Structural Modeling of Factors Influencing Auditory Distraction Induced by Voice Navigation Messages While Driving

Document Type : Original Article

Authors
Amir mohammad Sarlak
Seyed Ebrahim Abdolmanafi
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
10.22034/road.2026.582385.2488
Abstract
The increasing use of voice navigation systems and applications in urban driving has made auditory guidance messages one of the major sources of driver distraction. The aim of this study was to identify and structurally model the factors affecting auditory distraction caused by voice navigation messages. The statistical population consisted of Tehran drivers with experience in using voice navigation while driving. The study was conducted in two stages. In the first stage, drivers’ usage patterns were examined using a behavioral checklist. The results showed that 89% of drivers had experience using voice navigation systems, and 64% responded to auditory distraction by reducing speed and increasing safety distance. In the second stage, the relationships among the influencing factors were analyzed using Interpretive Structural Modeling (ISM). The results revealed a four-level hierarchical structure, in which sound intensity, sound quality, and timing of instruction delivery were placed at the first level; recognition of the output voice at the second level; number of instructions at the third level; and factors such as speaking speed, noisy environment, and guidance content at the fourth level. MICMAC analysis also showed that “guidance content” is a key and influential variable. Accordingly, improving message content and appropriately timing the delivery of instructions can help reduce driver distraction and enhance urban traffic safety.
Keywords
MICMAC Analysis
Auditory Distraction
Interpretive Structural Modeling (ISM)
Voice Navigation System
-Attri, R., Dev, N., and Sharma, V. (2013). Interpretive structural modelling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3–8.
-Basacik, D., Reed, N., and Robbins, R. (2011). Smartphone use while driving: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 14(6), 461–471.
-Caird, J. K., Willness, C. R., Steel, P., and Scialfa, C. (2008). A meta-analysis of the effects of cell phones on driver performance. Accident Analysis & Prevention, 40(4), 1282–1293.
-Chen, Y., and Liu, H. (2023). Effects of voice navigation systems on driving performance in congested traffic conditions. Transportation Research Part F: Traffic Psychology and Behaviour, 94, 123–134.
-Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S., Sudweeks, J., and Knipling, R. R. (2006). The 100 car naturalistic driving study: Phase II – Results of the 100 car field experiment. National Highway Traffic Safety Administration.
-Drews, F. A., Pasupathi, M., and Strayer, D. L. (2009). Passenger and cell phone conversations in simulated driving. Journal of Experimental Psychology: Applied, 14(4), 392–400.
-Ericsson. (2016). Ericsson mobility report. Ericsson.
-Garcia, A., Lopez, J., and Martinez, F. (2022). Effects of natural versus synthetic speech in vehicle navigation systems on driver attention. Applied Ergonomics, 98, 103583.
-Gopher, D., and Donchin, E. (1985). Workload: An examination of the concept. In K. R. Boff, L. Kaufman, and J. P. Thomas (Eds.), Handbook of perception and human performance. Wiley.
-Green, P. (2000). Crashes induced by driver information systems and what can be done to reduce them. International Journal of Vehicle Design, 27(1–4), 37–52.
-Guest, G., Bunce, A., and Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82.
-Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
-Horrey, W. J., and Wickens, C. D. (2006). Examining the impact of cell phone conversations on driving using meta analytic techniques. Human Factors, 48(1), 196–205.
-Kern, S., and Schmidt, G. (2009). Advanced driver assistance systems and driver distraction. Springer.
-Kim, H., Lee، S., and Park, J. (2021). Optimizing the timing of auditory navigation instructions to reduce driver cognitive load. Transportation Research Part F: Traffic Psychology and Behaviour, 79, 1–12.
-Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J., and Ramsey, D. (2006). The impact of driver inattention on near crash/crash risk: An analysis using the 100 car naturalistic driving study data. National Highway Traffic Safety Administration Report.
-Lee, J. D., Caven, B., Haake, S., and Brown, T. L. (2001). Speech based interaction with in vehicle computers: The effect of speech based email on drivers’ attention. Human Factors, 43(4), 631–640.
-Lee, J. D., Young, K. L., and Regan, M. A. (2008). Defining driver distraction. In M. A. Regan، J. D. Lee, and K. L. Young (Eds.), Driver distraction: Theory, effects, and mitigation. CRC Press.
-Li, Z., Wang, Y., and Chen, X. (2023). Deep learning analysis of driver behaviour during interaction with voice assistants in vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(5), 5123–5134.
-Maciej, J., and Vollrath, M. (2009). Comparison of manual vs. speech based interaction with in vehicle information systems. Accident Analysis & Prevention, 41(5), 924–930.
-McGehee, D. V., Lee, J. D., Rizzo, M., Dawson, J., and Bateman, K. (2007). Quantifying driver distraction in the automobile. Journal of Safety Research, 38(4), 391–399.
-Olson, R. L., Hanowski, R. J., Hickman, J. S., and Bocanegra, J. (2009). Driver distraction in