Abstract
The large number of work zone crashes has been a significant concern of transportation agencies and researchers. In the US, a work zone crash occurred every five minutes during 2015-2019. One approach for transportation agencies to reduce work zone crashes is to lower the speed within work zones, for example, posting speeding limits and installing speeding cameras. This approach is supported by studies that highlighted that average traffic speed is associated with crash risk. However, the findings of the relationship between traffic speed and crashes are inconsistent, which could lead to conflicting or even misleading interventions with the speed enforcement in work zones. Work zone presence could lead to the reduction of actual traffic speed that influences crash risk and, at the same time, directly impose effects on crash risks. It is challenging to rigorously separate these direct and indirect impacts. Furthermore, the actual impact of speed enforcement countermeasures on work zone crash risk has been rarely studied among the literature, providing limited knowledge on whether these countermeasures are effective in reducing crash risk near work zones in practice.
In this research project, we apply a comprehensive causal analysis and Web-GIS approach to enhance work zone safety through speed enforcement in Pennsylvania and Maryland. It contains three core initiatives. First, it develops a causal inference model to analyze the impact of work zones on crash risk controlling for traffic speed with the equational g-estimation and regression discontinuity design (RDD), using multiple large-scale and high-granular data sets. Second, it examines the work zone impact on crash risk under different speed enforcement countermeasures. Lastly, the research team creates an interactive Web-GIS platform for comprehensive traffic safety analysis in work zones, enabling stakeholders to access and analyze crashes related to work zones, speed enforcement measures, and other important crash contributors, with continuous data updates planned until 2025. This platform aims to identify high-risk areas and provide insights for safety improvements in work zones.
First, we will establish a rigorous causal inference model to infer the causal impact of work zones on crash risk when the traffic speed is controlled with high-granular and multi-source data sets. We propose to use an innovative approach, i.e., the combination of the sequential g-estimation and RDD, to examine the causal effect of the presence of work zones on crash occurrences when the traffic speed is controlled. The sequential g-estimation removes the effect of traffic speed on crash risk. RDD mitigates the potential confounding bias caused by roadway characteristics. The proposed method will be implemented using high-granular and multi-source data of thousands of work zones in PA and MD between 2018 and 2023 to control for the complex built and natural environments and reduce the associated bias of the estimation. The results can provide insights for most desired and actual traffic speeds to reduce work zone crash risk.
Second, we will examine the impact of work zones on crash risk under different speed enforcement countermeasures. We will apply the same framework in the first step to examine the heterogenous causal impact of work zones on crash risk under different speed enforcement countermeasures, including no speed enforcement, posting speed limit, and posting speed limit along with enforcement (e.g., automated speed enforcement and high-visibility enforcement), and compare the impacts for the work zones in PA and MD. In addition, we will further estimate these heterogenous impacts (by speed enforcement countermeasure) under various work zone characteristics, time of day, and traffic volumes. The results can offer information on how different speed enforcement countermeasures modifiy the causal impact of work zones on crash risk and, accordingly, provide implications for better deploying these countermeasures.
Third, we will build an interactive Web-GIS platform for work zone traffic safety analysis using the safety data in PA and MD. The digital platform provides users with an online interactive interface to explore all work zones in PA and MD by multiple aspects, including speed enforcement countermeasures, average speed, traffic volumes, roadway characteristics. In addition, the platform can help users identify high-risk locations, highlight potential crash contributors, and offer suggestions on how to improve work zone safety for each work zone based on their characteristics and locations. In addition, we will continue to collect and archive up-to-date data from various data providers in both PA and MD from 2024 to 2025 and enhance the web platform. The safety data providers include PennDOT, MDOT SHA, Waze, NOAA, and private data sources, including INRIX, TomTom, and Replica. We will integrate and analyze large-scale crash data and develop an additional function to the platform to visualize and forecast crash types, frequencies, and severity for each road segment in the two states, especially those with work zones and different speed enforcement countermeasures. With that said, the platform allows transportation agencies and other related stakeholders, such as urban planning departments, local communities, consulting firms, and academic institutions, to access historical, real-time, and forecasted traffic safety metrics for all work zones. We will continue to interview various data providers to enhance the quality and quantity of massive data in both states.
Description
Timeline
Strategic Description / RD&T
The research aligns with USDOT priorities of human factors with the objective of analyzing how human factors, such as socio-demographic, human interaction with roadway, bike/walking infrastructure, work zones would impact vulnerable users including work zone workers, and how to optimally create best built environment to improve roadway safety. This research also addresses data driven system safety priority by analyzing the safety of statewide work zones with the integration of multi-source data from GIS, infrastructure, land-use, Census data and traffic data. Likewise, the project aligns with the Safety21 UTC focus on promoting transportation systems safety.
