Background and Motivation

Crash predictability has long been a controversial issue. Bortkiewicz, usually considered the pioneer of modern crash research, stated that crash occurrences are random and thus inexplicable in his 1898 study (Elvik 2006). However, the development of modern analysis techniques has inspired various attempts to explore the causality of accidents. In recent days, it is suggested that scenario of crash-proneness do exist (Visser et al. 2007). Exploring the causes of motor vehicle crashes has become a pressing issue. Finding the causality of crashes is thus possibly one of the most effective ways to improve road safety and to prevent crashes from happening.

To enhance understanding of crashes, researchers have worked on mining aggregated crash data to extract crash patterns. Numerous contributing factors have been found critical to roadway safety. For example, rear-end accidents increased with the number of signal phases and width of traffic island (Chin and Quddus 2003).

Demographic characteristics such as age and gender also have been extensively studied (Clarke et al. 1998, Clarke et al. 1999, Chang and Yeh 2007). Despite the significant effect of single factors, recent research has claimed that crashes should be analyzed from a chain perspective (Elvik 2003, Wong and Chung 2007b, Verschuur and Hurts 2008, Wong and Chung 2008a, Wong and Chung 2008b). In addition to the scenario of crash occurrence, some remote factors of crash occurrence must be considered. For example, personality traits can be treated as prior-to-driving factors that affect risky driving behavior (Wong et al. 2010b, Wong et al. 2010c).

Exploring accident chains provides valuable clues that indicate accident-prone scenarios in which drivers usually have a higher risk of being involved in a dangerous situation. However, a crash-proneness driver driving in a crash-proneness scenario does not necessarily lead to the occurrence of crashes. Such accident-prone scenarios explain mostly the conditions in which drivers face higher risks of being involved in a crash, and possibly the mechanism through which such crashes occur. For example, Wong and Chung (2007b) found that young and inexperienced student drivers had an increased likelihood of being involved in off-road accidents on roads with speed limits between 51 and 79 kph under normal road conditions. The reality is that for each accident under certain conditions, there are numerous young and inexperienced student drivers who drive under identical conditions without experiencing accidents.

In fact, the majority of crashes are considered preventable, provided that the

surrounding area is properly observed by the driver and adequate maneuvers are successfully executed (Wong et al. 2010b).

It is clear that there is a missing link between crash proneness scenarios and the crash occurrences. Knowing the causality of crashes behind accident chains is the most crucial element in crash analysis and prevention. In fact, different drivers react differently in identical situations. While most drivers can still drive safely in a high accident risk scenario, but some fail to maintain safety, resulting in dangerous situations. Extraction of such crash pattern and possible crash-proneness driving population can only reveal partial nature of crash occurrence. The question thus remains: How do different reactions to identical conditions result in various outcomes.

Answers to the question rely on the understanding of drivers – the decision-maker of a running vehicle.

Research conducted in various countries has suggested that the human factor is the most important contributor to crash occurrence. Among those human factors, misallocating attention is one of the most critical cause of crashes or near-crash circumstances (Brown et al. 2000, McKnight and McKnight 2003, Underwood et al.

2003a, Underwood et al. 2003b, Chen et al. 2005, Dahlen et al. 2005, Underwood 2007, Di Stasi et al. 2009, Olson et al. 2009, Chan et al. 2010). In Taiwan, drivers failing to note roadway conditions accounted for 17% of the fatal crashes in 2011 (MOTC 2012). Presumably, a failure to allocate attention appropriately can be seen as the missing link between crash-prone scenario and crash occurrence within an accident chain. Problems of dividing limited attention resource would cause longer reaction time and higher crash possibility (Cheng et al. 2011). Thus, understanding the patterns of attention allocation is crucial to analyzing the relationship between crashes and ways to maintain situational awareness through visual transition.

Safe driving requires drivers to pay continued attention to various areas and to constantly update awareness of the driving environment. Information perception, which is the first stage of Ensley’s situational awareness, is the key step of comprehending, anticipating, and reacting against tasks or events (Endsley 1995).

Acquisition of incomplete or useless information will lead to insufficient comprehension of the current driving environment, misjudgment, rush reaction, and possibly to a crash. To drive safely, drivers must pay attention to multiple sources of information to make informed driving decisions. However, one’s mental resources are limited (Kahnemen 1973). Each driver has a central processor that determines the policy of attention allocation, which divides their mental resources within the limits of their mental capacity. Problems of divided attention may degrade one’s ability to detect potential threats while driving (de Waard et al. 2008, de Waard et al. 2009,

Marmeleira et al. 2009). Complex driving tasks with more information that drivers must attend to would cause drivers making more errors (Elvik 2006).

Distraction is one of major causes of attention misallocation. Shifting attention away from driving to undertake secondary tasks, such as answering cell phones, may increase the time required to perceive and react to external stimuli, and, thus, increase the risk of crashes (Neyens and Boyle 2007a, 2008). Providing drivers with information via in-vehicle information systems, such as GPS, is intended to help drivers more effectively plan the allocation of mental resources and prevent dangers from uncertainty. However, improper use of such devices can yield a negative effect and cause drivers to miss critical information (Liang et al. 2007, Wong and Chung 2007b, Vashitz et al. 2008). Horrey and Wickens (2007), a driving simulation experiment, stated that long glances over 1.6 seconds inside vehicles accounted for 86 percent of crashes. Klauer et al. (2006) also stated that shifting vision away from the forward area longer than 2 s increases the crash/near-crash risk by at least twofold.

It is obvious that a malfunction of attention allocation is the critical link that connects crash-prone scenario with crash occurrence. Misallocating attention may result in one’s awareness being distracted by useless information; thereby missing important information. In just a fraction of a second, one’s visual inattention can lead to unsuccessful information perception. Maneuvering without sufficient information of road conditions could generate unsafe situations easily and increases the likelihood of driver error. To explore the causality of crashes and to prevent them from happening, a functional mechanism for attention allocation is a vital issue that should be tackled. Knowledge of the patterns in which drivers allocate attention among multiple focal points provides insight into the information-seeking behavior of drivers and its relationship to safety.

Unlike those measurable attributes (such as roadway, environment or maneuver conditions) used in crash causation analyses, exploring attention allocation mechanism may face difficulties of observing a driver’s inherent behavior. Fortunately, the recent technique improvement enables the large scale data collection, including eye movement, bio-medical signal and associated maneuvering behavior. For example, the naturalistic driving studies were widely conducted for recording drivers’ every motion of attention allocation and maneuvering. Such a method provides ample opportunities for researchers to further explore drivers’ characteristics from mental and cognitive perspectives. Grabbing those chances would help explore the accident chain in deeper depth and bridge the missing link between crash occurrence and crash-proneness scenarios.