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High collision rates on horizontal curves compared with other roadway elements make them one of the most critical elements in a transportation network and thus good candidates for new studies, especially of the stability of a vehicle driving on a horizontal curve. In this regard, spiral transition curves as facilities to enter horizontal alignments are very important. While the superelevation changes in these curves have always been linear, the aim of this study was to evaluate non-linearity in the superelevation attainment function (SAF). Various scenarios were used in order to cover different situations. As a simulation tool, the CarSim software package was used to model three kinds of passenger cars (sport utility vehicle, utility truck and sedan). In addition, 16 types of spiral–curve–spiral road plans for three different conditions of road slope, six types of warping in the SAF and two types of functional speed were examined (a total of 1728 scenarios). The results showed that non-linearity might produce better results than other proposed methods; for example, it was found that a parabolic function reduced the lateral acceleration rate by over 50% for high superelevation and design speed. [ABSTRACT FROM AUTHOR]

An understanding of the change in follower behaviour as a driver characteristic in traffic simulations is necessary because these behavioural changes are frequently observed in traffic oscillations. The change in follower behaviour due to traffic acceleration and deceleration waves can be classified according to different behavioural patterns resulting from different driver reactions. Combinations of existing behavioural patterns (under-reaction–timid, over-reaction–timid and constant over-reaction–timid patterns) were thus analysed. The method of analysis was based on an artificial neural network model at the microscopic level in order to simulate the extent to which follower behaviour deviates from Newell's car-following model. The most influential parameters of all the behavioural patterns were determined. Based on the under-reaction–timid pattern, decreasing the reaction speed resulted in a change in behaviour due to the development of safe spacing at the reaction point. In addition, follower spacing was the most influential in the over-reaction–timid pattern, resulting in increasing and then decreasing behavioural change. In the constant over-reaction–timid pattern, the follower's speed resulted in increasing and then decreasing the change in behaviour. The application of behavioural change to an intelligent transportation system would improve safety. [ABSTRACT FROM AUTHOR]

Speed humps are in widespread use around the world. Despite their effective performance in increasing safety, they cause considerable damage to vehicles and discomfort to drivers and passengers. This paper investigates the dynamic response of vehicles on different types of speed humps using a multibody simulation software package that is popular in the automotive industry. Following this evaluation, two new profiles are recommended to optimize the dynamic performance of speed humps. A series of formulas are also presented to estimate the dynamic performance of passenger cars on flat-topped and parabolic humps based on vehicle speed, hump dimensions, and driving behavior while traversing the hump. The results show that, for flat-topped humps, the ramp length (or entrance slope) has the greatest effect on vehicle performance. It is found that the dynamic performance of the recommended profiles is much better than that of conventional humps. [ABSTRACT FROM AUTHOR]

The aim of this research was to predict operating speed by considering geometric and roadside factors. Although most previous studies have employed linear regression modelling (LRM) to predict operating speed, this study recommends structural equation modelling (SEM) for the prediction of operating speed on rural multi-lane highways. In addition to geometric variables, LRM takes roadside variables into account. When employing SEM in this work, two latent variables were defined, namely 'roadside effects' and 'geometric effects'. The first latent variable was the combination of land-use type, land-use density and number of accesses per segment, while the second was extracted from the segment length, highway grade, curvature, the presence of a guardrail and flat roadside slope, and the posted speed limits. The residual analysis and R2 values for LRM and SEM suggest that SEM demonstrates superior modelling performance compared with LRM. These results show the significant role of latent variables in predicting speed, which cannot be achieved through ordinary LRM. [ABSTRACT FROM AUTHOR]