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  • Mobility and traffic research  (10)
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  • Mobility and traffic research  (10)
  • 1
    Online Resource
    Online Resource
    Impact of Neighborhood Built Environments on Shopping Travel Modes in Shanghai, China
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 8 ( 2019-08), p. 669-681
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 8 ( 2019-08), p. 669-681
    Abstract: Walking is one of the most widely used means of transport. Neighborhood built environments have a direct influence on individuals’ daily commuting, recreational travel patterns, and shopping travel behavior. In Chinese cities, shopping activities are among the most frequent reasons for daily travel. Yet, research on the impact of neighborhood built environments on people’s shopping travel activities in high-density cities is limited. To fill this research gap, this study investigates how neighborhood built environments might affect pedestrians’ shopping travel activities in Shanghai, China. The data, which includes shopping travel patterns, perceived environmental characteristics, and individual socioeconomic status, were collected from a survey of 21 randomly selected neighborhoods in Shanghai in 2011. In total, data from 2,838 samples (participants) were collected. Multinomial logistic regression was used to investigate how neighborhood built environments affect residents’ choice of travel mode for shopping, that is, the likelihood of taking transit, driving, or biking vs. walking. Results showed that nearly half of people surveyed (43.3%) used walking as their primary shopping mode. Road network density, presence of primary schools, and average sidewalk width were positively correlated with the likelihood of using walking as the primary shopping mode. Gender, age, and car ownership were also significant in the model.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119844969
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
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  • 2
    Online Resource
    Online Resource
    Forecasting Traffic Speed during Daytime from Google Street View Images using Deep Learning
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications
    Abstract: Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. Then, the extracted features are entered into a graph convolutional neural network to predict traffic speed at different hours of the day. Our results show that the accuracy of the model can reach up to 86.5% and the Resnet18 model pretrained by Places365 is the best choice to extract scene features for traffic forecasting tasks. Finally, we conclude that the proposed model can predict traffic speed efficiently at the city scale and GSV images have the potential to capture information about human activities.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231169531
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 3
    Online Resource
    Online Resource
    Understanding the Relationships Among E-scooter Ridership, Transit Desert Index, and Health-Related Factors
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 12 ( 2022-12), p. 728-739
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 12 ( 2022-12), p. 728-739
    Abstract: This study aims to analyze electric scooter (e-scooter) markets in transit deserts and oases in the U.S. The four cities of Austin, Chicago, Portland, and Minneapolis were selected as case studies to determine the prevalence of e-scooter rides as related to locations with limited public transportation options. A t-test was performed to analyze the difference in the number of e-scooter rides between the transit deserts and transit oases. Overall, the arithmetic means of the e-scooter rides between the transit deserts and transit oases were not significantly different in Austin, Chicago, and Portland. The results confirm that the transit index score was among the top three predictors of trips in Austin, Minneapolis, and Portland. In Chicago, health-related characteristics such as crude prevalence of arthritis, diabetes, and obesity were found to be the most important predictors of trips in Chicago.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221097094
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
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  • 4
    Online Resource
    Online Resource
    Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 4 ( 2023-04), p. 287-297
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 4 ( 2023-04), p. 287-297
    Abstract: The COVID-19 pandemic has disrupted day-to-day lives and infrastructure across the United States, including public transit systems, which saw precipitous declines in ridership beginning in March 2020. This study aimed to explore the disparities in ridership decline across census tracts in Austin, TX and whether demographic and spatial characteristics exist that are related to these declines. Transit ridership data from the Capital Metropolitan Transportation Authority were used in conjunction with American Community Survey data to understand the spatial distribution of ridership changes caused by the pandemic. Using a multivariate clustering analysis as well as geographically weighted regression models, the analysis indicated that areas of the city with older populations as well as higher percentages of Black and Hispanic populations were associated with less severe declines in ridership, whereas areas with higher unemployment saw steeper declines. The percentage of Hispanic residents appeared to affect ridership most clearly in the center of Austin. These findings support and expand on previous research that found that the impacts of the pandemic on transit ridership have emphasized the disparities in transit usage and dependence across the United States and within cities.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231159906
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 5
    Online Resource
    Online Resource
    Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach
    SAGE Publications ; 2022
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2676, No. 7 ( 2022-07), p. 55-65
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 7 ( 2022-07), p. 55-65
    Abstract: Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221076426
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    The Geography and Equity of Crowdsourced Public Participation for Active Transportation Planning
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 1 ( 2019-01), p. 460-468
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 1 ( 2019-01), p. 460-468
