Chen, Peng; Liu, Qian; Sun, Feiyang. (2018). Bicycle Parking Security and Built Environments. Transportation Research: Part D, 62, 169 – 178.
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Abstract
The lack of secure bicycle parking is a serious but often neglected issue that discourages bicycling. Classical environment criminology theories try to explain the pattern of bicycle theft but provide limited insights into the relationship between crime and the built environment. This study examines the association between built environment factors and bicycle theft using a zero inflated negative binomial model to account for data over-dispersion and excess zeros. The assembled dataset provides variables pertaining to the road network, land use, bicycle travel demand, and socio-demographics. The key findings are as follows: (1) Bicycle theft is more likely to occur in areas for commercial purposes, areas with a high population or employment density, and areas with more bike lanes or sidewalks. (2) Bicycle theft is likely to occur at sites with more bike racks or bus stops. (3) Bicycle theft is more likely to occur at mid-blocks than at intersections. (4) Bicycle theft is more likely to occur in neighborhoods with a greater percentage of socially disadvantaged people and in neighborhoods where residents' median age is lower. (5) The likelihood of losing a bicycle is lower in areas with more bicycle trips. In general, the number of bicycle thefts increases in dense areas with more targets and decreases with greater natural guardianship provided by more passersby. With respect to policy implications, governments and transport planners should implement a geographically-differentiated surveillance strategy, encourage bicycling, improve the visibility of bike racks to the public, and promote surveillance and natural guardianship in densely developed areas.
Keywords
Bicycle Parking; Cycling; Bicycle Theft; Sociodemographic Factors; Bicycles; Environmental Aspects; Built Environment; Environment Criminology; Urban Design; Zero-inflated Negative Binomial Model; Crime; Theft; Risk; Opportunities; Behavior; Travel
Stewart, Orion Theodore; Moudon, Anne Vernez; Littman, Alyson; Seto, Edmund; Saelens, Brian E. (2018). The Association between Park Facilities and the Occurrence of Physical Activity during Park Visits. Journal Of Leisure Research, 49(3-5), 217 – 235.
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Abstract
Prior research has found a positive relationship between the variety of park facilities and park-based physical activity (PA) but has not provided an estimate of the effect that additional different PA facilities have on whether an individual is active during a park visit. Using objective measures of park visits and PA from an urban sample of 225 adults in King County, Washington, we compared the variety of PA facilities in parks visited where an individual was active to PA facilities in parks where the same individual was sedentary. Each additional different PA facility at a park was associated with a 6% increased probability of being active during a visit. Adding different PA facilities to a park appears to have a moderate effect on whether an individual is active during a park visit, which could translate into large community health impacts when scaled up to multiple park visitors.
Keywords
Accelerometer Data; Built Environment; Walking; Density; Health; Adults; Size; Gps; Attractiveness; Improvements; Measurement; Parks; Physical Activity; Quantitative Research; Urban Planning
Lindell, Michael K.; Arlikatti, Sudha; Huang, Shih-kai. (2019). Immediate Behavioral Response to the June 17, 2013 Flash Floods in Uttarakhand, North India. International Journal Of Disaster Risk Reduction, 34, 129 – 146.
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Abstract
The 2013 Uttarakhand flash flood was such a surprise for those at risk that the predominant source of information for their risk was environmental cues and, secondarily, peer warnings rather than official warnings. Of those who received warnings, few received information other than the identity of the flood threat. A survey of 316 survivors found that most people's first response was to immediately evacuate but some stayed to receive additional information, confirm their warnings, or engage in evacuation preparations. Unfortunately, engaging in these milling behaviors necessarily delayed their final evacuations. Mediation analysis revealed that psychological reactions mediated the relationship between information sources and behavioral responses. Further analyses revealed that immediate evacuation and evacuation delay were both predicted best by information search and positive affect, but correlation analyses indicated that a number of other models were also plausible. Final evacuation was best predicted by immediate evacuation and, to a significantly lesser extent, household together. Overall, results suggest that the Protective Action Decision Model (PADM) should be considered a useful framework for examining household responses to flash floods in developing countries like India. It supports the conclusion that a household's first warning source is a function of two distinct detection and dissemination systems within a community-an official system and an informal system. However, it fails to capture what pre-impact emergency preparedness entails for rapid onset events in a developing country context. Further research is needed to determine the relative importance of situational and cultural characteristics in producing these observed differences.
