Effects of Item Nonresponses on the Performance of Travel Mode Choice Models for Developing Cities: A Comparative Analysis

To enhance the quality of transportation planning and policy making, it is necessary to properly deal with the nonresponse issues in transport surveys. However, such nonresponse issues especially in developing countries have been ignored in literature. This paper first statistically identifies the missing patterns of item nonresponse (INR) in person trip survey data collected in developing cities and then analyzes the effects of INR on the performance of travel mode choice model (an aggregated multinomial logit model) based on expectation-maximization (EM) imputation method. As a case study, three developing cities representing three levels of INR are analyzed as follow. Firstly, the statistically significant social-economic attributes of trip makers and trip-context factors are identified with respect to INR in the missing pattern analysis by using Chi-Square test method. Secondly, EM imputation based on missing pattern analysis is applied to deal with missing data to obtain the unbiased data set as a benchmark. Thirdly, the null hypothesis that the model parameters estimated with and without imputation are equal is statistically tested using independent-sample T tests and further the internal validity performed in terms of R-squared coefficients is used to identify the discrepancy of model predictions between with and without imputations. Finally, one critical indicator – value of travel time (VOTT) is evaluated considering the effects of missing data. The results confirm that the respondents and non-respondents are quite different in terms of the social-economic background in the developing cities and further show that not only the missing rates but also the missing patterns greatly affect the performance of mode choice model in terms of model parameters and the prediction ability. The calculation of VOTT reveals that the VOTT affected by INR tends to be overestimated.

摘要为提高交通规划与决策质量，有必要妥善处理交通调查无回答问题。但是在相关研究中尤其在发展中国家此问题常被忽视。本文统计分析了发展中城市居民出行调查中项目无回答的缺失模式，进一步分析项目无回答作为模型缺失数据对基于期望最大化的数据修补的多项Logit集计的出行方式选择模型的影响。选择三个发展中城市代表调查无回答的三个等级做案例分析。首先，根据项目无回答缺失模式，利用Pearson开方检验得出重要的出行者社会经济属性和出行背景因素。其次，利用基于缺失模式分析的期望最大化估计，处理缺失数据得到无偏数据集作为基准。此外，利用独立样本的T检验了零假设，即有无期望最大化估计情况下的模型参数估计是相同的。用基于R平方系数的内在效度检验计算估计情况下的模型预测偏差。最后，考虑缺失数据的影响，评估一个重要的指标——出行时间价值。结果表明，在发展中城市，问卷回答者和不回答者的社会经济背景存在很大差异。缺失率和缺失模式均对出行方式选择模型的参数和预测精度有很大影响。考虑缺失模式的出行时间价值计算表明项目无回答对出行时间价值的影响被过高估计了。