Journal of Transport and Land Use

Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations

Jiaomin Wei, Yanyan Chen, Zhuo Liu, Yang Wang — Fri, 12 May 2023 00:00:00 -0700

The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.

Were COVID pedestrian streets good for business? Evidence from interviews and surveys from across the US

Hayden P. Andersen, Dillon T. Fitch-Polse, Susan L. Handy — Fri, 12 May 2023 00:00:00 -0700

During the COVID pandemic, at least 97 US cities closed downtown streets to vehicles to create commercial pedestrian streets with the goal of encouraging active travel and economic activity at safe social distances. This study addressed three questions about these programs for businesses located on a pedestrian street: 1) what factors influenced their feelings about the program; 2) what concerns did businesses located on pedestrian streets have; and 3) how did the pedestrian street program impact a business’s revenue as compared to other businesses in the area on streets that did not close. We created a geographic database of these pedestrian streets and identified nearly 14,000 abutting businesses, from which we collected interview and survey data. The interviews and survey results highlight key issues surrounding businesses’ experiences with pedestrian streets. Businesses abutting pedestrian streets had a slightly higher opinion of these programs than businesses not abutting these streets. A test of the effect of pedestrian street interventions on business revenue using a pseudo-control group showed the effect to be uncertain but, on average, negligible. The findings point to steps that cities can take to maximize the benefits of pedestrian streets for local businesses.

Heterogeneity in mode choice behavior: A spatial latent class approach based on accessibility measures

Jaime Pablo Orrego-Oñate, Kelly Clifton, Ricardo Hurtubia — Fri, 07 Apr 2023 00:00:00 -0700

We propose a method to estimate mode choice models, where preference parameters are sensitive to the spatial context of the trip origin, challenging traditional assumptions of spatial homogeneity in the relationship between travel modes and the built environment. The framework, called Spatial Latent Classes (SLC), is based on the integrated choice and latent class approach, although instead of defining classes for the decision maker, it estimates the probability of a location belonging to a class, as a function of spatial attributes. For each Spatial Latent Class, a different mode choice model is specified, and the resulting behavioral model for each location is a weighted average of all class-specific models, which is estimated to maximize the likelihood of reproducing observed travel behavior. We test our models with data from Portland, Oregon, specifying spatial class membership models as a function of local and regional accessibility measures. Results show the SLC increases model fit when compared with traditional methods and, more importantly, allows segmenting urban space into meaningful zones, where predominant travel behavior patterns can be easily identified. We believe this is a very intuitive way to spatially analyze travel behavior trends, allowing policymakers to identify target areas of the city and the accessibility levels required to attain desired modal splits.

Exploring a quantitative assessment approach for car dependence: A case study in Munich

Matthias Langer, David Durán-Rodas, Elias Pajares — Mon, 27 Mar 2023 00:00:00 -0700

While discussions are ongoing about the exact meaning of car dependence, its assessment has been primarily qualitative. The few quantitative approaches adopted so far have tended to analyze either high car use and ownership or a lack of public transport accessibility as indicators of car dependence. This study aims to quantitatively evaluate car dependence in Munich after merging these three aspects—car use, ownership, and lack of public transportation—and identify its associated potential spatial predictors. The exploratory approach is applied to traffic zones in the transit service area around Munich, Germany, which includes calculating an indicator for car dependence and its linkage with socio-spatial factors using multiple linear regression. For this purpose, traffic data from 2017 and census data from 2011 are used, which are the most recent available. It was found that car dependence is higher in suburban areas with low local numbers of employees, low land costs, and high average income tax payments. Identifying areas with higher car dependence and associated factors can help decision makers focus on or prioritize these areas in providing better access to alternative transportation and basic opportunities. Future research could focus on application in additional regions, using recent and aligned data, and further combinations with qualitative research.

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.