Showing papers in "Journal of Transport and Land Use in 2023"
TL;DR: In this paper , the authors 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.
Abstract: 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.
TL;DR: In this article , a systematic literature review of real-world mobility as a service (MaaS) applications and their effects on socio-territorial inequalities (STIs) is presented.
Abstract: Mobility as a service is a potential solution to mobility problems; however, it raises concerns about its relationship with socio-territorial inequalities (STIs). This paper contains a systematic literature review of real-world MaaS applications and their effects on STIs. From the principle of distributive justice, we adopted the Resources, Opportunities, Outcomes, and Wellbeing (ROOW) approach to assess cases. From 2009 papers on MaaS, we identified 20 that stood as real-world applications that considered equity impacts. Most studies were undertaken in Europe and Asia, neglecting countries in South America, Africa, and other low-income countries. They did not quantify the societal advantages of MaaS, while only a handful investigated the influence of MaaS over STIs. Results indicate that MaaS schemes contain at least three factors that may drive inequality: the lack of basic resources to enter the system, the systems’ limited geographic coverage, or MaaS users may simply not gain from the system’s intended accessibility benefits. In conclusion, MaaS could improve trip planning and access to new modes and low-density areas, but it is still perceived as expensive and only accessible to digitally literate people. This should be considered when defining MaaS governance, which remains (to date) underdeveloped, hindering private-public collaboration.
TL;DR: In this paper , the authors focus on those characteristics that create discomfort to riders who are walking between stations and destinations, with the aim of providing researchers and planners with a tool that can be used to identify pain points in any existing or potential station areas.
Abstract: Transit station area land-use characteristics can increase or decrease the perceived costs of riding rail relative to driving or taking other modes. This paper focuses on those characteristics that create discomfort to riders who are walking between stations and destinations, with the aim of providing researchers and planners with a tool that can be used to identify pain points in any existing or potential station areas. We propose and demonstrate a scalable, recomputable method of measuring pedestrian quality for trips that relies solely on datasets readily available for almost any location in the United States, and we compare results using data from a global source, OpenStreetMap. We illustrate our tool in neighborhoods surrounding the Blue Line light rail in Minneapolis, Minnesota, calculating the population-weighted distribution of land uses within pathway buffers of walks from stations to nearby destinations. We focus on land uses that pose a disutility to pedestrians such as major highways or industrial tracts, and we compare disamenity levels across station areas. Despite their simplicity, our measures capture important differences in land-use-related pedestrian experiences and reveal the inadequacy of using circular buffers to designate and characterize station catchment areas.
TL;DR: In this paper , the authors investigate the relationship between micro-mobility and the built environment (BE) at three levels of node (e.g., the emergence of docking stations and parking stops), link and network.
Abstract: Recent innovations in business models and technology have brought out new mobility systems, including shared and electric micro-mobility. A rapidly expanding strand of literature mirrors the micro-mobility’s exponential growth and popularity. While many studies analyze micro-mobility from operations, management and user perspectives, fewer works investigate the micro-mobility and built environment (BE) relationship. This paper systematically reviews the descriptive and empirical studies that investigate this relationship. It analyzes whether, similar to other transportation modes, micro-mobility (e.g., bike-sharing schemes and e-bikes/e-scooters) can potentially influence three BE aspects: urban design, land use, and transportation system. Furthermore, it outlines the recommended changes in the BE to support the micro-mobility and/or enhance the quality of the environment for non-users. This paper investigates the BE and micro-mobility relation at the three levels of node (e.g., the emergence of docking stations and parking stops), link (e.g., the street-level conflicts with walking/cycling/vehicle lanes) and network (e.g., infrastructure network creation and catchment area shifts). In addition, this relation is explored over time, based on the development stage of micro-mobility, the BE aspect (urban design, land use, or transport system), and spatial context (urban or rural). The findings are relevant for urban and transport planners, designers, researchers, policy makers and public authorities. They contribute to a much-needed evidence base for effective design and policy recommendations to accommodate micro-mobility in the BE to achieve a safe and inclusive public space.
TL;DR: In this article , the authors leveraged 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.
Abstract: 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.
TL;DR: In this article , car dependence is found to be higher in suburban areas with low local numbers of employees, low land costs, and high average income tax payments, which can help decision makers focus on or prioritize these areas in providing better access to alternative transportation and basic opportunities.
