Massachusetts Department of Transportation

Safety Data 

Visualization 

MassDOT’s IMPACT Tool and Promoting 

Safety Planning in Massachusetts

SAFETY DATA CASE STUDY 

FHWA-SA-21-078 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
i SAFETY DATA CASE STUDY FHWA-SA-21-078

Notice 

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement.
ii SAFETY DATA CASE STUDY FHWA-SA-21-078

1. Report No.

FHWA-SA-21-078

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Safety Data Visualization: MassDOT’s IMPACT Tool and Promoting Safety 

Planning in Massachusetts

5. Report Date

February 2022

6. Performing Organization Code

7.Author(s) 

Jeff Gooch, Ivy Huang, and Ian Hamilton

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive 

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Case Study, January 2020-June 2022 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche. Sarah Weissman Pascual provided editorial review and support for 

technical content.

16. Abstract

The Strategic Highway Safety Plan (SHSP) is a critical part of the Federal Highway Safety Improvement Program (HSIP). 

As part of the SHSP process, States are encouraged to take a broad view of safety needs and identify over-arching emphasis 

areas (EAs). This case study presents a safety analysis and technology improvement project conducted by the 

Massachusetts Department of Transportation (MassDOT) to increase access to roadway safety-related data for multidisciplinary and multi-jurisdictional stakeholders in the State. In 2020, MassDOT received a grant from the United States 

Department of Transportation (USDOT) as part of the USDOT’s competitive Safety Data Initiative (SDI) process. This 

grant allowed MassDOT to develop a series of tools and analyses to upgrade the State’s web-based safety data portal, 

IMPACT, and provide MassDOT’s safety partners across the State with greater access to data, capacity, and insights to 

encourage traffic safety initiatives and awareness. MassDOT’s innovative use of traditional and non-traditional data 

sources allowed the agency to analyze many of the State’s core safety priorities and EAs. The improvements to the 

IMPACT platform were the result of a collaborative process between MassDOT and the agency’s institutional and 

jurisdictional partners, and it will support and promote safety initiatives throughout Massachusetts.

17. Key Words: 

Data, Visualization, Systemic, Safety, 

Engagement

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 19 

22. Price

Technical Documentation Page
iii SAFETY DATA CASE STUDY FHWA-SA-21-078
iv SAFETY DATA CASE STUDY FHWA-SA-21-078

Acronyms 

Acronym Description

A Suspected Serious Injury

AADT Annual Average Daily Traffic

B Suspected Minor Injury

DOT Department of Transportation

EA emphasis area

FHWA Federal Highway Administration 

HSIP Highway Safety Improvement Program

K Fatal Injury 

MassDOT Massachusetts Department of Transportation

mph miles per hour

RPA Regional Planning Agency

RwD roadway departure 

SDI Safety Data Initiative

SHSP Strategic Highway Safety Plan

USDOT United States Department of Transportation
v SAFETY DATA CASE STUDY FHWA-SA-21-078

Table of Contents 

Introduction........................................................................................................................ 1

Background ........................................................................................................................ 2

IMPACT Enhancements and Development Process ..................................................... 3

Training............................................................................................................................... 9

Challenges .......................................................................................................................... 9

Conclusions and Lessons Learned .................................................................................10

References ........................................................................................................................12

List of Figures

Figure 1. Graphic. Screenshot of the IMPACT Safety Analysis Tools module........... 2

Figure 2. Graphic. Crash tree example for KAB speeding-related crashes. ............... 6

List of Tables

Table 1. Summary of risk factor methods used by emphasis area. ............................. 7

Table 2. Sample table of speeding risk factors and associated scores......................... 8
vi SAFETY DATA CASE STUDY FHWA-SA-21-078

Executive Summary

The Strategic Highway Safety Plan (SHSP) is a critical part of the Federal Highway Safety 

Improvement Program (HSIP). As part of the SHSP process, States are encouraged to take a 

broad view of safety needs and identify over-arching emphasis areas (EAs). This case study 

presents a safety analysis and technology improvement project conducted by the Massachusetts 

Department of Transportation (MassDOT) to increase access to roadway safety-related data 

for multi-disciplinary and multi-jurisdictional stakeholders in the State. In 2020, MassDOT 

received a grant from the United States Department of Transportation (USDOT) as part of the 