Keywords
Risk Perception; Mental Models; Warnings; Evacuation; Disaster; Tsunami; Communication; Earthquake; Beliefs; Hazard; Flash Flood; Warning; Psychological Reactions; India
Zhang, Jinlei; Chen, Feng; Shen, Qing. (2019). Cluster-based LSTM Network for Short-term Passenger Flow Forecasting in Urban Rail Transit. Ieee Access, 7, 147653 – 147671.
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Abstract
Short-term passenger flow forecasting is an essential component for the operation of urban rail transit (URT). Therefore, it is necessary to obtain a higher prediction precision with the development of URT. As artificial intelligence becomes increasingly prevalent, many prediction methods including the long short-term memory network (LSTM) in the deep learning field have been applied in road transportation systems, which can give critical insights for URT. First, we propose a novel two-step K-Means clustering model to capture not only the passenger flow variation trends but also the ridership volume characteristics. Then, a predictability assessment model is developed to recommend a reasonable time granularity interval to aggregate passenger flows. Based on the clustering results and the recommended time granularity interval, the LSTM model, which is called CB-LSTM model, is proposed to conduct short-term passenger flow forecasting. Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset. The results provide critical insights for subway operators and transportation policymakers.
Keywords
Traffic Flow; Neural-network; Prediction; Ridership; Models; Volume; Lstm; Short-term Passenger Flow Forecasting; Urban Rail Transit; K-means Clustering; Deep Learning
Mooney, Stephen J.; Bobb, Jennifer F.; Hurvitz, Philip M.; Anau, Jane; Theis, Mary Kay; Drewnowski, Adam; Aggarwal, Anju; Gupta, Shilpi; Rosenberg, Dori E.; Cook, Andrea J.; Shi, Xiao; Lozano, Paula; Moudon, Anne Vernez; Arterburn, David. (2020). Impact of Built Environments on Body Weight (The Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study. Jmir Research Protocols, 9(5).
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Abstract
Background: Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks. Objective: We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments. Methods: We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data. Results: We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values. Conclusions: Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions.
Keywords
Residential Location Choice; Physical-activity; Risk-factors; Food Desert; Neighborhood; Obesity; Association; Outcomes; Bmi; Accelerometer; Electronic Health Records; Built Environment; Washington; Geography; Longitudinal Studies
Duncan, Glen E.; Hurvitz, Philip M.; Moudon, Anne Vernez; Avery, Ally R.; Tsang, Siny. (2021). Measurement of Neighborhood-Based Physical Activity Bouts. Health & Place, 70.
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Abstract
This study examined how buffer type (shape), size, and the allocation of activity bouts inside buffers that delineate the neighborhood spatially produce different estimates of neighborhood-based physical activity. A sample of 375 adults wore a global positioning system (GPS) data logger and accelerometer over 2 weeks under free-living conditions. Analytically, the amount of neighborhood physical activity measured objectively varies substantially, not only due to buffer shape and size, but by how GPS-based activity bouts are identified with respect to containment within neighborhood buffers. To move the neighborhood-effects literature forward, it is critical to delineate the spatial extent of the neighborhood, given how different ways of measuring GPS-based activity containment will result in different levels of physical activity across different buffer types and sizes.
Keywords
Built Environment; Walking; Home; Accelerometry; Geographic Information Systems; Gps; Neighborhood; Physical Activity
Shen, Qing; Wang, Yiyuan; Gifford, Casey. (2021). Exploring Partnership Between Transit Agency And Shared Mobility Company: An Incentive Program For App-based Carpooling. Transportation, 48(5), 2585 – 2603.
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Abstract
How should public transit agencies deliver mobility services in the era of shared mobility? Previous literature recommends that transit agencies actively build partnerships with mobility service companies from the private sector, yet public transit agencies are still in search of a solid empirical basis to help envision the consequences of doing so. This paper presents an effort to fill this gap by studying a recent experiment of shared mobility public-private partnership, the carpool incentive fund program launched by King County Metro in the Seattle region. This program offers monetary incentives for participants who commute using a dynamic app-based carpooling service. Through descriptive analysis and a series of logistic regression models, we find that the monetary incentive to encourage the use of app-based carpooling generates some promising outcomes while having distinctive limitations. In particular, it facilitates the growth of carpooling by making carpooling a competitive commuting option for long-distance commuters. Moreover, our evidence suggests that the newly generated carpooling trips mostly substitute single-occupancy vehicles, thus contributing to a reduction of regional VMT. The empirical results of this research will not only help King County Metro devise its future policies but also highlight an appealing alternative for other transit agencies in designing an integrated urban transportation system in the era of shared mobility.
