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

Abstract

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.

Perception of urban park soundscape

“…20 Path analysis can give coefficients for estimating direct, indirect and total (direct plus indirect) effects of variables on each other. 21,22 Path analysis has been widely applied in not only social psychology and sociology 23 but also a number of acoustic studies to investigate the relationships among noise level, human perception, personal characteristics, and noise annoyance. [24][25][26] A number of major assumptions were made in formulating the path model.…”

Accessibility of Cities in the Digital Economy

“…ICTs can lower the cost of the conversation component (Leamer & Storper, 2001), but also facilitate physical spatial interaction. This discussion is reflected in the different types of relation between transport and ICTs identified in the literature (Salomon, 1986;Banister & Stead, 2004;Mokhtarian, 1990Mokhtarian, , 2002Cho & Mokhtarian, 2007): substitution (reduction, elimination), complementarity (stimulation, generation), modification (change time, mode, destination, etc. ), and neutrality (no impact of one medium on the other).…”

A social network perspective on ICT and urban travel

“…For example, it is easy to roughly assess how someone who begins to e-commute due to congestion may reduce his or her commuting. But what additional travel that person still engages in, given the newly saved time is challenging to assess (Choo and Mokhtarian, 2007).…”

“…Such technologies have moved our society from the "Industrial Era" to the ''Information Age'' and changed people's daily lifestyles as well as travel behaviour. As such, transportation and telecommunications have in common the characteristic that both are means of sharing information among people, and for that reason they are also closely interrelated with each other as substitutes and/or complements (Choo and Mokhtarian, 2007).…”

“…For example, it is easy to roughly assess how someone who begins to e-commute due to congestion may reduce his or her commuting. But what types of travel that person still engages in, in his/her free times, is difficult to measure (Choo and Mokhtarian, 2007). However, if a closer look is taken into the social networks of the individual it is possible to empirically generate data that can be used to calculate which (travel) relation is being substituted or complemented by ICT (Axhausen, 2008).…”