CHAPTER 6 C ONCLUSION AND R ECOMMENDATION
6.3 Recommendations
Results obtained in this study may not be able to represent rules of safe driving from the perspective of attention allocation. Yet, it is still fruitful for improving safety and for further studies. In this section, the recommendations for future studies were addressed in this section.
(1) This study utilized only the event database in 100-car. Sample drivers in this dataset eventually experience crashes. It is clearly that they encounter some undesired situations and allocate attention in an improper way. Therefore, results obtained in this does not necessarily represent a typical driver’s attention allocation pattern, nor a crash-free pattern. However, purpose of this study is to propose a method for analyzing attention allocation. Using 100-car data set enables the exploration of drivers’ vision transition among focal points. It is still a fruitful research for future application.
(2) The comparisons driver attention allocation patterns among crash, near-crash and baseline data will be needed. This study focus only on the data of which drivers eventually experience crashes or near-crashes. Including more levels of crash severity can help identify the possible pattern that drivers help to observe and prevent crashes. Comparing baseline and crash data in similar conditions can explore the difference of drivers gathering information from multiple sources, and possibly help researchers move one step closer to the causation of crashes.
(3) Crash occurrences are not necessarily resulted from the subject drivers’ fault.
Meanwhile, driving safely does not mean that the subject drivers did not make mistakes in driving or in attention allocation. Distinguishing these types of possible bias can be an essential issue when researchers intended to identify a risky pattern and a safe pattern. Moreover, in addition to exploring the crash-proneness patterns of attention allocation from the dimensions of
environmental conditions and driving tasks, another way of approaching this issue is to identify the risky driving population, particularly the aging or novice drivers.
(4) Owing to the data limitation, this study was not able to include attributers related to the characteristics of other vehicles on-road. For instance, whether a vehicle is located closely in front of the subject vehicle may vary the duration of glancing at forward side. Moreover, the concept of vehicle drivers’ domain cannot not be verified since the dataset did not provide the distance between drivers and the exact glanced point. Particularly, the boundary of reaction domain could be important clues for designing collision warning system. That is, once an obstacle crossing the reaction domain without triggering the changes of drivers’ attention allocation pattern, a warning could be delivered before the obstacle getting close to the critical domain.
(5) In addition, approximately 90% of off-road glances were shorter than 2.0 s when adopting 2-glance renewal cycles, which accounted for 90.74% of the generated renewal cycles. Apparently, most of the sample drivers were alert. Their attention was efficiently allocated and not glancing away from the front for too long in deteriorated situations. By contrast, off-road glances in 3-glance renewal cycles were unsafe and significantly different from those in 2-glance cycles, particularly in certain deteriorated conditions. Even the 3-glance renewal cycles accounted for only 7.14% of the generated renewal cycles, such a small proportion of driving patterns might contribute to most of the crash occurrences. Therefore, for crash prevention, further study is warranted for defective attention allocation patterns.
(6) The data adopted in this study was collected in United States. Driving culture, environment, behavior and complexity are different between the US and Taiwan.
To gain better insight of localized attention allocation patterns, it is crucial for government and university to develop programs of naturalistic driving for studies of driving behavior. Based on the localized data, concept proposed in this study could be a potential way for identifying the causations of crashes in Taiwan and possibly countermeasures for improving safety.
(7) The sequential glances made by different drivers are panel data. Relation between glances in a sequence and the heterogeneity among different driving conditions may cause the low fitness. In the future, mix logit could be considered for modeling the driver attention allocation.
R
EFERENCESAashto, 2010. Highway safety manual American Association of State Highway and Transportation Officials, Washington, DC.
Abdel-Aty, M., Pande, A., 2007. Crash data analysis: Collective vs. Individual crash level approach. Journal of Safety Research 38 (5), 581-587.
Al-Ghamdi, A.S., 2007. Experimental evaluation of fog warning system. Accident Analysis and Prevention 39 (6), 1065-1072.
Albertsson, P., Falkmer, T., 2005. Is there a pattern in european bus and coach incidents? A literature analysis with special focus on injury causation and injury mechanisms. Accident Analysis and Prevention 37 (2), 225-233.
Bagdadi, O., 2013. Assessing safety critical braking events in naturalistic driving studies. Transportation Research Part F: Traffic Psychology and Behaviour 16 (0), 117-126.
Basu, D., Hunt, J.D., 2012. Valuing of attributes influencing the attractiveness of suburban train service in mumbai city: A stated preference approach.
