Commute responses to employment decentralization: Anticipated versus actual mode choice behaviors of new town employees in Kunming, China

Abstract

This study examines workers" mode-choice responses to a typical job decentralization policy implemented in China"s urban developmentgovernment job relocation (GJR) to new towns in the urban periphery. Broadly, the literature suggests that job decentralization tends to increase car commuting; however, little is known about the effects of China"s GJR initiatives on individuals" commuting mode choices. Using Kunming as a case study, this study examines how workers" commuting mode choices have shifted in response to GJR policy. Our study analyzes two travel survey datasets that span the job relocation process: 1) stated preference (SP) data on workers" anticipated mode choices after a move of workplace to a planned new town; and 2) revealed preference (RP) data on workers" actual choices of commuting mode after their jobs were moved. The findings suggest that after job relocation, workers" actual commuting modes shift from more sustainable modes towards cars. The determinants of workers" mode choices differ substantially between the hypothetical and actual setting of job relocation. The anticipated mode choices are largely determined by socio-demographic characteristics whereas the actual mode choices are strongly influenced by travel time and housing locations. The evidence from this study offers two important implications for future planning practice of job decentralization.First, planners and policy-makers should be skeptical about the transportation benefits of job decentralization. Second, while SP surveys can assist planners to predict individuals" mode-choice responses, the robustness of SP results should be carefully assessed before translating into the evidence base for informing job decentralization policy-makings.

Toward More Sustainable Behavior: A Systematic Review of the Impacts of Involuntary Workplace Relocation on Travel Mode Choice

“…In ten studies (Aguiléra, Wenglenski, and Proulhac 2009; Bell 1991; Hanssen 1995; Meland 2007; Sprumont and Viti 2017; Vale 2013; Van Wee and Van Der Hoorn 2002; Walker, Thomas, and Verplanken 2015; Waygood, Kitamura, and Nakai 2007; Yang et al 2017), surveys were carried out before and after the relocation, while the rest of the studies used retrospective surveys or collected information through interviews with representatives of the companies or by reviewing the relevant planning documents. Some studies collected commute behavior information using a general classical travel survey, while others, such as (Hanssen 1995) and Sprumont and Viti (2017), collected commute mode choice information using a one-day and a two-week travel diary, respectively.…”

“…Some older studies mostly relied on descriptive analyses (Bell 1991; Daniels 1972, 1981; Wabe 1967), whereas some more recent studies had a focused research objective leading to a specific data collection process and less conventional methodological approaches (Sprumont and Viti 2017; Walker, Thomas, and Verplanken 2015; Yang et al 2017). Few studies used statistical models and exploited the data to identify the explanatory variables of the observed impacts on commuting behavior (Sprumont and Viti 2017; Sprumont et al 2014; Vale 2013; Yang et al 2017). Yang et al (2017) applied multinomial logit models, with revealed preference (RP) and stated preference (SP) data to explain the variation between anticipated and actual travel mode choice.…”

“…Few studies used statistical models and exploited the data to identify the explanatory variables of the observed impacts on commuting behavior (Sprumont and Viti 2017; Sprumont et al 2014; Vale 2013; Yang et al 2017). Yang et al (2017) applied multinomial logit models, with revealed preference (RP) and stated preference (SP) data to explain the variation between anticipated and actual travel mode choice. Using standard deviational ellipses (SDE) combined with cluster analysis, Sprumont and Viti (2017) also tried to capture activity spaces of a group of university employees whose activity-travel routine was disrupted to a great extent.…”

Urban Spatial Structure and Travel in China

“…Zhao et al 2014; J. Yang 2010), increasing automobile usage, and discouraging nonautomobile travel modes (Wu, Wang, and Li 2018; X. Yang et al 2017).…”

Location, location, relocation: how the relocation of offices from suburbs to the inner city impacts commuting on foot and by bike

“…The vast majority of literature concerning company relocation has been focused on movement away from the city centre to the suburbs or more general trends towards suburbanisation. This applies equally for studies performed outside of Norway ( [10][11][12][13][14][15][16][17][18][19]; Geographic Institute of Utrecht University 1990 in [20]) to those within Norway [21][22][23][24][25][26][27][28]. Common findings across all studies are increased car modal share typically a result of reduced public transport accessibility and favourable car parking allowances at the suburban location [17,20,23,27].…”