Creating Safer Streets Through Data Science — A Case Study

Executive Summary

Tens of thousands of people are killed or injured in traffic collisions each year. To improve road safety and combat life-threatening crashes, many U.S. cities have adopted Vision Zero, an initiative born in Sweden in the 1990s that aims to reduce traffic-related deaths and serious injuries to zero.

While many cities have access to data about where and why serious crashes occur, the use of predictive algorithms and advanced statistical methods to determine the effectiveness of different safety initiatives is less widespread. Therefore, DataKind, Microsoft and three U.S. cities — New York, Seattle and New Orleans — came together to help demonstrate how cutting edge and scalable solutions can be developed to help tackle a complex societal issue.

Each city had specific questions that they wished to address around local priorities for increasing traffic safety, to better understand the factors contributing to crashes and the potential impacts of different types of interventions. The DataKind team, working closely with local city transportation experts, brought together a wide variety of datasets such as information on past crashes, roadway attributes (e.g. lanes, traffic signals and sidewalks), land use, demographic data, commuting patterns, parking violations and existing safety intervention placements.

These inputs were leveraged to develop models that allowed cities to examine how different street characteristics impacts the injuries that occur, to determine the extent to which roadway user behavior and street design are contributing factors in crash occurrence and severity, to assess the effectiveness of interventions for increasing safety and guide the placement of future interventions.

The DataKind team also developed a model to help cities accurately and cost-effectively estimate “exposure” or total volume of vehicles on individual streets, a key factor in safety analyses as well as broader transportation planning activities.

Today, as a result of applying these models, the cities are better positioned to determine what kind of engineering, enforcement and educational interventions are effective and how to best allocate limited available resources.

Specifically,

  • In New York, with the new exposure model capability, the city can perform initial safety project feasibility studies more efficiently. When combined with DataKind’s crash models, the new capability will help the city test the potential impact different engineering, land use and traffic scenarios would have on total injuries and fatalities in the city.
  • In Seattle, the city focused on bicycle and pedestrian safety issues in order to gain insights that could contribute to the planning for more than $300 million in anticipated Vision Zero investments. The DataKind models identified collision patterns and factors that contributed to higher levels of injury severity, including whether a motor vehicle is making a right turn or left turn and the effectiveness of crosswalks in reducing crash severity. They also identified key variables affecting the likelihood of accidents taking place on particular stretches of road, including traffic volume, land use, number of traffic lanes, street width and pedestrian concentration.
  • And in New Orleans, the DataKind team created an Impact Assessment tool that will allow the city to compare various locations that are candidates for street treatments, such as bicycle lanes, and to evaluate the impact of implemented treatments over time.

In addition to aiding the participating cities in their efforts to make streets safer, the Vision Zero Labs project showed how data science and collaboration between the public and private sector can help benefit the greater good and produce innovative and scalable solutions to address complex civic issues like traffic safety. Cities around the world can adapt the methodologies and learnings to reduce traffic-related injuries and fatalities in their own communities.

DataKind Vision Zero Initiative: Purpose, Projects and Impacts

Visualization of an early version of the exposure model that estimates traffic volume by street in Seattle. This view shows the difference between the model’s estimates and actual measurements

Tens of thousands of people are killed or injured in traffic collisions each year. To improve road safety and combat life-threatening crashes, more than 25 U.S. cities have adopted Vision Zero, an initiative born in Sweden in the 1990’s that aims to reduce traffic-related deaths and serious injuries to zero. Vision Zero is built upon the belief that crashes are predictable and preventable, though determining what kind of engineering, enforcement and educational interventions are effective can be difficult and costly for cities with limited resources.

While many cities have access to data about where and why serious crashes occur to help pinpoint streets and intersections that are trouble spots, the use of predictive algorithms and advanced statistical methods to determine the effectiveness of different safety initiatives is less widespread. Seeing the potential for data and technology to advance the Vision Zero movement in the U.S., DataKind and Microsoft wondered: How might we support cities to apply data science to reduce traffic fatalities and injuries to zero?

Three U.S. cities — New York, Seattle and New Orleans — partnered with DataKind in the first and largest multi-city, data-driven collaboration of its kind to support Vision Zero efforts within the U.S. Each city had specific questions they wished to address related to better understanding the factors contributing to crashes and what types of engineering treatments or enforcement interventions may be most effective in helping each of their local efforts and increase traffic safety for all.

