"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff Data Scientist at Zendrive at Cypher 2018
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Going out for dinner in Mumbai during an extended stay, or planning for a long road-trip across the wild west of Rajasthan, the first thing one looks at is Maps, that informs the relative distance, estimated time and congestion areas of different routes for the drive. Zendrive built state-of-the-art technologies on its huge cache of driving data from smartphones and OBD, to add a significant dimension to the route mapping of Google, that is safety risk of the route. Essentially the technology is built on millions of drivers zipping through the route or segments thereof. Automobile Insurance expands in UBI- where it has been established that tracking a driver’s behavior behind the wheels (like Hard Brake, Speeding etc) can predict significant differences between their chances of collisions. Looking at the same event data from the road perspective, aggregating the relative event density on road stretches also predict the relative chances of collision on that segment. We have used map matching using GIS techniques, parametric density estimation and rare event modeling using quasi-Poisson GLM to analyze our data, build the models and finally implement the scoring system across the GIS route maps. Key learnings : Relation between dangerous driving events and collisions Route risk as an aggregate of all the drivers (or sample thereof) and their driving risk. The route you take for commute may determine your auto insurance. Outline : Usage Based Insurance : relation between collision rates and dangerous driving. Driving events : aggressive acceleration, hard brake, speeding, phone use, aggressive turns Poisson GLM modeling to predict collision rates using driving data Events on a road segment : map-matching using GIS techniques to split trips along road stretches, and aggregate such events along the spatio-temporal dimension across all drivers. Route risk of the road segment and any route comprising such segments. Driving risk along such routes and corresponding collision risks using transfer of the GLM model. Assignment of risks to drivers on their daily route of commute, to be used in UBI. www.analyticsindiasummit.com
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"Route risks using driving data on road segments" By Jayanta Kumar Pal Staff Data Scientist at Zendrive at Cypher 2018
- 1. Route Risks Using Driving Data on Road Segments Dr. Jayanta Kumar Pal, Staff Data Scientist, Zendrive 27 September 2018
- 2. Outline 1. Introduction : Automobile Collision and Its Global Impact 2. Which Roads are Riskier? 3. Collision Risk and Dangerous Events 4. Events on a Road Segment and the Route Risk Assignment 5. Impact and Directions: How to Reduce Collision Loss 2
- 3. Introduction: Automobile Collision and Its Global Impact
- 4. Collisions are Increasing Globally ● 60% of crashes attributed to driver factors. Rest are roadway and vehicle factors. ● 54 mn sustained injuries, 1.4 mn died worldwide (2013). ○ Africa (240 deaths per mn) ○ Europe (100 deaths per mn) ● BI and PD both on the rise. Automobile Collision and Its Global Impact 4
- 5. Zendrive Mission Make Roads Safer with Data and Analytics 5
- 6. Make Roads Safer with Data and Analytics ● Analysis: Identify unsafe driving patterns ● Coaching: Help drivers improve behavior ● Take Action: Zendrive joined Vision Zero, a national USDOT initiative Zendrive Mission: 6
- 7. Identify Risky Routes with two types of Data ● 150+ billion miles of driving data collected via our mobile SDK (50+ million installs) ● Street maps with GIS locations for road, rail track, intersections etc. Zendrive Mission: 7
- 8. Which Roads are Riskier?
- 9. Which are the Risky Roads? 1. Road types (Highway / motorway / trunk / residential). 2. Speed limits based on congestion / pedestrian access / sensitivity (school, hospital). 3. Road conditions (smooth / potholes), weather conditions (snow / rain). 4. More wheels on the road => more collisions. 5. Risk measured in collisions per mn miles. 9
- 10. Road Risk Hotspots ● Heat map of collisions identify : ○ Traffic intersections / lights / stop signs, ○ Highway entry-exit ramps, ○ Expressways with low visibility, ○ Construction zones. ● Spots where collisions are deadlier : ○ Pedestrian zones, ○ School and hospital areas, ○ High density residential zones etc. Which Roads are Riskier? 10
- 11. Using Zendrive Data to Assess the Risk ● Zendrive has 50 - 100/hour samples for every mile in US ● Collisions are very rare events (< 5%) ● Fortunately, we have a dense sample of dangerous events on all roads to build a predictive model. Which Roads are Riskier? 11
- 12. Collision Risk and Dangerous Events
- 13. Driving Events 13 GPS + Accelerometer + Gyroscope + Magnetometer + Gravity Aggressive acceleration Hard Brake Phone interaction (phone use + tapping) Overspeeding (speed limit infraction) Dangerous turns Collision risk (probability of collision / mile) + Improvement areas for drivers + Identification of potentially risky driving patterns
- 14. Zendrive Score and Collision Risk ● Function of all such events (along with their frequency and severity) ● Negative relation between our Zendrive Score and collision propensity. Collision Risk and Dangerous Events 14
- 15. ● Collision risk propensity built with collection of events on these roads. ● Low score => Higher chances of collisions (Risky traffic hotspots). Roads with More Events Lead to Higher Collision Risk Collision Risk and Dangerous Events 15
- 16. Events on Road Segment and the Route Risk Assignment
- 17. Route Risk Assessment While starting a trip from Los Angeles to Las Vegas, Google maps tells us which is the best route in terms of distance and estimated time. 17
- 18. Data Aggregation ● Substantial sample of drivers at any time on any road. ● GIS segments and map-matching used to mine through all trips on those stretches from GPS trails. ● Trip broken into such segments along with its events. Road Segment and Route Risk Assessment 18
- 19. Road Segment Safety Score ● For every road segment, we collect a set of trip segments and its events. ● Scoring framework gives the safety score of such a route. ● Lower the score => less safe, and more dangerous. ● This road segments are likely to have higher collision rates per miles driven as well. 80 90 100 68 80 Road Segment and Route Risk Assessment 19
- 20. Route Safety Risk ● Safety of the route = aggregate the safety score of such segments. ● Within every route, we have individual scores of road segments. ● This is such a map using a three color palette, for roads going from San Francisco to its neighbor cities like San Jose, Fremont, etc. Road Segment and Route Risk Assessment 20
- 21. Impact and Directions: How to Reduce Collision Loss
- 22. Possible Impacts of the Analysis ● Data-crunched spatio-temporal risk map of US (and global) roads. ● Huge impact envisaged. ● Risk map provides collision propensity of the routes ahead. How to Reduce Collision Loss 22
- 23. Temporal Road Safety 23 ● Road safety varies over time. ● Both diurnal and seasonal. ○ Winter snow ○ Late PM or early AM ● Our time-slot-by-time-slot (usually 15 mins of length) scoring is useful.
