Mining Big Data to Link Affordable Housing Policy with Traffic Congestion Mitigation in Beijing, China

PI: Marta C. Gonzalez (Department of Civil and Environmental Engineering, MIT) 


The increasingly unaffordable housing prices, sprawled urban growth patterns to chase lower-priced land for urban development, and their ripple effects including motorization, traffic congestion, air pollution and energy consumption increase have become major urban challenges in megacities in China. We propose to use Beijing, the capital city of China, as a case study, to address and disentangle these issues. Economical and Comfortable Housing (ECH) projects as well as price-controlled commodity housing have been the two major types of affordable housing in Beijing over the past fifteen years. In order to control housing price affordability, starting from 2014, the local government committed to have at least 40% of the newly supplied residential land for affordable housing projects, while the rest for commodity housing (of which 50% is dedicated to price-controlled housing projects)1. Given the great commitment of building more affordable housing projects in the future, we argue that the impacts of their location (e.g., on traffic demand and its negative consequences) will be significant, as the impacts of the ECH projects in the past.

In this research, we have three main objectives: 1) to use Big Data (mobile phone data) together with traditional household travel survey data for Beijing, to investigate the congested urban road users’ housing sources (i.e., where they live) and destination targets (i.e, where they work). This ability to identify the most congested roads and target their sources affected by the congestion will enable planners and policy makers to spot communities which experience the greatest travel delay, and to provide congestion mitigation policies for the targeted group in a more cost-effective way. 2) To investigate housing price affordability (price-to-income ratio) distribution in the metropolitan area, by crawling the housing transaction sales data for Beijing from online website, combined with the income data from the household travel survey in Beijing. 3) To associate the communities experiencing traffic congestions, and link this measure with housing price affordability index, price-to-income-ratio (PIR). We will study their relationship with driver sources of residential communities and driver destinations of employment zones, and provide policy guidance on transportation and housing alternatives for the future sustainable development of the city. This study will be particularly useful in facilitating an integrated planning of land-use and transportation in the metropolitan area, in guiding housing affordability policy together with alternative transport policy formation for the future of Beijing, and other cities with similar challenges in China.