Spatial models of active travel in small communities: Merging the goals of traffic monitoring and direct-demand modeling

Hankey, Steve; Lu, Tianjun; Mondschein, Andrew; Buehler, Ralph

doi:10.1016/j.jth.2017.08.009

Cited by 32 publications

(22 citation statements)

References 27 publications

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“…Direct demand models estimate pedestrian volumes along roadway segments and intersections using site and surrounding area characteristics. Street block face or mid-block count data have been used to model pedestrian volumes in New York, NY ( 2 ), Milwaukee, WI ( 3 ), Minneapolis, MN ( 4 , 5 ), and Blacksburg, VA ( 6 , 7 ). However, more recent direct demand pedestrian volume models have been developed from intersection crossing counts ( 8 – 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

A Pedestrian Exposure Model for the California State Highway System

Griswold

Medury

Schneider

et al. 2019

Transportation Research Record: Journal of the Transportation R

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For this study, we developed one of the first statewide pedestrian exposure models, using log-linear regression to estimate annual pedestrian crossing volumes at intersections on the California State Highway System. We compiled a database of more than 1,200 count locations, one of the largest ever used to create a pedestrian volume mode. We initially evaluated 75 explanatory variables for the model. The final model is based on the three land-use variables (employment density, population density, number of schools), four roadway network variables (number of street segments, intersections with principal arterial and minor arterial roadways, and four-way intersections), and the U.S. Census Bureau’s American Community Survey journey-to-work walk mode share that are readily available or fairly easy to create using basic geographic information system analysis. The resulting pedestrian volume model was used to estimate annual crossing volumes at more than 12,000 intersections along the California State Highway System. This is one of the first statewide pedestrian volume models, and California may represent the largest jurisdiction to date to adopt a single, system-wide pedestrian volume model.

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Section: Literature Reviewmentioning

confidence: 99%

A Pedestrian Exposure Model for the California State Highway System

Griswold

Medury

Schneider

et al. 2019

Transportation Research Record: Journal of the Transportation R

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“…2011 ; Hankey and Lindsey 2016 ; Miranda-Moreno and Fernandes 2011 ; Pulugurtha and Repaka 2008 ; Schneider et al. 2009 ; Strauss and Miranda-Moreno 2013 ) and in a few cases for annual average volumes ( Hankey et al. 2017b ).…”

Section: Introductionmentioning

confidence: 99%

Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas

Buehler

Hankey

2018

Environ Health Perspect

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Background:Walking and bicycling are health-promoting and environmentally friendly alternatives to the automobile. Previous studies that explore correlates of active travel and the built environment are for a single metropolitan statistical area (MSA) and results often vary among MSAs.Objectives:Our goal was to model the relationship between the built environment and active travel for 20 MSAs spanning the continental United States.Methods:We sourced and processed pedestrian and bicycle traffic counts for 20 U.S. MSAs (n=4,593 count locations), with 1–17 y of data available for each count location and the earliest and latest years of data collection being 1999 and 2016, respectively. Then, we tabulated land use, transport, and sociodemographic variables at 12 buffer sizes (100–3,000m) for each count location. We employed stepwise linear regression to develop predictive models for morning and afternoon peak-period bicycle and pedestrian traffic volumes.Results:Built environment features were significant predictors of active travel across all models. Areas with easy access to water and green space, high concentration of jobs, and high rates of active commuting were associated with higher bicycle and pedestrian volumes. Bicycle facilities (e.g., bike lanes, shared lane markings, off-street trails) were correlated with higher bicycle volumes. All models demonstrated reasonable goodness-of-fit for both bicyclists (adj-R2: 0.46–0.61) and pedestrians (adj-R2: 0.42–0.72). Cross-validation results showed that the afternoon peak-period models were more reliable than morning models.Conclusions:To our knowledge, this is the first study to model multi-city trends in bicycling and walking traffic volumes with the goal of developing generalized estimates of the impact of the built environment on active travel. Our models could be used for exposure assessment (e.g., crashes, air pollution) to inform design of health-promoting cities. https://doi.org/10.1289/EHP3389

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“…Several mechanisms have been proposed to alert drivers and provide traffic conditions notifications to improve on this issue. In the latter, the main objective is to detect which routes are coping with slow traffic, possible points of congestion, and sections with traffic interruption [25]. It is common for these mechanisms to use cables and sensors, such as piezoelectric, ultrasound, infrared, microwave, laser, and optics, to perform the counting of the vehicles [26].…”

Section: Introductionmentioning

confidence: 99%

“…Another issue is that surveillance cameras that were not originally specified or installed to use computer vision algorithms may have to be tweaked to this end [25]. Moreover, the use of computer vision algorithms may be impacted by several other factors such as the weather, the luminosity, and foreign objects on the roads, just to give some examples.…”

Section: Introductionmentioning

confidence: 99%

ATRIP: Architecture for Traffic Classification Based on Image Processing

et al. 2020

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With the increase of vehicles in large urban centers, there is also an increase in the number of traffic jams and accidents on public roads. The development of a proper Intelligent Transport System (ITS) could help to alleviate these problems by assisting the drivers on route selections to avoid the most congested road sections. Therefore, to improve on this issue, this work proposes an architecture to aid an ITS to detect, analyze, and classify the traffic flow conditions in real time. This architecture also provides a control room dashboard to visualize the information and notify the users about the live traffic conditions. To this end, the proposed solution takes advantage of computer vision concepts to extract the maximum information about the roads to better assess and keep the drivers posted about the traffic conditions on selected highways. The main contribution of the proposed architecture is to perform the detection and classification of the flow of vehicles regardless of the luminosity conditions. In order to evaluate the efficiency of the proposed solution, a testbed was designed. The obtained results show that the accuracy of the traffic classification rate is up to 90% in daylight environments and up to 70% in low light environments when compared with the related literature.

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Spatial models of active travel in small communities: Merging the goals of traffic monitoring and direct-demand modeling

Cited by 32 publications

References 27 publications

A Pedestrian Exposure Model for the California State Highway System

A Pedestrian Exposure Model for the California State Highway System

Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas

ATRIP: Architecture for Traffic Classification Based on Image Processing

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