- Email: [email protected]
Psychological factors that influence car-following and car-following model development
Transportation Research Part F 2 (1999) 213±219
www.elsevier.com/locate/trf
Psychological factors that in¯uence car-following and car-following model development Thomas A. Ranney * Transportation Research Center Inc., PO Box B 37, East Liberty, OH 43319, USA Received 3 January 2000 ; accepted 23 January 2000
Abstract This commentary on Brackstone and McDonaldÕs (1999) historical review of car-following models focuses primarily on ®ve issues: (i) Why has so much eort been devoted to car-following models? (ii) What assumptions do car-following models make about driver behavior? (iii) What factors in¯uence car-following? (iv) What improvements can be made to car-following models? and (v) Do we need a ÔnormativeÕ model of driver behavior? The review concludes that dierences between the approaches of trac engineers and trac psychologists to activities such as car-following have not led to a common understanding of behavior, which is required if the challenge of anticipating how people will drive in other circumstances with dierent in-car systems is to be successfully overcome. Ó 2000 Published by Elsevier Science Ltd. All rights reserved. Keywords: Close following
1. Introduction Brackstone and McDonaldÕs (1999) historical review of car-following models describes the signi®cant amount of eort trac engineers have devoted to this topic for over 45 years. The authors assert that this topic has increased in importance and conclude that a model of normative driver behavior is needed to further improve car-following models. The authorsÕ decision to submit this review to a journal, that deals with psychological issues and is presumably read mostly by trac psychologists, appears to represent a plea for help in developing models of normative driving behavior. However, when considered from a trac psychology perspective, the paper
*
Tel.: +1-937-666-4511. E-mail address: [email protected] (T.A. Ranney).
1369-8478/00/$ - see front matter Ó 2000 Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 9 - 8 4 7 8 ( 0 0 ) 0 0 0 1 0 - 3
214
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
raises numerous questions concerning the role of psychological factors in car-following models. In particular, it would have been helpful for this audience if the authors had described the motivation for developing car-following models and their success in accomplishing the tasks they were designed to accomplish. Similarly, a discussion relating the underlying assumptions concerning behavioral mechanisms to the inadequacies of existing models would have provided a more compelling story for this audience. Finally, the paper would have bene®ted from a more detailed discussion of the circumstances that create the need for more sophisticated models and their expected performance criteria. The remainder of this article, therefore, will consider some of the psychological aspects of car-following, ®rst by addressing some of the questions that were raised by this review. 2. Why has so much eort been devoted to car-following models? The trac engineering motivation for studying car-following is presented by Homburger, Keefer and McGrath (1982), who suggest that by examining the manner in which individual vehicles follow one another and from the joint behavior of pairs of vehicles, the behavior of a single-lane trac stream can be deduced. Trac stability is a logical extension of car-following theory (May, 1990), where local stability refers to the response of a following vehicle in a single pair of vehicles and asymptotic stability refers to the manner in which lead vehicle ¯uctuations are propagated through an entire stream of vehicles in the same lane. Microscopic models of individual car-following behavior form the building blocks for macroscopic models of trac ¯ow, which are used to assess the eects of interventions such as changes in roadway geometry, trac signal timing, delineation, signing, etc. Trac psychologistsÕ motivations for studying car-following range from ®nding ways to reduce the incidence of rear-end collisions to describing the skills, abilities, and errors involved in car-following. In this work, researchers have adopted dierent assumptions concerning the behavioral mechanisms involved in car-following. For example, Evans (1991), who conducted a signi®cant amount of research in this area, asserted that ``most driving is spent constrained by a vehicle in front,'' suggesting that drivers generally have little choice but to follow a lead vehicle. In contrast, Rockwell (1972) interpreted the ``rather large headway variations'' observed in on-road studies as evidence of dierent driving strategies, rather than individual dierences in car-following skills. Trac psychologists have also developed measures of carfollowing performance. In particular, Brookhuis, De Waard and Mulder (1994) de®ned coherence, phase shift, and gain to characterize the tracking aspects of car-following. They used these measures to assess the eects of impairment, due to alcohol, drugs, or using a cellular phone while driving. Trac engineers and trac psychologists study car-following for dierent reasons. Models suitable for trac engineering purposes may therefore not be appropriate for trac psychologistsÕ purposes, and vice versa. For example, Homburger et al. (1982) suggest that while more complex models may do a better job of representing the behavior of a single pair of vehicles, the more simplistic models have proven more suitable for studying trac stability. The usefulness of any model, as with any tool, relates to how well it accomplishes the task for which it was designed.
