Combining speed and acceleration to detect reckless driving in the informal public transport industry

Abstract

Abstract-The informal transport industry in Sub-Saharan Africa is notoriously dangerous, leading to many fatalities annually. This paper presents an innovative way of monitoring driver behaviour, in real-time, by taking into account road design standards, vehicle dynamics, and passenger comfort. Two models are presented that each combines acceleration and speed data into an erratic driving detection algorithm. The first model is developed though the evaluation of empirical results taken from trips in a minibus taxi, and subjectively gauging recklessness from a passenger's perspective. The second model presents a novel use of commonly used civil engineering principles, used in road design. Evaluation of the models, using actual minibus data, demonstrates that both successfully detect reckless driving, but the second model proves to be simpler and less processor intensive.

Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

“…By comparing jerk energy with a preset threshold, these driving errors could be successfully detected. A similar study conducted by Zeeman and Booysen (2013) identified reckless driving patterns also through accelerometery and GPS. Different from Chakravarty et al (2013), driving events in this study were first pre-classified into urban driving and highway driving.…”

“…An abnormal session is defined as a session of accelerometer readings, which contains more than five sequential peaks (greater than 0.38g m/s 2 ) per second. Through empirical tests and the results of the prior work described above, 0.38g m/s 2 is used as the threshold peak value to separate driving errors from normal driving behaviors (Chakravarty et al, ; Júnior et al, ; Zeeman & Booysen, ). Mean value of z‐axis gyroscope (mean z‐gyro) readings . The z ‐axis readings of the gyroscope reflect directly the vehicle rotation caused by performing turn or lane changes.…”

Mutual interferences of driving and texting performance

“…Several researchers are beginning to explore smartphone applications that can monitor impaired driving performance (Dai et al, 2010;Hammoud & Zhang, 2008;He et al, 2013;Johnson & Trivedi, 2011;You et al, 2013;Zeeman & Booysen, 2013;), such as drunk, distracted, aggressive and drowsy driving. However, no attempts have been made to detect texting while driving yet.…”

“…If an application can monitor texting while driving, it can potentially be easier to implement, and can complement current efforts in law enforcement or social campaigns. Researchers have attempted to detect drunk driving (Dai, Teng, Bai, Shen, & Xuan, 2010), cognitively distracted driving , aggressive driving (Johnson & Trivedi, 2011;Zeeman & Booysen, 2013), and drowsy driving (Hammoud & Zhang, 2008;He et al, 2013). However, to our best knowledge, no application has been developed that detects texting while driving.…”

A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

“…In contrast, the threshold for abrupt acceleration or hard braking used in some studies is 0.1 g [30], 0.0875 g [31], and 0.15 g [32]. In addition, according to the ndings in Zeeman and Booysen [33], Eboli et al [34], and Eboli et al [35], speed or road condition should be considered for characterizing drivers' behavior and the acceleration threshold is found to be reduced with an increase in speed. Also, the threshold range for positive acceleration on city roads in Brussels, as de ned in Vlieger et al [36], is 0.85~1.10 m/s 2…”