Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (2018)

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5 Connected and automated vehicles (CAVs) are quickly expanding in the automobile and transportation industry and are expected to become a major share of the market in the next decade. Agencies are preparing themselves for this disruptive change, which can bring safety, mobility, and operational benefits. NCHRP Project 20-102, “Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies—Task-Order Support,” was initiated to assess the impacts of CAVs on state and local transportation agencies. The program’s objectives are to: • Identify critical issues associated with CAVs that state and local transportation agencies and AASHTO will face, • Conduct research to address those issues, • Conduct related technology transfer and information exchange activities. One of the policy catalysts to help achieve greater market penetration of CAVs is thought to be dedicating lanes for their priority or exclusive use as an initial incentive to encourage CAV ownership. The objective of this research, conducted under Task 08 of NCHRP Project 20-102, was to develop guidance for identifying conditions amenable to dedicating lanes for CAV users through modeling and simulation, as well as other qualitative ways such as investigations of laws and regulations. The balance of this chapter discusses and expands on the objectives and scope of this research and the purpose of this report. 1.1 Project Objective The primary objective of NCHRP Project 20-102(08), “Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles,” was to develop guidance for identifying and describing conditions amenable to dedicating lanes for CAV users. To achieve this objective, the project team undertook six tasks that concluded with the development of this research report and guidance document (Figure 1.1). • More specifically, these tasks involved: Identify the categories of benefits and disbenefits for dedicated lane (DL) users, non-DL users, and owners and operators of the facilities when dedicating lanes to CAV users; • Evaluate existing modeling and analytical frameworks to assess the impact of dedicating lanes to CAV users, specifically for applications such as Cooperative Adaptive Cruise Control (CACC), and to propose a specific project approach; • Identify diverse case study sites to help the researchers study the impacts and challenges of dedicating lanes to CAV users and propose specific case study sites for use in this project. C H A P T E R 1 Introduction

6 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles • Apply the approach proposed in Task 2 to the specific sites identified in Task 3 to investigate the impacts and challenges of dedicating lanes to CAV users; • Investigate existing laws and regulations that govern or constrain agencies in dedicating lanes to CAV users; and • Develop specific guidance on identifying and describing amenable or challenging conditions to dedicating lanes to CAV users, to be included in the final research report. Figure 1.1 also presents a basic workflow, showing the relationships among the different tasks associated with the project 1.2 Background Connected vehicles (CVs) use various communication technologies to exchange informa- tion with other cars on the road: vehicle-to-vehicle (V2V) communication, roadside infrastruc- ture such as vehicle-to-infrastructure (V2I) or infrastructure-to-vehicle (I2V) communication, and the “cloud.” These technologies can help improve vehicle safety, vehicle efficiency, and commute times (U.S.DOT n.d.c). Consequently, dedicated short-range communication (DSRC) is expected to be one of the leading communication technologies to be used in a CV environment (FHWA 2017a). In the classical sense, CVs may offer little or no automated control; rather, they focus on enhancing driver awareness through advanced warnings received by the in-vehicle communica- tion equipment. The CVs may transmit a range of messages containing various types of infor- mation about the condition of the vehicle and higher-level applications of interest to the driver and/or passengers. The most elementary (and likely to be legally required) message broadcast by an in-vehicle device (or onboard equipment) is known as the Basic Safety Message (BSM) (SAE International 2016a). The BSM contains information about a vehicle’s speed, position, size, heading, acceleration, and other representative elements (U.S.DOT n.d.a). When other vehicles or roadside devices receive this information, applications can use the data to provide warnings, Identify Categories of Benefits and Disbenefits Evaluate Existing Modeling/Analytical Framework Identify Diverse Case Study Sites Suitable for Evaluation Select, Adapt, and Apply to Evaluation Approach Identify Typical Laws and Regulations Develop Guidance Document 1 2 3 4 5 6 Figure 1.1. Project task overview.

