Comparative analysis of the safety effects of electric bikes at signalized intersections

Bai, Lu; Liu, Pan; Chen, Yuguang; Zhang, Xin; Wang, Weixu

doi:10.1016/j.trd.2013.02.001

Cited by 107 publications

(67 citation statements)

References 3 publications

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“…As found in previous studies, the aberrant riding behaviors by ebikes increase the risk of conflicts, resulting in high likelihood of crash [10,11]. E-bike riders with frequent aggressive driving behaviors are found to be 2.5% points less likely to use e-bike license plate.…”

Section: E-bike Riders'supporting

confidence: 63%

“…Research on e-bikes has focused on the growing use [5], the health and environmental effects [6,7], and the aberrant riding behavior [8][9][10][11]. Safety research has focused on safety comparisons between e-bikes and regular bicycles [12][13][14], injury patterns among e-bike users ( [15][16][17]; Feng et al, 2017), and modeling faults in e-bike fatal crashes [18].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

Guo

Zhou

et al. 2017

Journal of Advanced Transportation

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The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP) model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use.

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Section: E-bike Riders'supporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

Guo

Zhou

et al. 2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…A likely explanation for this increase in e-bike injuries is the rapid increase in e-bike use. Several studies have focused on video analysis of Chinese intersections and found that e-bike behavior is as bad or slightly worse than bicyclist behavior (Wu et al, 2012;Bai et al, 2013;Du et al, 2013;Zhang and Wu, 2013).…”

Section: Bicycle Safetymentioning

confidence: 99%

Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders

Langford

Chen

Cherry

2015

Accident Analysis & Prevention

135

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“…Bai et al [16] evaluated the risktaking behaviors of bicycles at signalized intersections using traffic conflicts technique. They found that about 77.7% of conflicts were caused by the risky behavior of the automobiles drivers that in particular did not yield right-ofway to electric-bikes/bicycles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In such situation, bicycle traffic could have an impact on the operation of vehicles and increase vehicle delays within road segment areas. A review of the literature shows that most previous studies focused on evaluating the interactions between bicycles and vehicles within the intersections in urban areas [16][17][18][19][20][21]. However, such model cannot be directly used for the analysis of street sections.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

Wang

et al. 2017

Journal of Advanced Transportation

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Many urban streets are designed with on-street bike lanes to provide right-of-way for bicycle traffic. However, when bicycle flow is large, extensive passing maneuvers could occupy vehicle lanes and thus cause interferences to vehicle traffic. The primary objective of this study is to evaluate how bicycle traffic affects vehicle operation on urban streets with bike lanes. Data were collected on six street segments in Nanjing, China. The cumulative curves were constructed to extract traffic flow information including individual bicycle and vehicle speeds and aggregated traffic parameters such as flow and density. The results showed that as bicycle density on bike lanes continuously increases faster bicycles may run into vehicle lanes causing considerable reductions in vehicle speeds. A generalized linear model was estimated to predict the vehicle delay. Results showed that vehicle delay increases as bicycle flow and vehicle flow increase. Number of vehicle lanes and width of bike lane also have significant impact on vehicle delay. Findings of the study are helpful to regions around the world in bike infrastructure design in order to improve operations of both bicycles and vehicles.

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Comparative analysis of the safety effects of electric bikes at signalized intersections

Cited by 107 publications

References 3 publications

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

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