Table 1 Summary of previous studies on speeding behavior.
Objective | Methodology | Variables | Data | Results | Reference |
|---|---|---|---|---|---|
Identifying contributing factors to severe injuries in speeding and non-speeding crashes | Random parameter binary logit model | Driver/Occupant characteristics Roadway, crash, and environmental characteristics Vehicle characteristics | Crash data in Thailand from 2012 to 2017 | Central, eastern, and southern regions increased severe injuries in speeding crashes Under the influence of alcohol and van increased non-speeding crashes Van, passenger car, pickup truck, running-off-road on straight and hitting guardrails and mounting traffic island decreased fatal and severe injuries in speeding crashes Restraint, truck, and running-off-road on straight and hitting guardrail decreased severe and fatal non-speeding crashes | |
Identifying the pattern of motorcycle speeding crashes | Cluster and correspondence analysis | Driver/Occupant characteristics Roadway, crash, and environmental characteristics Vehicle characteristics | Louisiana crash data from 2010 to 2016 | Two lanes, low traffic volume, undivided curves, older motorcyclists, intersections, right angle and left turn crashes, and at-grade locations were found to be associated with speeding motorcycle crashes | |
Investigating the factors that increase the likelihood of speeding | Beta binomial regression model Survey | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | Naturalistic driving data from Strategic Highway Research Program Driver questionnaire answers | The probability of speeding decreased among increasing age groups Drivers aged between 16 to 24 years showed a 1.5 times higher likelihood of speeding in comparison to drivers 80 or more The odds of speeding at lower posted speed limits were higher | |
Investigating the too fast for rainy conditions between male and female drivers | Random parameters multinomial logit model | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | Florida crash data from 2015 to 2017 | Male and female drivers generally perceive and react to rainy weather conditions in fundamentally different ways | |
Evaluating the relationship between trip/driving/roadways features and speeding behavior | Classification-based association algorithm | Roadway, crash, and environmental characteristics | Naturalistic driving data collected by SPMD program | Longer trips and driving on roadways with higher functional classification led to longer speeding events Lower functional class, experiencing congestion before a speeding event, and the presence of a median were associated with higher speeding patterns | |
Evaluating the effects of socio-demographic factors on motorcycles speeding behavior | Linear network autocorrelation models | Driver/Occupant characteristics | Mahasarakham University Social Network Survey 2018 | males showed a higher rate of speeding behavior in comparison with females Drivers’ age is associated with speeding behavior | |
Determining the factors that influence speeding violation behavior | Binary logical regression model | Roadway, crash, and environmental characteristics | Electronic law enforcement data from the public security administration of Wujiang | License plate, season, speeding area, position, and rainfall are the major contributing factors to speeding violations | |
Exploring the effect of different BAC levels on drivers’ speed | Simulation | Driver/Occupant characteristics | 82 drivers’ response | Drivers drove up to 8.78 and 4.13 km/h faster than sober states in rural and urban conditions Crash probabilities increased up to 3 and 3.5 times when drivers were driving alcohol-impaired in rural and urban areas | |
Determining contributing factors to severe crash injuries in China | Principal component analysis Hierarchical clustering | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | China crash data from 2007 to 2013 | Speeding and overloading of passengers were primary contributing factors to severe injuries Lack of or nonstandard roadside safety infrastructure and slippery roads due to rain, snow, or ice were secondary factors | |
Identifying contributing factors to crash injury severity | Partial proportional odds model | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | Crash records collected on rural two-lane highways in China | Speeding as well as drivers’ age (25–39), alcohol consumption, pedestrian involvement, asphalt pavement, and angle collision type significantly increased crash injury severity | |
Investigating the contributing factors to crash severities | Generalized ordered logit/partial proportional odds | Driver/Occupant characteristics Roadway, crash, and environmental characteristics Vehicle characteristics | Crash data from 2012 to 2013 on one of Ethiopia’s main and busiest highways | Speeding has varying coefficients on different levels of crash severity, and it has the highest coefficient in serious injury and fatal crashes | |
Categorizing crashes into fatal and non-fatal crashes and identifying the contributing factors to them | Logistic regression model | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | Riyadh crash data from 2004 to 2011 | Speeding, non-intersection location, and point of collision (head-on) were found to be significant | |
Identifying factors that increase the probability of speeding-related crashes | Single-variable table analysis CART | Driver/Occupant characteristics Roadway, crash, and environmental characteristics | FARS and NASS GES data | Younger drivers (21–25 years old) and drivers with prior speeding convictions were more likely to be involved in speeding-related crashes |
