Safety-in-Numbers for route choice of bicycle trips: A choice experiment approach for commuters

Safety-in-Numbers (SiN) implies that the risk of collision per road user is less when there are more road users. Although the available literature has confirmed the existence of SiN as an objective measure of safety, the effect on perceived safety, especially in the context of bicycle riders, has received much less attention. This study investigates the SiN effect on the perceived safety of bicycle riders that influences route choice behavior. A stated preference survey was performed in the South Delhi district of Delhi. The effect of attributes like posted speed limit, the volume of motorized traffic, bicycle infrastructure, and bicycle traffic/ crowding on route choice behavior was investigated. A binary logit model was developed to quantify the effect of these attributes on route choice. The results indicate that, in general, riders prefer routes with more bicycle traffic, hence validating SiN. But the effect does not always hold. For some riders, in the presence of dedicated bicycle infrastructure, when the perceived safety is higher, the presence of more bicycle traffic acts as crowding and demotivates riders to choose that route. The study also reveals that riders prefer routes with a low volume of motorized traffic and dedicated bicycle infrastructure. The outcomes suggest that a policy that encourages infrastructural development to provide lateral separation will encourage more people, hence increasing bicycle mode share as well as the perceived safety of riders.

Gender differences in driving control of young alcohol-impaired drivers

BACKGROUND

Male and female drivers exhibit different degrees of vehicle control while driving under the influence of alcohol. However, this interaction between alcohol and gender is understudied. The present study examined the effects of different alcohol levels on the driving control of male and female drivers with the help of driving simulator experiments in heterogeneous traffic conditions.

METHOD

Forty young drivers (20 males and 20 females) completed simulated driving at four Blood Alcohol Concentration (BAC) levels: 0% (control), 0.03%, 0.05% and 0.08%. Driving impairment in vehicle control was measured in terms of average speed, acceleration variability and reaction time of drivers. Repeated-measures ANOVA tests were conducted and regression models were developed for male and female drivers to quantify the effects of BAC levels and driver characteristics on the driving control measures.

RESULTS

Significant effects of gender were observed for average speed (p <  0.001) and acceleration variability (p = 0.015) but not for reaction time of drivers (p =  0.891). Further, the effect of BAC was significant in all the three measures of vehicle control (p <  0.001). Driving control improved with increasing age of male drivers while caffeine consumption was observed as an alcohol-antagonizing factor in female drivers.

CONCLUSION

The findings suggest that vehicle control of female drivers is more likely to get affected even at low BAC levels, providing evidence that they belong to critical section of driving community in terms of alcohol-related impairment. The findings may help in discouraging drinking and driving among male and female drivers.

Modelling braking behaviour and accident probability of drivers under increasing time pressure conditions

Drivers apply brakes to reduce the speed of a vehicle based on the perceived risk while approaching a certain event. Inadequate or excessive braking can lead to serious consequences. The current study analyses the braking behaviour and accident probability of the drivers under increasing time pressure conditions. Two perilous events (pedestrian crossing and obstacle overtaking) were designed to examine Brake Pedal Force (BPF) and Brake-To-Maximum Brake (BTMB) transition time on a driving simulator. Eighty-five Indian licensed drivers drove the simulator in three different time pressure conditions: No Time Pressure (NTP) (baseline), Low Time Pressure (LTP), and High Time Pressure (HTP). Random parameters Tobit model was used for analysing BPF and duration analysis approach was considered for BTMB analysis. Further, generalized linear mixed model with logit link function was used to study the effect of BPF and BTMB on accident probability of the drivers. The model results showed that gender, driving profession, approach speed, age, driving history, and driving condition significantly affected braking behaviour of the drivers. It was observed that in pedestrian crossing event, LTP and HTP driving conditions resulted in 42.31 % and 87.28 % increase in BPF and 13 % and 23 % reduction in BTMB respectively with respect to NTP driving condition and the corresponding changes were slightly lower in case of obstacle overtaking event. The accident probability model showed that female drivers needed 119.70 % and 186.08 % more BPF and 37.55 % and 58.51 % less BTMB in LTP and HTP driving conditions respectively to have equivalent risk levels as observed for male drivers. Further, non-professional drivers had to increase their BPF by 166.83 % in LTP and 219.93 % in HTP to offset their increased accident risk as compared to professional drivers under time pressure conditions.