USDOT’s competitive Safety Data Initiative (SDI) process. This grant allowed MassDOT to 

develop a series of tools and analyses to upgrade the State’s web-based safety data portal, 

IMPACT, and provide MassDOT’s safety partners across the State with greater access to data, 

capacity, and insights to encourage traffic safety initiatives and awareness. MassDOT’s 

innovative use of traditional and non-traditional data sources allowed the agency to analyze 

many of the State’s core safety priorities and EAs. The improvements to the IMPACT platform 

were the result of a collaborative process between MassDOT and the agency’s institutional and 

jurisdictional partners, and it will support and promote safety initiatives throughout 

Massachusetts.
1 SAFETY DATA CASE STUDY FHWA-SA-21-078

Introduction 

The Strategic Highway Safety Plan (SHSP) is a critical part of the Federal Highway Safety 

Improvement Program (HSIP). As part of the SHSP process, States are encouraged to take a 

broad view of safety needs and identify over-arching emphasis areas (EAs; e.g., lane departure, 

intersection safety, or age-related issues) for attention over a multi-year period. These plans 

not only inform the priorities of a State department of transportation (DOT), but they also 

provide an avenue for multi-disciplinary and multi-jurisdictional collaboration. An SHSP can help 

align the activities of engineers, law enforcement, emergency services, and educators, as well as 

provide a means for a State DOT to communicate with its regional and local stakeholders.

Purpose and Need

This case study presents a safety analysis and technology improvement project conducted by 

Massachusetts Department of Transportation (MassDOT) to increase access to roadway safetyrelated data for multi-disciplinary and multi-jurisdictional stakeholders in the State. In 2020, 

MassDOT received a grant from the United States Department of Transportation (USDOT) as 

part of the USDOT’s competitive Safety Data Initiative (SDI) process. This grant allowed 

MassDOT to develop a series of tools and analyses to upgrade the State’s web-based safety 

data portal, IMPACT, and provide MassDOT’s safety partners across the State with greater 

access to the agency’s data, capacity, and insights to encourage traffic safety initiatives and 

awareness. 

Target Audience: 

• Executive leadership. 

• Information technology staff. 

• Data managers, analysts, and stewards. 

• Stakeholders involved in the SHSP process, including planning, engineering, education, 

law enforcement, emergency response staff. 

• Local technical assistance program managers and staff.
2 SAFETY DATA CASE STUDY FHWA-SA-21-078

Background 

IMPACT originally went live in 2018. Initial functionality included dashboards for high-level data 

review, the ability to query and download crash data, automatic report generation, and the 

ability to develop cross-tabulations of crash data. Although access to data was helpful for its 

partners, MassDOT soon identified the need to provide safety analysis and results in the tool. 

With USDOT’s SDI grant, MassDOT developed a Safety Analysis Tools module for IMPACT that

includes crash-based network screening maps, risk-based network screening maps, a crash tree 

maker, and a test of proportions tool. Figure 1 is a screenshot of IMPACT’s Safety Analysis Tools

module. 

These tools and visualizations fill a critical need for MassDOT; they provide data-driven outputs 

to support project selection for MassDOT’s HSIP and SHSP programs. They also make these 

outputs accessible to the State’s stakeholders and the general public. MassDOT’s risk-based 

network screening results follow the EAs outlined in the State’s SHSP. The accompanying risk 

maps allow MassDOT to inform safety stakeholders about locations of greatest safety risk and 

proactively plan projects with the greatest need. Prior to this project, MassDOT’s safety 

program was primarily based on a hot spot approach; this hot spot analysis process identified 

intersections for safety projects based on crash history.

While this addressed locations that experienced a high frequency of crashes, a hot spot 

approach does not account for the infrequency of severe crashes; these are susceptible to 

regression to the mean and tend not to cluster year after year. A network screening approach 

based on risk rather than crash history alone identifies and proactively addresses risk factors 

that could contribute to severe crashes before they occur. This case study focuses on the 

Figure 1. Graphic. Screenshot of the IMPACT Safety Analysis Tools module.

© MassDOT
3 SAFETY DATA CASE STUDY FHWA-SA-21-078

process MassDOT employed to identify risk factors for most EAs in the State’s SHSP and 

visualize the results to encourage data-driven decision-making across the State.