Transportation Research Part a-Policy and Practice 46 (9), 1465-1476.
Bayam, E., Liebowitz, J., Agresti, W., 2005. Older drivers and accidents: A meta analysis and data mining application on traffic accident data. Expert Systems with Applications 29 (3), 598-629.
Ben-Akiva, M.E., Lerman, S.R., 1985. Discrete choice analysis: Theory and application to predict travel demand Mit Press.
Bennett, J.D., Passmore, D.L., 1985. Multinomial logit analysis of injury severity in united-states underground bituminous coal-mines, 1975-1982. Accident Analysis and Prevention 17 (5), 399-408.
Blanco, M., Biever, W.J., Gallagher, J.P., Dingus, T.A., 2006. The impact of secondary task cognitive processing demand on driving performance. Accident Analysis and Prevention 38 (5), 895-906.
Borowsky, A., Shinar, D., Oron-Gilad, T., 2010. Age, skill, and hazard perception in driving. Accident Analysis and Prevention 42 (4), 1240-1249.
Boyle, L.N., Mannering, F., 2004. Impact of traveler advisory systems on driving speed: Some new evidence. Transportation Research Part C 12 (1), 57-72.
Brookhuis, K., De Waard, D., 1999. Limiting speed, towards an intelligent speed adapter (isa). Transportation Research Part F: Traffic Psychology and Behaviour 2 (2), 81-90.
Brown, T., Lee, J., Mcgehee, D., 2000. Attention-based model of driver performance in rear-end collisions. Transportation Research Record: Journal of the
Transportation Research Board 1724, 14-20.
Bruyas, M.-P., Brusque, C., Debailleux, S., Duraz, M., Aillerie, I., 2009. Does making a conversation asynchronous reduce the negative impact of phone call on driving? Transportation Research Part F-Traffic Psychology and Behaviour 12 (1), 12-20.
Caird, J.K., Hancock, P.A., 1994. The perception of arrival time for different oncoming vehicles at an intersection. Ecological Psychology 6 (2), 83-109.
Caird, J.K., Willness, C.R., Steel, P., Scialfa, C., 2008. A meta-analysis of the effects of cell phones on driver performance. Accident Analysis and Prevention 40 (4), 1282-1293.
Caliendo, C., Guida, M., Parisi, A., 2007. A crash-prediction model for multilane roads. Accident Analysis and Prevention 39 (4), 657-670.
Can, V.V., 2013. Estimation of travel mode choice for domestic tourists to nha trang using the multinomial probit model. Transportation Research Part a-Policy and Practice 49 149-159.
Carbaugh, J., Godbole, D.N., Sengupta, R., 1998. Safety and capacity analysis of automated and manual highway systems. Transportation Research Part C-Emerging Technologies 6 (1-2), 69-99.
Carbonell, J.R., 1966. A queuing model of many-instrument visual sampling. IEEE Transactions on Human Factors in Electronics HFE-7 157-164.
Carbonell, J.R., Ward, J.L., Senders, J.W., 1986. A queuing model of visual sampling experimental validation. IEEE Transactions on Man-Machine Systems MMS-9 82-87.
Castro, M., Paleti, R., Bhat, C.R., 2013. A spatial generalized ordered response model to examine highway crash injury severity. Accident Analysis and Prevention 52 188-203.
Chan, E., Pradhan, A.K., Pollatsek, A., Knodler, M.A., Fisher, D.L., 2010. Are driving simulators effective tools for evaluating novice drivers' hazard anticipation, speed management, and attention maintenance skills? Transportation Research Part F-Traffic Psychology and Behaviour 13 (5), 343-353.
Chang, H.-L., Yeh, T.-H., 2007. Motorcyclist accident involvement by age, gender, and risky behaviors in taipei, taiwan. Transportation Research Part F: Traffic Psychology and Behaviour 10 (2), 109-122.
Chang, L.-Y., Wang, H.-W., 2006. Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis and Prevention 38 (5), 1019-1027.
Chang, L.Y., Chen, W.C., 2005. Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research 36 (4), 365-375.
Chapman, P., Underwood, G., Roberts, K., 2002. Visual search patterns in trained and untrained novice drivers. Transportation Research Part F: Traffic Psychology and Behaviour 5 (2), 157-167.
Chen, W.H., Lin, C.Y., Doong, J.L., Trb, 2005. Effects of interface workload of in-vehicle information systems on driving safety. Human performance;
simulation and visualization. pp. 73-78.