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
215
3. What assumptions do car-following models make about driver behavior? Brackstone and McDonaldÕs review portrays an evolution of models characterized by increasingly sophisticated assumptions about driver behavior. These models have progressed from the initial work of Pipes, which incorporated the assumption that drivers behave according to the safe following rule in the California Motor Vehicle Code, adopting at least one-car length of following distance for every 10 miles per hour (e.g. May, 1990), through ForbesÕ early assumptions that following distance is based on the reaction time required to perceive the need to decelerate and apply the brakes, to the General MotorsÕ models, ®ve generations of which adopted the assumption that the car-following response (acceleration or deceleration) was a function of a stimulus, represented by the relative velocity of the lead vehicle and ``sensitivity,'' which itself is a function of the spacing between vehicles. The search for the stimulus used by drivers in car-following appears to represent the most concerted eort to explore the psychological factors in carfollowing. According to Evans (1991), the most successful stimulus is the relative speed divided by spacing, and the least successful stimulus is the spacing between the vehicles. Furthermore, based on the ®nding that driversÕ ability to judge relative speed is inversely proportional to inter-vehicle spacing, Evans concludes that the primary cue cannot simply be the change in visual angle subtended by the lead vehicle. Later models incorporated the possibility that following drivers may use information from more than one vehicle ahead to anticipate the actions of the lead vehicle. Finally, as described by Brackstone and McDonald, the Helly model incorporates a threshold mechanism, whereby the driver will not reassess the required deceleration until the discrepancy between estimated and actual spacing exceeds a speci®ed criterion. The incorporation of a threshold mechanism would appear to bring this model conceptually close to risk-threshold models of driving behavior (e.g. Ranney, 1994); however the Helly model threshold is not intended to represent a purely behavioral mechanism, since it compares the driverÕs estimation of spacing with the actual spacing, presumably not directly available to the driver. Therefore, while car-following models incorporate some assumptions about underlying behavioral mechanisms, they are clearly not intended to be purely behavioral models of car-following. 4. What factors in¯uence car-following? Most generally, car-following refers to a situation in which a vehicleÕs speed and longitudinal position are in¯uenced by the vehicle immediately ahead of it in the same travel lane. Car-following is characterized by the headway (time or distance between vehicles) and the degree to which the following vehicle tracks the velocity changes of the lead vehicle. With respect to headway, Rockwell (1972) cites estimates, which suggest that overtaking or coupling occurs at headways between approximately 9 and 4 s. Car-following involves headways between 4 and .5 s. Ohta (1993) suggests that drivers are most comfortable in a zone in which headways vary between 1.1 and 1.7 s. The tracking aspects of car-following are used in experimental settings but cannot be accessed in observational studies of trac. Numerous factors in¯uence car-following behavior. Although not all have been supported empirically, Rockwell (1972) suggested that car-following behavior is in¯uenced by ``driving
216
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
goals, road curvature, relative velocity, stream speed, whether car-following is elected or imposed, and the length of time in the coupled state.'' More recently, Evans and colleagues conducted a series of observational studies to identify individual factors that contribute to risk-taking behavior. Headways were used as a measure of risk taking. Evans and Wasielewski (1983) found that headway increased with driver age and that males adopted shorter headways than females. Evans, Wasielewski and Von Buseck (1982) found shorter headways for drivers not wearing seat belts. In some experimental studies, subjects are given instructions concerning car-following. For example in a closed-course experiment, Korteling (1990) found that older and brain-damaged drivers were less accurate than controls when asked to track the speed changes of a lead vehicle. Brookhuis et al. (1994) conducted a series of experiments in which subjects followed an experimental vehicle whose driver varied speeds within a speci®ed range. They found that telephoning while driving, alcohol, antihistamines, and time-on-task aected driversÕ abilities to follow a lead vehicle. Speci®cally, while drivers were able to accurately reproduce the velocity changes of a lead vehicle, they were signi®cantly slower in responding when the impairment factor was present. Ohta (1993) also conducted a series of experiments in which subjects followed a researcher-driven lead vehicle under four dierent sets of instructions. He found dierences in headway selection based on the type of instruction and classi®ed drivers according to their apparent strategy for selecting headways. He then administered a number of psychological tests and related the results to observed headway selection strategy. He concluded that drivers who adopted short time headways displayed unstable aect and a low level of safety consciousness. OhtaÕs results reveal