Pedestrian Crash Hot Spots

Identification Methods for Pedestrian Crash Hot Spots

Research Team:

Dr. Aditya Medury
Dr. Offer Grembek

Funding Organization:

California Department of Transportation (Caltrans)

Publications and Resources:

  • Medury, A., & Grembek, O. (2016). Dynamic programming-based hot spot identification approach for pedestrian crashes. Accident Analysis & Prevention, 93, 198-206.

Study Description:

Network screening techniques are widely used by state agencies to identify locations with high crash concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile crashes. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of some of the widely used network screening methods has not been tested.

In order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption.

Figure 1 of hotspot identification

Figure 1. An example of hot spots identified using sliding window method and dynamic programming

Another aspect of this research is in addressing the limitations of identifying hot spots in the absence of pedestrian volume information and subsequently safety performance functions. Thus, our research is also evaluating different crash frequency-based hot spot prioritization methods, which include the following techniques:

  1. Assessing the significance of dominant crash type presence:
    1. Pattern recognition-based methods [2]
    2. Probability of specific crash types exceeding threshold proportion (HSM [3]
  2. Addressing regression-to-mean using crashes in reference populations:
    1. Excess predicted average crash frequency using method of moments (HSM

An underlying challenge for utilizing some of the existing techniques devised for automobile crashes is that existing crash databases do not include any pedestrian typologies, unlike those defined for automobile crashes (e.g., broadside, sideswipe, rear-end, etc.). As part of this research, we also propose a pedestrian crash typology (Figure 2) using clustering techniques applied on location attributes (crosswalk, roadway, lighting conditions), as well as mode-specific movement preceding crash (e.g., automobile turning or moving straight, pedestrian crossing in the crosswalk or walking along the roadway).

Figure 2 of proposed pedestrian crash typology

Figure 2. Proposed pedestrian crash typology


  1. Medury, A., & Grembek, O. (2016). Dynamic programming-based hot spot identification approach for pedestrian crashes. Accident Analysis & Prevention, 93, 198-206.
  2. Kononov, J. (2002). Identifying locations with potential for accident reductions: Use of direct diagnostics and pattern recognition methodologies. Transportation Research Record: Journal of the Transportation Research Board, (1784), 153-158.
  3. National Research Council (US). Transportation Research Board. Task Force on Development of the Highway Safety Manual, & Transportation Officials. Joint Task Force on the Highway Safety Manual. (2010). Highway safety manual (Vol. 1). AASHTO.