Connected Vehicle Data Safety Applications (TTI-05-01)

Statewide District Segment SPF Data Project Description: The large-scale assessment of driving behavior impacts on traffic safety and on-going surveillance is hindered by data collection difficulties, small sample sizes, and high costs. Today’s connected vehicles (CV) now offer massive volumes of observed driving behavior data from newer vehicles that come with myriad electronics and sensors that monitor the state of the vehicle, environmental conditions, and its driver’s actions. This project evaluated the viability CV data in roadway safety applications with the objective of improving the existing predictive crash methods. The project team prepared safety data for statewide rural, 2-lane, TxDOT-maintained roadway segments to support a safety performance function model. Data Scope: This data includes the model results from a safety performance function model and includes segment node ID, predicted crash count, expected crash count, and the difference between expected and prediction crash counts for statewide rural, 2-lane, TxDOT-maintained roadway segments. The data set includes 188,758 segments and is CSV tabular format. Data Specification: Please see the Data Specification document below. Urban Intersection SPF Data Project Description: The large-scale assessment of driving behavior impacts on traffic safety and on-going surveillance is hindered by data collection difficulties, small sample sizes, and high costs. Today’s connected vehicles (CV) now offer massive volumes of observed driving behavior data from newer vehicles that come with myriad electronics and sensors that monitor the state of the vehicle, environmental conditions, and its driver’s actions. This project evaluated the viability CV data in roadway safety applications with the objective of improving the existing predictive crash methods. The project team prepared safety data at intersections in the urban area of Tyler district, Texas to support a safety performance function model. Data Scope: This data includes the model results from a safety performance function model and includes intersection node ID, control type (signalized, unsignalized), number of legs, and predicted and expected crash frequencies at urban intersections in the TxDOT Tyler District. The data set includes 1,532 intersections and is CSV tabular format. Data Specification: Please see the Data Specification document below.

全州辖区路段分段安全性能函数（Safety Performance Function，简称SPF）数据项目说明：
针对驾驶行为对交通安全的影响及持续监测开展大规模评估，常因数据采集难度大、样本量偏小及成本高昂而受阻。如今，联网汽车（Connected Vehicles，简称CV）搭载了大量电子设备与传感器，可监测车辆状态、环境状况及驾驶员操作，从而提供海量实测驾驶行为数据。本项目旨在优化现有碰撞预测方法，评估了联网汽车数据在道路安全应用中的可行性。项目团队针对全州范围内由得克萨斯州交通局（Texas Department of Transportation，简称TxDOT）养护的双向两车道乡村路段，整理了交通安全数据以支撑安全性能函数模型的构建。
数据范围：本数据集包含安全性能函数模型的运行结果，涵盖全州范围内由TxDOT养护的双向两车道乡村路段的路段节点ID、预测碰撞次数、期望碰撞次数，以及期望碰撞次数与预测碰撞次数的差值。本数据集共包含188758个路段，采用CSV表格格式存储。
数据规范：详见下文的数据规范文档。

城市交叉口安全性能函数数据项目说明：
针对驾驶行为对交通安全的影响及持续监测开展大规模评估，常因数据采集难度大、样本量偏小及成本高昂而受阻。如今，联网汽车（CV）搭载了大量电子设备与传感器，可监测车辆状态、环境状况及驾驶员操作，从而提供海量实测驾驶行为数据。本项目旨在优化现有碰撞预测方法，评估了联网汽车数据在道路安全应用中的可行性。项目团队针对美国得克萨斯州泰勒辖区城区内的交叉口整理了交通安全数据，以支撑安全性能函数模型的构建。
数据范围：本数据集包含安全性能函数模型的运行结果，涵盖TxDOT泰勒辖区城市交叉口的交叉口节点ID、控制类型（信号控制、无信号控制）、交叉口支路数量，以及预测碰撞频次与期望碰撞频次。本数据集共包含1532个交叉口，采用CSV表格格式存储。
数据规范：详见下文的数据规范文档。