Cheng, A.S.K., Ng, T.C.K., Lee, H.C., 2011. A comparison of the hazard perception ability of accident-involved and accident-free motorcycle riders. Accident Analysis and Prevention 43 (4), 1464-1471.
Chimba, D., Sando, T., Kwigizile, V., 2010. Effect of bus size and operation to crash occurrences. Accident Analysis and Prevention 42 (6), 2063-2067.
Chin, H.C., Quddus, M.A., 2003. Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections.
Accident Analysis and Prevention 35 (2), 253-259.
Choo, S., Mokhtarian, P.L., 2004. What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice. Transportation Research Part a-Policy and Practice 38 (3), 201-222.
Clarke, D.D., Forsyth, R., Wright, R., 1998. Machine learning in road accident research: Decision trees describing road accidents during cross-flow turns.
Ergonomics 41 (7), 1060-1079.
Clarke, D.D., Forsyth, R., Wright, R., 1999. Junction road accidents during cross-flow turns: A sequence analysis of police case files. Accident Analysis and Prevention 31 (1–2), 31-43.
Cooper, P.J., Zheng, Y., 2002. Turning gap acceptance decision-making: The impact of driver distraction. Journal of Safety Research 33 (3), 321-335.
Courtney, S.M., 2010. Short-term memory: Psychological and neural aspects. In:
George, F.K., Michel Le, M., Thompson, R.F. eds. Encyclopedia of behavioral neuroscience. Academic Press, Oxford, pp. 210-215.
Creaser, J.I., Rakauskas, M.E., Ward, N.J., Laberge, J.C., Donath, M., 2007. Concept evaluation of intersection decision support (ids) system interfaces to support drivers’ gap acceptance decisions at rural stop-controlled intersections.
Transportation Research Part F 10 (3), 208-228.
Crundall, D., Van Loon, E., Underwood, G., 2006. Attraction and distraction of attention with roadside advertisements. Accident Analysis and Prevention 38 (4), 671-677.
Dahlen, E.R., Martin, R.C., Ragan, K., Kuhlman, M.M., 2005. Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis and Prevention 37 (2), 341-348.
Davoodi, S.R., Hamid, H., Arintono, S., Muniandy, R., Faezi, S.F., 2011. Motorcyclist rear brake simple perception-response times in rear-end collision situations.
Traffic Injury Prevention 12 (2), 174-179.
Davoodi, S.R., Hamid, H., Pazhouhanfar, M., Muttart, J.W., 2012. Motorcyclist perception response time in stopping sight distance situations. Safety Science 50 (3), 371-377.
De Waard, D., Dijksterhuis, C., Brookhuis, K.A., 2009. Merging into heavy motorway traffic by young and elderly drivers. Accident Analysis and Prevention 41 (3), 588-597.
De Waard, D., Kruizinga, A., Brookhuis, K.A., 2008. The consequences of an increase in heavy goods vehicles for passenger car drivers’ mental workload and behaviour: A simulator study. Accident Analysis and Prevention 40 (2), 818-828.
Di Stasi, L.L., lvarez-Valbuena, V., Cañas, J.J., Maldonado, A., Catena, A., Antolí, A., Candido, A., 2009. Risk behaviour and mental workload: Multimodal assessment techniques applied to motorbike riding simulation. Transportation Research Part F: Traffic Psychology and Behaviour 12 (5), 361-370.
Dingus, T.A., Klauer, S.G., Neale, V.L., Petersen, A., Lee, S.E., Sudweeks, J., Perez, M.A., Hankey, J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z.R., Jermeland, J., Knipling, R.R., 2006. The 100-car naturalistic driving study, phase ii – results of the 100-car field experiment. DOT-HS-810-593. National Highway Traffic Safety Administration, D.C.
Donmez, B., Boyle, L.N., Lee, J.D., 2007. Safety implications of providing real-time feedback to distracted drivers. Accident Analysis and Prevention 39 (3), 581-590.
Dozza, M., 2013. What factors influence drivers’ response time for evasive maneuvers in real traffic? Accident Analysis & Prevention in press.
Dukic, T., Ahlstrom, C., Patten, C., Kettwich, C., Kircher, K., 2013. Effects of electronic billboards on driver distraction. Traffic Injury Prevention 14 (5), 469-476.
Dukic, T., Broberg, T., 2012. Older drivers’ visual search behaviour at intersections.
Transportation Research Part F: Traffic Psychology and Behaviour 15 (4), 462-470.
