Strategic and Tactical Guidance for the Connected and Autonomous Vehicle Future

Autonomous vehicle (AV) and Connected vehicle (CV) technologies are rapidly maturing and the timeline for their wider deployment is currently uncertain. These technologies are expected to have a number of significant societal benefits: traffic safety, improved mobility, improved road efficiency, reduced cost of congestion, reduced energy use, and reduced fuel emissions. State and local transportation agencies need to understand what this means for them and what they need to do now and in the next few years to prepare for the AV/CV future. In this context, the objectives of this research are as follows: Synthesize the existing state of practice and how other state agencies are addressing the pending transition to AV/CV environment Estimate the impacts of AV/CV environment within the context of (a) traffic operations—impact of headway distribution and traffic signal coordination; (b) traffic control devices; (c) roadway safety in terms of intersection crashes Provide a strategic roadmap for INDOT in preparing for and responding to potential issues This research is divided into two parts. The first part is a synthesis study of existing state of practice in the AV/CV context by conducting an extensive literature review and interviews with other transportation agencies. Based on this, we develop a roadmap for INDOT and similar agencies clearly delineating how they should invest in AV/CV technologies in the short, medium, and long term. The second part assesses the impacts of AV/CVs on mobility and safety via modeling in microsimulation software Vissim

Evaluating the Impacts of Time-of-Day Tolling on Indiana Roadways

In recent decades, several agencies have implemented tolls on their highway, bridge, or tunnel infrastructure for purposes that include mitigation and revenue generation. The Indiana Department of Transportation (INDOT) sought to investigate the feasibility of time-of-day tolling at a specific section of Indiana’s highways, and therefore commissioned this research study. Recognizing that traditional ex ante methods of tolling evaluation such as surveys and public hearings are time intensive and costly, this project developed a simulation package to measure the expected impacts of TOD tolling in terms of revenue, travel delay, and welfare. The report developed an analytical model and accompanying software tool (a TOD tolling analysis pack) to capture the relationship between the time-of-day toll and route choice, and to evaluate quickly, the impacts of various TOD tolling scenarios. The research products help the analyst to easily visualize traffic flows on roadways and to estimate the revenue, monetary savings, travel times, speeds, vehicle hours of travel, vehicle miles of travel, and welfare in response to different scenarios. The TOD tolling analysis pack reduces drastically, the time and effort in evaluating proposed TOD tolling initiatives for the future. The visualization features illustrate traffic diversions due to tolling implementation and display the most impacted road segments. Further, the TOD tolling analysis pack can be integrated seamlessly with INDOT’s existing TransCAD models and with Google Maps to provide users with the capability of acquiring additional pertinent information on the study area

Mobility and Safety Impacts of Autonomous Vehicles

Connected and Autonomous Vehicles (CAV) are revolutionizing the automotive space. We are at the cusp of a, once in a century, transformation in the automotive space. This work strives to understand, analyze and provide insights on the various dimensions this transition is going to impact. We begin with the exploration of the CAV landscape which is in a continuous state of flux. We attempt to examine, analyze and evaluate this space using semi-structured interviews with experts from across the whole country. The interviews are supported additionally by survey questions which further capture the expert views quantitatively. This initial exploratory study leads us to the central questions of this study which include (1) Modeling of SAE (Society of Automotive Engineers) vehicles from level 0 to level 5 using a simulation framework (2) Analysis of mobility and safety impacts of SAE vehicles. (3) Building a predictive model of the risk level of autonomous vehicles based on trajectory information. For the modeling of AVs, the different levels of SAE were mapped to particular functionalities. Each of these functionalities were then modeled using the external driver model (EDM) and were tested on VISSIM to evaluate their performance. The mobility impacts of these models were tested on a highway and an intersection environment. The analysis were conducted for 100% penetration levels for each SAE and also for different penetration levels One of the most important benefits of AVs that has been touted by OEMs and DOTs alike, are the safety benefits of CAVs. Among many industries which will be affected by the safety aspects of CAVs, insurance industry is one of them. An immediate challenge that lies in front of them will be to evaluate the risk level of different SAE classes of vehicles. This will be especially true as most of the SAE level data is unavailable or very scarce. To overcome this limitation, we propose a novel methodology to identify risky driver behavior for every SAE level. The framework includes the utilization of surrogate safety measures modified for SAE levels. The trajectory data created from SAE level simulation is used as the data set for model training and testing which predicts driving risk. The models evaluated are logistic regression, decision trees and neural networks. This framework provides a foundation for modeling the riskiness of autonomous vehicles in traffic networks