Deployment Plan
July – September 2024
1. Briefs and Demos to Maryland DOT SHA, PennDOT, Caltrans, toXcel Inc.
October – December 2024
1. Briefs and Demos to Maryland DOT SHA, PennDOT, Caltrans, toXcel Inc.
January – March 2024
1. Briefs and Demos to other areas, such as FHWA Turner-Fairbank research center
April – June 2024
1. Briefs and Demos to Maryland DOT SHA, PennDOT, Caltrans, toXcel Inc.
2. Develop research report.
3. Develop a prototype dashboard web-GIS application for PA/MD state roads
4. Develop policy brief for legislators.
Overall plan
We will work closely with the State of PA, MD, CA to implement this research. The team consisting of the PI, research scientist and phd students will hold a bi-weekly coordination calls to discuss difficulties encountered and proposed solutions, and to outline plans for completing the scope of work, key milestones and deliverables. When performing the tasks, we will together meet with project managers, engineers and staff at those state agencies who provides feedback/comments for each month, to ensure the model development and testing are consistent with state DOTs’ view, and the tasks are aligned with the partners’ needs.
In terms of implementation barriers, we will evaluate and prioritize barriers as the project progresses. Main potential barriers for this project are to identify work zone locations and configurations, and utilize them as independent variables to develop safety causal model. If any risks or barriers are identified during the project, we will use our domain expertise to find alternative methods or to seek professional help from both data and methodology perspectives utilizing resources from CMU and deployment partners, and more broadly, through the CMU’s National University Transportation Center on Safety (Safety21). The core CMU team at Mobility Data Analytics Center (MAC) has sufficient and somewhat overlapping expertise in safety data analytics, travel behavioral model, and GIS that we can reallocate personnel if needed. Another advantage is that this research team at MAC have been intensively collaborating on research projects in the past three years with necessary data analytics, GIS and traffic engineering skill sets, including three successful MDOT research projects, two successful PennDOT research projects and thus are able to reduce or eliminated those barriers.
Upon the completion of this project, we plan to actively seek both industrial and federal funding based on this initial development. Our framework is applicable to any large traffic networks with safety data and work zone information. This generality will attract attentions from various public agencies and non-profit organizations to better deploy safe roadway infrastructure and road construction project. Potential funding agencies/collaborators include the Department of Transportation, Federal Highway Administration, TRB, state DOTs, MPOs/RPOs, and local non-profits and mobility service companies.
Expected Outcomes/Impacts
The goal of this research is to develop a comprehensive and universally applicable framework of work zone safety models and tools for regional transportation networks. This includes estimating the work zone impact on crash risk under different speed enforcement countermeasures. Enhanced knowledge and awareness about the effects of various speed enforcement countermeasures on work zone impacts on crash risks, especially on roads with a history of fatal accidents. Improved understanding of how multi-source data can aid public agencies in achieving greater transparency and making more informed decisions regarding work zone planning and operations, especially in the context of evolving technologies and the increasing availability of diverse data sources.
Expected Outputs
The work zone speed enforcement framework will be accessible through open-source codes available online, complemented by a prototype web application. This application will leverage multi-modal data collected over several years from PA and MD, offering user interfaces to manage various work zone scenarios, including different weather conditions and traffic volumes. It will also enable visualization of crash risks for selected road segments. A case study in PA will assist public agencies in setting guidelines for work zone planning and operation. We plan to actively collaborate with local transportation agencies, including PennDOT and MDOT SHA, to assess their interest in these tools and integrate them into their regular operations.
Modeling scripts: The scripts for work zone safety analysis under different speed enforcement countermeasures, with a description of different causal variables used in the model. By June 30, 2025, he scripts will be shared with state DOT officials for establishing work zone safety analysis.
Web-GIS platform: The scripts and database files of a prototype Web-GIS platform for work zone traffic safety analysis. By June 30, 2025, the platform will be hosted in the server located in either CMU or local transportation agencies.
Final report: A technical report summarizing all data sets, innovations, technical details of proposed econometric models, solution algorithms, case studies, and findings. By June 30, 2025, the report will be fully edited and ready for publication in academic journals.
TRID
There lacks a digital interactive platform to conveniently explore the safety of work zones. Although online crash dashboards have been widely offered by both federal and local transportation agencies, such as the Fatality Analysis Reporting System by the National Highway Traffic Safety Administration and the Crash Dashboard by the Florida Department of Transportation, none of these platforms have incorporated work zone data or provided functions to explore the safety of work zones. The National Work Zone Safety Information Clearinghouse hosts a static web page with limited crash data related to work zones, offering no flexibility for users to customize and export datasets.
https://cdan.dot.gov/DataVisualization/DataVisualization.htm
https://www.flhsmv.gov/traffic-crash-reports/crash-dashboard/
https://workzonesafety.org/work-zone-data/
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| hcain@andrew.cmu.edu |
Cain, Heather |
CEE |
Other |
Staff - Business Manager |
| seanqian@cmu.edu |
Qian, Sean |
CEE |
PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$95000.00
Total Project Budget (from all funding sources)
$200001.58
Documents
| Type |
Name |
Uploaded |
| Data Management Plan |
dmp_aowE6f4.docx |
May 23, 2024, 9:46 p.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| Maryland Department of Transportation |
Deployment & Equity Partner Deployment & Equity Partner |
| toXcel Inc |
None |