Eidehall, A., Pohl, J., Gustafsson, F., Ekmark, J., 2007. Toward autonomous collision avoidance by steering. Ieee Transactions on Intelligent Transportation Systems 8 (1), 84-94.
Elvik, R., 2003. Assessing the validity of road safety evaluation studies by analysing causal chains. Accident Analysis and Prevention 35 (5), 741-748.
Elvik, R., 2006. Laws of accident causation. Accident Analysis and Prevention 38 (4), 742-747.
Elvik, R., 2008. A survey of operational definitions of hazardous road locations in some european countries. Accident Analysis and Prevention 40 (6), 1830-1835.
Endsley, M.R., 1995. Toward a theory of situation awareness in dynamic-systems.
Human Factors 37 (1), 32-64.
Falkmer, T., Gregersen, N.P., 2001. Fixation patterns of learner drivers with and without cerebral palsy (cp) when driving in real traffic environments.
Transportation Research Part F: Traffic Psychology and Behaviour 4 (3), 171-185.
Fambro, D.B., Koppa, R.J., Picha, D.L., Fitzpatrick, K., Nat Res, C., 1998. Driver perception-brake response in stopping sight distance situations. Transportation Research Record: Journal of the Transportation Research Board 1628, 1-7.
Galpin, A., Underwood, G., Crundall, D., 2009. Change blindness in driving scenes.
Transportation Research Part F: Traffic Psychology and Behaviour 12 (2), 179-185.
Gershon, P., Ben-Asher, N., Shinar, D., 2012. Attention and search conspicuity of motorcycles as a function of their visual context. Accident Analysis and Prevention 44 (1), 97-103.
Geurts, K., Thomas, I., Wets, G., 2005. Understanding spatial concentrations of road accidents using frequent item sets. Accident Analysis and Prevention 37 (4), 787-799.
Gkritza, K., Kinzenbaw, C.R., Hallmark, S., Hawkins, N., 2010. An empirical analysis of farm vehicle crash injury severities on iowa's public road system. Accident Analysis and Prevention 42 (4), 1392-1397.
Golob, T.F., Recker, W.W., 2003. Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions. Journal of Transportation Engineering-Asce 129 (4), 342-353.
Habib, K.M.N., Day, N., Miller, E.J., 2009. An investigation of commuting trip timing and mode choice in the greater toronto area: Application of a joint discrete-continuous model. Transportation Research Part a-Policy and Practice 43 (7), 639-653.
Hatfield, J., Chamberlain, T., 2008. The effect of audio materials from a rear-seat audiovisual entertainment system or from radio on simulated driving.
Transportation Research Part F-Traffic Psychology and Behaviour 11 (1), 52-60.
Hill, J.D., Ng Boyle, L., 2007. Driver stress as influenced by driving maneuvers and
roadway conditions. Transportation Research Part F-Traffic Psychology and Behaviour 10 (3), 177-186.
Ho, C.S., Charles, 2008. The multisensory driver: Implications for ergonomic car interface design Ashgate, Hampshire.
Hooper, K.G., Mcgee, H.W., 1983. Driver perception-reaction time: Are revisions to current specification values in order? Transportation Research Record: Journal of Transportation Research Board 904, 21-30.
Horrey, W.J., Wickens, C.D., 2007. In-vehicle glance duration - distributions, tails, and model of crash risk. Transportation Research Record: Journal of the Transportation Research Board 2018, 22-28.
Horrey, W.J., Wickens, C.D., Consalus, K.P., 2006. Modeling drivers' visual attention allocation while interacting with in-vehicle technoligies. Journal of Experimental Psychological: Applied 12 (2), 67-78.
Kahnemen, D., 1973. Attention and effort Prentice-Hall Inc, New Jersey.
Kass, S.J., Beede, K.E., Vodanovich, S.J., 2010. Self-report measures of distractibility as correlates of simulated driving performance. Accident Analysis and Prevention 42 (3), 874-880.
Kato, H., Kaneko, Y., Inoue, M., 2010. Comparative analysis of transit assignment:
Evidence from urban railway system in the tokyo metropolitan area.
Transportation 37 (5), 775-799.
Khorashadi, A., Niemeier, D., Shankar, V., Mannering, F., 2005. Differences in rural and urban driver-injury severities in accidents involving large-trucks: An exploratory analysis. Accident Analysis and Prevention 37 (5), 910-921.
Kiefer, R.J., Hankey, J.M., 2008. Lane change behavior with a side blind zone alert system. Accident Analysis and Prevention 40 (2), 683-690.
