Detection Distances to Crosswalk Markings

Fitzpatrick, Kay; Chrysler, Susan T; Iragavarapu, Vichika; Park, Eun Sug

doi:10.3141/2250-01

Cited by 7 publications

(4 citation statements)

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“…Crosswalks marked with paint are the safer places to cross a street because they are visible to drivers. When the marking patterns are zebra crossings, the degree of identification by drivers increases compared to other marking patterns such as bar pairs or transverse lines (Fitzpatrick et al., 2010).…”

Section: Resultsmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routes.

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Section: Resultsmentioning

confidence: 99%

Characterizing zebra crossing zones using LiDAR data

Esmorís

Vilariño

Arango

et al. 2023

Computer aided Civil Eng

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“…Given that only one site was included in the model, the estimated effect could be because of site-specific factors rather than the continental marking. Given that other studies show differences in driver behavior and preferences given crosswalk markings, additional data on this marking type would be helpful to explore this issue more thoroughly ( 38 , 39 ).…”

Section: Model Results and Discussionmentioning

confidence: 99%

Examining the Relationships between Multimodal Environments and Multitasking Driving Behaviors

Zou

Guo

Khaloei

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This study examined multitasking behaviors of drivers in environments that include large numbers of pedestrians and cyclists, using video and vehicle data from the second Strategic Highway Research Program (SHRP2). The study includes 15 sites in both Seattle, WA, and Tampa, FL, U.S., (nine pedestrian and six cyclist locations), including three marking and signal types for crosswalks and two types for bike treatments. A total of 1,458 SHRP2 traversals with time-series data and forward videos were extracted with face/dash videos for about 50% of these traversals. Forward video coding was conducted for all daytime traversals starting from one block before to one block after the selected site. Face/dash video was coded for all traversals with pedestrians or cyclists. A matched set of traversals without pedestrians or cyclists were also coded. The final data set included 458 traversals with coded data on multitasking behavior and the multimodal environment. Mixed-effect binary logistic regression models were used to examine the associations of pedestrian/cyclist presence and the facility type with drivers’ multitasking behavior. The findings show that the presence of pedestrians/cyclists and facility types could be related to drivers’ multitasking behavior. The findings can provide the foundation for future studies that examine safety for non-motorists with respect to infrastructure design, signage, and policies. There is also the potential to provide insights into assistive driving systems within automated vehicles, which are discussed in this paper.

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“…Visibility: The literature provides clear evidence that HVCs are more visible to both drivers and pedestrians compared with basic markings. Fitzpatrick et al’s rigorous 2010 field visibility study found that drivers detect continental (i.e., longitudinal) markings at a midblock crossing at about “twice the distance upstream as the transverse markings during daytime conditions.” Daytime detection distance was longer than nighttime ( 2 ). It is not well established how or whether the increased visibility translates to effectiveness (e.g., reduced crashes or increased driver yielding), but in the absence of more definitive information, increased visibility intuitively seems to make for a more effective marking.…”

Section: Introductionmentioning

confidence: 97%

“…Associated treatments: Many other studies have reported increased effectiveness associated with HVCs but only in conjunction with other treatments—including illuminated overhead signs and pedestrian refuge islands ( 2, 3, 9–13 ).…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Driver Yielding at Uncontrolled Crosswalks

Alston¹,

Schroeder²,

Brown³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This study evaluated driver yielding behavior at basic- versus high-visibility crosswalk (HVC) markings on undivided major street approach legs of two-way stop-controlled intersections. The research team measured and compared driver yielding rates using staged pedestrian crossings at sites with HVCs and at sites with basic markings. Study sites were undivided two-lane roadways with relatively low speeds (25 to 35 mph speed limits) and volumes (up to 616 two-way vehicles per hour during crossings)—half with basic crosswalk markings, half with HVCs. In addition to site characteristic data (crossing distance, presence of bike lane, presence of on-street parking, location context), the study included the collection of vehicle volumes and approach speeds, which enabled statistical modeling of yielding in relation to site characteristics and driver speeds. In total, the study collected 1,188 individual observations at 32 sites in four states. The results showed that, under certain conditions, HVCs were associated with increased driver yielding relative to crossings with basic markings. The study further showed that an approaching driver’s speed and prevailing site speeds had a strong and consistent negative effect on driver yielding. Site context and other treatments present at the site had an impact, but the relationship between driver speed and yielding was robust and statistically significant across all tests. Drivers exceeding 40 mph were unlikely to yield to a pedestrian waiting at a marked crosswalk, suggesting that an agency should consider supplemental treatments to increase driver yielding at such locations.

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Detection Distances to Crosswalk Markings

Cited by 7 publications

References 0 publications

Characterizing zebra crossing zones using LiDAR data

Characterizing zebra crossing zones using LiDAR data

Examining the Relationships between Multimodal Environments and Multitasking Driving Behaviors

Factors Affecting Driver Yielding at Uncontrolled Crosswalks

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