IMPACT Enhancements and Development Process

USDOT’s SDI grant process required MassDOT to conduct analysis on a condensed 12-month 

timeline. This required parallel efforts that simultaneously conducted systemic analyses and 

developed a custom visualization module in the State’s IMPACT tool. This section documents 

how MassDOT incorporated relevant stakeholder feedback to develop a series of systemic 

analyses and maps. These maps visualize the highest priority locations for potential safety 

improvements across the State and for each Regional Planning Agency (RPA).

Stakeholder Engagement

MassDOT actively engaged potential users throughout the project to solicit input, including a 

kickoff meeting during the scoping stage and mid-way through the project. MassDOT held 

webinar-style meetings and invited potential users at Federal, State, regional, and local agencies, 

as well as consultants and representatives from public health, law enforcement, and public 

advocacy groups. MassDOT used these meetings to gather input on the usability of the tools, as 

well as preferred data formats produced by the application. MassDOT also held a recorded 

webinar at the conclusion of the SDI grant process to update stakeholders on project progress 

and functionality. 

Systemic Analysis Process

Based on stakeholder input and the project scoping process, MassDOT prepared a series of 

customized systemic analyses to address many of the State’s most important safety needs. This 

process included data collection, focus crash and facility type development, and risk scoring 

methodology formulation.

Data Approach

MassDOT performed systemic safety analyses to develop risk-based network screening maps 

for 12 EAs in the State’s 2018 SHSP. These EAs are loosely divided into two categories: EAs 

where the targeted countermeasure approach is typically engineering-focused (e.g., rumble 

strips for roadway departure), and EAs where countermeasures may be more behaviorallyfocused (e.g., targeted education and enforcement for occupant protection). 
4 SAFETY DATA CASE STUDY FHWA-SA-21-078

The complete list of EAs considered in MassDOT’s analysis include the following:

• Bicycles. 

• Distracted driving.

• Impaired driving.

• Intersections. 

• Large vehicles or trucks. 

• Motorcycles. 

• Occupant protection (or unbelted vehicle occupants).

• Older drivers (65 and older).

• Pedestrians. 

• Roadway departures (RwD) - urban and rural.

• Speeding and aggressive driving. 

• Young drivers (24 and younger). 

Individual systemic analysis identified potential risk factors associated with each EA. MassDOT 

used the following steps, based on the Federal Highway Administration’s (FHWA’s) Systemic 

Safety Project Selection Tool: 

1. Identify the focus crash types. 

2. Identify the focus facility types. 

3. Perform the risk factor analysis. 

4. Develop a risk factor scoring approach. 

5. Rank results based on risk category.

MassDOT used statewide crash and road inventory data for all EAs. Depending on the nature 

of each EA (either engineering or behavioral), MassDOT applied other supporting geospatial 

datasets (e.g., horizontal curves and Census data), reports, and tabular files. Examples of these 

supplementary datasets include: 

• Aging-related health and access data from the University of Massachusetts-Boston.

• Census estimates.

• Citation records.

• Driver’s license records.

• Environmental justice metrics (e.g., proportion of non-white population, limited English 

proficiency households, and median household income). 

• Freight corridors. 

• Hospital and emergency services locations.

• Intersections. 

• Motorcycle ownership. 

• School locations. 
5 SAFETY DATA CASE STUDY FHWA-SA-21-078

Identify Focus Crash Types 

MassDOT identified focus crash types using a 5-year crash dataset from 2013 to 2017, using the 

most recent year of closed crash data at the time of the analysis. The approach to identify the 

focus crash type was tailored for different EAs. Engineering EAs used a crash tree analysis, 

guided by engineering judgment. For instance, MassDOT identified angle crashes at 

intersections to further define the focus crash type. MassDOT focused solely on 

overrepresentation of severe crashes for behavioral EAs. The project team used crash severity 

as a filter on the focus crash type, and the target severity levels varied by sample size. While 

most of the systemic analysis focused on fatal injury (K) and suspected serious injury (A) 

crashes, suspected minor injury (B) crashes were included for some EAs to address low sample 

size concerns.

