Search

Vibrancy is one of the most desirable outcomes of transit-oriented development (TOD). The vibrancy of a metro station area (MSA) depends partially on the MSA’s built-environment features. Predicting an MSA’s vibrancy with its built-environment features is of great interest to decision makers as these features are often modifiable by public interventions. However, little has been done on MSAs’ vibrancy in existing studies. On the one hand, seldom has the vibrancy of MSAs been explicitly explored, and measuring the vibrancy is essential. On the other hand, because MSAs are interconnected, one MSA’s vibrancy depends on the MSA’s features and those of relevant MSAs. Hence, selecting a suitable metric that quantifies spatial relationships between MSAs can better predict MSAs’ vibrancy. In this study, we identify four single-dimensional vibrancy proxies and fuse them into an integrated index. Moreover, we design a two-layer graph convolutional neural network model that accounts for both the built-environment features of MSAs and spatial relationships between MSAs. We employ the model in an empirical study in Shenzhen, China, and illustrate (1) how different metrics of spatial relationships influence the prediction of MSAs’ vibrancy; (2) how the predictability varies across single-dimensional and integrated proxies of MSAs’ vibrancy; and (3) how the findings of this study can be used to enlighten decision makers. This study enriches our understandings of spatial relationships between MSAs. Moreover, it can help decision makers with targeted policies for developing MSAs towards TOD.

Excess commuting, which concerns the differences between the actual commute and the optimal (minimum) commute afforded by a given distribution of jobs and housing, i.e., urban form, has been extensively studied across disciplines. In the existing excess commuting framework, the optimal commute considers commuting efficiency but overlooks commuting equity, which is defined as the variation in commuting cost across workers before and after the optimisation. The framework also overlooks the variation in commuting frequency across workers for a period of interest, which also affects the overall commuting cost for the period. In this paper, we propose a novel excess commuting framework using a Greedy-Initialisation-based Genetic Algorithm, where the optimal commute accounts for commuting efficiency and equity and commuting-frequency variation simultaneously. We illustrate and calibrate the framework using one-month metro smart card data in Shanghai. Comparing with two other existing models, the Greedy-Initialisation-based Genetic Algorithm can generate a commuting pattern that balances commuting efficiency and commuting equity, which the existing commuting framework and corresponding algorithms cannot.

The excess commuting framework has advanced a series of metrics through which a city or a region’s jobs-housing balance and commuting efficiency can be measured. This study seeks to add to the conceptual development and extension of the excess commuting framework. Specifically, it considers the carrying capacity (of links) and related congestion issues in the excess commuting framework and demonstrates that overlooking these characteristics has important implications for excess commuting metrics. Drawing on an empirical case study, it shows that when carrying capacity and traffic congestion are accounted for, the observed commute is longer than otherwise. Excess commuting tends to be higher than its counterparts in previous excess commuting studies. The findings suggest that future excess commuting studies should take account of carrying capacity and congestion in determining excess commuting metrics. Moreover, high-quality connections (preferably via public transport) between jobs and housing allied with sufficient carrying capacity of popular links/routes for commuters are crucial preconditions for cities and regions to harvest the full benefits of jobs-housing balance policies targeted at the reduction of the average commute distance and vehicle miles travelled.

Using multiday continuous smartcard data in 2020, we investigate group-based travel in Hong Kong metro system by identifying metro riders intentionally traveling in groups (ITGs). ITGs serve as our proxies for citywide physical social interactions. Considering ITG members are interrelated through group-based trips, we construct a social network (an ITG network) formed by ITGs to explore the network properties and structures of ITG activities. Examining ITGs both before and during the COVID-19 pandemic, we measure the spatial patterns of ITGs and their dynamics across locales and over time. We find that the degree of the ITG network follows a heavy-tailed distribution. The network size and interconnections vary across time. Some ITG members are more influential vertices than others in maintaining the networks’ topological properties. We illustrate how new data and methods can be used to explore in-person interactions and social activity patterns in transit-reliant cities.