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Improving the Credibility of, and Compliance with, Speed Limits: a Real-World Approach

D. I. V. AS. ANR. Improving the Credibility of, and Compliance with, Speed Limits: a Real-World Approach. N. Hautière 1 , P. Charbonnier 2 , E. Dumont 1 , S. Glaser 1 , E. Violette 3. 1 LCPC, Paris 2 LRPC de Strasbourg, ERA 27, Strasbourg 3 CETE Normandie-Centre, ERA 34, Rouen.

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Improving the Credibility of, and Compliance with, Speed Limits: a Real-World Approach

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  1. D I V AS ANR Improving the Credibility of, and Compliance with, Speed Limits: a Real-World Approach N. Hautière1, P. Charbonnier2, E. Dumont1, S. Glaser1, E. Violette3 1 LCPC, Paris 2 LRPC de Strasbourg, ERA 27, Strasbourg 3 CETE Normandie-Centre, ERA 34, Rouen

  2. Presentation Outline • Introduction • Rough points • Our position • Speed limits computation: state of the art • How to improve drivers’ compliance with speed limits? • Process • Examples • On-board solution: ARCOS • Roadside solution: SARI • Cooperative solution: DIVAS • Perspectives

  3. Introduction • Fact There is a strong link between accidentology and speed. • Problem Permanent speed limits (signs) do not help road users to adapt their speed in case of transient difficulties - Meteorology: rain, fog, wet road, ice on the road - Traffic, road works, lack of maintenance… • Solution Adaptive and customized speed limits. 110 light rain 90 strong rain

  4. Introduction: Rough Spots(at least in France) • The duality of speed limits • Speed limits have different functions, e.g.: • to ensure homogeneous behaviours, • to ensure coherence with road related risks. • To comply with speed limits, road users must be aware that speed limits are related to the riskand not only to speed enforcement. • Problem: do we communicate on the speed or on the risk? • Compliance • Prior to the introduction of automatic speed enforcement, speed limits were designed knowing that they would not be respected. • Today, posted speed limits are no longer suitable because they are complied with. • Liability • Legal issues are problematic for adaptive speed limits.

  5. We focus on the scientific aspect of the problem. We seek to compute credible safe* speed limits.(*) i.e. risk-related We consider isolated vehicles, only interacting with the infrastructure. Introduction: Our Position

  6. Empirical approach Actual speeds are measured in nominal conditions  all the parameters are integrated Problem: only appliesin nominal conditions  not adaptive  not credible Computational approach Based on physical models for specific situations many parametersare omitted: driver, car, visibility... Problem: not customized  needs to be pessimistic not credible Speed Limits Computation: State of the Art

  7. How to Improve Drivers’ Compliance with Speed Limits? • Hypothesis Credible speed limits are better complied with. • Question How to make speed limits credible? • Answer By making them adaptive and customized.

  8. Process • Combine empirical and computational approaches Usual approach: Our approach: with SL = Speed Limit MSL = Mandatory Speed Limit NSL = Nominal Speed Limit empirical model f(pi) = speed decrement needed to maintain nominal risk level pi = transient risk factor computational approach

  9. ExamplesOn-board solution • We make it customized(on-board = more credible), • ARCOS Project First use of risk functions • Example: the SAVV (speed warning in curves) • Website: http://www.arcos2004.com/ Source: ARCOS Project

  10. Alert threshold ExamplesRoadside solution • We make it adaptive • SARI / IRCAD (roadside = addresses all drivers) • Problem: drivers are not aware of the risk in bad conditions (particularly with skid resistance) • We must set a warning threshold in the speed distribution. • Website: http://www.sari.prd.fr/ Risk function Source: Lacroix Traffic

  11. ExamplesCooperative Solution • We make SLs both adaptive and customized. • We generalizeroad-related risks byadding meteorological risks, and by combining risk factors (with ranking, rather than simply decrementing). • This is one objective of the DIVASProject. Source: ARCOS Project Source: PReVENT Maps&Adas

  12. Local actors Industrials Research institutes Universities DIVAS: Dialog between Infrastructure and Vehicles to Improve the Road Safety (1) • Type of project: French ANR 2006 • Promoted by PREDIT GO9 • Timeframe: May 2007-May 2010 • Cost budget: 4 M€(ANR funding1.3M€) • Coordinator: LCPC • Philippe Lepert • Nicolas Hautière • Consortium: 15 partners Competitiveness clusters Source: LARA (ENSMP/INRIA)

  13. DIVAS: Dialog between Infrastructure and Vehicles to Improve the Road Safety (2) • The DIVAS project is building a global a vehicles – infrastructure information exchange system • It aims at preparing its implementation, in terms of: • technology, • acceptability, • credibility. • The project is focussed on the role of: • the infrastructure characteristics • the role of the road operators in the deployment of such systems. • It aims at providing each vehicle with an individualized safety indicator along a route, • It mainly takes into account the road geometry, the road surface conditions and the visibility conditions. • Web site: http://or.lcpc.fr/divas-fr/ • Reference: • N. Hautière, P. Lepert. “Infrastructure - Vehicles Dialogue to Improve Road Safety: The DIVAS Approach”. To appear in Transport Research Arena (TRA), Ljubljana, Slovenia, April 21 – 25, 2008

  14. DIVASMeasuring Actual Speeds (empirical approach) • “Reference” drivers with instructions, in nominal conditions. • Record the speed profile along the road (and other information also) with an instrumented vehicle. • Calibratespeed profiles using roadside speed measurements at different spots. • Build a nominal speed profile. • Infer nominal risk for a specific situation (e.g. “brick wall”) Source: LAVIA Project

  15. 1st Level Application:Consolidation of Vertical Signalling • Signs provide the permanent speed limits, posted by the road operator (or police). • Posted speed should be coherent with nominal speed in order to be credibleDiscrepancies should be studied, baring in mind the duality of posted speed limits.

  16. 2nd Level Application: Adaptation of Speed Limits to Keep Constant Risks (computational approach) • Risk models are chosen with respect to the studied risk factor • The DV is computed to have a constant risk (R) compared to the nominal risk (RN) • Example:brick wall risk model and wet road • Nominal risk: we compute the gravity of an accident at impact speed SN into a wall at t=2s SNDAN EESN* RN • Wet road leads to a reduction of skid resistance S DA  EES  R > RN • Knowing the actual road surface conditions, we can compute DS / S’=S-DS  R’=RN • Assumption: computing S’=S-DS is more credible than computing S’=f(skid resistance), which was used for example in ALZIRAproject. *EES = Equivalent Energy Speed (cf. LAB PSA/RENAULT)

  17. Perspectives • We argue that crediblerisk-related speed limits would be better complied with. • We proposed an approach to computecredible speed limits by making them adaptive and customized. • We are testing the approach in the framework of DIVAS project dealing with cooperative systems. • In the coming next months, we will see if our approach is relevant or not. Mid-term seminar November 2008 Today May 2007 May 2010 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Time (Quarter) DIVAS agenda

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