Issue link: http://uwashington.uberflip.com/i/903202
18 Pacific Northwest Transportation Consortium • Project: Streamlining the Crash Reporting Process in the Pacific Northwest • PI: Kevin Chang (UI), kchang@uidaho.edu • Co-Investigators: David Hurwitz (OSU), Michael Lowry (UI) There are over five million traffic crashes reported annually in the United States [NHTSA] and the documentation process for every single crash begins at the scene of the incident with information gathered by a member of the law enforcement community or by the private citizens. This information is subsequently transmitted to a local and state agency for data entry, processing, and aggregation. Given the volume of incidents and the multiple handoffs between different parties, the likelihood for transmission error and interpretation deviation necessitate a cradle-to-grave examination of this reporting process. Furthermore, each state has developed its own independent tracking system, rendering data comparisons across state boundaries to be inconsistent. These collective issues justify the need to examine crash reporting and to identify a process where data entry is streamlined to best meet the needs of all system users that include, but are not limited to: law enforcement, local and state agency data analysts, national and state agency safety offices, and researchers and academicians who must rely on good data to draw conclusions and recommend purposeful safety improvements. The objectives for this project include the development of a cradle-to-grave crash reporting process in the Pacific Northwest that maximizes usability, accuracy, and accessibility for incident responders, local and state agencies, and citizens and academicians. This includes determining where the introduction of errors occurs in each state's reporting process and the root causes of those errors. • Project: Freeway Traffic Safety and Efficiency Enhancement through Adaptive Roadway Lighting and Control Enabled by Connected Sensor and Infrastructure Networks • PI: Yinhai Wang (UW), yinhai@uw.edu • Co-Investigators: Zhibin Li (UW), Haizhong Wang (OSU) Roadway safety and efficiency are adversely affected by bad weather conditions and environmental factors such as inappropriate lighting, sun glare, etc. Recent studies have found that uniform roadway lighting does not necessarily make a roadway safer. Instead, adaptive lighting based on roadway, traffic, environmental, and weather conditions is likely to achieve better safety, efficiency, and energy conservation benefits than conventional lighting schemes. Although the benefits of combining adaptive lighting and active traffic management (a control strategy demonstrated effective in reducing crashes and enhancing vehicle throughput) may seem obvious, very little research has been done along this line. The objective of this project is to develop an adaptive roadway lighting methodology and a supporting simulation platform through which it can be tested. The methodology will consider multi-source data including roadside sensor outputs, weather data, roadway geometrics, and elevation data as inputs in order to determine an optimal lighting strategy, as well as active traffic management (ATM) strategies. Specifically, this project will conduct research to evaluate the feasibility and value of controlling the roadway lighting system based on site—and time specific characteristics (i.e., weather, traffic, pavement marking conditions). Communication bandwidth offered by the ROAM (ROSS Open Antenna Management) system (produced by Acuity Brands) will be used for coordinating illumination from light to light, as well as provide for between lights and roadway users via their mobile devices. Existing data archived in the DRIVE Net system (e.g., traffic detector data, probe vehicle data, roadway safety data, etc.) can be used to calibrate a simulation platform that will allow experimentation with and testing of the methodology before putting it into practice. Such a connected system that combines adaptive lighting and active traffic control strategies is expected to significantly increase safety and efficiency.