MassDOT used crash-level, vehicle-level, and person-level data as needed to query the relevant 

crashes for each EA. An example focus crash type identified by MassDOT for the Large Vehicle 

EA (engineering) is rear-end, non-intersection crashes involving heavy trucks. Meanwhile, focus 

crash types for the Occupant Protection EA (behavioral) is any severe crash involving an 

unbelted vehicle occupant. 

Identify Focus Facilities

MassDOT did not identify focus facility types for behavioral EAs because the common 

countermeasures—education and enforcement—are community-level countermeasures that do 

not necessarily apply to a specific class of road. Additionally, behavioral crashes, such as 

impaired driving or unbelted driving, occur on all roadway types. The Perform Risk Factor Analysis

section contains more information on how MassDOT applied community-level data to road 

segments. 

For engineering EAs, MassDOT used crash trees to identify the focus facility type (figure 2). 

The crash trees typically used a focus crash type (where applicable), area type, functional 

classification, and posted speed limit. MassDOT identified the combinations that accounted for 

the largest proportions of focus crash types and focus facility types. For example, focus facility 

types for the Large Vehicle EA were Interstates and Principal Arterial Freeways and 

Expressways for a focus crash type of rear-end, non-intersection large vehicle crashes.
6 SAFETY DATA CASE STUDY FHWA-SA-21-078

Perform Risk Factor Analysis

MassDOT used three methods to identify the potential risk factors: binary logistic regression, 

mileage exposure analysis, and negative binomial regression (table 1). These methods varied 

depending on the EA and the underlying data. For the roadway departure EA, mileage exposure 

analysis compared the portion of KA (or KAB) crashes against the portion of segment length (in 

miles) for the roadway characteristic of interest. Although MassDOT explored using vehicle 

miles traveled as the primary exposure metric, the agency decided that road mileage was a 

more reliable indicator across the State. MassDOT then selected risk factors by selecting 

roadway characteristics that were “overrepresented” for a combination of KA or KAB crashes 

compared to the mileage.

Figure 2. Graphic. Crash tree example for KAB speeding-related crashes. 

Source: USDOT
7 SAFETY DATA CASE STUDY FHWA-SA-21-078

Table 1. Summary of risk factor methods used by emphasis area.

Emphasis Area Method Approach 

Intersections Binary Logistic Regression

Large Vehicles or Trucks Binary Logistic Regression

Speeding and Aggressive Driving Binary Logistic Regression

Motorcycles Binary Logistic Regression

Bicycles Binary Logistic Regression

Pedestrians Binary Logistic Regression

RwD - Urban and Rural Mileage Exposure Analysis

Impaired Driving Negative Binomial Regression

Distracted Driving Negative Binomial Regression

Older Drivers Negative Binomial Regression

Occupant Protection Negative Binomial Regression

Young Drivers Negative Binomial Regression

For the remaining engineering EAs, MassDOT used binary logistic regression to identify risk 

factors. This probabilistic modeling technique assesses the probability that an event has 

occurred (i.e., a KA motorcycle crash) at a given segment based on the model inputs. Agresti 

(2007) provides more background information on this method. The odds ratio metric helped 

identify final risk factors, where a value greater than one represents an independent increase in 

the probability of a KA crash occurring on that segment for a given input variable. These 

models typically included standard road segment factors, such as number of lanes, posted speed 

limit, shoulder width, and others. MassDOT used segment length as an offset and treated the 

rest of the variables as binary inputs, including developing binned binary variables for continuous 

variables.

For behavioral EAs, MassDOT used negative binomial regression to identify community-level 

characteristics that are associated with higher frequencies of focus KA crashes. Negative 

binomial regression is a commonly used analysis method for over-dispersed count data (i.e., the 

variance exceeds the mean of the observed data). The dependent variable in the model is the 

total KA crashes for the EA of interest, making a count model appropriate for the data. 

Example independent variables included an offset of mile-years for the town as well as 

proportions of mileage by facility type, population density, citation history, alcohol sales 

establishments, and proportion of young-licensed drivers (age 15-24) in a town. 

