ACCESS: An agent-based model to explore job accessibility inequalities
Introduction
In spite of the changes currently taking place in labor relations worldwide, such as the automation and modernization of activities, a substantial portion of trips in urban centers are work-motivated. The relevance of these work-motivated travels reflects the long tradition of researchers studying it, as travelling to a job is frequently reported by people as the most important travel activity (Grengs, 2015).
One convenient way of measuring how efficient a transport system is in providing access to opportunities is through accessibility measures. During the last fifty years, accessibility concepts have become central to territorial planning (Batty, 2009). Accessibility, or “the potential for reaching spatially distributed opportunities”, is considered to be the joint result of the geographical distribution of transport and activities (Paez, Scott, & Morency, 2012). Several authors have contributed to its definition, applications and overviews (Anas, 1983; Batty, 2009; El-Geneidy & Levinson, 2006; Geurs & van Wee, 2004; Hagerstrand, 1970; Handy & Niemeier, 1997; Hansen, 1959; Iacono & Levinson, 2017; Ingram, 1971; Lei & Church, 2010; Paez et al., 2012; Stewart, 1948).
Through the previous accessibility definition, it is observed that accessibility measures are capable of identifying the distributional effects of transport projects and policies across specific population groups and regions (van Wee & Geurs, 2011). Accessibility inequality is discussed by various authors in terms of its definitions and measures (Di Ciommo & Shiftan, 2017; Litman, 2002; Neutens, Schwanen, Witlox, & De Maeyer, 2010), the impacts of public transport improvements on equality (Bills & Walker, 2017; Chen, Ni, Xi, Li, & Wang, 2017; Currie et al., 2009; Monzon, Ortega, & Lopez, 2013; Pereira, Banister, Schwanen, & Wessel, 2017) and the potential benefits of multimodal transit trips (Boarnet, Giuliano, Hou, & Shin, 2017; Pritchard, Tomasiello, Giannotti, & Geurs, 2019).
To alleviate job accessibility inequalities, public policies on transport and land use can be implemented, but their outcomes are not straightforward due to the high complexity of their interaction in the urban environment. The land use and transport cycle, as discussed by Paquette, Ashford, and Wright (1972), illustrates the complex and reciprocal influence of both components. The cycle begins with the addition of transportation facilities, which results in increased accessibility and land value, leading to changes in the land use that further increase the trip generation and, thus, resulting in higher traffic needs (Paquette et al., 1972). Throughout this process, transport and land use, which are components of the gentrification process, can influence incoming residents and the burden of displaced residents, especially in cities with high transportation costs (Eckerd, Kim, & Campbell, 2019).
The work developed by Mejia-Dorantes and Lucas (2014) shows the outcomes from transport and land-use integrated policies by comparing the London Jubilee Line Extension and the Madrid Metrosur impacts on economic and land use development. The authors concluded that, besides the existing economic conditions, an economic uplift occurred around stations with land use plans to stimulate urban densities, mixed land uses, and pedestrian and cycling access.
To better understand the impacts of transport and land use policies on urban environments, it is important to develop models with the capability to simulate different transport interventions and land use policies and incorporate their dynamics to provide resulting outcomes. According to Wise, Crooks, and Batty (2017), methodologies on agent-based modeling have shown the capacity to emulate the relations between urban systems and transportation. The work developed by Torrens and Nara (2007) shows that a hybrid agent-cellular automata model is useful in representing human behavior in complex urban systems to explore gentrification process. In addition to the inherent simplification on model creation, the simulation of transport and land use policies allows insights to be obtained on what parameters are more relevant and the efficiency of different policy scenarios.
This work aims to investigate the impacts of public transport and land use policies on job accessibility inequalities through ACCESS. This model allows for outcomes on job accessibility inequality to be simulated based on different transport and land use policies while considering the working population stratified into different social groups. The model simulates the residential location choice of the working population based on the job accessibility and neighborhood status.
ACCESS is empirically evaluated using data from Sao Paulo, which is a megacity in a global south developing country that presents a remarkable unequal accessibility condition among different social groups. The municipality of Sao Paulo, Brazil is one of the five largest cities in the world, with approximately 12 million inhabitants (UN, 2018). It has a core-periphery pattern, where the central area concentrates most of the jobs, wealthy neighborhoods and public transport supply, while the periphery has resulted from an unplanned expansion and is mainly occupied by the poor and less educated population (Moreno-Monroy & Ramos, 2015). Sao Paulo metropolitan region is formed by 39 municipalities. Despite being the main economic and financial center of Brazil, responsible for 19% of the Brazilian GDP (Haddad, Hewings, Porsse, Van Leeuwen, & Vieira, 2015), it presents huge social inequalities (Slovic, Tomasiello, Giannotti, Andrade, & Nardocci, 2019). A bus network and a railway system integrate the municipalities in the metropolitan region (Moreno-Monroy, Lovelace, & Ramos, 2018).
The remainder of this article is organized as follows. In Section 2, the application of transport in residential location choice models is reviewed. In Section 3, we present ACCESS, its submodels and specifications. In Section 4 we show the results considering the calibration and the case study experiments. Finally, the discussions and conclusions are presented in Section 5.
Section snippets
Transport in agent-based residential location choice models
Agent-based models are increasingly being utilized to evaluate environmental and social systems due to their framework, which allows interactions between individual heterogeneous entities to be investigated across space and time (Millington, O'Sullivan, & Perry, 2012).
Agent-based models that use transport proxies in residential location choice were applied in several studies. In many cases, transport was incorporated by a distance proxy (Feitosa, Le, & Vlek, 2011; Guo, Buchmann, & Schwarz, 2019
The ACCESS model
The ACCESS model focuses on job accessibility and its inequalities through social groups and space. This model adopts a generative approach (Epstein, 1999), where the spatial patterns of the residential location that result in different accessibility conditions among social groups emerge from agents that are initially randomly located. In the model, three groups of agents with different relocation priorities in terms of job accessibility and neighborhood status dispute the housing market in an
Experiments
In addition to the baseline scenario described in Section 3.3, five alternative experiments were simulated. All experiments began with agents being randomly allocated in the environment and with the α parameter from utility function being set to 0.6 for the high-status group, 1 for the middle-status group and 0.2 for the low-status group.
In the first experiment, two new public mass rail transport lines, which are part of the government plan of investments in transport infrastructure, were
Discussions and conclusions
This paper aimed to simulate the potential impacts of new transport infrastructure along with land use policies related to social housing and job programs. To explore the impacts of land use policies on job accessibility inequalities, we developed ACCESS.
ACCESS was conceived to be a tool to support decision-making by providing insights into different transport and land use policy impacts on alternative realities, as well as to stimulate the debate on job accessibility inequalities and how to
Acknowledgments
The authors would like to acknowledge the financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (Finance Code 001); the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) [grant number: 310908/2017-5] and [grant number: 421423/2018-8]; the São Paulo Research Foundation (FAPESP) [grant number:15/50127-2]; and the University of São Paulo dean office. We also would like to thank Dr. Patricia S. Lavieri, the Department of
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