The Impact of Commercial Parking Utilization on Cyclist Behavior in Urban Environments

Jashami, Hisham; Cobb, Douglas P.; Hurwitz, David; McCormack, Edward; Goodchild, Anne; Sheth, Manali

doi:10.1016/j.trf.2020.07.014

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…A Tukey HSD post hoc pairwise comparison was used to calculate the group means for each of the main effects as shown in Figure 10. This comparison shows the influence of each factor with all other factors held constant ( 27 , 28 ). Across the vehicle types, the rounded RS was about 2 dB louder than the sinusoidal RS.…”

Section: Resultsmentioning

confidence: 99%

Evaluating In-Vehicle Sound and Vibration during Incursions on Sinusoidal Rumble Strips

Horne

Jashami

Monsere

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Rumble strips (RS) are a countermeasure used to reduce roadway-departure crashes by providing audible and haptic alerts to the driver when a vehicle is departing the roadway. This study evaluated the feasibility of using sinusoidal RS as a substitute for more traditional rounded RS. A van, a passenger car, and a heavy vehicle were equipped with sound and vibration sensors to measure the interior noise and haptic feedback of each RS design. A set of typical conditions (with interior climate control fan and radio turned on) were also tested. Data from 75 RS strikes were analyzed. Experimental results demonstrated that the rounded RS doubled interior noise for the passenger car and van (11.3 dBA, 10.0 dBA) but the sinusoidal RS also generated a clearly noticeable interior alert for the passenger car and van (5.8 dBA, 4.6 dBA). The haptic alert provided an increase over the human perception threshold of vibration for all vehicles. The sinusoidal RS interior alert was detectable and within the acceptable range, but not clearly noticeable (5 dBA) when the climate control and radio were active. Alert levels for the rounded RS were >10 dBA, doubling the amount of interior noise for all ambient factor groups (11.2–14.4 dBA).

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Section: Resultsmentioning

confidence: 99%

Evaluating In-Vehicle Sound and Vibration during Incursions on Sinusoidal Rumble Strips

Horne

Jashami

Monsere

et al. 2020

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Similar behavioral adaptations were observed in situations when the HGV was reversing, which made nearly half of the cyclists react in some way (e.g., riding in the opposite traffic lane, going around the reversing truck, waiting in the cycle lane while the truck was reversing). Related insights are provided by Jashami et al (2020) , who concluded that larger loading zones for trucks in the proximity of cyclists resulted in the cyclists adopting slower speed and greater lateral distances from the loading zone even when the zone size was not directly obstructing the trajectory of the cyclists. Pokorny and Pitera (2019) reported that in a scenario where HGVs and cyclists will continue after having stopped at the red phase at traffic lights, cyclists accelerated faster than and thus “escaping” the trucks’ proximity.…”

Section: Resultsmentioning

confidence: 98%

“…More specifically, two of these implicit cues are related to the appearance and characteristics of HGVs (e.g., large/heavy, often driven by a professional driver) and VRUs (e.g., unprotected, wearing helmet, gender) that may set expectations and affect behavior in terms of clearance and acceleration ( Walker, 2007 ). The rest of the implicit cues reflect communication via movement, position, and timing (kinesics, proxemics, and chronemics nonverbal behavior): HGV driver adopting a “flying”, “accelerative”, or “piggybacking” strategy when overtaking a cyclist ( Dozza et al, 2016 ), HGV passing a VRU in close proximity ( Chuang et al, 2013 ; Beck et al, 2019 ; Garcia et al, 2020 ; Beck et al, 2021 ), cyclists adapting their trajectory depending on the position of the HGV ( Pitera et al, 2017 ; Jashami et al, 2020 ), pedestrian passing the obstructing HGV by stepping onto the roadway ( Colley et al, 2020 ). When it comes to explicit communication, one of the cues identified is based on strategic positioning (proxemics) to request perception and involves cyclists selecting a more visible position when the HGV is present ( Pokorny and Pitera, 2019 ), while the other one involves HGVs displaying colors, symbols, and text to VRUs in their vicinity via eHMI ( Colley et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

et al. 2022

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This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

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“…In addition, there is a severe mismatch between the rapid growth of non-motorized vehicles and the construction of motor vehicle-oriented transportation facilities [ 7 ]. Competition between motorized and non-motorized vehicles for limited road space has intensified, triggering widespread conflict, especially at road sections with large traffic volume [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations

Zhang

Zhou

et al. 2022

IJERPH

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Cyclists occupying motorized vehicle lanes disrupt road traffic order and increase collisions. Exploring the contributing factors could help develop countermeasures to regulate such behaviors. The purpose of this study is to explore the intrinsic features influencing the behavior of cyclists in occupying motorized vehicle lanes at different bicycle facilities. We investigated a total of 34,631 cycling behavior samples in the urban area of Pingdingshan, China. A Bayesian random parameter logit model was used to account for the unobserved heterogeneous effects. The experimental results of all bike facilities demonstrate that the bike type, dividing strip type, bike lane width, temporary on-street parking, and whether it is a working day significantly affect cyclists’ occupying motorized vehicle lane behaviors. Factors associated with unobserved heterogeneity are age, barriers dividing strip, vehicle lane numbers, bike volume, vehicle volume, and daily recording time intervals. Comparing the estimated model of five type bike lane facilities across different dividing strips, we find that cyclists have a significantly different occupying probability and the heterogeneity factors of the various bike facilities also have their focus. When the non-motorized road conditions become more open, the cyclist behavior becomes more random and the heterogeneity factors become broader.

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The Impact of Commercial Parking Utilization on Cyclist Behavior in Urban Environments

Cited by 16 publications

References 8 publications

Evaluating In-Vehicle Sound and Vibration during Incursions on Sinusoidal Rumble Strips

Evaluating In-Vehicle Sound and Vibration during Incursions on Sinusoidal Rumble Strips

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

Exploring Unobserved Heterogeneity in Cyclists’ Occupying Motorized Vehicle Lane Behaviors at Different Bike Facility Configurations

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