Introduction 7 alerts, and advisories that can mitigate danger in certain situations. For example, a driver in a vehicle nearing an intersection could receive notifications of another car that is about to run a red light. Similarly, a driver in a vehicle approaching a blind curve could receive alerts about an oncoming car—currently out of sight beyond the curve—that is swerving into the driver’s lane to avoid an object on the road. Roadside equipment, consisting of infrastructure-based devices that also receive BSMs, can broadcast messages such as MapData and Signal Phase and Timing (SPaT), which in-vehicle devices receive and use to continuously monitor the infrastructure-based warnings and alerts. Automated vehicles (AVs) are those in which some portion of the dynamic driving task occurs without direct driver inputs to control the steering, acceleration, and braking. Highly automated driving systems are designed so that the driver is not expected to constantly monitor the roadway while operating in self-driving mode (SAE International 2018). According to SAE International, vehicle automation can fall under five different levels of automation: • Level 1 (Driver Assistance). This level represents the driving mode-specific execution by a driver assistance system of either steering or acceleration/deceleration using information about the driving environment and with the expectation that the human driver performs all remaining aspects of the dynamic driving task. • Level 2 (Partial Automation). This level represents the driving mode-specific execution of both steering and acceleration/deceleration using information about the driving environ- ment and with the expectation that the human driver performs all remaining aspects of the dynamic driving task. • Level 3 (Conditional Automation). This level represents the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene. • Level 4 (High Automation). This level represents the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task within a specifically constrained operational design domain, even if a human driver does not respond appro- priately to a request to intervene. • Level 5 (Full Automation). This level represents the full-time performance by an auto- mated driving system of all aspects of the dynamic driving task (DDT) under all roadway and environmental conditions that can be managed by a human driver. In the above definitions, the DDT includes the following decision-making tasks that are performed while driving: • Operational driving tasks. These tasks include the operational aspects such as steering, braking, accelerating, and monitoring the vehicle and roadway. • Tactical driving tasks. These tasks involve higher-level decision making such as responding to events or determining when to change lanes to achieve a higher headway or expected desired speeds. • Strategic driving tasks. These tasks are strategic in nature, such as determining destinations and waypoints, selecting parking spots, and so forth. The operational design domain (ODD) is the set of operating conditions within which a given driving automation system is specifically designed to function. The ODD includes, but is not limited to, environmental, geographical, and time-of-day restrictions, and/or the requisite presence or absence of certain traffic or roadway characteristics. For example, a dedicated lane with clear lane markings and free-flow traffic is an ODD for a system with Adaptive Cruise Control and Lane Centering.