that both stable personality factors and transient motivational dierences, re¯ecting driversÕ responses to dierent instruction sets, can in¯uence headway selection. Together, these studies indicate signi®cant individual dierences in car-following. Fig. 1 presents a conceptual model of the relative contribution of dierent categories of factors in in¯uencing car-following behavior. According to this model, car-following occurs primarily at intermediate levels of service, represented by car-following zone 2 in Fig. 1. Under free-¯ow conditions (zone 1), car-following may occur, but only if the driver chooses to drive closely to another vehicle. Under congested conditions (zone 3), drivers have little choice about following the lead vehicle closely. The model also indicates that within zone 2, car-following is determined by a combination of the constraints imposed by other vehicles, individual factors and situational factors, and that the relative contribution of these factors diers as a function of trac congestion. In particular, drivers have considerably more freedom to adopt dierent car-following strategies under conditions near level of service A. However, as trac density increases, the car-following task changes gradually from self-paced to forced-paced, where the actions of other vehicles increasingly dictate the behavior of the following vehicle. Near level of service F, trac conditions are the predominant factors and the contributions of situational factors and individual dierences are minimal. 5. What improvements can be made to car-following models? Car-following models typically assume that drivers intend or are forced to follow vehicles. As suggested by Fig. 1, this assumption may be adequate for situations in which trac imposes
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
217
Fig. 1. Factors in¯uencing car-following behavior.
signi®cant constraints on following drivers. However, where possible, many drivers are likely to ®nd ways to retreat from this in¯uence thus restoring their freedom to allocate attentional resources among the components of driving and the increasing number of in-vehicle activities that nowadays compete for these attentional resources. Moreover, contemporary interactions among road users may re¯ect dierent motives for selecting headways. For example, in heavily congested trac, drivers may adopt uncomfortably short following distances to prevent vehicles in adjacent lanes from entering the gap immediately in front of their vehicle. Finally, in heavily congested conditions in which drivers are forced to respond closely to lead vehicles for extended periods, carfollowing may be characterized by an increasing number of attentional lapses, resulting in vehicle control errors (e.g. dangerously short headways). Car-following models could thus be improved by incorporating these intentional and unintentional sources of variability. In addition, car-following models could be improved by matching parameter values more closely to trac conditions.
6. Do we need a normative model of driver behavior? The complexity of car-following behavior is suggested by the large number of factors that in¯uence car-following behavior. Moreover, as shown repeatedly in laboratory studies of human
218
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
performance (e.g. Neisser, 1976), humans are very adaptable and will develop new strategies for new situations. Given the ever-increasing variety of driving situations, including changes in the driving task associated with dierent technologies, and the corresponding variety of skills and abilities required, it is unlikely that a comprehensive normative model of driver behavior will ever be feasible (see Ranney, 1994). In contrast, the development of better models of car-following behavior applicable to well-de®ned situations is probably a more worthwhile approach. However, if as suggested by Brackstone and McDonald, the target situations involve Advanced Vehicle Control and Safety Systems (AVCSSs), then the required models will need to be extended to predict driversÕ interactions with, and responses to, such systems ± a problem that is considerably dierent from car-following behavior.
7. Conclusion Car-following behavior represents a unique area of inquiry because it is of interest both to trac engineers and psychologists. If, as suggested Brackstone and McDonald, there is a new impetus emerging from the ITS arena, then perhaps this will provide a common incentive for these two disciplines to work together on this topic. One scenario for cooperative progress would involve trac engineers specifying the operational parameters associated with situations to which car-following models must apply and the desired performance criteria for the models. In turn, trac psychologists would develop behavioral models that incorporate contemporary thinking concerning the eects of motivational, attentional, and situational factors. However, because trac engineers and trac psychologists have approached car-following from dierent perspectives and with dierent knowledge bases, it will be necessary to develop a common understanding of the problem including speci®cation of the behavioral assumptions underlying these models and the relationship of these assumptions to model structures. Hopefully, the eort devoted to developing operational de®nitions will facilitate validation studies. Within this optimistic scenario, there is no reason why such models cannot be extended to include predictions of driversÕ interactions with in-vehicle technologies.
