Comparative analysis & modelling for riders’ conflict avoidance behavior of E-bikes and bicycles at un-signalized intersections

With the increasing popularity of electric-assist bikes (E-bikes) in China, U.S. and Europe, the corresponding safety issues at intersections have attracted the attention of researchers. Understanding the microscopic behavior of E-bike riders during conflicts with other road users is fundamental for safety improvement and simulation modeling of E-bikes at intersections. This study compared the conflict avoidance behaviors of E-bike and conventional bicycle riders using field data extracted from video recordings of different intersections. The impact of conflicting road user type and gender on E-bikes and bicycles were analyzed. Compared with bicycles, E-bikes appeared to enable more flexibility in conflict avoidance behavior. For example, E-bikes would behave like bicycles when conflicting with motor vehicles/Ebikes, and behave more like motor vehicles when conflicting with bicycles/pedestrians. Based on this, we built an Extended Cyclist Conflict Avoidance Movement (ECCAM) model, which can represent the conflict avoidance behavior of E-bikes/bicycles at mixed traffic flow un-signalized intersections. Field data were applied to validate the proposed model, and the results are promising

The effects of traveler risk-taking behaviors on system evolution processes.

<p>Fig 4(a) and 4(b) compare the effect difference between risk aversion and risk proneness attitudes. Fig 4(c) and 4(d) show the influences of parameter σ on both fluctuation function Θ^<i>t</i> and endogenous risk attitude .</p

Notions and the corresponding definitions.

<p>Notions and the corresponding definitions.</p

Comparison of effects between two different risk attitude evolution schemas.

<p>Fig 5(a) corresponds to the case of endogenous risk attitudes, and Fig 5(b) corresponds to the case of exogenous risk attitudes.</p

Updatings of the risk attitude parameter with different values of parameter <i>σ</i>.

<p>A larger <i>σ</i>-value corresponds to a larger change rate of .</p

Evolution of the system with endogenous risk attitudes (<i>σ</i> = 0.9).

<p>Fig 3(a)~3(d) shows the influences of parameters <i>α</i>, <i>β</i> and <i>θ</i> on both steady state and evolution process of the dynamic system.</p

Additional file 1 of Two laser-assisted hatching methods of embryos in ART: a systematic review and meta-analysis

Supplementary Material 1