Dr. Zhou is responsible for teaching Public Transport System and Transport Policy and Planning courses at The University of Hong Kong. His research focuses on transport/urban planning and policy as well as how big/open data and related analytics can improve/enhance them. He has authored 59 articles in internationally refereed journals. Of those articles, 21 involve big and/or open data (BOD) and related analytics.
- June 12: The Planning and Governance urban mobility: The Devil is in the Detail
Can TODness Improve (Expected) Performances of TODs? An Exploration Facilitated by Non-Traditional DataTransit-oriented developments (TODs) in general and TODness in particular require concerted, conscious and continuous investment and efforts. Given this, it is legitimate and understandable that decision-makers, scholars and the public expect as many positive outcomes from TODs and TODness as possible. Non-traditional data (NTD) has provided new opportunities for us to quantify TODness and expected outcomes we have for it. This paper introduces NTD-facilitated or NTD-based indicators for TODness and the expected outcomes. Based on empirical studies of Shenzhen, it identifies the aspects of TODness that have the most impacts on the expected outcome and differentiates the magnitude of impacts of different TODness indicators across days of a week. To validate the relevance and usefulness of the indicators for TODness and expected outcomes mentioned above, we will run regression models between them. In these models, the dependent variable is one of the expected outcomes we deem important: the transit riders per hour, the smartphone users per hour and the ratio of the transit riders per hour to the smartphone users per hour (weekdays vs. weekends). The independent variables are the TODness index and the three secondary indices about some aspects of TODness introduced by Gu et al. (2018) and two extra variables (the number of points of interest (POIs) with at least one Weibo checkin and the Simpson index for all the Weibo POIs) based on Weibo data. The regression results show that not the same set of independent variables influence the number of transit riders. On weekdays, it is POIs and Diversity that significantly influence the number of transit riders. On weekends, Simpson index, together with POIs and Diversity significantly influence the number of transit riders. In terms of the ratio of transit riders to smartphone users, the results indicate that only Design is a good predictor and its magnitude of impacts is negligible. The above findings can provide useful references for TOD-related plan formulation and decision-making who want to increase transit ridership and transit shares across TODs, regardless of which level of TODness they have achieved respectively.