Acknowledgement The author would like to thank John R. Bloom®eld for his help in developing the ideas presented in this manuscript. References Brackstone, M., & McDonald, M. (1999). Car following: a historical Review. Transportation Research F: Trac and Transport Psychology 1, 2, 181±196. Brookhuis, K., De Waard, D., & Mulder, B. (1994). Measuring driving performance by car-following in trac. Ergonomics, 37, 427±434. Evans, L. (1991). Trac safety and the driver. New York: Van Nostrand Reinhold.
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
219
Evans, L., & Wasielewski, P. (1983). Risky driving related to driver and vehicle characteristics. Accident Analysis and Prevention, 15, 121±136. Evans, L., Wasielewski, P., & Von Buseck, C. R. (1982). Compulsory seat belt usage and driver risk-taking behavior. Human Factors, 24, 41±48. Homberger, W. S., Keefer, L. E., & McGrath, W. R. (1982). Transportation and trac engineering handbook (second ed.). Englewood Clis, NJ: Prentice-Hall. Korteling, J. E. (1990). Perception-response speed and driving capabilities of brain-damaged and older drivers. Human Factors, 32, 95±108. May, A. D. (1990). Trac ¯ow fundamentals. Englewood Clis, NJ: Prentice Hall. Neisser, U. (1976). Cognition and reality: principles and implications of cognitive psychology. San Francisco, CA: W. H. Freeman. Ohta, H. (1993). Individual dierences in driving distance headway. In A. G. Gale, Vision in vehicles IV. Amsterdam: Elsevier. Ranney, T. A. (1994). Models of driving behavior: a review of their evolution. Accident Analysis and Prevention, 26, 733±750. Rockwell, T. (1972) Skills, judgment, and information acquisition in driving. In T. W. Forbes, Human factors in highway trac safety research. New York: Wiley.
www.elsevier.com/locate/trf
Psychological factors that in¯uence car-following and car-following model development Thomas A. Ranney * Transportation Research Center Inc., PO Box B 37, East Liberty, OH 43319, USA Received 3 January 2000 ; accepted 23 January 2000
Abstract This commentary on Brackstone and McDonaldÕs (1999) historical review of car-following models focuses primarily on ®ve issues: (i) Why has so much eort been devoted to car-following models? (ii) What assumptions do car-following models make about driver behavior? (iii) What factors in¯uence car-following? (iv) What improvements can be made to car-following models? and (v) Do we need a ÔnormativeÕ model of driver behavior? The review concludes that dierences between the approaches of trac engineers and trac psychologists to activities such as car-following have not led to a common understanding of behavior, which is required if the challenge of anticipating how people will drive in other circumstances with dierent in-car systems is to be successfully overcome. Ó 2000 Published by Elsevier Science Ltd. All rights reserved. Keywords: Close following
1. Introduction Brackstone and McDonaldÕs (1999) historical review of car-following models describes the signi®cant amount of eort trac engineers have devoted to this topic for over 45 years. The authors assert that this topic has increased in importance and conclude that a model of normative driver behavior is needed to further improve car-following models. The authorsÕ decision to submit this review to a journal, that deals with psychological issues and is presumably read mostly by trac psychologists, appears to represent a plea for help in developing models of normative driving behavior. However, when considered from a trac psychology perspective, the paper
*
Tel.: +1-937-666-4511. E-mail address: [email protected] (T.A. Ranney).