- 24. Commuter Study ● Commuters in the Bay Area ● Study identified risk spots, high risk time intervals ● US-101 is safer than I-280 (counter- intuitive). ● Lunch time was perceived to be high risk compared to the peak commute time. ● https://www.zendrive.com/commute/ How to Reduce Collision Loss 24
- 25. Policymaker Recommendation ● Participation in the road safety policies of urban, state and federal level groups. ● Active measures are recommended, like reduce lane passings, widen the lanes, introduce medians etc for highways identified. ● Intersections outlined to the city traffic overseers, so that measures like longer light durations, introduction of stop signs, outlawing certain turns are taken. How to Reduce Collision Loss 25
- 26. Come learn more about Zendrive. Questions? 26
- 27. Thank you for your attention. 27
Editor's Notes
- Collisions are increasing everywhere… Collisions account for huge human and property losses all over the world. In 2013, 54 million people sustained injuries on road, resulting in 1.4 million deaths. Africa has the highest per capita deaths (240 per million), Europe has the lowest (100 per million). ~ 60% of crashes can be attributed to driver factors. Rest are roadway and vehicle factors. Bodily injury (BI) and Property Damage (PD) both are on the rise worldwide.
- Why - Make roads safer with data and analytics How - Save money, save lives What - Mobile driver analytics platform Zendrive has developed state-of-the-art technology to monitor driver behavior through mobile sensor data merged with GIS information. Driving on roads include many dangerous events, such as hard brakes and phone use, that lead to higher probability of collision. Drivers who have more such events per mile, or have history of severe such events, tend to have a higher propensity of collision. Similarly, roads with higher frequency of such events are riskier than other roads. Real time collision detection enables swift assistance from family or caregivers, reducing time for intervention.
- Our stated objective is to make roads safer. Part of it is done by making drivers aware of their dangerous driving, and encouraging them to forego such habits. But another crucial part is to educate everyone about the safety risks of routes, and recommending the authority to take appropriate actions. Vision Zero is an initiative by USDOT, launched in Nov 2016, that Zendrive participate in. Cities have already taken that pledge, (outlined here), and Zendrive route risk is likely to play a huge role in it.
- Our stated objective is to make roads safer. Part of it is done by making drivers aware of their dangerous driving, and encouraging them to forego such habits. But another crucial part is to educate everyone about the safety risks of routes, and recommending the authority to take appropriate actions. Vision Zero is an initiative by USDOT, launched in Nov 2016, that Zendrive participate in. Cities have already taken that pledge, (outlined here), and Zendrive route risk is likely to play a huge role in it.
- Project introduction
- Zendrive’s data pervades across all roads, all time, to the extent of having x samples in every mile at any 15-minute period (in US). However, this is only a small fraction (< 5%) of all cars on the road, and a small sample of all collisions on these roads. Collisions being very rare events, such a sample is inadequate to assess the risk of all road segments. Fortunately, we have a sufficiently dense sample of dangerous events on all roads to build a predictive model.
- Zendrive score is a function of all such events (along with their frequency and severity) We validate the negative relation between our Zendrive Score and collision propensity (plot shown here) Since collisions are reported as count per million miles, we aggregate drivers in score ranges, and compare their collision rate. The decreasing line clearly establishes the efficacy of the score as a predictor of collision.
- The model for collision risk propensity of a road is, therefore, built using the collection of events happening on these roads. We built scores for the road segments in the same way they are computed for drivers. Roads with lower score have higher chances of collisions, identifying risky traffic hotspots in the process. We corroborate the small set of collisions to check if they mostly happen on such traffic hotspots.
- Our target is to assign a safety score to these options, so that the user can choose to avoid the route which has a higher chance of collisions. Collisions occur many times due to the fault of other drivers, represented by a random driver who is sharing the same road at that time. Route is made up of short road segments, each one potentially having different safety risk.
- The time component of road safety is considered under the scope of this framework as well. Road risk assignment has a highly variant time-of-day and seasonal component. Same road in winter snow has a much higher collision propensity than in mild summer. Similarly, late PM or early AM driving are significantly different compared to the daytime driving. Our time-slot-by-time-slot (usually 15 mins of length) captures this variation and proposes route accordingly.