After identifying community-level risk factors for the behavioral EAs, MassDOT further 

identified segment-level risk factors by reviewing the roadway characteristic data linked to the 

crash data. This methodology conformed to a similar approach in FHWA’s Crash Data

Summary Template. MassDOT identified any roadway characteristics which were statistically 

overrepresented for KA focus crashes compared to focus crashes of all severities as risk 

factors. This resulted in a two-tier series of risk factors for behavioral EAs, with segments 

receiving a town-level risk score and a supplemental segment-level risk score.
8 SAFETY DATA CASE STUDY FHWA-SA-21-078

Develop Risk Factor Scoring Approach

The number and the nature of identified risk factors varied for each EA; MassDOT developed 

unique scoring schemes for each risk factor and for each EA. Typically, a risk factor was scored 

between 0 and 1 depending on the risk factor characteristics, professional judgement, and the 

regression model results. For example, in the motorcycle crash analysis, the project team 

identified three RPAs as high-risk areas based on the logistic regression. Once MassDOT 

identified the most appropriate form for each risk factor score, the total risk score for a 

segment or town is the sum of the individual risk factor scores normalized by the number of 

risk factors (which ranged between 0 and 100 percent). Table 2 provides an example of this 

scoring method with respect to the speeding EA.

Table 2. Sample table of speeding risk factors and associated scores.

Variable Risk Factor Criteria Example Segment 

Characteristic

Risk

Score

Annual average daily 

traffic (AADT)

AADT between 20,000 and 40,000 

vehicles per day 25,000 0.875 

Proportion of younger 

drivers in census tract

Proportion of younger drivers in 

census tract between 0.15 and 0.21 0.12 0 

Divided or undivided Undivided segment Undivided 1 

Weighted average 

degree of curvature 

(degrees per 100 ft)

Weighted average degree of 

curvature 10 or more degrees per 

100 ft

11 0.6 

Posted speed limit in 

miles per hour (mph) Posted speed limit of 30 mph 30 1 

Sidewalk (left, right, or 

both) 

No sidewalk present on the 

segment 

None 1 

Right Shoulder Type Right shoulder is stable Stable 1 

RPA 

Segment is in the Pioneer Valley 

Planning Commission or 

Southeastern Regional Planning and 

Economic Development District

Southeastern Regional 

Planning and Economic 

Development District

1 

Total Risk Score: 6.475 

Normalized Risk Score: 81% 

Rank Results Based on Risk Category

MassDOT ranked the total risk factor results for each EA to identify locations for targeted 

countermeasures. MassDOT used a percentile rank approach to sort the normalized risk 

scores. Sites ranked in the top 5 percentile were categorized as “Primary Risk Sites,” and sites 

ranked in the next 10 percentiles (85th to 95th percentiles) were categorized as “Secondary Risk 
9 SAFETY DATA CASE STUDY FHWA-SA-21-078

Sites.” MassDOT excluded all other sites from the IMPACT visualization. MassDOT applied the 

percentile rankings at the statewide- and RPA-levels, producing two maps for each EA. 

Visualization

After assigning normalized risk scores to the relevant segments, MassDOT visualized the results 

in the IMPACT Risk-Based Network Screening tool. This tool provides a statewide map of 

segments that users can query against using different levels of interest, such as town, RPA, 

MassDOT engineering district, and route. For these maps, “primary risk sites” are colored red 

and “secondary risk sites” are colored blue; all other segments are gray. The only segments 

shown in the maps are focus facility type segments. The visualization also includes a data grid 

which provides attribute details for the segments, as well as dashboards that summarize

segment characteristics by category.

Training

Although MassDOT does not plan any formal trainings for its statewide stakeholders specific to 

the latest enhancements, MassDOT recorded a webinar in fall 2021 to demonstrate the tool. 

This recording provides an initial tutorial for new users and could form the foundation of future 

training efforts across the State. Existing IMPACT modules (i.e., prior to the enhancements) 

have guided tutorials and recorded trainings to support the tool’s use. 

Challenges

MassDOT encountered a few challenges during the development of the risk-based network 

screening maps. The following sections briefly describe some of the noteworthy challenges for 

agencies considering a similar data sharing, analysis, and visualization approach. 