    Abstract: Transportation planners increasingly use new forms of online public participation alongside traditional in-person approaches, including crowdsourcing tools capable of encouraging geographically specific input. Digital involvement may be particularly valuable in exploring methods to plan at a megaregional scale. Research is beginning to address digital inequalities, recognizing that broadband and smartphone access may restrict opportunities for disadvantaged groups. However, the geography and equity of participation remain pragmatic issues for practice and research. This paper reviews the geography and equity of the participation methods in Austin, Texas for active transportation (bicycling and pedestrian) through three approaches to co-produce informed plans: in-person meetings, public participation geographic information system (PPGIS), and an emerging smartphone platform that logs trips and encourages input on route quality. In addition to spatial analysis with standard deviational ellipses, we include qualitative case analysis to contextualize the geographic and equity implications of different participation approaches. Results show that both online techniques resulted in a larger geography for participation than in-person meetings, with the regional PPGIS covering the most area. However, review of the income levels in each area shows that use of the smartphone-based crowdsourcing platform was aligned with lowest-income areas. This study shows that online participation methods are not homogeneous regarding geography or equity. In some contexts, smartphone applications can help reach lower-income communities, even when compared with in-person meetings. Crowdsourcing tools can be valuable approaches to increase geography and equity of public participation in transportation planning.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198118823498
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
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  • 7
    Online Resource
    Online Resource
    Identifying Hospital Deserts in Texas Before and During the COVID-19 Outbreak
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 4 ( 2023-04), p. 813-825
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 4 ( 2023-04), p. 813-825
    Abstract: In this study, we proposed a GIS-based approach to analyzing hospital visitors from January to June 2019 and January to June 2020 with the goal of revealing significant changes in the visitor demographics. The target dates were chosen to observe the effect of the first wave of COVID-19 on the visitor count in hospitals. The results indicated that American Indian and Pacific Islander groups were the only ones that sometimes showed no shift in visitor levels between the studied years. For 19 of the 28 hospitals in Austin, TX, the average distance traveled to those hospitals from home increased in 2020 compared with 2019. A hospital desert index was devised to identify the areas in which the demand for hospitals is greater than the current hospital supply. The hospital desert index considers the travel time, location, bed supply, and population. The cities located along the outskirts of metropolitan regions and rural towns showed more hospital deserts than dense city centers.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221095745
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 8
    Online Resource
    Online Resource
    Durations of Dockless E-Scooter Trips Before and During the COVID-19 Pandemic in Austin, TX: An Analysis Using Hazard-Based Duration Models
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 4 ( 2023-04), p. 629-640
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 4 ( 2023-04), p. 629-640
    Abstract: The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people’s travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin’s Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users’ behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor’s degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area; therefore, it is vital to extend e-scooter service areas to cover underserved communities.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221138807
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 9
    Online Resource
    Online Resource
    Grocery Shopping : How Individuals and Built Environments Influence Choice of Travel Mode
    SAGE Publications ; 2011
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2230, No. 1 ( 2011-01), p. 85-95
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2230, No. 1 ( 2011-01), p. 85-95
    Abstract: This research investigated the influences of socioeconomic characteristics of individual travelers and of the environments where the travelers live and shop on choice of travel mode for grocery shopping. The data on travel for grocery shopping came from 2,001 respondents to the 2009 Seattle Obesity Study survey in King County, Washington. Eighty-eight percent of the respondents drove to their grocery stores, whereas 12% used transit or taxis, walked, biked, or carpooled. The addresses of 1,994 homes and 1,901 primary grocery stores used by respondents were geographically coded. The characteristics of built environments in the neighborhoods around homes and grocery stores and the distances between those homes and stores were measured in a geographic information system. Four binary logistic models estimated the impact of individual socioeconomic characteristics, distance, and built environments around homes and grocery stores on the travel mode used for grocery shopping. Fourteen variables were significantly related to mode choice. The strongest predictors of driving to the grocery store were more cars per adult household member, more adults per household, living in a single-family house, longer distances between homes and grocery stores (both the stores used and the nearest stores), and more at-ground parking around the grocery store used. Higher street density, more quick-service restaurants around homes, and more nonchain grocery stores near the primary grocery store used were related to not driving. Results suggested that reductions of distances between homes and grocery stores, clustering of grocery stores and other food establishments, and reductions in the amount of the parking around them could lead to less driving for grocery shopping.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2230-10
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2011
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  • 10
    Online Resource
    Online Resource
    Using Deep Learning to Understand Travel Demands in Different Urban Districts
    SAGE Publications ; 2021
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2675, No. 7 ( 2021-07), p. 117-128
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2675, No. 7 ( 2021-07), p. 117-128
    Abstract: Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198121994582
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
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