Assessing Impact of Urban Form Measures on Nonwork Trip Mode Choice After Controlling for Demographic and Level-of-Service Effects
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The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice have been investigated. The travel data used for analysis are from the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song in 2002 and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment by using the Portland data. The results of the multinomial logit mode choice model indicate that mixed uses promote walking behavior for nonwork activities.Keywords:
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THIS PAPER DESCRIBES BRIEFLY THE WORK DONE IN DEVELOPING A MODEL OF TRAVEL MODE CHOICE. THE MODEL THAT HAS BEEN DEVELOPED REPRODUCED EXISTING CONDITIONS TO A FAIRLY HIGH DEGREE OF ACCURACY, IN THE LIMITED TESTS TO WHICH IT HAS BEEN SUBJECTED. IN THE PAST, MOST MODELS HAVE BEEN DEVELOPED AS A STAGE IN A TRAFFIC STUDY FOR A PARTICULAR AREA. AS SUCH THE MODELS ARE REQUIRED TO FIT INTO AN EXISTING FORECASTING PROCESS AND THE USE VARIABLES FOR WHICH DATA HAVE ALREADY BEEN COLLECTED. THE AUTHOR OF THIS PAPER DECIDED TO APPROACH THE PROBLEM DIFFERENTLY, AND DETERMINE FIRST OF ALL WHAT REASONS PEOPLE CONSCIOUSLY USE TO DECIDE WHICH TRAVEL MODE TO USE. A SURVEY WAS CARRIED OUT TO COLLECT DETAILS OF THE USUAL TRAVEL HABITS, INCLUDING TRAVEL TIMES AND COSTS, AND A QUESTION WAS ASKED TO DETERMINE ALL THE REASONS WHICH INFLUENCED THE CHOICE OF MODE FOR THE WORK JOURNEY. A SECOND SURVEY WAS ALSO MADE TO PROVIDE A CHECK. FROM AN EXAMINATION OF ALL THE REASONS GIVEN AND THE NUMBERS OF PEOPLE GIVING EACH REASON, IT BECAME CLEAR THAT THE MOST IMPORTANT FACTORS ARE TRAVEL TIME, TRAVEL COST, COMFORT AND CONVENIENCE. /AUTHOR/
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People usually travel with different preferences to reach the location of their leisure activities, where transport mode is a decisive factor. Previous studies focus on developing discrete choice models for people where a trip purpose is considered as an explanatory variable. In this paper, discrete choice modeling is applied to model the behavior of leisure travelers, to understand the impact of several factors on the transport mode choice, such as sociodemographic variables. A sample of 1100 travelers from a household survey in Budapest is used in the analysis. The sample includes the daily activity plans of leisure travelers and their sociodemographic as well as economic characteristics. A Multinomial Logit (MNL) model is applied, where the data are examined across travel time variables and travel characteristics, such as travel time, travel cost, age, gender, income, and car ownership. The developed models are used to predict the probability of using the different transport modes based on the characteristics of the travelers and their trips. The model indicates that time and the cost impact travel negatively. While other variables show varied impacts on the transport mode selection. The output of this study is important for decision-makers to understand the patterns of leisure tips in a city.
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Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.
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Data analysis plays a key role in supporting the development of sustainable transportation. Using the large-scale household mobility survey data collected in Milan, Italy during 2005–2006, we study whether the large-scale data contribute to improving accuracy in estimating household travel modes. This paper presents three machine learning methods including multinomial logit (MNL) model, random forest (RF) and support vector machine (SVM) to estimate the household travel mode. Their model accuracies are 70.41%, 71.89%, 72.74% respectively under the full sample size. It is found that the accuracies of these three methods fluctuate fiercely when the sample size is less than 20,000 and then stabilize gradually with continuous increasing it. After stabilization occurs, accuracies with these three methods do not significantly increase as the sample size continues to increase. We also study the travel characteristics derived from the large-scale survey data, which is fundamental for developing a sustainable transportation system. The collected data items include five explanatory variables, i.e., household size (HS), vehicle ownership, household income (HI), travel distance, travel time and one response variable (i.e., household travel mode), which includes public transport (PT), private car, usage of PT and private car simultaneously and the others travel modes (e.g., walk). We further investigate the importance of explanatory variables in terms of estimating household travel mode choice with the MNL model. It is found that vehicle ownership is the most critical factor influencing household travel mode choice, followed by travel distance, travel time, HS and HI. The ranking result is consistent with the RF approach.
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This paper explores the impacts of personal characteristics and the spatial structure on travel behaviour, especially mode choice. The spatial structure is described among other things by accessibility measures. The models are estimated using structural equation modelling (SEM). The models are based on the 1992 Upper Austrian travel survey and the Upper Austrian transport model. The results highlight the key roles of car ownership, gender and work status in explaining the observed level and intensity of travel. The most important spatial variable is the number of facilities which can be reached by a household. The municipality based variables and the accessibility measures have rather little explanatory power. The reasons for this low explanatory power are considered. Although the findings in this study indicate that the spatial structure is not a decisive determinant of traffic, the results provide useful hints for possible policy alternatives.
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