Abstract: 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.
TL;DR: In this article , the authors present a framework to integrate cross-section survey data between distinct geographic areas, which can be obtained from cordon surveys or mobile phone data, to optimize destination choice models with limited destination capacities in geographical regions usually characterized by travel demand asymmetries.
Abstract: Destination choice models are a key component of any transport and land-use model. Applications in agent-based models allow for destination choice on an individual level including personal variables, like trip purpose, or situational variables. Commonly applied methodologies stem from econometrics, discrete choice theory and utility maximization using either revealed or stated preference data. This paper presents a framework to integrate cross-section flows between distinct geographic areas, which can be obtained from cordon surveys or mobile phone data. Proposed optimization methodology—based on extended shadow price theory—accommodates these complementary data sources as spatially distributed constraints, in addition to the destination capacity constraints such as workplaces. The new generic and robust optimization methodology accounts for constraints as observed on cross-section flows and destination capacities while maintaining econometric choice model theory. As a proof of concept, the suggested methodology is successfully applied in a real-case, agent-based application covering the tri-national Basel region with about 2 million residents, and a large set of 2 · 104 distinct destination alternatives. Due to different wage levels in all three countries and other reasons, the region’s cross-border commuter flows are highly asymmetric. Including data on cross-border flows obtained from a cordon survey, the choice model’s mean deviation declines by 20% and more on a cross-section level and even more so on a choice alternative level, compared to calculations ignoring shadow prices. Moreover, multiple scenario calculations show considerable improvements in planning and forecasting applications. The results demonstrate the suitability and relevance of the proposed approach to optimize destination choice models with limited destination capacities in geographical regions usually characterized by travel demand asymmetries.
TL;DR: In this article , the authors developed 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.
Abstract: 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.
TL;DR: In this paper , a novel land-use identification framework is proposed to quantify land use characteristics using traffic-flow and traffic-events data, and two widely used Ensemble learning methods: Random Forest and Adaboost, are introduced to classify the land use type and fit the landuse density.
Abstract: 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.
TL;DR: Wang et al. as discussed by the authors analyzed the correlation of built environment with DBS-metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study, and found significant positive influence of greenery and presence of barriers on trip density and cycling speed.
Abstract: 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.
TL;DR: In this article , the authors studied the effect of pedestrian street interventions on business revenue using a pseudo-control group and found that businesses abutting pedestrian streets had a slightly higher opinion of these programs than businesses not abutted these streets.
Abstract: 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.
TL;DR: Wang et al. as discussed by the authors investigated the impacts of the 15-minute neighborhood described in the 2018 standards on activity space, using mobile network data in Qingdao, China, where a total of 42,991 subscribers of China Mobile are randomly sampled.
Abstract: 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.
TL;DR: In this paper , the authors studied the ease of access to mosques at walkable distances of 400 m and 800 m radii in twelve selected neighborhoods in Abu Dhabi and Dubai in 2012 and found that good pedestrian accessibility results from an effective interplay between street design, plot densities, network intersection density, strategic placement of alleys, and mosques ratio and spatial distribution.
Abstract: More than a house of worship, religious buildings have a critical and authoritative role in the social and political life of people. Yet, such places of divine and spirit have received limited attention in transportation and urban planning research. This research evaluates accessibility to one kind of religious institution: mosques. The article studies the ease of access to mosques at walkable distances of 400 m and 800 m radii in twelve selected neighborhoods in Abu Dhabi and Dubai. Analysis uses the gravity metric under two network scenarios: streets only, and the combined network of streets and alleys. Gravity values demonstrate three types of accessibility to mosques: plots without access, plots with minimum access to one mosque, and plots with choice access to more than one mosque. Findings show neighborhoods have experienced an erratic decrease in accessibility to mosques. In both cities, percentages of plots with an overall accessibility to mosques, (sum of both minimum and choice), were higher in the pre- and-early-suburban phases. With the inclusion of alleyways, the overall accessibility percentages increased in many cases. The study reveals that good pedestrian accessibility results from an effective interplay between street design, plot densities, network intersection density, strategic placement of alleys, and mosques’ ratio and spatial distribution.