Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent-Based Modelling Approach

Abstract

Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers are more likely to break local and fixed speed limits in urban environments. Analysis conducted by the Department for Transport found that the UK’s accident prevention measure’s cost is estimated to be £33bn per year. Therefore, there is a strong motivation to investigate the causes of roadside collisions in urban environments to better prepare traffic management, support local council policies, and ultimately reduce collision rates. This study utilises agent-based modelling as a tool to plan, experiment and investigate the relationship between speeding and vehicle density with collisions. The study found that higher traffic density results in more vehicles travelling at a slower speed, regardless of the degree to which drivers comply with speed restrictions. Secondly, collisions increase linearly as speed compliance is reduced for all densities. Collisions are lowest when all vehicles comply with speed limits for all densities. Lastly, higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels, increasing the likelihood of congestion around these sites. This work, when extended to real-world applications using empirical data, can support effective road safety policies.

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Electric Energy Consumption in Urban Space

“…Similarly, the road network is designed around a built-up environment that contains all the characteristics of a dense urban street network with varying speed limits and intersection rules adopted from the UK Speed Limits [34]. Lastly, the model was used by researchers looking at how driver behaviour impacts collision rates [24].…”

“…These agents inherit similar characteristics as real-world vehicles; they have four wheels, steering angle, traction, mass and drag. Each agent applies a set of rules outlined in the article [24].…”

“…It provides the means to plan, design and experiment with micro heterogeneous agents in an artificial, computational environment. ABMs have been utilised in various domains to explain complex phenomena such as those that occur in crime [12,13], ecology [14][15][16], economics [17,18], sociology [19,20], geography [21,22] and transportation [23,24]. One advantage of using ABMs is that they are able to represent a richer and more detailed set of individual actors and therefore potential policy alternatives and outcomes than alternative statistical models [25].…”

“…The urban road network consists of 1295 roads which vehicles drive on and 354 intersections which consist of traffic rules (Algorithm 1, [24]). The road network has been designed to depict a small urban town.…”

Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study

“…Almost all ABMs implement some aspect of decision-making [17,18,6,19,20]. However, the frameworks applied to these models vary in their purpose and complexity.…”