Kim, J.-K., Kim, S., Ulfarsson, G.F., Porrello, L.A., 2007. Bicyclist injury severities in bicycle-motor vehicle accidents. Accident Analysis and Prevention 39 (2), 238-251.
Kim, J.-K., Ulfarsson, G.F., Shankar, V.N., Kim, S., 2008. Age and pedestrian injury severity in motor-vehicle crashes: A heteroskedastic logit analysis. Accident Analysis and Prevention 40 (5), 1695-1702.
Kim, K., Yamashita, E.Y., 2007. Attitudes of commercial motor vehicle drivers towards safety belts. Accident Analysis & Prevention 39 (6), 1097-1106.
Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., Ramsey, D.J., 2006. The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data. DOT-HS-810-594. National Highway Traffic Safety Administration, D.C.
Koch, C., Tsuchiya, N., 2006. Attention and consciousness: Two distinct brain
processes. Trends in Cognitive Science 11 (1), 16-22.
Konstantopoulos, P., Chapman, P., Crundall, D., 2010. Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving. Accident Analysis and Prevention 42 (3), 827-834.
Konstantopoulos, P., Crundall, D., 2008. The driver prioritisation questionnaire:
Exploring drivers' self-report visual priorities in a range of driving scenarios.
Accident Analysis and Prevention 40 (6), 1925-1936.
Kostyniuk, L., Eby, D., 1998. Exploring rear-end roadway crashes from the driver's perspective. Human Factors Division, Transportation Research Institute, Michigan University, Ann. Arbor.
Koustanaï, A., Van Elslande, P., Bastien, C., 2012. Use of change blindness to measure different abilities to detect relevant changes in natural driving scenes.
Transportation Research Part F: Traffic Psychology and Behaviour 15 (3), 233-242.
Kvålseth, T., Crossman, E.F.W., Kum, K., 1976. The effect of cost on the sampling behavior of human instrument monitors. In: Sheridan, T., Johannsen, G. eds.
Monitoring behavior and supervisory control. Springer US, pp. 393-403.
Laberge, J.C., Creaser, J.I., Rakauskas, M.E., Ward, N.J., 2006. Design of an intersection decision support (ids) interface to reduce crashes at rural stop-controlled intersections. Transportation Research Part C-Emerging Technologies 14 (1), 39-56.
Laureshyn, A., Svensson, , Hydén, C., 2010. Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation.
Accident Analysis & Prevention 42 (6), 1637-1646.
Lee, B.H.Y., Schofer, J.L., Koppelman, F.S., 2005. Bicycle safety helmet legislation and bicycle-related non-fatal injuries in california. Accident Analysis and Prevention 37 (1), 93-102.
Lehtonen, E., Lappi, O., Summala, H., 2012. Anticipatory eye movements when approaching a curve on a rural road depend on working memory load.
Transportation Research Part F: Traffic Psychology and Behaviour 15 (3), 369-377.
Levin, L.D., T., Henriksson, P., Mårdh, S., Sagberg, F., 2009. Older car drivers in norway and sweden - studies of accident involvement, visual search behaviour, attention and hazard perception. VTI rapport 656A. VTI, Norway.
Liang, Y., Reyes, M.L., Lee, J.D., 2007. Real-time detection of driver cognitive distraction using support vector machines. IEEE Transactions on Intelligent Transportation Systems 8 (2), 340-350.
Liu, B.-S., Lee, Y.-H., 2005. Effects of car-phone use and aggressive disposition during critical driving maneuvers. Transportation Research Part F: Traffic Psychology and Behaviour 8 (4–5), 369-382.
Loo, B.P.Y., Wong, S.C., Hau, T.D., 2006. Introducing alternative fuel vehicles in hong kong: Views from the public light bus industry. Transportation 33 (6), 605-619.
Maltz, M., Shinar, D., 2007. Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers. Transportation Research Part F-Traffic Psychology and Behaviour 10 (4), 345-357.
Malyshkina, N.V., Mannering, F.L., 2009. Markov switching multinomial logit model:
An application to accident-injury severities. Accident Analysis and Prevention 41 (4), 829-838.
Marell, A., Westin, K., 1999. Intelligent transportation system and traffic safety - drivers perception and acceptance of electronic speed checkers. Transportation Research Part C-Emerging Technologies 7 (2-3), 131-147.
