Connected Car Data: The Next Generation of Surrogate Safety Analytics

When an intersection sees a high frequency of hard braking, or a road section see s high volume s of vehicle speeding, on e might think that it’s a high – risk area for road safety and proactive measures should be implemented to prevent crashes from occurring . P roving the intuition with data , however, hasn’t been easy .

D ata such as hard braking or speeding – which wa s impossible to collect on scale a few years ago – are becoming available through connected car data and quickly being leveraged by innovative transportation agencies to identify safety “ hot spots ” , i.e., locations more prone to accidents . Such data can be collected for n etwork -wide stud ies or for pinpoint ing a particular intersection or road segment , allowing road safety experts to investigate citizen complaints , prioritize projects, dig deeper into driver behaviour s causing accidents , and design counter measures .

If your goals are to reduce crash frequency and crash severity, and to do it as early as possible, keep reading.

The Rise of Surrogate Safety Data

Collision data has long informed road safety analysis, but it is always delayed, requiring years of reported incidents.

Surrogate safety measures are non-crash measures, such as near misses that could have resulted in a crash, but fortunately did not. They are useful indicators for measuring the risk of crash occurrence by giving visibility into driver behaviour and patterns.

Because this data can be obtained faster, it enables safety experts to take a proactive approach to predicting accidents and take preventative actions more timely and efficiently.

Connected Car Data for Safety Studies

C onnected car data refers to data generated by vehicles that are connected to the internet , such as harsh accelerations, harsh brakes, speed, seatbelt violations and more. As a form of surrogate data, connected car data offers the benefits , such as unparalleled visibility into driver behaviours , that traditional methods can’t for safety studies and risk screening modelling . C onnected car data also offers network-wide coverage without using any roadside sensors, such as cameras or lidar , making it useful for study ing large networks economically and efficiently.

Using D river B ehaviours for Risk Screening and Accident Prediction

How accurately can driver behaviours predict accidents though? And what driver behaviours are the most important for predicting accidents? We started by studying two common driver behaviours: harsh acceleration and deceleration .

Thanks to the high-resolution quality of connected car data, we can pinpoint these two driver behaviours to any intersection or road segment, as well as isolate the behavioral data based on their relationship with another vehicle or with a Vulnerable Road User.

Driver Behaviours Matter – A Study

To analyze whether har sh acceleration s and de celerations (which we call event data) can predict accidents accurately , and their ranked import ance as predictors , we did a correlation stud y between historic crash counts and event data using the Spearman C orrelation A nalysis M ethodology .

Spearman R ank C orrelation is a non-parametric statistical method used to measure the strength and direction of the relationship between two ranked variables . In our case, variables are surrogate and traditional safety metrics such as average speed , number of crashes , and connected car event data .

The Spearman rank correlation coefficient ranges between -1 and 1. A value of -1 indicates a perfect negative correlation, e.g., when one variable (such as average speed) increases, the other variable (such as count of crashes) decreases . A value of 1 indicates a perfect positive correlation, e.g., when one variable increases (such as average speed) , the other variable also increases ( such as count of crashes ) . A value of 0 indicates no correlation between the two variables.

We used this methodology to observe if an increase in harsh accelerations correlated with the number of crashes to determine whether harsh accelerations can be a pre dictor of crashes.

SMATS looked at accident data from 2015 to 2019 from the W ashington S tate D epartment of T ransportation ’s (WSDOT) crash data portal for 1 , 710 road segments in Bellevue City. Driv er behavio u r data (such as harsh accelerations/decelerations) , sourced through Wejo’s connected car database from January and Februar y of 2020, were also analyzed.

We found that driv er behavio u r s had a higher importance ranking than traditional variables (e.g., AADT) for predicting crashes. In fact, h ar sh acceleration was the most important variable for determining hotspots in road segments .

Using the same methodology , we analyzed the same driver behavioural data for the same period at 109 intersections in Bellevue City. The finding was consistent with the study of road segments. For intersections, harsh acceleration was also the most important variable for determining hotspots .

The T akeaway: The N ext Generation of R isk Screen ing Model s is H ere

The future of risk screening projects is faster, more efficient, and exciting!

Why do we say so?

• Driver behaviours can now be quantified using connected car event data.
• Connected car event data is highly correlated with historical crashes. Specifically , harsh acceleration and deceleration rank higher than AADT and speed in importance as accident predictors.
• Driver behaviours are proven significant crash predictors for both road segments and intersections.

Requiring no roadside sens ors , connected car data is perfectly suited in next – generation risk screening models for large or small road networks to identify hotspots and assess project impact, saving time and cost .

Book a demo to check out our risk screening model using connected car data. It can be used as a standalone, or supplementary to SPF to improve prediction accuracy.