Journal of Transport and Land Use

End of the line: The impact of new suburban rail stations on housing prices

Rhea Acuña — Mon, 20 Feb 2023 00:00:00 -0800

This study leverages the staggered opening of new Metro stations in a suburb of Washington, DC to estimate the impact of proximity to public rail transit on housing prices. Both hedonic and repeat sales models indicate that housing prices increase as distance increases, suggesting that living near public transportation in Prince George’s County is primarily viewed as a disamenity. For properties at one mile from the nearest station, the preferred repeat sales model estimates a marginal price increase of 4.6 percent for a one-mile increase in distance. I argue that the suburban environment may be key in explaining the results. In the suburbs, a greater share of the population relies on automobiles, and rail stations are typically equipped with large parking lots. The suburban environment allows households the opportunity to both benefit from public transportation access and mitigate the negative externalities associated with living right next to the station.

Integrating transit and TNC services to improve job accessibility: Scenario analysis with an equity lens

Lingqian Hu, Sai Sun — Tue, 24 Jan 2023 00:00:00 -0800

With the rapid growth of Transportation Network Company (TNC) services and the continued decline of transit ridership, existing research has proposed and some transit agencies have implemented programs that integrate transit and TNC services. This paper expands the research area to examine the equity implications of such integrations, focusing on job accessibility improvements for low-income workers. We develop an analytical framework that compares improvements in accessibility to jobs under different hypothetical scenarios in which TNC travel serves as the last-mile connection of transit services. Using the city of Chicago for the case study, this research confirms that such transit-TNC integration increases job accessibility for all low-income workers throughout the city, but it also pinpoints nuanced differences in the accessibility improvements among workers of different races, ethnicities, and sexes during peak and off-peak hours.

Using traffic data to identify land-use characteristics based on ensemble learning approaches

Jiahui Zhao, Zhibin Li, Pan Liu — Fri, 13 Jan 2023 00:00:00 -0800

The land-use identification process, which involves quantifying the types and intensity of human activities at a regional level, is a critical investigation step for ongoing land-use planning. One limitation of land-use identification practices is that they are based on theoretical-driven models using survey and socioeconomic data, which are often considered costly and time consuming. Another limitation is that most of these identification methods cannot incorporate the effect of daily human activity, resulting in some significant spatial heterogeneity being ignored. In this context, a novel land-use identification framework is proposed to quantify land-use characteristics using traffic-flow and traffic-events data. Regarding the identification models, two widely used Ensemble learning methods: Random Forest and Adaboost, are introduced to classify the land-use type and fit the land-use density. The case study collected the transit vehicle positions, traffic events, and geo-tagged data at the regional level in the San Francisco Bay Area, California. The results demonstrated that this framework with Ensemble learning was significantly accurate at identifying land-use characteristics in both the type classification and density regression tasks. The result averages improved 12.63%, 12.84%, 11.05%, 5.44%, 12.84% for Area Under ROC Curve (AUC), Classification Accuracy (CA), F-Measure (F1), Precision, and Recall, respectively, in classification tasks and 56.81%, 21.20%, 47.29% for Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively, in regression tasks than other models. The Random Forest model performs better in labels with high regularity, such as education, residence, and work activities. Apart from the accuracy, the correlation analysis of the error term also showed that the result was consistent with people’s common sense of land-use characteristics, demonstrating the interpretability of the proposed framework.

The activity space and the 15-minute neighborhood: An empirical study using big data in Qingdao, China

Lin Lin, Tianyi Chen — Fri, 13 Jan 2023 00:00:00 -0800

Daily travel distance in urban China has substantially increased. The spatial layout of the 15-minute neighborhood, which supports local living and encourages walking and biking, was detailed in the Urban Residential District Planning and Design Standards in China in 2018. This study investigates the impacts of the 15-minute neighborhood described in the 2018 standards on activity space, using mobile network data in Qingdao, China. A total of 42,991 subscribers of China Mobile are randomly sampled. The 15-minute neighborhood attributes are objectively measured for sampled residents individually. Our study shows that not all 15-minute neighborhood attributes are associated with smaller activity space. Commercial retail services and green open space, which were found to increase walking and physical activity, do not reduce activity space. On the other hand, public services such as primary school and middle school, bus stops, neighborhood centers, and sports facilities within walking distance are positively associated with smaller activity space.