Marmeleira, J.F., Godinho, M.B., Fernandes, O.M., 2009. The effects of an exercise program on several abilities associated with driving performance in older adults. Accident Analysis and Prevention 41 (1), 90-97.
Martens, M.H., Fox, M.R.J., 2007. Do familiarity and expectations change perception?
Drivers’ glances and response to changes. Transportation Research Part F 10 (6), 476-492.
Matas, A., Raymond, J.L., 2008. Changes in the structure of car ownership in spain.
Transportation Research Part a-Policy and Practice 42 (1), 187-202.
Mcevoy, S.P., Stevenson, M.R., Woodward, M., 2007. The contribution of passengers versus mobile phone use to motor vehicle crashes resulting in hospital attendance by the driver. Accident Analysis and Prevention 39 (6), 1170-1176.
Mcintyre, S., Gugerty, L., Duchowski, A., 2012. Brake lamp detection in complex and dynamic environments: Recognizing limitations of visual attention and perception. Accident Analysis and Prevention 45 (0), 588-599.
Mcintyre, S.E., 2008. Capturing attention to brake lamps. Accident Analysis and Prevention 40 (2), 691-696.
Mcknight, A.J., Mcknight, A.S., 2003. Young novice drivers: Careless or clueless?
Accident Analysis and Prevention 35 (6), 921-925.
Metz, B., Schömig, N., Krüger, H.-P., 2011. Attention during visual secondary tasks in driving: Adaptation to the demands of the driving task. Transportation Research Part F: Traffic Psychology and Behaviour 14 (5), 369-380.
Miller, S., Kirlik, A., Kosorukoff, A., Byrne, M.D., 2004. Ecological validity as a mediator of visual attention allocation in human-machine systems.
AHFD-04-17/NASA-04-6. Aviation Human Factors Division Institute of Aviation, University of Illinois at Urbana-Champaign, Savoy, Illinois.
Mizuno, K., Kajzer, J., 1999. Compatibility problems in frontal, side, single car collisions and car-to-pedestrian accidents in japan. Accident Analysis and Prevention 31 (4), 381-391.
Molin, E.J.E., Brookhuis, K.A., 2007. Modelling acceptability of the intelligent speed adapter. Transportation Research Part F-Traffic Psychology and Behaviour 10 (2), 99-108.
Montella, A., 2011. Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types.
Accident Analysis and Prevention 43 (4), 1451-1463.
Montella, A., Aria, M., D’ambrosio, A., Mauriello, F., 2012. Analysis of powered two-wheeler crashes in italy by classification trees and rules discovery.
Accident Analysis and Prevention 49 (0), 58-72.
Moore, D.N., Schneider, W.H., Savolainen, P.T., Farzaneh, M., 2011. Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident Analysis and Prevention 43 (3), 621-630.
Moray, N., 1986. Monitoring behavior and supervisory control. In: Boff, K.R. ed.
Handbook of perception and human performance. Wiley, New York, pp.
40.1-40.51.
Motc, 2012. Road traffic accidents (a1) by violates and types of first party http://www.motc.gov.tw/uploaddowndoc?file=view/2011/d3180.xls&filedispla y=d3180.xls&flag=doc (accessed May 15.2013).
Nabatilan, L.B., 2007. Factors that influence visual attention and their effects on safety in driving: An eye movement tracking approach. The Louisiana State University, USA.
Neale, V.L., Klauer, S.G., Knipling, R.R., Dingus, T.A., Holbrook, G.T., Petersen, A., 2002. The 100 car naturalistic driving study, phase 1 – experimental design.
DOT HS 809 536. National Highway Traffic Safety Administration, D.C.
Neale, V.L., Perez, M.A., Lee, S.E., Doerzaph, Z.R., 2007. Investigation of driver-infrastructure and driver-vehicle interfaces for an intersection violation warning system. Journal of Intelligent Transportation Systems 11 (3), 133-142.
Neuman, T.R., 1989. New approach to design for stopping sight distance.
Transportation Research Record: Journal of the Transportation Research Board 1208, 14-22.
Neyens, D.M., Boyle, L.N., 2007a. The effect of distractions on the crash types of teenage drivers. Accident Analysis & Prevention 39 (1), 206-212.
Neyens, D.M., Boyle, L.N., 2007b. The effect of distractions on the crash types of teenage drivers. Accident Analysis and Prevention 39 (1), 206-212.
Neyens, D.M., Boyle, L.N., 2007b. The effect of distractions on the crash types of teenage drivers. Accident Analysis and Prevention 39 (1), 206-212.