Evaluating the Effects of Traffic on Driver Stopping and Turn Signal Use at a Stop Sign

2007 ◽  
Vol 27 (2) ◽  
pp. 27-35 ◽  
Author(s):  
Angela R. Lebbon ◽  
John Austin ◽  
Ron Van Houten ◽  
Louis E. Malenfant
Keyword(s):  
Turn Signal ◽  
Stop Sign ◽  
Signal Use

Characteristics of turn signal use at intersections in baseline naturalistic driving

2015 ◽  
Vol 74 ◽  
pp. 1-7 ◽  
Author(s):  
John M. Sullivan ◽  
Shan Bao ◽  
Roy Goudy ◽  
Heather Konet
Keyword(s):  
Turn Signal ◽  
Naturalistic Driving ◽  
Signal Use

Turn signal use among car drivers and motorcyclists at intersections: a case study of Da Nang, Vietnam

2019 ◽  
Vol 128 ◽  
pp. 25-31 ◽  
Author(s):  
Duy Quy Nguyen-Phuoc ◽  
Anh Thi Phuong Tran ◽  
Chris De Gruyter ◽  
Inhi Kim ◽  
Diep Ngoc Su
Keyword(s):  
Turn Signal ◽  
Signal Use

Turn signal use among motorcyclists and car drivers: The role of environmental characteristics, perceived risk, beliefs and lifestyle behaviours

2020 ◽  
Vol 144 ◽  
pp. 105611 ◽  
Author(s):  
Duy Quy Nguyen-Phuoc ◽  
Chris De Gruyter ◽  
Oscar Oviedo-Trespalacios ◽  
Su Diep Ngoc ◽  
Anh Thi Phuong Tran
Keyword(s):  
Perceived Risk ◽  
Environmental Characteristics ◽  
Turn Signal ◽  
Lifestyle Behaviours ◽  
Risk Beliefs ◽  
Signal Use

The Additive Impact of Group and Individual Publicly Displayed Feedback: Examining Individual Response Patterns and Response Generalization in a Safe-Driving Occupational Intervention

2010 ◽  
Vol 34 (5) ◽  
pp. 338-366 ◽  
Author(s):  
Timothy D. Ludwig ◽  
E. Scott Geller ◽  
Steven W. Clarke
Keyword(s):  
Individual Response ◽  
Response Patterns ◽  
Safe Driving ◽  
Baseline Design ◽  
Turn Signal ◽  
Group Feedback ◽  
Individual Feedback ◽  
The Individual ◽  
Signal Use ◽  
Feedback Phase

Additive effects of publicly posting individual feedback following group goal-setting and feedback were evaluated. The turn-signal use of pizza deliverers was studied in a multiple baseline design across two pizza stores. After baseline observations, pizza deliverers voted on a group turn-signal goal and then received 4 weeks of group feedback on their turn-signal use (i.e., group feedback phase). Then, for the next 4 weeks, individual turn-signal use means were publicly posted along with the group feedback (i.e., individual feedback phase). Deliverers at Store A ( n = 24) increased their use of turn signals from a mean of 5% during baseline to 16.9% during the group feedback phase and then to 30% during the individual feedback phase. Turn-signal use at Store B (n = 20) increased from 28.9% during baseline to 43.6% during group feedback phase and to 56% during the subsequent individual feedback phase. Individual analyses suggested that deliverers who improved the target behavior during group feedback phase did not increase their turn-signal use further when individual feedback was added. Conversely, most deliverers who did not improve during the group feedback phase increased their turn-signal use when individual feedback was added. Complete intersection stopping increased concurrently with the turn-signal intervention phases from baseline means of 12% and 30% at Store A and B, respectively, to means of 21% and 48% during the interventions.

Analysis of fatal crashes due to signal and stop sign violations

2004 ◽  
Author(s):  
Brittany N. Campbell ◽  
John D. Smith ◽  
Wassim G. Najm
Keyword(s):  
Stop Sign ◽  
Fatal Crashes

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

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