Smart cities have become a popular concept because they have the potential to create a sustainable and livable urban future. Smart mobility forms an integral part of the smart city agenda. This paper investigates “smart mobility” from the angle of sustainable commuting practices in the context of smart cities. This paper studies a multivariate multiple regression model within a panel data framework and examines whether increasing access to broadband Internet connections leads to the choice of a sustainable commuting mode in Australian local government areas. In this case, access to the Internet is used as a proxy for determining urban smartness, and the use of different modes of transport including working at home is used to investigate sustainability in commuting behavior. The findings show that an increasing access to broadband Internet reduces the level of working from home, public transport use, and active transport use, but increases the use of private vehicles, perhaps to overcome the fragmentation of work activities the Internet creates. How to overcome the need for car-based travel for fragmented work activities while increasing smartness through the provisioning of broadband access should be a key smart city agenda for Australia to make its cities more sustainable.

Introduction

The concept, “smart city,” has become almost ubiquitous both in academia and in policy circles due to its potential to address a range of negative effects of rapid urbanization (e.g., congestion, CO2 emissions), industrialization (e.g., air and soil pollution) and consumerism practices (Mahbub et al., 2011; Wiig, 2015; Taamallah et al., 2017; Trindade et al., 2017). While a number of scholars and smart city sceptics raise their concerns about the ongoing global smart city movement (Yigitcanlar and Lee, 2014; Kunzmann, 2015; Angelidou, 2017), many levels of government—local, regional, state, national, and supra national—across the globe still continue to jump on the smart cities bandwagon (Townsend, 2013; Komninos, 2016). Due to diverse disciplinary and sectoral perspectives, there is no common consensus on the definition of smart cities (Angelidou, 2014; Albino et al., 2015). However, these cities are generally seen as localities that effectively utilize strategic planning approaches and innovative solutions to improve the quality of life of their communities, including ecological, cultural, political, institutional, social, and economic components (Neirotti et al., 2014; Yigitcanlar, 2016). Smart cities are also an umbrella concept that contain various sub-elements, ranging from smart economy to smart living, smart governance to smart people, and smart mobility to smart environment (Lee et al., 2014; Lara et al., 2016; Chang et al., 2018). Because emissions generated from transport causes about a quarter to one-third of greenhouse gas (GHG) emissions, smart mobility forms an integral part of the smart city agenda (Creutzig et al., 2015; Yigitcanlar, 2015; Arbolino et al., 2017). The smart mobility concept, in the fashionable sense, is defined as “integrating the sustainable and smart vehicular technologies, and the cooperative-ITS [intelligent transport systems], accelerated with the cloud-server and big-data based vehicular networks” (Kim et al., 2015, p.59). Similarly, Chun and Lee (2015) see smart mobility as a comprehensive and smarter future traffic service in combination with smart technology. However, in the traditional sense, smart mobility is basically all about reducing congestion, greenhouse gases, and other vehicular emissions, and fostering faster, greener, and cheaper transportation options (Spinney et al., 2009). Moving smartly surely depends on an efficient means of active and public transport systems having a low environmental impact, a network of safe and continuous cycle lanes and walkways, and interchange parking that avoids city congestion (Yigitcanlar and Kamruzzaman, 2014, 2015; Chun and Lee, 2015). In other words, mobility cannot be considered “smart” if it is not also sustainable (Yigitcanlar et al., 2015; Garau et al., 2016).