This repository is associated with the research study titled "Drivers' Behavior Confronting Fixed and Point-to-Point Speed Enforcement Cameras: Agent-Based Simulation and Translation to Crash Relative Risk Change" authored by Seyed Amir Ahmad Safavi-Naini, Shayan Sobhani, Mohammad-Reza Malekpour, Kavi Bhalla, Saeid Shahraz, Rosa Haghshenas, Seyyed-Hadi Ghamari, Mohsen Abbasi-Kangevari, Nazila Rezaei, Seyed Taghi Heydari, Negar Rezaei, Kamran B. Lankarani, Farshad Farzadfar.
The data provided in this repository contains aggregated simulation results, which have been used to translate our simulation findings into crash relative risk changes.
In this innovative approach, we conducted a comparative assessment of various enforcement interventions within our community, considering different contextual factors such as compliance with traffic regulations, traffic volume, and telematics usage. These instructions outline the steps to evaluate the cost-effectiveness or effectiveness of proposed interventions before their implementation.
Gather data samples from a significant number of participants within our hometown.
Characterize the behavior of our community by determining the proportion of three distinct driver groups:
Group A: Law-abiding drivers - those who consistently adhere to speed limits across all regions, not limited to camera zones. Group B: Carefree drivers - those who exceed speed limits, even within camera zones. This behavior may be attributed to ineffective regulations or personal interests. Group C: Adaptive drivers - individuals who reduce their speed to conform to limits solely within camera zones, adopting a deceleration-acceleration pattern.
Determine the ratio of these driver groups to create a behavioral model for our community. Our study compared intervention effectiveness across four community profiles, each characterized by a unique driver behavior ratio.
Replicate our simulation steps (or conduct more advanced simulations if you have additional information and resources).
Utilize the power model formula to translate simulation results into crash relative risk. In this formula, the speed's relationship with a crash is raised to the power of 2 (for normal crashes), 3 (for serious injury crashes), or 4 (for deadly crashes). Refer to our references for further details. This serves as a concise and user-friendly guide.
In our case, we did not perform this step. However, one can translate the change in Crash Relative Risk into a quantification of reduced crash risk, affecting the quality and quantity of human lives. Subsequently, calculate the cost of crashes by assigning a value to each human life (whether in terms of quality or quantity). This approach allows us to indirectly determine the benefits of transitioning from the current intervention to the planned one, such as adding another fixed-speed camera or converting a fixed-speed camera to a point-to-point speed camera.
By identifying the flow of road traffic and considering previous effectiveness measurements, we can calculate annual effectiveness (expressed in dollars, for example).
Next, calculate the annual cost of speed enforcement. This entails calculating the expenses associated with changing the current intervention to your desired new strategy, which includes infrastructure, equipment, maintenance, and more, over a one-year period.
Now, you can formulate the cost-effectiveness ratio and assess the logical viability of your new solution compared to the current one.
Keep in mind that this is an indirect finding with multiple assumptions during calculations. However, having a quantified rationale is preferable to relying solely on the opinions of individuals, which often occurs in decision-making processes. This method brings us one step closer to evidence-enhanced decision-making and evidence-based policymaking. Furthermore, this approach is cost-effective and can be implemented in countries with limited resources.