Data Quality

MassDOT accomplished the series of analyses and completed the tool despite occasional 

challenges with data quality or missing data. MassDOT assessed these data on a case by case 

basis and made tradeoffs when necessary. To overcome missing datasets that might directly link 

to key safety indicators (e.g., bicycle and pedestrian counts), MassDOT applied known 

surrogates (e.g., population and employment density and access to a motor vehicle) to narrow 

the focus on locations of highest risk for certain EAs. MassDOT plans to refine the analyses as 

future data become available (e.g., probe data for speed and exposure).
10 SAFETY DATA CASE STUDY FHWA-SA-21-078

Crash Data

The process of finalizing a year of crash data in Massachusetts can be a time-consuming process. 

Even though the analyses were performed between 2020 to 2021, MassDOT used their last 5 

years of “closed” crash data, 2013 through 2017. Users of the tool expressed concern when 

informed of this, and MassDOT intends to regularly update the results with more recent crash

data when possible.

Road Inventory

MassDOT’s road inventory data has several very short segments due to the segmentation 

process applied by the agency’s geographic information system service. When modeling, 

MassDOT excluded short segments from their analysis to remove potential bias arising from 

these otherwise uninformative locations for roadway safety analysis and project development. 

However, these short segments present an issue for visualization. As a result, MassDOT 

aggregated these short segments to more practical lengths by dissolving the road inventory 

segments using the risk factor characteristics.

Visualization

MassDOT made minor compromises in the map visualizations given the differences in data 

between EAs. Performance could be significantly reduced for the tool to be completely dynamic 

and produce customized statistics on the fly. As a result, MassDOT had to assess tradeoffs

between the data to be visualized, the visualization methodology, and ensuring a quality user 

experience for users of all capabilities. 

Conclusions and Lessons Learned

Accessibility to data is a critical part of managing a statewide safety program. This not only 

includes the data required to conduct safety analysis, but it also means delivering the results of 

these analyses and insights to all decision makers across the community. Through a USDOT 

SDI grant, MassDOT was able to perform an extensive systemic analysis of nearly all of their 

SHSP EAs and introduce a proactive component to the agency’s entire safety program.

MassDOT’s innovative use of traditional and non-traditional data sources allowed the agency to 

analyze many of the State’s core safety priorities and EAs; while behavioral EAs are typically 

excluded from systemic analysis approaches, community-level data allowed MassDOT to 

identify priority road segments for more multi-disciplinary interventions. For future 

improvements, MassDOT noted that the tool could potentially include the ability to overlay and 

combine results across EAs. This would allow stakeholders to efficiently track and address 

segments that meet several risk criteria across SHSP EAs.

The improvements to the IMPACT platform were the result of a collaborative process between 

MassDOT and the agency’s institutional and jurisdictional partners, and it will support and 
11 SAFETY DATA CASE STUDY FHWA-SA-21-078

promote safety initiatives throughout the State. IMPACT’s visualization capabilities will engage 

stakeholders across Massachusetts, many of which may have had limited information in the past, 

and allow safety stakeholders from all backgrounds to contribute to safety efforts in their 

communities.
12 

References

 Agresti, A. (2007). An Introduction to Categorical Data Analysis. Second Edition. John 

Wiley & Sons, Inc., New York.

 Federal Highway Administration. (2021). “Strategic Highway Safety Plan” (website). 

Available online: https://safety.fhwa.dot.gov/shsp/. 

 Massachusetts Department of Transportation. (2018). Massachusetts Strategic Highway 

Safety Plan, Boston, MA. Available online: https://www.mass.gov/doc/massachusettsshsp-2018/download. 

 Massachusetts Department of Transportation. (2021). “IMPACT Home” (website).

Available online: https://apps.impact.dot.state.ma.us/cdp/home.

 Preston, H., Storm, R., Dowds Bennett, J., and Wemple, B. (2013). Systemic Safety 

Project Selection Tool, Report No. FHWA-SA-13-019, Washington, D.C. Available 

online: https://safety.fhwa.dot.gov/systemic/fhwasa13019/.

 University of Massachusetts-Boston. (2018). Healthy Aging Data Report: Highlights from 

Massachusetts, 2018, Boston, MA. Available online: 

http://mahealthyagingcollaborative.org/wpcontent/uploads/2018/12/MA_Healthy_Aging_Highlights_2018.pdf.

Contact

Massachusetts Department of Transportation

Bonnie Polin, State Safety Engineer

bonnie.polin@state.ma.us