Keywords
Shared Mobility; Public-Private Partnership; App-based Carpooling; Incentive Fund; Transit Agencies; Incentives; Commuting; Public Transportation; Mobility; Regression Analysis; Regression Models; Partnerships; Vehicles; Car Pools; Private Sector; Occupancy; Transportation Systems; Mass Transit; Transportation Planning; Empirical Analysis; Urban Transportation
Bassok, Alon; Hurvitz, Phil M.; Bae, C-H. Christine; Larson, Timothy. (2010). Measuring Neighbourhood Air Pollution: The Case of Seattle’s International District. Journal Of Environmental Planning & Management, 53(1), 23 – 39.
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Abstract
Current US regulatory air quality monitoring networks measure ambient levels of pollutants and cannot capture the effects of mobile sources at the micro-scale. Despite the fact that overall air quality has been getting better, more vulnerable populations (children, the elderly, minorities and the poor) continue to suffer from traffic-related air pollution. As development intensifies in urban areas, more people are exposed to road-related air pollution. However, the only consideration given to air quality, if any, is based on ambient measures. This paper uses an inexpensive, portable Particle Soot Absorption Photometer (PSAP) to measure Black Carbon (BC) emissions, a surrogate for diesel fuels emissions, in Seattle's International District. With the aid of a GPS receiver, street-level BC data were geocoded in real space-time. It was found that pollution levels differed substantially across the study area. The results show the need for street-level air pollution monitoring, revisions in current land use and transportation policies, and air quality planning practice.
Keywords
Emission Standards; Air Pollution; Atmospheric Deposition; Social Groups; Waste Products; Sanitary Landfills; Black Carbon; Freeway Air Pollution Sheds (faps); Land Use; Mobile Monitoring; Neighbourhood Air Quality; Aerosol Light-absorption; Respiratory Health; Coefficient; Exposure; Symptoms; Children; Pollutants; Particles; Exhaust; Asthma
Stover, Victor W.; Bae, C.-H. Christine. (2011). Impact of Gasoline Prices on Transit Ridership in Washington State. Transportation Research Record, 2217, 1 – 10.
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Abstract
Gasoline prices in the United States have been extremely volatile in recent years and rose to record high levels during the summer of 2008. According to the U.S. Energy Information Administration, the average U.S. gasoline price for the year 2008 was $3.26 a gallon, which was the second highest yearly average in history when adjusted for inflation. Transportation agencies reported changes in travel behavior as a result of the price spike, with transit systems experiencing record ridership and state departments of transportation reporting reductions in traffic volumes. This study examined the impact of changing gasoline prices on transit ridership in Washington State by measuring the price elasticity of demand of ridership with respect to gasoline price. Ordinary least-squares regression was used to model transit ridership for transit agencies in 11 counties in Washington State during 2004 to 2008. The price of gasoline had a statistically significant effect on transit ridership for seven systems studied, with elasticities ranging from 0.09 to 0.47. A panel data model was estimated with data from all 11 agencies to measure the overall impact of gasoline prices on transit ridership in the state. The elasticity from the panel data model was 0.17. Results indicated that transit ridership increased as gasoline prices increased during the study period. The findings were consistent with those from previous studies on the topic.
Keywords
Time-series Analysis; Gas Prices; Elasticities; Demand
Hong, Jinhyun; Shen, Qing. (2013). Residential Density and Transportation Emissions: Examining the Connection by Addressing Spatial Autocorrelation and Self-Selection. Transportation Research Part D-transport And Environment, 22, 75 – 79.
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Abstract
This paper examines the effect of residential density on CO2 equivalent from automobile using more specific emission factors based on vehicle and trip characteristics, and by addressing problems of spatial autocorrelation and self-selection. Drawing on the 2006 Puget Sound Regional Council Household Activity Survey data, the 2005 parcel and building database, the 2000 US Census data, and emission factors estimated using the Motor Vehicle Emission Simulator, we analyze the influence of residential density on road-based transportation emissions. In addition, a Bayesian multilevel model with spatial random effects and instrumental variables is employed to control for spatial autocorrelation and self-selection. The results indicate that the effect of residential density on transportation emissions is influenced by spatial correlation and self-selection. Our results still show, however, that increasing residential density leads to a significant reduction in transportation emissions. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords
Urban Form; Travel; Transportation Emissions; Residential Density; Confounding By Location; Self-selection