PW 1327 Status analysis of non-engineering considerations of road accidents along toll roads, E01, E02 & E03- a case study of sri lanka

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Abstract

The global road causality rate is continuously climbing as motorization levels grow, and the situation is particularly acute in developing countries. In particular, developing countries suffer a high rate of road crashes, thus contributing up to 80% of world road fatalities. Developing nations cannot afford loss to their economies and loss of young and economically active sector of the population. Because of these losses, economies of developing countries are becoming unsustainable.

At present Sri Lanka is experiencing an average of eight (8) deaths each day and the trend had been a continuously rising phenomena. Despite laws and regulations been introduced from time to time, the situation is continuously keep on raising. The expressway network amounting up to a total length exceeding 180 km had been constructed accommodating all acceptable geometric standards as such there would be road crashes resulting due to deficiencies in geometrics. Since opening for public this expressway network had so far experienced a total of 5115 road accidents and among them 2560 had been fatal accidents. This growing situation warranted to have an in depth study to find out the extent of non-technical reasons to have a this sort of eventuality within the expressway network.

Analysis of a total of 5115-toll road crashes revealed that reasons for more than 80% of these accidents could not be explained only on technical terms. Indiscipline behaviors, relaxation of law enforcement, misbehaviors, excessive speeds, drowsiness, drunkenness were the dominant reasons to have an alarming growth of road crashes within the expressway Network. Analysis further revealed that without a comprehensive in depth study of ‘human limitations’ a self-explaining road design warranting minimum road crashes could not be attained. Findings were useful to develop explanatory variables characterizing ‘Human limitations’ to be used in accident predictive models.

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