To help the cities answer these questions, DataKind launched its first ever Labs project, led by DataKind data scientists Erin Akred, Michael Dowd, Jackie Weiser and Sina Kashuk. A DataDive was held in Seattle to help support the project. Dozens of volunteers participated in the event and helped fuel the work that was achieved, including volunteers from Microsoft and the University of Washington’s E-Science Institute, as well as many other Seattle data scientists.

The DataKind team also worked closely with local city officials and transportation experts to gain valuable insight and feedback on the project and access a wide variety of datasets such as information on past crashes, roadway attributes (e.g. lanes, traffic signals, and sidewalks), land use, demographic data, commuting patterns, parking violations, and existing safety intervention placements.

The cities provided information about their priority issues, expertise on their local environments, access to their data, and feedback on the models and analytic insights. Microsoft enabled the overall collaboration by providing resources, including expertise in support of the collaborative model, technical approaches, and project goals.

Overall, the work accomplished by the Vision Zero Labs team proved to be invaluable for the cities of New York, Seattle and New Orleans, equipping them with powerful insights, models and tools that can help inform future planning to prevent severe traffic collisions and keep all road users safe. With this knowledge, the cities can better determine how to best allocate resources and investments towards improvements in infrastructure and policy changes.

In addition to aiding the participating cities in their efforts to make streets safer, the project showed how data science can be effectively used to address complex civic issues like transportation safety. A particular example is the technique developed in this project around estimating road use volume even when complete data is lacking. This technique is relevant both for safety analyses and broader transportation planning activities. These are the kinds of cutting edge and scalable solutions DataKind’s Labs projects aim to deliver to achieve sector wide impact.

The project also showed how collaboration between the public and private sector and amongst partner organizations can help benefit the greater good and result in innovative and scalable solutions to address complex and critical issues like traffic safety. Cities around the world will be able to benefit from the results of the Vision Zero Labs project and can adopt the methodologies and learnings from the work to reduce traffic-related injuries and fatalities in their own communities.

Below are detailed descriptions of the specific local traffic safety questions each city asked, the data science approach and outputs the DataKind team developed, and the outcomes and impacts these analyses are providing each city.

New York: Estimating Street Volumes and Understanding How Street Design Can Reduce Injuries

Map showing street improvement projects locations and change in crashes in New York

Local Question: According to the City of New York, on average, vehicles seriously injure or kill a New Yorker every two hours, with vehicle collisions being the leading cause of injury-related death for children under 14 and the second leading cause for seniors. Looking to improve traffic safety on its streets, the city wanted to understand what existing interventions are working and where there is potential for improvement to help inform how the city can better allocate its resources to protect its residents.

Data Science Approach and Outputs: The team leveraged datasets from New York City’s Department of Transportation, NYC OpenData, New York State and other internal city data to examine the effectiveness of various street treatments to help inform the city’s future planning and investment of resources. Lacking some of the data necessary to address the actual impact of existing street treatments, the team looked to answer other crucial questions regarding traffic safety that could help benefit the city.

Before they could answer these questions, they first needed to answer a more basic one — how many cars are on the road? Knowing the total volume of road users or “exposure” is necessary to understand the true rate of crashes, but most cities don’t have this data available. To overcome this, the team designed an innovative exposure model that can accurately estimate traffic volume in streets throughout the city. The model has two main components. The first is an algorithm that propagates traffic counts on a single street segment to adjacent street segments. It assumes that traffic on one city block is very similar to traffic on adjacent blocks. This process can be run many times and allows one to widely propagate traffic count values along neighboring streets. However, some streets may not have any nearby traffic counts available, so the second component of the model is a machine learning model, with high predictive accuracy, that predicts traffic volumes on streets based on their characteristics.

The team also created a crash model for New York, allowing the city to examine individual locations and test how different street characteristics impacts the number of injuries. For example, the city may be able to look at a particular street and determine whether it is safer for the street to be a one- or two-way road.

Outcomes: The exposure model will prove to be invaluable to the City of New York, filling a crucial void in vehicle volume data that many cities face. With it, the city can now perform initial safety project feasibility studies very quickly and provide context for a variety of other safety research work that requires an “exposure” rate. The model can also be altered to estimate other defined traffic volume measures, like peak hour traffic volumes. It can also help inform future work related to traffic congestion and citywide vehicle usage.

New York can also use the crash models to test the potential impact different engineering, land use and traffic scenarios would have on total injuries and fatalities in the city. They will continue to build upon the work started by DataKind, as the models developed set the stage for future research in crash prediction, congestion relief and city safety projects.

