The Relationship of Vehicle Type Choice to Personality, Lifestyle, Attitudinal, and Demographic Variables

This research focuses on exploring the travel attitude, personality lifestyle, and mobility factors affecting individuals' vehicle type choices, as well as developing a disaggregate choice model of vehicle type based on both these factors and typical demographic variables. A literature review looks at studies related to vehicle type choice models, vehicle use models, and mobility. The study then describes the characteristics of vehicle classification model used in the study, and the key explanatory variables included in the vehicle type choice model. The relationships of vehicle type to travel attitude personality, lifestyle, mobility, and demographic variables are then individually examined u sing one-way ANOVA and chi-squared tests. A multinomial logit model is then developed for vehicle type choice

Impacts of Home-Based Telecommuting on Vehicle-Miles Traveled: A Nationwide Time Series Analysis

This study estimates the impact of telecommuting on personal transportation using a multi-variate time series analysis of aggregate nationwide data spanning 1966-1999 for all variables except telecommuting, and 1988-1998 for telecommuting. Three dependent variables were modeled, in direct and per-capita forms: ground vehicle miles traveled (VMT), airline passenger miles traveled (PMT), and the sum of those two variable, loosely referred to as total miles traveled. The first part of the analysis modeled each dependent variable (1966-1999) as a function of conventional variables representing economic activity, the cost of transportation transportation supply, and demographics. In the second part of the study, the residuals of the first part (1988-1998) were modeled as a function of the number of telecommuters. Secondary data sources were used for the study. After the modeling results are presented, the study offers several public policy recommendations, based on the conclusion that telecommuting appears to have a statistically significant, albeit modes in magnitude, effect on reducing travel

The Effects of the Emission Cost on Route Choices of International Container Ships

Maritime freight shipping has increased significantly and air pollution from international ships has grown accordingly, having serious environmental effects all over the world. This paper analyzes the effects of the emission cost on ocean route choices, focusing on international container ships. First, the paper formulates a freight network model that captures decisions and interactions of ocean carriers and port terminal operators in the maritime freight transport system. Then, the emission cost is calculated based on an activity-based approach as a component of the ocean transportation cost function. A case study is examined to find if the emission cost affects ocean route choices. The results indicate that the optimal ocean route and transportation cost are changed distinctively due to the emission cost. The research discusses how the emission cost plays a role in route changes and why ocean carriers have to consider these costs in their routing decisions

Aggregate Relationships between Telecommunications and Travel: Structural Equation Modeling of Time Series Data

The purpose of this study is to explore the aggregate relationships (substitution, complementarity, or neutrality) between telecommunications and travel and to compare such relationships across transportation modes. This study first presents a conceptual model, considering causal relationships among travel, telecommunications, land use, economic activity, and socio-demographics. Then, based on the conceptual model, the aggregate relationships between telecommunications (local telephone calls, toll calls, and mobile phone subscribers) and travel (VMT, transit passengers, and airline PMT) are explored in a comprehensive framework, using structural equation modeling of national time series data spanning 1950-2000 in the U.S. At the most detailed level, individual and joint structural equation models for telecommunications and ground travel or airline travel were developed, using selected subsets of the endogenous variables, and then the causal relationships between the two were compared by mode. The model results suggest that most significant causal relationships between telecommunications and travel are complementary. That is, as telecommunications demand increases, travel demand increases, and vice versa. The only exceptions are the two causal relationships between transit passengers and mobile phone subscribers, which are substitutive. Furthermore, there are a number of neutral (zero net) effects of telecommunications on travel or vice versa. Overall, causal effects between telecommunications and travel are different among their modes. However, most of them are complementary regardless of the causal direction. At a less detailed level, composite indices for eight endogenous variable categories were constructed by combining the variables of a given category into a single composite indicator for that category through confirmatory factor analysis. Then, structural equation models for travel and wired (telephone calls) or mobile (mobile phone subscribers) telecommunications were estimated, using the composite indices and sociodemographic variables. The estimated models also support that the aggregate relationship between actual amounts of telecommunications and travel is complementarity, albeit asymmetric in directional weight. That is, as travel demand increases, telecommunications demand increases, and (to a lesser extent) vice versa. Consequently, the empirical results from both levels of structural equation modeling strongly suggest that the aggregate relationship (or system-wide net effect) between actual amounts of travel and telecommunications is complementarity, not substitution

Identification of Causal Relationship between Attitudinal Factors and Intention to Use Transportation Mode

Based on the theory of planned behavior, this study identifies the causal relationship between attitudinal factors and intention to use transportation mode. A structural equation model was developed based on twelve hypotheses. The main findings and implications of this study are as follows. First, people who want to express themselves through cars have a high intention to use personal vehicles, and they purchase cars for this purpose. If the shared vehicle service provides a vehicle rental that reflects individual tastes, those who want to own the vehicle will use the shared vehicle. This could be a solution to the parking problem. Second, those who perceive travel as a disutility have a low intention to use public transportation. If fare discounts are applied when transferring public transportation and micro-mobility, it is expected that the use of public transportation will increase due to reduction of access time for public transportation. Third, people who like to drive have a high intention to use personal vehicles and micro-mobility. Providing space for driving cars as a leisure activity may be one of the ways to prevent traffic accidents that may occur in the future due to a mixed flow of autonomous vehicles and conventional vehicles

Identification of Causal Relationship between Attitudinal Factors and Intention to Use Transportation Mode

Based on the theory of planned behavior, this study identifies the causal relationship between attitudinal factors and intention to use transportation mode. A structural equation model was developed based on twelve hypotheses. The main findings and implications of this study are as follows. First, people who want to express themselves through cars have a high intention to use personal vehicles, and they purchase cars for this purpose. If the shared vehicle service provides a vehicle rental that reflects individual tastes, those who want to own the vehicle will use the shared vehicle. This could be a solution to the parking problem. Second, those who perceive travel as a disutility have a low intention to use public transportation. If fare discounts are applied when transferring public transportation and micro-mobility, it is expected that the use of public transportation will increase due to reduction of access time for public transportation. Third, people who like to drive have a high intention to use personal vehicles and micro-mobility. Providing space for driving cars as a leisure activity may be one of the ways to prevent traffic accidents that may occur in the future due to a mixed flow of autonomous vehicles and conventional vehicles

Telecommunications and travel demand and supply: Aggregate structural equation models for the US

Disaggregate studies of the impacts of telecommunications applications (e.g. telecommuting) on travel have generally found a net substitution effect. However, such studies have all been short-term and small-scale, and there is reason to believe that when more indirect and longer-term effects are accounted for, complementarity is the likely outcome. At least two aggregate studies have focused on the relationships between telecommunications and travel from economic perspectives (consumer and industry). However, both use the monetary value of consumption or transactions rather than actual activity measures (e.g. miles, number of calls), and neither fully explains the direct and indirect causal relationships between the two. The purpose of this study is to develop a conceptual model in a comprehensive framework, considering causal relationships among travel, telecommunications, land use, economic activity, and socio-demographics, and to explore the aggregate relationships between telecommunications and travel, using structural equation modeling of national time series data spanning 1950–2000 in the US. In this paper we focus on number of telephone calls as the measure of telecommunications, and passenger vehicle–miles traveled as the measure of transportation. Future research will investigate additional measures of these two constructs. Our empirical results strongly support the hypothesis that telecommunications and travel are complementary. That is, as telecommunications demand increases, travel demand increases, and vice versa. These results offer a more realistic picture to policy makers and transportation planners than has been available till now, and suggest useful directions for them to develop transportation or telecommunications strategies designed to reduce traffic congestion, air pollution, and energy consumption