A Systematic Methodology to Study Driving Adaptation Effects under Flooding Conditions

Abstract

Flooding events not only cause physical damage to the road infrastructure and roadside systems, yet they potentially lead to significant traffic disruptions over the network which have a large impact on the sustainability of urban cities. According to several climate change scenarios, flooding events as a result of intense precipitation are expected to continue into the future becoming more frequent and severe. Therefore, it is essential that engineers design and manage transport networks as efficiently as possible under adverse weather conditions in order to build more resilient transport systems. To do so, professionals need to understand and analyse the impacts of flooding on transport networks. Modelling the impacts of flooding events on the road network is nowadays possible by the use of integrated simulation techniques that combine a flood simulation model with a microscopic traffic model. With these modelling techniques, the geographic location, severity and other impacts of flooding on the road network can be predicted and studied. Nevertheless, in order to perform a consistent traffic simulation under flooding conditions, a thorough analysis of driving behaviour under such conditions needs first to be accomplished, which is a research field that has been scarcely investigated thus far. Therefore, the main contribution of this thesis is to provide a systematic methodology for studying driving adaptation effects under flooding conditions at two complementary levels, namely microscopic and macroscopic level. This consists on a multilevel approach that uses microscopic traffic data of vehicles driving through flooding to extend the study to a macroscopic level. A video analysis procedure called the `The 3-Step Video Analysis Approach´ (3SVAA) is developed by the author to extract microscopic traffic parameters from video recording of vehicles driving under different flood depths. This methodology is implemented through an empirical study that analyses two videos of vehicles crossing waterlogged stretches part of the A94 and A93 major roads in Scotland (UK). First, the 3SVAA is introduced as a suitable data collection technique to extract microscopic traffic variables (i.e. vehicle's speed, time headways and spacing) from video recording of vehicles driving through flooding. More specifically, these parameters are obtained from the vehicle's trajectories in the space (m)-time(s) diagram, which is the main outcome of this video analysis approach. Subsequently, by applying a multilevel analysis, the study can be extended to a macroscopic level by correlating macroscopic variables (i.e. average speed, flow and density) with their microscopic counterparts through the fundamental relationship of traffic flow. This allows to estimate part of the fundamental diagrams of vehicles travelling under different flood depths, and therefore to study flood impacts on free flow speed and capacity.