详情

Safety Performance for Active Transportation Modes using Exposure Models

项目名称：	Safety Performance for Active Transportation Modes using Exposure Models
摘要：	The need to develop a modally integrated and well-functioning transportation systems is clear. It is in the interest of agencies to provide safe, accessible, and reliable systems for all, including users who walk, bike, and use mobility assistive devices. At the same time, the financial resources for local and state agencies operating these systems are dwindling, and many agencies are having difficulty maintaining their current assets at a state of good repair, let alone funding for new infrastructure. This often leads to projects being built that prioritize motorized vehicle mobility over facilities that move people and advance the use of active transportation modes (walking and biking) as part of accessible, healthy, and connected communities. In addition to funding challenges, facilities that support active transportation modes often receive less priority because the data and decision-making tools are not available to assess the potential safety performance tradeoffs when evaluating alternatives that includes facilities for walking and biking. This research will investigate a means to assess the safety performance of projects for multiple modes of transportation in the suburban and urban environments. To fully support all modes and to improve the safety and equity of the system, it is critical to develop a method to determine potential use of the system by those who bike, walk and use mobility assisted devices. Counts of people walking and biking are often unavailable in these environments or have not been collected in a manner that is usable for crash prediction or for a comparisons of the needs of all users to support equitable decisions. Because of this lack of information, the benefits and opportunities for active transportation are often overlooked. This research will look at key factors simultaneously to evaluate the safety performance of urban and suburban facilities using characteristics such as speed, roadway width, (lanes/shoulders/medians), sidewalks, bicycle lanes/tracks, land use, network connectivity, and access management. These factors will be used to determine current and future system usage by the active transportation modes if compatible land use, or walkable and bikeable facilities were to be provided. In addition, various design treatments for active users in different contexts will be evaluated using proven exposure prediction methods for active transportation modes to examine how these designs effect the likelihood of crashes. Exposure models allow organizations to incorporate predictive information into decisions when volume data for those who walk or bike are not available and organizations do not have the funds to collect data at every location where a project might be developed. Even if data are collected in some locations for individual projects, state and local transportation agencies are unlikely to have sustainable funding available for regularly collecting active transportation volume data; therefore, these critical modes lack the necessary data to justify their consideration during project planning and development. This lowers the potential for inclusion of transportation facilities for active transportation modes, particularly in lower income areas where significant gaps for active transportation modes exists. It is intended that this research advance a decision-making framework that considers how best to achieve an integrated multimodal approach on the public roadway system. The proposed research would develop methods for using exposure volumes that could be used in other tools, such as the models currently under development in NCHRP Project17-84, “Pedestrian and Bicycle Safety Performance Functions for the Highway Safety Manual” and other systemic safety tools. In addition, this research would develop additional safety performance functions to supplement the NHCRP Project17-84 models with additional functional classes and contexts. This proposal is developed with the intent to supplement other ongoing predictive modeling and systemic tools for active transportation. The objective of this research is to advance the predictive methodologies for pedestrians and bicyclist by using exposure (e.g., pedestrian any bicyclist volume) prediction. These exposure models can then be used to develop multivariate safety performance functions and systemic analysis tools for use in planning, design and operational decision-making. The safety performance functions will address additional functional classes and contexts to supplement those developed being develop under NCHRP Project17-84. This proposed research will develop models and predictive methods that can be used by state and local agencies of all sizes to determine potential exposure, which will allow this information to be used to determine the likely safety performance at a given location. The multivariate nature of this approach allows for the necessary planning, design, and operational considerations in different combinations and contexts upfront, so the needed information related to potential safety performance can be used in decision-making throughout planning and the project development process for active transportation modes. This research will also develop guidance for determining appropriate considerations and modifications to the public right of way to provide for an equitable system for people biking and walking in a manner that reduces the likelihood of crashes involving these users and the likelihood of death or serious injury of these users in the event of a crash. The research will be conducted in two phases. Phase I will include at least the following tasks: Task 1—Review safety literature to identify the pedestrian exposure factors associated with highway safety; Task 2—Identify data needs and potential data sources for developing exposure models and safety prediction models for the identification of appropriate multimodal roadway locations, as well as development of a data collection plan; Task 3—Prepare a Phase II work plan indicating a methodological framework for developing exposure and safety prediction models; and Task 4—Prepare an interim report documenting the literature review, datasets, and proposed work plan. Tasks to be performed in Phase II will include at least the following: Task —Execute the work plan developed in Task 4 and approved by the NCHRP panel; Task 6—Conduct the statistical analysis (i.e., exposure and safety prediction) using the proposed methodologies that have been approved by the NCHRP panel; Task 7—Develop a user-friendly guideline to facilitate the implementation of research outcomes and how these models can be used to inform multimodal decision-making in different design contexts and for different modal priorities, including a discussion of how changes in the road environment may change safety outcomes for all users and how roadway (mitigation) strategies can be applied to address the respective changes; and Task 8—Prepare final deliverables and guidelines documenting the research development process, including all assumptions, research results, and the guidelines on incorporating exposure factors in safety research. The final set of deliverables should include the research datasets with a data dictionary, and the program code used for the development of any statistical models to support future research and reproducibility of research results. The AASHTO Committee on Safety and the TRB Highway Safety Performance Committee have identified this research as a high priority. This research will have a significant influence on how the state departments of transportation analyze pedestrian and bicyclist accommodations and will provide the ability to make use of additional analytical tools.
状态：	Proposed
资金：	700000
资助组织：	National Cooperative Highway Research Program<==>Federal Highway Administration<==>American Association of State Highway and Transportation Officials (AASHTO)
项目负责人：	Jawed, Inam
开始时间：	20210527
主题领域：	Highways；Pedestrians and Bicyclists；Planning and Forecasting；Safety and Human Factors