Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework

Abstract

In order to contain commuting distance growth and relieve traffic burden in mega-city regions, it is essential to understand journey-to-work patterns and changes in those patterns. This research develops a planning support model that integrates increasingly available mobile phone data and conventional statistics into a theoretical urban economic framework to reveal and explain commuting changes. Base-year calibration and cross-year validation were conducted first to test the model’s predictive ability. Counterfactual simulations were then applied to help local planners and policymakers understand which factors lead to differences in commuting patterns and how different policies influence various categorical zones (i.e. centre, near suburbs, sub-centres and far suburbs). The case study of Shanghai shows that jobs–housing co-location results in shorter commutes and that policymakers should be more cautious when determining workplace locations as they play a more significant role in mitigating excessive commutes and redistributing travel demand. Furthermore, land use and transport developments should be coordinated across spatial scales to achieve mutually beneficial outcomes for both the city centre and the suburbs. Coupled with empirical evidence explaining commuting changes over time, the proposed model can deliver timely and situation-cogent messages regarding the success or failure of planned policy initiatives.

The Impacts of Air Pollution on Mental Health: Evidence from the Chinese University Students

“…A plethora of research has evidenced that mental disorders are associated with environmental changes [16][17][18]. According to a self-assessed mental health survey, Xue et al [16] reported that an increase in air pollution is associated with a higher probability of self-assessed mental health problems.…”

What does urban informatics add to planning support technology?

“…It supplements decades of PSS literature presenting novel approaches to core model development (Basu andFerreira, 2020: 1397;Jia et al, 2020Jia et al, : 1418 with an increasing focus on the integration of the planning process and the applications of PSS technologies (Jiang et al, 2020(Jiang et al, : 1343Page et al, 2020Page et al, : 1508. User interface developments are also presented (Pettit et al, 2020(Pettit et al, : 1490Yang et al, 2020Yang et al, : 1474 as are urban analytics that utilize recently available big data sets (Pan et al, : 1456Yang, 2020Yang, : 1440. These additions help demonstrate new opportunities for the integration of planning support research, urban informatics, and smart cities.…”

“…Instrumental value is produced by enabling feedback solicited from planners and local stakeholders directly implicate PSS model outcomes and improvements (Cai et al, 2020(Cai et al, : 1380Pettit, 2020Pettit, : 1490Page et al, 2020Page et al, : 1508. Planner and stakeholder inputs help to create multiple scenarios that can inform planning consequences and policy decisions (Yang, 2020(Yang, : 1440Yang et al, 2020Yang et al, : 1474Xu et al, 2020Xu et al, : 1361. The normative value of participation is realized in the smart city ideal of transparent, participatory, and collaborative open government and data (Deal et al, 2017b;Pereira et al, 2017).…”

“…Pan et al ( : 1456 and Jia et al (2020Jia et al ( : 1418 use large, voluminous, and diverse data sources (address change data from postal records and POI data from navigation systems) to better understand the science of cities and advance the theories of complex urban systems. Xu et al (2020Xu et al ( : 1361 and Yang (2020Yang ( : 1440 contribute to the understanding of urban dynamics, urban mobility and their resulting impacts. Though these theoretical findings do not immediately, nor intuitively support practical planning, understanding complex urban phenomena is the first step toward future iterations that will ultimately become useful for planning practice.…”

School Commuting: Barriers, Abilities and Strategies toward Sustainable Public Transport Systems in Yogyakarta, Indonesia

“…For example, a study stated that if parents use cars or motorcycles for their commuting trips, the likelihood of taking their children to school by private vehicles increases [28]. Meanwhile, Yan found that journey-to-work patterns significantly influence the mode choice decision in Shanghai [29]. Lin and Chang also stated that when the location of the children's school is close to parents' offices or located along the parent's travel route, parents tend to escort their children to school by private vehicle on the way to the workplace [30].…”