LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Wen Cheng


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LTC, Jack R. Widmeyer Transportation Research Conference, 11/04/2011, Wen Cheng

  1. 1. A New Method to Identify Hot Spots in roadway Network<br />BY<br />Wen Cheng, Ph.D., PE<br /> Assistant Professor<br />Civil Engineering Department<br />Cal Poly Pomona<br />
  2. 2. Presentation Outline<br />Hotspot Identification (HSID) background<br />Description of the new proposed HSID method<br />Application of the new method<br />Summary and Conclusions<br />
  3. 3. Crashes in Real Life: Huge Burden<br />
  4. 4. Crashes in the U.S.<br />42,116 fatalities (i.e., 115 persons killed/day)<br />1.51 fatalities/100M VMT<br />14.79 fatalities/100K Population<br />231 Billion Economic Cost<br />41% alcohol-related fatalities<br />29.7% Speed-related fatal crashes<br />Source: 2005 NHTSA<br />
  5. 5. Crashes in California<br />Source: 2005 CA Statistical Abstract <br />
  6. 6. How to address safety challenges<br />ISTEA legislation (1991) and TEA-21(1998) required each state develop safety management systems. <br />SAFETEA-LU (2005) establishes a new core Highway Safety Improvement Program<br />structured and funded to make significant progress in reducing highway fatalities<br />
  7. 7. Safety Management System Basic Components<br />
  8. 8. Current Practice of HSID <br />Rank a set of candidate locations in terms of the registered accident counts or expected long-term accident counts <br />General assumptions<br />accident process can be viewed as a sequence of Bernoulli trials <br />Accident counts of the set of locations follow negative binomial distribution<br />
  9. 9. State-of-the-art HSID Method: Bayesian<br />Where:<br />f (k)= prior pdf of k’s of reference population (similar sites ), it follows gamma distribution whose parameters are obtained based on empirical data.<br />f (K|k)= pdf of accident counts on a specific site whose expected safety is k, it is Poisson distribution.<br />f (K)= pdf of a group of collected accident counts.<br />f ( k|K)= posterior pdf of λ of the site which has x counts.<br />
  10. 10. General Process of Diagnosis<br />
  11. 11. General Process of Countermeasure Selection<br />
  12. 12. Problems of Current Safety Practice<br />The safety management components are somewhat isolated<br />Much less safety resources are allocated to the first step: HSID<br />Much more resources are invested in the subsequent steps.<br />Result: the substantial resources invested in the subsequent steps could be wasted on the sites that are wrongly identified in the first step<br />
  13. 13. The New HSID Method Proposed<br />Streamline the safety improvement process by incorporating the crash type and crash severity into HSID step.<br />Crash type recognition: facilitate the crash diagnosis process.<br />Crash Severity recognition: facilitate the economic evaluation of countermeasure alternatives.<br />All information is incorporated under Bayesian framework.<br />
  14. 14. Case Study: City of Corona<br />Crossroads Collision Database software<br />298 Intersections: 141 signalized and 157 non-signalized <br />Crash period: 10 years (2000~2009). <br />A set of roadway factors: minor ADT, major ADT, speed limit, etc.<br />
  15. 15. Method Evaluation<br />Crash data divided into 2 groups: Before period (2000~2004), After (2005~2009)<br />Use top 10% locations as crash hotspots<br />Evaluation criteria: overall crash costs of future period for all crashes and crash types.<br />Results: type-and-severity recognition method shows advantages over the typical one in terms of both criteria.<br />
  16. 16. Discussion on Future Direction<br />Crash data from other cities or counties are needed.<br />Data sample size (298) is relatively small.<br />Special statistical modeling techniques are required to address issues associated with crash type and severity.<br />