1369-8478/00/$ - see front matter Ó 2000 Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 6 9 - 8 4 7 8 ( 0 0 ) 0 0 0 1 0 - 3
214
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
raises numerous questions concerning the role of psychological factors in car-following models. In particular, it would have been helpful for this audience if the authors had described the motivation for developing car-following models and their success in accomplishing the tasks they were designed to accomplish. Similarly, a discussion relating the underlying assumptions concerning behavioral mechanisms to the inadequacies of existing models would have provided a more compelling story for this audience. Finally, the paper would have bene®ted from a more detailed discussion of the circumstances that create the need for more sophisticated models and their expected performance criteria. The remainder of this article, therefore, will consider some of the psychological aspects of car-following, ®rst by addressing some of the questions that were raised by this review. 2. Why has so much eort been devoted to car-following models? The trac engineering motivation for studying car-following is presented by Homburger, Keefer and McGrath (1982), who suggest that by examining the manner in which individual vehicles follow one another and from the joint behavior of pairs of vehicles, the behavior of a single-lane trac stream can be deduced. Trac stability is a logical extension of car-following theory (May, 1990), where local stability refers to the response of a following vehicle in a single pair of vehicles and asymptotic stability refers to the manner in which lead vehicle ¯uctuations are propagated through an entire stream of vehicles in the same lane. Microscopic models of individual car-following behavior form the building blocks for macroscopic models of trac ¯ow, which are used to assess the eects of interventions such as changes in roadway geometry, trac signal timing, delineation, signing, etc. Trac psychologistsÕ motivations for studying car-following range from ®nding ways to reduce the incidence of rear-end collisions to describing the skills, abilities, and errors involved in car-following. In this work, researchers have adopted dierent assumptions concerning the behavioral mechanisms involved in car-following. For example, Evans (1991), who conducted a signi®cant amount of research in this area, asserted that ``most driving is spent constrained by a vehicle in front,'' suggesting that drivers generally have little choice but to follow a lead vehicle. In contrast, Rockwell (1972) interpreted the ``rather large headway variations'' observed in on-road studies as evidence of dierent driving strategies, rather than individual dierences in car-following skills. Trac psychologists have also developed measures of carfollowing performance. In particular, Brookhuis, De Waard and Mulder (1994) de®ned coherence, phase shift, and gain to characterize the tracking aspects of car-following. They used these measures to assess the eects of impairment, due to alcohol, drugs, or using a cellular phone while driving. Trac engineers and trac psychologists study car-following for dierent reasons. Models suitable for trac engineering purposes may therefore not be appropriate for trac psychologistsÕ purposes, and vice versa. For example, Homburger et al. (1982) suggest that while more complex models may do a better job of representing the behavior of a single pair of vehicles, the more simplistic models have proven more suitable for studying trac stability. The usefulness of any model, as with any tool, relates to how well it accomplishes the task for which it was designed.
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
215
3. What assumptions do car-following models make about driver behavior? Brackstone and McDonaldÕs review portrays an evolution of models characterized by increasingly sophisticated assumptions about driver behavior. These models have progressed from the initial work of Pipes, which incorporated the assumption that drivers behave according to the safe following rule in the California Motor Vehicle Code, adopting at least one-car length of following distance for every 10 miles per hour (e.g. May, 1990), through ForbesÕ early assumptions that following distance is based on the reaction time required to perceive the need to decelerate and apply the brakes, to the General MotorsÕ models, ®ve generations of which adopted the assumption that the car-following response (acceleration or deceleration) was a function of a stimulus, represented by the relative velocity of the lead vehicle and ``sensitivity,'' which itself is a function of the spacing between vehicles. The search for the stimulus used by drivers in car-following appears to represent the most concerted eort to explore the psychological factors in carfollowing. According to Evans (1991), the most successful stimulus is the relative speed divided by spacing, and the least successful stimulus is the spacing between the vehicles. Furthermore, based on the ®nding that driversÕ ability to judge relative speed is inversely proportional to inter-vehicle spacing, Evans concludes that the primary cue cannot simply be the change in visual angle subtended by the lead vehicle. Later models incorporated the possibility that following drivers may use information from more than one vehicle ahead to anticipate the actions of the lead vehicle. Finally, as described by Brackstone and McDonald, the Helly model incorporates a threshold mechanism, whereby the driver will not reassess the required deceleration until the discrepancy between estimated and actual spacing exceeds a speci®ed criterion. The incorporation of a threshold mechanism would appear to bring this model conceptually close to risk-threshold models of driving behavior (e.g. Ranney, 1994); however the Helly model threshold is not intended to represent a purely behavioral mechanism, since it compares the driverÕs estimation of spacing with the actual spacing, presumably not directly available to the driver. Therefore, while car-following models incorporate some assumptions about underlying behavioral mechanisms, they are clearly not intended to be purely behavioral models of car-following. 