The team was able to leverage the work started in New York City to help develop and refine the approaches for both Seattle and New Orleans.

Seattle: Understanding How Street Design, Driver Behavior and the Surrounding Environment Contribute to Crashes

This “exposure” model developed for New York and Seattle shows estimates of citywide traffic volume, a key piece of information needed for advanced analyses that most cities don’t have

Local Question: While Seattle has seen a 30 percent decline in traffic fatalities over the last decade, traffic collisions are still a leading cause of death for Seattle residents age 5 to 24. Older adults are also disproportionately affected, so this trend could grow as the population ages. To supplement the findings of the City’s Bicycle and Pedestrian Safety Analysis project and provide policy makers and engineers with actionable information for developing and implementing interventions, Seattle sought to find out what mid-block street designs are most correlative with collisions involving vulnerable roadway users and what the probability of such collisions occurring is at identified locations.

Data Science Approach and Outputs: Using Seattle’s collision, roadway traffic, exposure data and environment characteristics, the DataKind team developed models to uncover collision patterns involving pedestrians or bicyclists and determined the extent to which contributing circumstance and street design are correlated with collision rates, as well as the severity level of specific types of crashes. The team also applied the methodology developed for their work with New York to calculate exposure or total traffic volume citywide for Seattle.

By incorporating incident-specific information such as time of day, weather, lighting conditions and behavioral aspects, the team was also able to further develop a crash model to evaluate elements that may contribute to crashes at intersections and to what extent driver behavior, road conditions and street design played a role.

Outcomes: The DataKind team was able to determine several variables that had the greatest impact on mid-block collisions — traffic volume, land use, number of traffic lanes, street width and pedestrian concentration were the most demonstrative inputs associated with collisions.

For instance, it was found that the fact of whether a motor vehicle is making a right turn or left turn at a given intersection will influence the severity of the collision. Researchers were also able to identify in which months of the year incidences of crashes appeared to be better or worse. Interestingly, the number of crosswalks was found to be significant and that more crosswalks at an intersection showed reduction in the severity of crashes.

With these insights, Seattle will be able to pinpoint high risk areas and the factors that can be addressed to help reduce future crashes. The city recently passed a levy to fund multi-modal transportation improvements city-wide and the results from this project, along with additional safety studies, will help guide more than $300 million in Vision Zero investments over the next nine years. 

New Orleans: Evaluating the Effectiveness of Street Treatments

Local Question: While New Orleans hasn’t officially adopted Vision Zero, the city government and community are working together to make roads safer. In 2014, New Orleans was named a “silver” level bicycle-friendly community by the League of American Bicyclists and had the eighth highest share of bicycle commuters among major U.S. cities. New Orleans also leads Southern U.S. cities in bicycle commuting. Yet, a disproportionately high number of the state’s pedestrian crashes occur in New Orleans and the number of bicycle crashes doubled from 2010 to 2014.

To help the city protect its growing number of roadways users, New Orleans wanted to understand the impact that future installation of street treatments, such as bike lanes and traffic signage, could have on preventing traffic injuries and fatalities.

Data Science Approach and Outputs: The DataKind team created an Impact Assessment tool that could be used to test the effectiveness of installed treatments, which would then be used to better inform the placement of future street treatments, both individual interventions and groups of interventions applied simultaneously.

Specifically, the tool takes a set of treatment locations and uses different statistical methods to create sets of comparison locations. These comparison locations are used as a point of reference to gauge the impact of the treatment on traffic safety by comparing crash rates before and after the installation of interventions to similar intersections that did not receive interventions. The tool includes visualizations to examine generated comparison groups, as well as methods for using manually selected comparison groups.

As an example, New Orleans could select a treatment, such as a bike lane, and compare the crash rates before and after the bike lane was installed. The city can then compare these crash rates to other comparison sites. The comparison sites are especially important because they allow the city to prepare for outside factors, such as overall growth in population or traffic. The crash rate could actually increase at a treatment site but this may be due to other factors such as large increases in traffic. When comparing a treatment site with similar untreated sites, we can see if the crash rate increased at a lower rate, thus indicating an improvement in safety due to the treatment. 

Outcomes: New Orleans has integrated the Impact Assessment tool into their systems and will be collecting more data to maximize the tool’s potential and evaluate the effectiveness of additional street features. These findings will help inform the placement of future street treatments.

“Making streets safer for all New Orleanians is a major priority of ours,” said Oliver Wise, director for the City of New Orleans’ Office of Performance and Accountability. 

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