Successful Strategies for Adoption of Safety Cameras – New York City, NY

Background

Speed is a persistent traffic safety issue; particularly in areas with high pedestrian and/or bike users. One effective enforcement strategy that has been utilized is Automated Speed Enforcement (ASE), more recently termed “safety cameras.”

However, agencies have often struggled with implementing safety cameras due to citizen concerns, legislative resistance, speeding not being perceived as a safety issue, and privacy issues. Implementation has also battled the perception that automated enforcement is a “money grab.”

Due to the high number of pedestrians and bicyclists, New York City (NYC) had a particular interest in the use of safety cameras. In 2013, pedestrian and bicyclist crashes accounted for 28 percent of all police reported crashes but made up 65 percent of fatalities in New York City. Additionally, unsafe speed was noted as a contributing factor in 7 percent of all crashes but accounted for 25 percent of fatal crashes.

New York City faced typical oppositions to safety cameras such as legislative restrictions and citizen resistance. They successfully instituted a safety camera program in school zones through several strategies.

Consistent Speed Limits for Vulnerable Road Users – Examples from Various Agencies

Background

Speed limits are sometimes inconsistent within a jurisdiction for similar roadways. In some cases, this is because speed limits are applied to roadway sections based on characteristics which may not be obvious to the driver. For instance, speed limits on one roadway classified as a collector are set at 35 mph while another collector with similar characteristics is set at 30 mph due to a higher crash history. Since both appear similar to drivers, they are likely to apply the speed they believe is the most suitable to both roadways.

In other cases, as noted by “Methods and Practices for Setting Speed Limits: An Informational Report,” varying levels of experience, use of different procedures, as well as subjective procedures for determining speed limits can lead to inconsistencies in setting speed limits within or between jurisdictions. In either case, inconsistency violates driver expectancy and can lead to drivers disregarding speed limits.

Data Management & Spatial Integration: Missouri’s Transportation Management

This case study documents how the Missouri Department of Transportation’s (MoDOT’s) Transportation Planning Division (TPD) coordinates with the Information Systems unit (the agency’s information technology unit) and the State’s Traffic Records Coordinating Committee (TRCC) to form a leadership group that supports the State’s data management and integration activities. The State’s Transportation Management System (TMS) stores all transportation-related data maintained by the agency. This Oracle-based database, directly managed by TPD, ties all asset data to a single, all public roads base map and linear referencing system (LRS). This provides a tabular LRS location and a spatial location compatible with geographic information systems (GIS) software for all data elements located along Missouri’s public roads network. This spatial orientation supports several data management and integration efforts between different business units within MoDOT, as well as with external partners that manage relevant safety data. The DOT committed to spatial data integration early in the development of the TMS, and it has led to the flexible and expandable repository that exists today.

Acadiana Planning Commission: Data Governance - Louisiana’s Local Government Partnerships

The Acadiana Planning Commission (APC) is the metropolitan planning organization (MPO) covering the Lafayette, Louisiana urbanized area. Although Lafayette proper is highly urban, the MPO in total covers a seven-parish area in southern Louisiana that also includes low density rural communities. The number and diversity of local partnerships that comprise APC's stakeholder base represent a common challenge for transportation agencies as they collect safety-related data and make data-driven decisions.

As part of Federal Highway Administration (FHWA)-sponsored data integration and planning projects in Louisiana, APC and the State Department of Transportation and Development (DOTD) created a joint data governance group. From this initial effort, APC developed partnerships and processes that help the region manage the data necessary to support APC’s roadway safety goals. The core lesson of APC’s success has been its leadership in bringing local agencies into the regional data governance group. APC forged the inter-agency and inter-departmental partnerships that are a fundamental feature of their data governance programs. The result is better quality safety data tailored to fit the analytic tools and methods APC and DOTD use. As the region’s highway safety lead agency, APC uses the improved data to support comprehensive local road safety planning and efficient project development. The regional data governance benefited State-level partners like DOTD and the Center for Analytics and Research in Transportation Safety (CARTS) at Louisiana State University. Participating in regional data governance gives the State agencies a venue for communicating standards, providing technical assistance, and collecting safety-related data. State practitioners also gain insights into local road safety issues. This regional approach to data collection and maintenance in Acadiana will serve as an example for the rest of the State as Louisiana promotes shared data governance processes and a unified transportation safety mission.

Kentucky Transportation Cabinet – Kentucky’s Network Screening Process

This purpose of this case study is to describe Kentucky’s network screening methodology for all State-owned roads, as well as local roads classified as a collector street or above. The Kentucky Transportation Cabinet’s (KYTC) Highway Safety Improvement Program requires a data-driven process to identify sites with a potential safety need and prioritize projects. The KYTC partnered with the University of Kentucky’s Kentucky Transportation Center (KTC) to develop a network screening approach to prioritize locations statewide to be targeted for future safety improvement projects. This network screening approach addresses five focus areas: 1) Roadway Departure Corridors, 2) Cable Barrier, 3) High Friction Surface Treatment (HFST) Segments, 4) HFST Ramps, and 5) Intersections. The KTC analyzed statewide enterprise road, traffic, and crash data to develop safety performance functions (SPFs) that predict crashes on all facilities encompassed by each focus area. The KTC used cumulative residual (CURE) plots to assess SPF model performance and identify outliers or issues inherent in the dataset that lead to worse model fit. The CURE plot approach also underscores the importance of thoughtful and homogenous site segmentation for improved performance and meaningful network screening results. This network screening methodology applies a State-specific approach to rank locations based on higher-than-expected crashes and associated crash costs.

