Automated Methods for Traffic Data Collection and Surrogate Measures of Safety Hasselt University

Nicolas Saunier [email protected]

March 19rd 2015

My Journey to Transportation

1

1998-2001: Telecom ParisTech engineer in man-machine interface and artificial intelligence

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

2 / 44

My Journey to Transportation

1

1998-2001: Telecom ParisTech engineer in man-machine interface and artificial intelligence

2

2001-2005: Ph.D. with Sophie Midenet in Computer Science from Telecom ParisTech, working at INRETS (IFSTTAR) on “Influence of traffic control in a signalized intersection on the risk of road users; Stream-based learning of safety indicators through data selection”

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

2 / 44

My Journey to Transportation

1

1998-2001: Telecom ParisTech engineer in man-machine interface and artificial intelligence

2

2001-2005: Ph.D. with Sophie Midenet in Computer Science from Telecom ParisTech, working at INRETS (IFSTTAR) on “Influence of traffic control in a signalized intersection on the risk of road users; Stream-based learning of safety indicators through data selection”

3

2005-2009: Postdoc at UBC with Tarek Sayed, developping video analysis for surrogate safety analysis

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

2 / 44

My Journey to Transportation

1

1998-2001: Telecom ParisTech engineer in man-machine interface and artificial intelligence

2

2001-2005: Ph.D. with Sophie Midenet in Computer Science from Telecom ParisTech, working at INRETS (IFSTTAR) on “Influence of traffic control in a signalized intersection on the risk of road users; Stream-based learning of safety indicators through data selection”

3

2005-2009: Postdoc at UBC with Tarek Sayed, developping video analysis for surrogate safety analysis ´ 2009-: Professor in Polytechnique Montreal

4

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

2 / 44

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

3 / 44

Introduction

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

4 / 44

Introduction

Surrogate Measures of Safety

Looking for (complementary) measures of safety that do not require to wait for accidents to happen ´ 2006]: in the safety hierarchy, Hypothesis [Svensson and Hyden, all events have a relationship to accidents (safety) that may be of different nature Automation using video sensors and computer vision cheap hardware, open source software

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

5 / 44

Introduction

The Particularities of our Approach

Automated video analysis

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Introduction

The Particularities of our Approach

Automated video analysis Develop an automated, robust and generic probabilistic framework for surrogate safety analysis

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Introduction

The Particularities of our Approach

Automated video analysis Develop an automated, robust and generic probabilistic framework for surrogate safety analysis for all types of road users and road environments

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Introduction

The Particularities of our Approach

Automated video analysis Develop an automated, robust and generic probabilistic framework for surrogate safety analysis for all types of road users and road environments generalize the concept of collision course: importance of motion prediction methods

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Introduction

The Particularities of our Approach

Automated video analysis Develop an automated, robust and generic probabilistic framework for surrogate safety analysis for all types of road users and road environments generalize the concept of collision course: importance of motion prediction methods improve existing indicator(s) before inventing new ones

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Introduction

The Particularities of our Approach

Automated video analysis Develop an automated, robust and generic probabilistic framework for surrogate safety analysis for all types of road users and road environments generalize the concept of collision course: importance of motion prediction methods improve existing indicator(s) before inventing new ones

Better understand collision processes and the similarities between interactions with and without a collision for safety estimation

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

6 / 44

Automated Video Analysis

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

7 / 44

Automated Video Analysis

Processing Steps

1

Video data collection

2

Data preparation

3

Moving road user detection, tracking and classification

4

Motion prediction

5

Safety indicators

6

Interpretation

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

8 / 44

Automated Video Analysis

Step 1: Video Data Collection

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

9 / 44

Automated Video Analysis

Step 2: Data Preparation In particular, camera calibration: homography and distortion (if any)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

10 / 44

Automated Video Analysis

Step 2: Data Preparation

In particular, camera calibration: homography and distortion (if any)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

10 / 44

Automated Video Analysis

Step 3: Moving Road User Detection, Tracking and Classification

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

11 / 44

Automated Video Analysis

Step 3: Moving Road User Detection, Tracking and Classification

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

11 / 44

Automated Video Analysis

Step 3: Moving Road User Detection, Tracking and Classification

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

11 / 44

Automated Video Analysis

Validating Cyclist Counts in Mixed Traffic Counting Accuracy for Cycle Tracks at Road Segments (5 minutes intervals, 170 points)

Counting Accuracy for Cycle Tracks at Road Segments (15 minutes intervals, 54 points)

60

140

y=x

120

40 30

y = 0,96x + 0,0862 R² = 0,9749

20 10

AUTOMATED COUNTING

AUTOMATED COUNTING

50

y=x

100 80 60

y = 0,9668x + 0,0831 R² = 0,9888

40 20

0

0 0

10

20

30

40

MANUAL COUNTING

´ N. Saunier, Polytechnique Montreal

50

60

0

20

40

60

80

100

120

140

MANUAL COUNTING

(a)

(b)

(c)

(d)

March 19rd 2015

12 / 44

Automated Video Analysis

Validating Cyclist Counts in Mixed Traffic Counting Accuracy for No Cycle Tracks at Road Segments (5 minutes intervals, 71 points)

Counting Accuracy for No Cycle Tracks at Road Segments (15 minutes intervals, 21 points)

50

100

y=x

y=x

80

30

20

y = 0,931x + 0,7323 R² = 0,954

10

AUTOMATED COUNTING

AUTOMATED COUNTING

40

60

40

y = 0,9304x + 2,2115 R² = 0,9768

20

0

0 0

10

20

30

MANUAL COUNTING

´ N. Saunier, Polytechnique Montreal

40

50

0

20

40

60

80

100

MANUAL COUNTING

March 19rd 2015

12 / 44

Automated Video Analysis

Disaggregated Vehicle Speed Validation 90

90 y = 1x + 8,7 R² = 0,8898

y = 1x + 3,645 R² = 0,7796

80

70

Extracted Speed (km/h)

