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Route risks using driving data on road segments

Submitted by Jayanta Pal (@jayantapal) on Wednesday, 20 June 2018

Technical level: Intermediate

Abstract

Going out for dinner in Cincinnati during an extended stay, or planning for a long road-trip across the wild west of US, 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 HMM, 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.

Outline

1.Usage Based Insurance : relation between collision rates and dangerous driving.
2.Driving events : aggressive acceleration, hard brake, speeding, phone use, aggressive turns
3.Poisson GLM modeling to predict collision rates using driving data
4.Events on a road segment : map-matching using HMM to split trips along road stretches, and aggregate such events along the spatio-temporal dimension across all drivers.
5.Route risk of the road segment and any route comprising such segments.
6.Driving risk along such routes and corresponding collision risks using transfer of the GLM model.
7.Assignment of risks to drivers on their daily route of commute, to be used in UBI.

Speaker bio

Jayanta is the leading data scintist in Zendrive, who has helped built its algorithms from scratch, and create the product for its entirety. He has done his undergraduate and Masters in ISI Kolkata, and PhD in Statistics from University of Michigan, Ann Arbor. He has stints in both academia (Assistant Professor, Duke University) and industry research groups(as statistician in HP, GM, and GE) before joining Zendrive as its first Data Scientist in 2014.

Comments

  • Zainab Bawa (@zainabbawa) Reviewer a year ago

    Hello Jayanta, we need to detailed slides for this proposal to fully evaluate it.

  • Abhishek Balaji (@booleanbalaji) Reviewer 10 days ago

    Hi Jayanta,

    We’d like to consider this for the 2019 edition of The Fifth Elephant. All proposals require slides and preview video for evaluation.Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    We need your updated slides and preview video by Jun 27, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

  • Abhishek Balaji (@booleanbalaji) Reviewer 6 hours ago

    Moving this to reject, since the proposer is not interested in reworking this proposal.

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