Indiana Department of Transportation – Indiana’s State Road 37 Improvement Project

This case study presents an interchange alternatives analysis from the Indiana Department of Transportation (INDOT). The analysis supported a multi-agency planning and engineering effort that involved INDOT, the Indianapolis Metropolitan Planning Organization, Hamilton County, Town of Fishers, and City of Noblesville. These agencies identified State Road (SR) 37 from 126th Street in Fishers to SR 32/38 in Noblesville as a candidate for significant mobility and safety improvements. The SR 37 corridor project had two primary needs: 1) reduce existing and forecasted congestion at signalized intersections within the study area, and 2) reduce the crash frequency and rate at identified intersections. INDOT targeted five, at-grade signalized intersections along the study corridor for interchange improvements. The safety analysis applied State-specific safety performance functions (SPFs) and crash modification factors (CMFs) derived from the American Associate of State Highway and Transportation Officials (AASHTO) Highway Safety Manual (HSM) to predict crashes for "Build" and "No-Build" scenarios over a 20-year period between 2018 and 2038. The INDOT analysis encountered several key challenges, including key technical inputs for the Interactive Highway Safety Design Model (IHSDM) software and the application of the HSM to future design alternatives; for instance, INDOT did not apply the Empirical-Bayes (EB) method due to the significant change in the design and operational performance of the corridor between the Build and No-Build scenarios. INDOT’s ingenuity and engineering judgment allowed the agency to navigate many of these challenges, and the analysis predicted that the Build alternative, although originally proposed for its traffic operational improvements, should yield a safety benefit and reduce crashes compared to the No-Build alternative future.

Yale-Kilgore Road Safety and Traffic Assessment, ID

This case study presents a safety analysis by the FHWA, Western Federal Lands Highway Division (WFLHD) Highway Safety Team. The WFLHD used the Interactive Highway Safety Design Model (IHSDM) software as part of the design process for the rehabilitation of Yale-Kilgore Road. Yale-Kilgore Road is a county owned and operated two-lane undivided road located in Clark and Fremont counties in Idaho (ID). The western half of the corridor in Clark County is unpaved and transitions to paved asphalt at the county line with Fremont County. Although the road is county owned and operated, a substantial portion of the corridor falls within the Caribou-Targhee National Forest, and the eastern terminus of the corridor is less than 30 miles from an entrance to Yellowstone National Park.

High Visibility Enforcement – City of Oro Valley, AZ

Background

The Oro Valley Police Department (OVPD) has created a data-driven initiative to improve traffic safety in the town of Oro Valley, Arizona. The program is called "HiVE" or High Visibility Enforcement, designed to target intersections that have high crash rates. HiVE is described as an “educational” initiative rather than a strict enforcement detail with the following two primary components:

• OVPD publishes HiVE’s future deployment dates and times to television, print, radio, and social media. This is to alert the community about the increased visibility of law enforcement and to improve communications between the police and citizens. Partnering with local media is a key component of the HiVE
• During scheduled deployments, OVPD places six motorcycle officers in and around the targeted intersections. Motorcycle officers actively enforce traffic violations during peak travel times. The graphic below shows the HiVE logo developed for communications and program identification.

OVPD reminds motorists not to engage in distracted driving or other driving behaviors that contribute to avoidable injury or fatal vehicle crashes.

Targeted Reporting of Speeding-Related Crashes – Arizona DOT

Background

Speed Too Fast for Conditions (STFC) is a field provided on most agency crash forms. The intent is to label scenarios where a driver was traveling below the posted speed limit but the speed at the time of the crash was not appropriate for prevailing environmental conditions and was a contributor to the crash. However, significant variations exist in interpreting the definition of the environment when coding crash forms. As a result, it is often left to the attending officer's interpretation.

In Arizona, historically STFC was defined as “Traveling at a speed that was unsafe for the road, weather, traffic or other environmental conditions at the time.” In many cases, an officer would include the behavioral or human environment and could interpret driver incapacity (Driving Under the Influence (DUI), impaired, distracted, fatigued) as a condition that would warrant traveling at a lower speed regardless of actual roadway conditions. For instance, a drunk driver on dry daytime roads traveling under the speed limit could be coded as Speed Too Fast for Conditions if the officer felt the state of impairment warranted a lower speed. Depending on the attending officer’s interpretation, there may be scenarios in which no speed is safe for conditions¹. While it is important to address these crashes, solutions should focus on the root cause of the crash when feasible. Countermeasures geared specifically towards speeding, such as Dynamic Speed Feedback Signs (DSFS), lane narrowing, or use of landscaping, may be less effective when the driver is impaired. Rather areas with a high number of impaired crashes should be targeted with countermeasures that address the impairment, such as enforcement.

Georgia DOT Uses Curve Safety Assessment Devices for High Friction Surface Treatment Site Selection

Problem

RwD crashes are a major emphasis area for Georgia. To address this crash type, the Georgia Department of Transportation (GDOT) investigated and implemented several solutions to crashes in horizontal curves. One solution that GDOT is using to improve safety is the installation of high friction surface treatment (HFST) at curves where roadway departure crashes have the greatest likelihood of occurring. GDOT initially identified these curves using a traditional ball bank indicator. This approach is both time and resource intensive requiring two to three workers (driving, reading, and writing). Therefore, a new approach was tested: a market-ready curve safety assessment device that utilizes an electronic ball bank indicator traditionally used for setting safe speeds in curves. This market-ready device also has a website/database where all the raw data could be stored and access at any time.

Solution

GDOT rents devices for each of its Districts to collect data on all curves on the road network. Once the data is collected, GDOT manually locates each curve on the road network since data from the devices are imported as individual data points and may not be geospatially located correctly. GDOT will use the tool and database developed from the Georgia Tech research project to determine potential safety projects. Crash and historical probe speed data will be used with the curve data to determine which projects are to be implemented first by using a benefit/cost ratio ranking.