A New Method to Identify Hot Spots in roadway Network

BYWen Cheng, Ph.D., PE Assistant Professor

Civil Engineering DepartmentCal Poly Pomona

Presentation Outline• Hotspot Identification (HSID) background• Description of the new proposed HSID

method• Application of the new method• Summary and Conclusions

Crashes in Real Life: Huge Burden

Crashes in the U.S.• 42,116 fatalities (i.e., 115 persons

killed/day)• 1.51 fatalities/100M VMT• 14.79 fatalities/100K Population• 231 Billion Economic Cost• 41% alcohol-related fatalities• 29.7% Speed-related fatal crashes

Source: 2005 NHTSA

Crashes in California

Source: 2005 CA Statistical Abstract

How to address safety challenges• ISTEA legislation (1991) and TEA-21(1998)

required each state develop safety management systems.

• SAFETEA-LU (2005) establishes a new core Highway Safety Improvement Program– structured and funded to make significant

progress in reducing highway fatalities

Safety Management System Basic Components

1• Identification of hot spots

2• Diagnosis of crash problems

3• Selection of countermeasures

4• Evaluation of countermeasures

Current Practice of HSID • Rank a set of candidate locations in terms

of the registered accident counts or expected long-term accident counts

• General assumptions– accident process can be viewed as a

sequence of Bernoulli trials –Accident counts of the set of locations follow

negative binomial distribution

State-of-the-art HSID Method: Bayesian

)(

)|()()|(

Kf

kKfkfKkf

Where:

f (k)= prior pdf of k’s of reference population (similar sites ), it follows gamma distribution whose parameters are obtained based on empirical data.

f (K|k)= pdf of accident counts on a specific site whose expected safety is k, it is Poisson distribution.

f (K)= pdf of a group of collected accident counts.

f ( k|K)= posterior pdf of λ of the site which has x counts.

General Process of Diagnosis

1• Identification of dominant accident types of hot

spots

2• Data amplification by detailed on-site

investigation (maybe include conflict studies)

3• Identification of dominant factors and/or road

features

4• Determine the nature of the accident problem

General Process of Countermeasure Selection

1• Determine the range of measures likely to influence the

dominant accident types and road features

2• Select countermeasures expected to reduce the number and

severity of accidents of the type dominant at the location

3

• Check that adopted countermeasures do not have undesirable consequences, either in safety terms or in traffic efficiency terms

Problems of Current Safety Practice• The safety management components are

somewhat isolated• Much less safety resources are allocated to

the first step: HSID• Much more resources are invested in the

subsequent steps.• Result: the substantial resources invested in the

subsequent steps could be wasted on the sites that are wrongly identified in the first step

The New HSID Method Proposed• Streamline the safety improvement process by

incorporating the crash type and crash severity into HSID step.

• Crash type recognition: facilitate the crash diagnosis process.

• Crash Severity recognition: facilitate the economic evaluation of countermeasure alternatives.

• All information is incorporated under Bayesian framework.

Case Study: City of Corona• Crossroads Collision Database software• 298 Intersections: 141 signalized and 157

non-signalized • Crash period: 10 years (2000~2009). • A set of roadway factors: minor ADT, major

ADT, speed limit, etc.

Method Evaluation• Crash data divided into 2 groups: Before period

(2000~2004), After (2005~2009)• Use top 10% locations as crash hotspots• Evaluation criteria: overall crash costs of future

period for all crashes and crash types.• Results: type-and-severity recognition method

shows advantages over the typical one in terms of both criteria.

Discussion on Future Direction• Crash data from other cities or counties are

needed.• Data sample size (298) is relatively small.• Special statistical modeling techniques are

required to address issues associated with crash type and severity.