By Tom Vanderbilt
A New York Times impressive Book
One of the easiest Books of the Year
The Washington Post • The Cleveland Plain-Dealer • Rocky Mountain News
In this outstanding, vigorous, and eye-opening research, Tom Vanderbilt examines the perceptual limits and cognitive underpinnings that make us worse drivers than we expect we're. He demonstrates why plans to guard pedestrians from vehicles frequently bring about extra injuries. He uncovers who's likely to honk at whom, and why. He explains why site visitors jams shape, outlines the accidental results of our quest for protection, or even identifies the commonest mistake drivers make in parking lots. Traffic is approximately greater than riding: it's approximately human nature. it's going to switch the best way we see ourselves and the area round us, and it could even make us higher drivers.
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Additional info for Traffic: Why We Drive the Way We Do (and What It Says About Us)
Information and Communication Technology (ICT) and ITS can be used to assess and manage these risks. Advanced modeling techniques are required for assessing risks and evaluating measures to improve the management of hazardous goods transport in urban traffic networks. Different risk models can produce different “optimal” paths for a hazmat shipments between a given origin–destination (Erkut and Verter, 1998). Five categories of risk models have been identified: (1) traditional risk, (2) population exposure, (3) incident probability, (4) perceived risk, and (5) conditional risk.
Beroggi, G. E. , and Wallace, W. A. (1995). Operational control of the transportation of hazardous materials: An assessment of alternative decision models. Management Science 41 (12): 1962–1977. Birge, J. , and Louveaux, F. (1997). Introduction to stochastic programming. New York: Springer. Bok, J. H. , and Park, S. (1998). Robust investment model for longrange capacity expansion of chemical processing networks under uncertain demand forecast scenarios. Computers and Chemical Engineering 22 (7): 1037–1049.
The effect of road freight vehicles and heavy goods vehicles (HGVs) on traffic accidents has recevied very little attention (Hiselius 2004; Ramírez et al. 2009). The relationship of ramp section design and truck accident rates was investigated by Zaloshnja and Miller (2004). An interview questionnaire survey of professional drivers, including truck drivers was used to identify factors contributing to the probability of falling asleep and crash risk (Tzamalouka et al. 2005). Applications of intelligent transport systems for the prevention of traffic accidents caused by freight traffic have also been investigated (Palkovics and Fries 2001; Sarvi and Kuwahara 2008).