4. What factors in¯uence car-following? Most generally, car-following refers to a situation in which a vehicleÕs speed and longitudinal position are in¯uenced by the vehicle immediately ahead of it in the same travel lane. Car-following is characterized by the headway (time or distance between vehicles) and the degree to which the following vehicle tracks the velocity changes of the lead vehicle. With respect to headway, Rockwell (1972) cites estimates, which suggest that overtaking or coupling occurs at headways between approximately 9 and 4 s. Car-following involves headways between 4 and .5 s. Ohta (1993) suggests that drivers are most comfortable in a zone in which headways vary between 1.1 and 1.7 s. The tracking aspects of car-following are used in experimental settings but cannot be accessed in observational studies of trac. Numerous factors in¯uence car-following behavior. Although not all have been supported empirically, Rockwell (1972) suggested that car-following behavior is in¯uenced by ``driving
216
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
goals, road curvature, relative velocity, stream speed, whether car-following is elected or imposed, and the length of time in the coupled state.'' More recently, Evans and colleagues conducted a series of observational studies to identify individual factors that contribute to risk-taking behavior. Headways were used as a measure of risk taking. Evans and Wasielewski (1983) found that headway increased with driver age and that males adopted shorter headways than females. Evans, Wasielewski and Von Buseck (1982) found shorter headways for drivers not wearing seat belts. In some experimental studies, subjects are given instructions concerning car-following. For example in a closed-course experiment, Korteling (1990) found that older and brain-damaged drivers were less accurate than controls when asked to track the speed changes of a lead vehicle. Brookhuis et al. (1994) conducted a series of experiments in which subjects followed an experimental vehicle whose driver varied speeds within a speci®ed range. They found that telephoning while driving, alcohol, antihistamines, and time-on-task aected driversÕ abilities to follow a lead vehicle. Speci®cally, while drivers were able to accurately reproduce the velocity changes of a lead vehicle, they were signi®cantly slower in responding when the impairment factor was present. Ohta (1993) also conducted a series of experiments in which subjects followed a researcher-driven lead vehicle under four dierent sets of instructions. He found dierences in headway selection based on the type of instruction and classi®ed drivers according to their apparent strategy for selecting headways. He then administered a number of psychological tests and related the results to observed headway selection strategy. He concluded that drivers who adopted short time headways displayed unstable aect and a low level of safety consciousness. OhtaÕs results reveal that both stable personality factors and transient motivational dierences, re¯ecting driversÕ responses to dierent instruction sets, can in¯uence headway selection. Together, these studies indicate signi®cant individual dierences in car-following. Fig. 1 presents a conceptual model of the relative contribution of dierent categories of factors in in¯uencing car-following behavior. According to this model, car-following occurs primarily at intermediate levels of service, represented by car-following zone 2 in Fig. 1. Under free-¯ow conditions (zone 1), car-following may occur, but only if the driver chooses to drive closely to another vehicle. Under congested conditions (zone 3), drivers have little choice about following the lead vehicle closely. The model also indicates that within zone 2, car-following is determined by a combination of the constraints imposed by other vehicles, individual factors and situational factors, and that the relative contribution of these factors diers as a function of trac congestion. In particular, drivers have considerably more freedom to adopt dierent car-following strategies under conditions near level of service A. However, as trac density increases, the car-following task changes gradually from self-paced to forced-paced, where the actions of other vehicles increasingly dictate the behavior of the following vehicle. Near level of service F, trac conditions are the predominant factors and the contributions of situational factors and individual dierences are minimal. 5. What improvements can be made to car-following models? Car-following models typically assume that drivers intend or are forced to follow vehicles. As suggested by Fig. 1, this assumption may be adequate for situations in which trac imposes
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
217
Fig. 1. Factors in¯uencing car-following behavior.
signi®cant constraints on following drivers. However, where possible, many drivers are likely to ®nd ways to retreat from this in¯uence thus restoring their freedom to allocate attentional resources among the components of driving and the increasing number of in-vehicle activities that nowadays compete for these attentional resources. Moreover, contemporary interactions among road users may re¯ect dierent motives for selecting headways. For example, in heavily congested trac, drivers may adopt uncomfortably short following distances to prevent vehicles in adjacent lanes from entering the gap immediately in front of their vehicle. Finally, in heavily congested conditions in which drivers are forced to respond closely to lead vehicles for extended periods, carfollowing may be characterized by an increasing number of attentional lapses, resulting in vehicle control errors (e.g. dangerously short headways). Car-following models could thus be improved by incorporating these intentional and unintentional sources of variability. In addition, car-following models could be improved by matching parameter values more closely to trac conditions.
