Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Liu, Jun; Khattak, Asad J.; Li, Xiaobing; Nie, Qifan; Ling, Ziwen

doi:10.1016/j.jsr.2020.02.006

Cited by 48 publications

(9 citation statements)

References 24 publications

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“…People younger than 30 and older than 49 had a higher likelihood of incidents ending in injury based on both our summary statistics and random forest modelling, despite comprising a lower proportion of total https://BikeMaps.org reports. Our results corroborate other similar research findings on associations between older age and bicycling injury (Vanparijs et al 2015; Prati et al 2017; Chen and Shen 2019; Liu et al 2020; Meuleners et al 2020). For example, an Australian study found that although older bicyclists were less likely to experience a crash than younger bicyclists, they were more likely to be injured (Poulos et al 2015).…”

Section: Discussionsupporting

confidence: 93%

Spatial variation in bicycling risk based on crowdsourced safety data

Fischer

Sersli

Nelson

et al. 2022

Canadian Geographies / Géographies canadiennes

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Falls and single bicycle incidents with infrastructure, roads, and railroads are relatively frequent incidents and lead to injury.• Crowdsourced data can supplement official reports to improve bicycling safety analysis.• Narrative and contextual details help inform interventions that can reduce injury for bicyclists.Bicycling-related injury data are difficult to obtain from official reports, which capture only about 20% of crashes and often lack coordinates, injury outcomes, and narratives needed for understanding where and why incidents occurred. Crowdsourced data on bicycling safety provides new opportunities for the study of bicycling injury and risk. Our goal was to quantify factors that influence the spatial variation in unsafe bicycling across a city, based on self-reports of bicycling incidents. To meet this goal, we leveraged BikeMaps.org, a global tool for reporting bicycling safety incidents, drawing on data from Metro Vancouver. We summarized incident conditions that led to injury, developed a model to identify predictors of injury using random forest regression, and mapped bicycling incident hot spots. Our results

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Section: Discussionsupporting

confidence: 93%

Spatial variation in bicycling risk based on crowdsourced safety data

Fischer

Sersli

Nelson

et al. 2022

Canadian Geographies / Géographies canadiennes

View full text Add to dashboard Cite

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“…Feature extraction is completed by CL, whereas the pooling layer is responsible for coping with the size of the image and avoiding overfitting it. If the size of the input image and kernel is represented by I(n ×m) and K(a, b), respectively, then the two-dimensional convolution operation can be represented by Equation (5).…”

Section: Customization Of the Pre-trained Modelmentioning

confidence: 99%

“…According to a recent study [2,3], over speeding is the main cause of accidents. In the rescue operation, the location of the accident spot is important [4,5]. In the case of heavy traffic or a city location, emergency assistance will be available shortly, but in lowtraffic areas or highways, it is difficult to provide emergency aid on time.…”

Section: Introductionmentioning

confidence: 99%

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

et al. 2022

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As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

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“…However, the increasing number of road networks created for vehicles has contributed to a higher frequency of collisions on roads. Bicyclists are more vulnerable to such accidents than other road users [5], [6]; however, they have a low priority in terms of allocating a safe space in many countries. Less protected groups, including pedestrians, bicyclists, and motorcyclists, account for nearly half of all deaths from collisions on roads [7].…”

Section: Introductionmentioning

confidence: 99%

Mechanism underlying the impact of perception time on detection and recognition distance: New insights into bicycle safety

Abdur

Kojima

Kato

et al. 2022

Preprint

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Perception time (PT) is a major factor affecting a driver’s ability to detect and recognize a bicycle. Researchers have attempted to enhance the PT and gain insights into why drivers fail to see bicyclists before collisions despite looking, even when they are at a safe driving distance from the bicycle. Previous studies have focused on the detection distance and recognition distance of bicycles within 600 milliseconds (ms). PT is the main factor for avoiding collisions; however, it has been shown that a control bicycle as well as treated bicycles can be detected from greater distances. This study aims to evaluate the early detection and recognition of bicycles owing to the impact of conspicuity treatments such as white stripes on a red background (WRED), a high-visibility jacket (HVJ), reflective tape (RT), and their combinations in order to achieve longer detection and recognition distances under day/night conditions. The detection and recognition distances of WRED tire treatment were compared with those of an HVJ, RT, and their combinations, based on PTs of 250 and 600 ms. The same treatments were applied and compared at the required PT for the safe driving distance of a bicycle. The respondents provided their perceptions based on video surveillance data presented on a computer screen. The detection and recognition distance of WRED treatment combined with an HVJ was significantly greater under all conditions except twilight with car headlights and nighttime with car headlights for a PT of 600 ms. Furthermore, for this combination, the PT was significantly shorter under all conditions except nighttime with car headlights. The effects of gentle self-signaling of a bicycle via the combination of WRED treatment and an HVJ can reduce the PT for detecting a bicycle and increase the detection and recognition distance under all lighting conditions. Passive safety measures based on these results can support drivers, who might otherwise look but fail to see bicyclists in time. In summary, the combination of WRED treatment with an HVJ is strongly recommended to achieve cost-effective self-signaling of a bicycle.

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Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Cited by 48 publications

References 24 publications

Spatial variation in bicycling risk based on crowdsourced safety data

Spatial variation in bicycling risk based on crowdsourced safety data

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

Mechanism underlying the impact of perception time on detection and recognition distance: New insights into bicycle safety

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