Journal of Transport and Land Use <p>The Journal of Transport and Land Use is the leading international journal that publishes original interdisciplinary papers on the interaction of transport and land use. The Editors welcome original submissions across the globe and from a wide range of domains, including engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems.</p> en-US <p>Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under <a href="">Creative Commons Attribution-Noncommercial License 4.0</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</p> (Ying Song) (Arlene Mathison) Fri, 13 Jan 2023 11:12:49 -0800 OJS 60 End of the line: The impact of new suburban rail stations on housing prices <p class="p1"><span class="s1">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.</span></p> Rhea Acuña Copyright (c) 2023 Rhea Acuña Mon, 20 Feb 2023 00:00:00 -0800 Integrating transit and TNC services to improve job accessibility: Scenario analysis with an equity lens <p class="p1"><span class="s1"> 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. </span></p> Lingqian Hu, Sai Sun Copyright (c) 2023 Lingqian Hu, Sai Sun Tue, 24 Jan 2023 00:00:00 -0800 Using traffic data to identify land-use characteristics based on ensemble learning approaches <p class="p1"><span class="s1">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 </span><span class="s2">Area Under ROC Curve (</span><span class="s1">AUC), </span><span class="s2">Classification Accuracy (</span><span class="s1">CA), F-Measure (F1), Precision, and Recall, respectively, in classification tasks and 56.81%, 21.20%, 47.29% for </span><span class="s2">Mean Squared Error (</span><span class="s1">MSE), </span><span class="s2">Root Mean Square Error (</span><span class="s1">RMSE), and </span><span class="s2">Mean Absolute Error (</span><span class="s1">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.</span></p> Jiahui Zhao, Zhibin Li, Pan Liu Copyright (c) 2023 Jiahui Zhao, Zhibin Li, Pan Liu Fri, 13 Jan 2023 00:00:00 -0800 The activity space and the 15-minute neighborhood: An empirical study using big data in Qingdao, China <p class="p1"><span class="s1">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.</span></p> Lin Lin, Tianyi Chen Copyright (c) 2023 Lin Lin, Tianyi Chen Fri, 13 Jan 2023 00:00:00 -0800