8 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Requests to intervene are notifications by the automated driving system (ADS) to fallback- ready users that they should promptly perform the DDT fallback. The DDT fallback may entail: • The user (a human driver) resuming manual operation of the vehicle (i.e., taking active control over one or more driving tasks), or • The user taking steps to achieve a minimal risk condition (i.e., by taking control of all DDTs long enough to safely park the vehicle), or • The ADS itself responding to achieve a minimal risk condition. The DDT fallback is the response by the user, either to perform the DDT or to take whatever steps are needed to achieve a minimal risk condition after occurrence of a DDT performance- relevant system failure or upon ODD exit, or the response by an ADS to achieve minimal risk condition, given the same circumstances. CAVs use the data provided over the wireless communication links to and from the vehicles to support driving automation systems at any of the five SAE International levels. CAV technolo- gies are quickly expanding in the transportation and automotive markets and are envisioned to bring tremendous operational, safety, environmental, and institutional impacts at sizable mar- ket penetration rates (MPRs) (Shladover and Bishop 2015). The CAV MPR can be the percent- age of vehicles on the roadways that are equipped with CAV technologies. To foster increased CAV market penetration within the near future, agencies are exploring a variety of strategies. One strategy, based on lessons learned from the implementation of managed lanes, is dedicating lanes to these special vehicles. By providing CAV users preferred access to some specific rights- of-way, CAV DL strategies could improve the performance of the transportation system and expedite the deployment of CAV applications. One highly researched CAV technology is the CACC system, which uses a combination of sensors and V2V and I2V communication to enable a vehicle to adjust its speed automatically to the speed of the preceding vehicle in the same lane (Shladover et al. 2015). Given the level of research and development activity associated with CACC systems, the research team decided to use CACC as the evaluation model for conducting CAV research in this project. The team also utilized dynamic speed harmonization (DSH), which dynamically adjusts the speeds of equipped vehicles on a freeway, in response to downstream congestion, to improve throughput and reduce shockwaves and the associated possibility of secondary crashes (Ma et al. 2016). For NCHRP 10-102(08), CAVs were defined as the class of vehicles that use CV technology to support some safety-critical and efficiency improvement functionality, and use automated control to manage the dynamic driving task for certain driving modes. In the modeling con- ducted for this project task, the CAVs were assumed to be partially automated (SAE Interna- tional’s Level 2). Specifically, the CAVs were assumed to have an automated driving system performing the operational driving tasks (steering, braking, accelerating, and monitoring the vehicle and roadway) under a cooperative vehicle-following mode when in a DL. It was assumed that, under all other situations in the model, a human driver was operating the vehicle. Addition- ally, it was assumed that for all vehicles, including the CAVs, the human drivers performed all tactical and strategic driving tasks. 1.3 Report Overview This report documents the analysis conducted as part of this research project, presents the analysis results, and provides generalized guidance. The report is organized into 10 chapters, as follows: • Chapter 1: Introduction. This chapter introduces the research report, expands on the project scope, and defines CAVs in terms of automated versus human control.

Introduction 9 • Chapter 2: Categories of Benefits and Disbenefits to Stakeholders. This chapter summarizes the literature review and identifies the types of stakeholders benefited (or disbenefited) when dedicating lanes to CAVs, factors influencing these benefits, and the potential performance measures. • Chapter 3: Connected and Automated Vehicle Applications. Several CAV applications exist in the research industry today. Assessing all of them was beyond the scope of this study. This chapter documents the research team’s approach to down-selecting two CAV applications and suitable modeling techniques for use in this project. • Chapter 4: Case Study Site Selection. This chapter documents the approach to choosing the right case study sites for conducting the analysis. The project team used modeling and simulation-based analysis to evaluate potential benefits and parameter sensitivity of CAV applications on overall traffic efficiency and safety. • Chapter 5: Analysis and Evaluation Approach. This chapter documents the researchers’ analysis and evaluation approach to conducting simulation-based analysis on the selected case study sites. The chapter also expands on some of the analysis assumptions that readers should be aware of when utilizing the results and recommendations provided in this report. • Chapter 6: Evaluation Results. This chapter expands on the results of the modeling and simulation-based analysis. The results are categorized based on sensitivity parameters analyzed in this research project. This chapter is highly technical and should be read in conjunction with the technical tables and figures provided. • Chapter 7: Review of Laws and Regulations Regarding Dedicating Lanes. This chapter sum- marizes the literature review and identifies the laws and regulations regarding dedicating lanes to specific categories of users. Historically, lanes have been dedicated to HOVs, motorcycles and bicycles, buses, alternative fuel vehicles, and trucks. • Chapter 8: Guidance on Operational Characteristics and Impacts. Based on the evalua- tion results presented in Chapter 6 and the laws and regulations presented in Chapter 7, this chapter expands on specific guidance for agencies interested in dedicating lanes to CAVs. • Chapter 9: Future Research Directions. Although this project expanded the current spectrum of understanding with respect to dedicating lanes to CAVs, unknowns remain that require further research to understand how different applications may impact managed lane facilities. Chapter 9 presents specific research directions that may need to be undertaken in the near future. • Chapter 10: Conclusions. This chapter summarizes the project analysis.