,full-load ratio of public transportation as constraints,the author solves the model by way of branch and bound algorithm.In addition,the author also analyzes the travel structure,travel expense,government investment,energy consumption and emission constraint and other indicator data in Beijing and estimates the best urban travel structure by model.(3)Travel activities forecast model based on the low carbon target.First,the paper analyzes citizens' daily travel data by connecting the travel activities with transportation mode in accordance with the sequence to form a combination chain,including one-day activities sequence,purpose of each activity,transportation mode and other information.Then the paper codes the combination chain and changes it into 0-1 code which can be recognized and calculated by computer.Then,by application of the neural network prediction model,and simulated training of the combination chain model through training data,it predicts citizens' travel activities and obtains the change in travel quantity.Finally,the paper makes analysis of examples by model,analyzes the per capita income doubling and cancellation of the license-plate lottery measures and estimates the change in citizens' travel volume and traffic carbon emissions.(4)Traffic mode selection model based on the low carbon target.By application of 4th comprehensive traffic survey data in Beijing,the paper analyzes the elements affecting individual's choice of traffic mode,builds the individual traveller's traffic mode selection model based on Mixed Logit model and calculates the model parameters based on optimized annealing algorithm.In addition,by example analysis,the paper predicts and evaluates the effect of congestion charging and public bus speed increase on change of traffic mode and carbon emissions.