6. Do we need a normative model of driver behavior? The complexity of car-following behavior is suggested by the large number of factors that in¯uence car-following behavior. Moreover, as shown repeatedly in laboratory studies of human
218
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
performance (e.g. Neisser, 1976), humans are very adaptable and will develop new strategies for new situations. Given the ever-increasing variety of driving situations, including changes in the driving task associated with dierent technologies, and the corresponding variety of skills and abilities required, it is unlikely that a comprehensive normative model of driver behavior will ever be feasible (see Ranney, 1994). In contrast, the development of better models of car-following behavior applicable to well-de®ned situations is probably a more worthwhile approach. However, if as suggested by Brackstone and McDonald, the target situations involve Advanced Vehicle Control and Safety Systems (AVCSSs), then the required models will need to be extended to predict driversÕ interactions with, and responses to, such systems ± a problem that is considerably dierent from car-following behavior.
7. Conclusion Car-following behavior represents a unique area of inquiry because it is of interest both to trac engineers and psychologists. If, as suggested Brackstone and McDonald, there is a new impetus emerging from the ITS arena, then perhaps this will provide a common incentive for these two disciplines to work together on this topic. One scenario for cooperative progress would involve trac engineers specifying the operational parameters associated with situations to which car-following models must apply and the desired performance criteria for the models. In turn, trac psychologists would develop behavioral models that incorporate contemporary thinking concerning the eects of motivational, attentional, and situational factors. However, because trac engineers and trac psychologists have approached car-following from dierent perspectives and with dierent knowledge bases, it will be necessary to develop a common understanding of the problem including speci®cation of the behavioral assumptions underlying these models and the relationship of these assumptions to model structures. Hopefully, the eort devoted to developing operational de®nitions will facilitate validation studies. Within this optimistic scenario, there is no reason why such models cannot be extended to include predictions of driversÕ interactions with in-vehicle technologies.
Acknowledgement The author would like to thank John R. Bloom®eld for his help in developing the ideas presented in this manuscript. References Brackstone, M., & McDonald, M. (1999). Car following: a historical Review. Transportation Research F: Trac and Transport Psychology 1, 2, 181±196. Brookhuis, K., De Waard, D., & Mulder, B. (1994). Measuring driving performance by car-following in trac. Ergonomics, 37, 427±434. Evans, L. (1991). Trac safety and the driver. New York: Van Nostrand Reinhold.
T.A. Ranney / Transportation Research Part F 2 (1999) 213±219
219
Evans, L., & Wasielewski, P. (1983). Risky driving related to driver and vehicle characteristics. Accident Analysis and Prevention, 15, 121±136. Evans, L., Wasielewski, P., & Von Buseck, C. R. (1982). Compulsory seat belt usage and driver risk-taking behavior. Human Factors, 24, 41±48. Homberger, W. S., Keefer, L. E., & McGrath, W. R. (1982). Transportation and trac engineering handbook (second ed.). Englewood Clis, NJ: Prentice-Hall. Korteling, J. E. (1990). Perception-response speed and driving capabilities of brain-damaged and older drivers. Human Factors, 32, 95±108. May, A. D. (1990). Trac ¯ow fundamentals. Englewood Clis, NJ: Prentice Hall. Neisser, U. (1976). Cognition and reality: principles and implications of cognitive psychology. San Francisco, CA: W. H. Freeman. Ohta, H. (1993). Individual dierences in driving distance headway. In A. G. Gale, Vision in vehicles IV. Amsterdam: Elsevier. Ranney, T. A. (1994). Models of driving behavior: a review of their evolution. Accident Analysis and Prevention, 26, 733±750. Rockwell, T. (1972) Skills, judgment, and information acquisition in driving. In T. W. Forbes, Human factors in highway trac safety research. New York: Wiley.