The Highway Safety Information System (HSIS) is a database that maintains crash data, roadway inventory, and traffic volume data for several US states. It is an excellent source of data to highway safety research and can be used to investigate many research questions. However, to prepare an analysis-ready roadway safety dataset based on the HSIS or any databases that store the data in multiple different subsets and follow linear referencing, the researchers should integrate multiple datasets, merge or unmerge and remove certain inconsistent records, and finally clean the dataset. The HSIS staff is usually accommodating and eager to help, but sometimes the nature of data needs is complicated and laborious.
A tool named Roadway Safety Data Integrator (RSDI) was developed for combining, segmenting, and selecting homogeneous HSIS roadway segments and also crash assignment by desired crash fields (e.g., crash severity or type). The RSDI tool can be helpful for integrating different safety-related datasets such as roadway inventory (including grade, curve, and other subsets), traffic volume, and crash data; also, it can do required segmentation and identify the homogeneous roadway segments over the desired years of study that are the basis for development and calibration of the HSM predictive models. The RSDI tool can be used for similar purposes and not only limited to the HSIS data. It can be used for segmentation and finding homogeneous segments of any datasets that follow linear referencing.
The data consists of the following items:
- The RSDI tool and its guide
- Integrated and raw HSIS data from states of Illinois (R2U: 2005-10) and Washington (R2U: 2010-15)
R2U: Rural two-lane, two-way roads
This data is generated for a registry-based unmatched cohort study. We identified trauma patients requiring intubation during resuscitation in the emergency department, then extracted relevant trauma related parameters and outcome data from the trauma registry of the hospital and patients’ health records.
The hospital trauma registry captures any patients who met trauma resuscitation team activation criteria, who were triaged as critical or emergency in the ED, who died (excluding death prior to arrival to ED) and were admitted to intensive care units (ICU)/high dependency units (HDU). Injury and outcome data were prospectively collected and entered into the registry.
This file contains all the coreflooding experimental data used in the current study. Each experiment structure contains the 3D porosity, permeability, and CO2 saturation maps. All 3D maps are inlet first. Part of the data is processed from datasets contained in Reynolds et al. (2018).
The traffic flow in southeast Asian countries comprises of vehicles of various static and dynamic characteristics (mixed traffic conditions). In these conditions, there exists free movement (lane-less movement) behaviour particularly amongst the two-wheelers. In this traffic regime, the manoeuvre of vehicles is a complex phenomenon leading to integrated driving behaviour. The aim of this article is to assess the lane-changing behaviour of vehicles under mixed traffic stream at the merged section of urban roads. The lane-changing model MOBIL (Minimising Overall Braking Induced by Lane Changes) is combined with the Intelligent Driver Model (IDM) as an underlying car-following model to implement lane-changing rules for different vehicle classes, upon applying the "Politeness", and "Vehicle-type" factors. The traffic flow during peak hours at the merging section is analysed microscopically through video data. Based on these data, the merging behaviour models for studying discretionary and mandatory lane changing behaviours are developed. The merging manoeuvre data from video recording is utilised to calibrate and validate the models. The findings from this article enable one to understand the lane-changing movements of different vehicle-types at merging sections. Also, the developed models can be utilised to mimic congested traffic stream realistically under mixed traffic conditions. Finally, this study provides a deeper insight into the safety management at uncontrolled junctions, thus enabling one to implement various traffic control measures.