A Citywide Location-Allocation Framework for Driver Feedback Signs: Optimizing Safety and Coverage of Vulnerable Road Users

Driver feedback signs (DFSs) are being adopted increasingly by municipalities around the world, as they have been proven to be a cost-effective countermeasure that improves road safety. However, research is still needed on developing a location-allocation framework to determine the optimal implementation strategies for DFS placement. Hence, the main aim of this paper is to formulate a location-allocation optimization problem with the objective of reducing vehicular collisions (ΔC) while enhancing spatial coverage for vulnerable road users and facilities (Cov). Two distinct planning scenarios, namely, an all-new and expansion scenario, were proposed in the framework. It was found that ΔC and Cov can be improved by up to 149.44% and 69.27%, respectively, in the all-new scenario. Two expansion scenarios were done with 10 and 20 additional units into the system. It was found that ΔC can be improved by up to 30.22% and 51.61% for the additional 10 and 20 DFSs, respectively. Likewise, the Cov can be improved by up to 14.64% and 29.27%, respectively. This framework provides decision makers with the freedom to simulate and optimize their DFS network by balancing the needs of the road users, vulnerable facilities, and traffic safety in locating DFSs over an urban road network.

Before-and-After Empirical Bayes Evaluation of Citywide Installation of Driver Feedback Signs

Speeding is a leading factor that contributes to approximately one-third of all fatal collisions. Over the past decades, various passive/active countermeasures have been adopted to improve drivers’ compliance to posted speed limits to improve traffic safety. The driver feedback sign (DFS) is considered a low-cost innovative intervention that is being widely used, in growing numbers, in urban cities to provide positive guidance for motorists. Despite their documented effectiveness in reducing speeds, limited literature exists on their impact on reducing collisions. This study addresses this gap by designing a before-and-after study using the empirical Bayes method for a large sample of urban road segments. Safety performance functions and yearly calibration factors are developed to quantify the sole effectiveness of DFS using large-scale spatial data and a set of reference road segments within the city of Edmonton, Alberta, Canada. Likewise, the study followed a detailed economic analysis based on three collision-costing criteria to investigate if DFS was indeed a cost-effective intervention. The results showed significant collision reductions that ranged from 32.5% to 44.9%, with the highest reductions observed for severe speed-related collisions. The results further attested that the benefit–cost ratios, combining severe and property-damage-only collisions, ranged from 8.2 to 20.2 indicating that DFS can be an extremely economical countermeasure. The findings from this study can provide transportation agencies in need of implementing cost-efficient countermeasures with a tool they need to design a long-term strategic deployment plan to ensure the safety of traveling public.

Simulation of the Wear and Handling Performance Trade-off by Using Multi-objective Optimization and TameTire

ABSTRACT Optimization is a key tool used by automakers to efficiently design and manufacture vehicles. During vehicle design, much effort is devoted to efficiently simulate and optimize as many vehicle parameters as possible to save development costs during physical testing. One area of vehicle development that heavily relies on physical testing and subjective driver feedback is the tire design process. Optimizing tire parameters relies either on this subjective feedback from trained drivers, or use of existing tire data or scaling of a reference tire model simulate the desired design change and provide feedback. These data are often difficult to obtain and properly scale to represent the appropriate design changes. Michelin's TameTire model is a force and moment tire model. It includes thermal tire effects and is physically derived, thereby allowing quick access to scaling factors to change a tire's behavior based on pertinent tire design changes such as tread depth and tread stiffness. In this paper, a multi-objective optimization is performed to observe the trade-off between tire wear and handling performance by using the scaling factors available in the TameTire model.