How Machine Learning Algorithms Are Predicting Pedestrian Accident Hotspots
Pedestrian safety is becoming an increasing worry in urban areas. In 2022, the Governors Highway Safety Association (GHSA) reported 2.37 pedestrian fatalities for every billion vehicle miles traveled (VMT). Given the rising number of vehicles and pedestrians on the roads, cities are looking for innovative ways to enhance safety.
To address this issue, cities use machine learning (ML) and big data analytics as practical tools. These technologies enable cities to forecast potential accident hotspots before they result in tragedies. By examining traffic patterns, pedestrian behavior, and environmental elements, these algorithms can pinpoint high-risk areas for collisions.
With the help of predictive insights, local authorities can implement specific safety measures to decrease the chances of pedestrian accidents. This blog post discusses the application of ML in preventing pedestrian collision incidents.
A Harrowing Incident in Colorado Springs Rekindles the Need for Smarter Solutions
As per a report from The Gazette (2024), a man was seriously injured on a late Friday night. The incident occurred in Colorado Springs in September 2024, after being struck by a car on North Powers Boulevard. The accident took place near a major intersection known for high traffic flow. Despite various safety measures, pedestrian accidents like this continue to happen in cities across the globe.
For seeking compensation related to such injuries, there are many ways. The most successful way is getting professional expertise from a local Colorado Springs auto accident attorney. They can help victims and their families navigate complex legal proceedings.
Even if getting justice may be possible, it is crucial to focus on preventing such incidents at the initial level. Fortunately, the rise of machine learning (ML) and extensive data analysis is helping cities predict pedestrian accident hotspots. Hence, such advancement can potentially prevent such future tragedies.
The Power of Machine Learning in Traffic Safety
Machine learning algorithms have transformed numerous sectors, and traffic safety is no different. Cities are using ML to analyze large datasets related to traffic patterns, accident records, road conditions, and pedestrian behavior.
These algorithms can detect traffic trends and forecast the locations where accidents are most likely. For example, ML can highlight areas with a history of pedestrian activity, and inadequate lighting, contributing to higher accident rates.
Similarly, in the Colorado Springs collision, using machine learning to monitor traffic could have helped. The intersection at Powers and Palmer Park Boulevard might have been identified as high-risk. With this information, city planners and local authorities could have implemented focused safety measures. Some of the instances include improved lighting, enhanced pedestrian crossings, or increased signage.
How Cities Use Big Data and Machine Learning to Predict Hotspots?
Machine learning models rely on large sets of data to operate effectively. Urban areas gather information from a variety of sources, including traffic cameras, road sensors, and GPS systems. By inputting this data into machine learning algorithms, cities can accurately predict where pedestrian accidents are likely to happen.
The process involves teaching the ML model using data from known areas with frequent pedestrian accidents. With time, the algorithm learns which factors most indicate accidents, like road width, vehicle speed, weather conditions, and pedestrian volume. Once trained, the model can be used in new locations to pinpoint potential accident hotspots before they occur.
For instance, ML could mark a stretch of road as a danger zone due to consistent high speeds and heavy traffic. To enhance safety, authorities can take proactive steps, like reducing speed limits or installing pedestrian crossing signals.
How Machine Learning Can Prevent Single-Car Pedestrian Accidents?
Accidents involving single cars and pedestrians, such as the one in Colorado Springs, can occur under seemingly normal conditions. It can take place even in clear weather, on dry roads, and during scenes with no obvious distractions.
As per Springs Law Group, all drivers are responsible for driving safely and reasonably while behind the wheel of a car. In case of mishaps, they also emphasize the importance of being forthright and clear when sharing details of the incident.
Despite this, accidents can still happen due to various human errors or negligence. You may come across multiple incidents in your social media feed or even see a few Instagram reels every day. In these situations, machine learning algorithms play a vital role. They can identify hidden risk factors that human drivers or city planners may overlook.
For example, an algorithm may observe that accidents occur frequently on a specific road, even though it appears safe. Data might reveal that drivers often overlook pedestrians at this location due to poor lighting or an intersection angle. With this information, city planners can make informed decisions to reduce risks, They can improve street lighting or adjust traffic signals.
Predictive Analytics in Action
Several cities have already begun using machine learning to predict and prevent pedestrian accidents. For example, the Vision Zero initiative uses predictive analytics to identify dangerous intersections in New York City. With the data from the Department of Transportation and other city databases, ML models analyze patterns of dangerous driving behaviors.
Another innovative approach involved the development of exposure models, which estimate traffic volume across different streets in the city. These models help determine where accidents are most likely to occur, enabling the city to plan targeted interventions.
Additionally, crash models have been implemented to assess the impact of street design changes on safety. For example, they can predict whether converting a two-way street into a one-way might reduce accidents.
Similarly, in Chicago, researchers use machine learning and artificial intelligence to study pedestrian accident patterns and recommend road design and traffic management changes. This has already led to interventions in several neighborhoods that have historically seen higher pedestrian accident rates.
FAQs
What factors contribute to pedestrian accidents?
Several factors lead to vehicle-pedestrian crashes. Key factors include the type of collision, the cause of the accident, the age of the pedestrian, and junction control mechanisms. Pedestrian behavior, roadway characteristics, vehicle speed, and driver attentiveness also influence crash severity and frequency.
How do cities use big data to predict pedestrian accident hotspots?
Cities collect data from traffic cameras and accident reports. ML algorithms analyze this data to identify patterns and predict where pedestrian accidents are most likely to happen. Techniques like decision trees, random forests, and logistic regression help in identifying influential factors that contribute to its severity.
Can machine learning completely prevent pedestrian accidents?
While machine learning improves the ability to predict accident-prone areas, it cannot eliminate accidents. Human behavior and environmental factors still play a role, but ML significantly enhances preventive safety efforts. For instance, ML models like neural networks can be trained to detect pedestrians in complex environments, even in occlusion scenarios.
Tragic incidents (the one that took place on North Powers Boulevard) serve as a reminder that pedestrian safety remains a challenge. Nevertheless, machine learning presents a potent solution for anticipating and averting accidents before they happen.
With the use of extensive data and predictive algorithms, urban areas can locate areas with high rates of pedestrian accidents. This way, it can implement precise measures to safeguard vulnerable road users. As more cities adopt these technologies, streets will become safer and pedestrian accidents will decrease.
