Study Type Major Data Needs Criteria/approaches to Identify Secondary Crash

Raub (1997); Karlaftis et al. (1999) static incident < clearance time+15 min(ute), < 1 m(ile)

Latoski et al. static incident < clearance time+15 min, < 3 m

Hirunyanitiwattana & Mattingly (2006) static crash data < 2 h(our), < 2 m

Zhan et al. (2008) static incident < clearance time+15 min, < 2 m, lane closure

Khattak et al. (2009) static incident < actual duration, < 1 mile upstream

Moore et al. (2004); Kopitch & Saphores (2011) static incident data < 2 h, < 2 m [both dir(ection)]

Chang & Rochon (2011) static incident data < 2 h, < 2 m; < 0.5 hour, < 0.5 mile (other dir)

Green et al. (2012) static crash data < 80 minutes, < 6,000ft; < 1,000ft (other dir)

Zhan et al. (2009) dynamic incident data maximum queuing model

Sun & Chilukuri (2005, 2010); dynamic incident data incident progression curves

Zhang & Khattak (2010) dynamic incident data deterministic queuing model

Haghani et al. (2006); Cho & Miller-Hooks (2010) dynamic incident + simulated traffic data Impact based on simulated speed contour map

Vlahogianni et al. (2010, 2011, 2012) dynamic incident + monitor + sensor data identify influential area by ASDA model

Yang et al. (2013ab) dynamic crash + sensor data spatiotemporal impact by speed contour map

Chung (2013) dynamic crash + sensor data crash impact region by speed contour map

BIG DATA FOR TRAFFIC SAFETY PERFORMANCE EVALUATION

Hong Yang, Kaan Ozbay, Ender Faruk Morgul, Kun Xie

UrbanMITS Laboratory, Department of Civil &Urban Engineering,

Center for Urban Science + Progress, New York University

Motivation Objectives

Literature Review

Improved Identification Algorithm

Spatiotemporal Impact of Secondary Crashes

Conclusions & Discussion

Traffic incidents cause delays & secondary crashes

Secondary crashes induce more operational & safety issues

Reducing the risk of secondary crashes is of high priority

Knowledge of secondary crashes is very limited

Identification of secondary crashes was a great challenge

Discuss the issues of existing approaches for secondary crash identification

Develop the virtual sensor based traffic data collection methodology

Improve the identification algorithm based on traditional sensor data

Develop an on-line scalable approach for automating identification procedure

Provide readily deployable identification tool for large-scale network use

No standard criteria to establish the fixed thresholds

The thresholds are too subjective to be reliable

Hardly reflect the actual impact of each incident

Possible Bias: Underestimate and overestimate

Weak assumption: maximum queue at clearance time

Queuing model-based: deterministic

Queuing model-based: off-line method

Depend on detailed incident information

Sensor-based: limit instrumented roads

Framework

Virtual Sensor

Potential Data Sources:

Google Map

Bing Map

MapQuest

Other maps with API

The travel time between two virtual sensors is measured and the average travel speed is interpreted.

A virtual sensor is a user created marker on map. It has coordinate information and can be accessed by API.

Data Validation

Virtual sensor output need to capture normal traffic & recurring congestion

Virtual sensor output need to capture incidents interrupt on traffic

Virtual sensor output should match traditional sensor output

Demos: virtual sensor vs detector

Primary vs. Secondary

What if a simple approach is used?

Actual impact exceeded Threshold 1

Actual impact is with Threshold 2

Challenge:

Missing information for establishing a threshold

Impact of each primary crash varies

Progression of QueueDynamic progression of queue:

Queue can exist even incident is cleared

Maximum queue can exist before and after the primary incident is cleared

Presence of Impact:

It can last over long time and space.

A reliable way is needed to account for the dynamic characteristics of impact of primary incident .

Step 1: Construct speed contour Maps (SCM)

Step 2: Develop a representative speed contour map (RSCM)

Step 3: Construct a binary speed contour Map (BSCM)

The speed measurements derived from the virtual sensors are

extracted to develop SCM

Each pair of virtual sensors will keep recording the data

Challenge:

How many virtual sensors are needed for a

given link?

Sensitivity: What are the internal algorithm

for crowd-sourcing data by those maps?

The time interval to obtain the virtual sensors

should be relative small (i.e., 5-minute)

Construct RSCM based

on historical data

collected by virtual

sensors

A sampling strategy is

used to sample the

recorded data

What if there is no special events?

Highlight the impact of special events

Challenge:

Does the facility have a

representative

condition?

The difference between

the SCM and the RSCM

highlights the incident

impact

The identification

algorithm is then needed

to technically determine

whether the secondary

crash exists or not

Challenge:

How to find the

difference?

What are the reasonable

criteria to define the

impact area?

Sub-step 4.1: Build line equation

Sub-step 4.2: Predict the coordination of special points

Sub-step 4.3: Detect the K cells that contain the short line segments

Sub-step 4.4: Check the binary speed measurement of each cell

Crash B is identified as a secondary crash of crash A

Crash C is NOT identified as a secondary crash of crash A

E LX X L L

E L

S SS T T S

T T

( , )j is t 1 1( , )j is t

( , )q qS T

1

( , )K

k q qk

V S T Q

Traditional approaches for secondary

crash identification is largely limited

by the availability of infrastructure-

based sensors.

A virtual-sensor approach is proposed

to collect the third-party open source

traffic data.

The virtual-sensor data is acceptable in

comparison with output from roadside

traffic sensors deployed by the

transportation agencies.

The automatic identification approach

is developed to capture the secondary

crashes in an efficient and reliable way.

The proposed approaches enable on-

line identification for secondary crash

on a large network even traffic

sensors are not available.

The approach is readily available in

practice.

If you have question, please contact: Hong.Yang@nyu.edu

Development of an On-line Scalable Approach for Identifying Secondary Crashes