Extracted Speed (km/h)

80

60 50 40 30 20 10

70 60 50 40 30 20 10

0

0 0

10

20

30

40

50

60

70

Observed Speed (km/h)

´ N. Saunier, Polytechnique Montreal

80

90

0

10

20

30

40

50

60

70

80

90

Observed Speed (km/h)

March 19rd 2015

13 / 44

Automated Video Analysis

Road User Classification in Dense Mixed Traffic

ROC Curves

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

14 / 44

Automated Video Analysis

Tracking Parameter Optimization

Video sequence

Groundtruth tracks

Tracker

Tracker parameters i

Fitness function

Tracker traces i

Tracker traces i+1

´ N. Saunier, Polytechnique Montreal

Fitness i

List of previous solutions and their parameters

Comparison function

Mutation function

Fitness i

March 19rd 2015

15 / 44

Automated Video Analysis

Tracking Parameter Optimization

TI - CALIBRATION Subway Station

TI - TEST NY Crosswalk

Poly Corridor

1

1

0.8

0.8

0.6

0.6

MOTA

MOTA on test sequences

Poly Corridor

0.4

NY Crosswalk

0.4

0.2

0.2

0

0

-0.2

Subway Station

-0.2

TrOPed Improvement

Manual Calibration

´ N. Saunier, Polytechnique Montreal

TrOPed Improvement

Manual Calibration

March 19rd 2015

15 / 44

Traffic Intelligence

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

16 / 44

Traffic Intelligence

Open Science Principle of independent reproducibility Need to share data and tools used to produce the results public datasets and benchmarks [Saunier et al., 2014] public / open source software: adoption and contributions by researchers and practitioners

Traffic Intelligence open source project

https://bitbucket.org/Nicolas/trafficintelligence

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

17 / 44

Traffic Intelligence

“Traffic Intelligence”

Tools for video analysis and transportation data (a.k.a. trajectories) analysis Open source: MIT license (most permissive) Three parts 1

2 3

“Independent” project “Trajectory Management and Analysis” (C++): trajectory I/O (SQLite), trajectory similarity/distance measures Feature-based tracking (C++): feature tracking and grouping Trajectory data analysis (Python): tracking results loading, behaviour, interaction and safety analysis

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

18 / 44

Traffic Intelligence

Interfaces

It is first thought of as a library than as an end-user “click & run” tool It provides end-user programs: feature tracker executable, Python scripts the command line is not a bug, but a feature for automation/parallelism configuration files

No (few) graphical user interface: contributions or commercial add-ons?

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

19 / 44

Traffic Intelligence

2-minute Rant on Programming

Transportation engineers are deep in “big data” or “data science”: more and more data is coming our way and spreadsheets are ill-suited for the task Computing is the new math

Prototyping ! = software engineering iterative development: interpreter and interactive data exploration and processing (e.g. Python with scientific packages) “premature optimization is the root of all evil”

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

20 / 44

Traffic Intelligence

Performance Feature tracking performance depends on video resolution and special post-processing requirements such as stabilisation or lens correction (for distortion). A typical one hour 800 × 600 video is processed with current consumer-grade hardware in about an hour. A typical one hour 1280 × 960 video with correction for distortion can be processed in about two hours. Basic analysis on one of these trajectory sequences takes between 5 minutes and 30 minutes, depending on traffic in the scene, while conflict analysis, particularly motion patterns, can typically take anywhere between 1 to 48 hours to complete. Conflict analysis processing times are very sensitive to the interaction complexity of the scene. (Intel Core i7 3770k CPU) [St-Aubin et al., 2014] ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

21 / 44

Traffic Intelligence

Other Characteristics Multi-platform: C++ compiles on Windows and Linux, Python Documentation (Doxygen) and explicit naming The program is (partially) tested Python modules: cvutils, events, indicators, metadata, ml, moving, (objectsmoothing, pavement, poly-utils,) prediction, (processing,) storage, (traffic engineering,) ubc utils, utils Python scripts: (classify-objects.py,) display-trajectories.py, safety-analysis.py, compute-clearmot.py, play-video.py, compute-homography.py, (train-object-classification.py, ), create-bounding-boxes.py, undistort-video.py, delete-tables.py(, rescale-homography.py)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

22 / 44

Surrogate Measures of Safety

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

23 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

A traffic conflict is “an observational situation in which two or more road users approach each other in space and time to such an extent that a collision is imminent if their movements remain unchanged”

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 4: Motion Prediction

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

24 / 44

Surrogate Measures of Safety

Step 5: Safety Indicators

t1

0.7 0.3

t2 0.4

2

0.6

1 ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

25 / 44

Surrogate Measures of Safety

Step 5: Safety Indicators

Using of a finite set of predicted trajectories, enumerate the collision points CPn and the crossing zones CZm . Safety indicators can then be computed: X P(Collision(Ui , Uj )) = P(Collision(CPn )) Pn

P(Collision(CPn )) tn P(Collision(Ui , Uj )) P P(Reaching(CZm )) |ti,m − tj,m | pPET (Ui , Uj , t0 ) = m P m P(Reaching(CZm )) TTC(Ui , Uj , t0 ) =

´ N. Saunier, Polytechnique Montreal

n

March 19rd 2015

25 / 44

Surrogate Measures of Safety

Step 5: Safety Indicators

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

25 / 44

Surrogate Measures of Safety

Collision Probability

Step 5: Safety Indicators 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

2

3

4 Time (second)

5

6

2

3

4 Time (second)

5

6

TTC (second)

3.0 2.5 2.0 1.5 1.0 0.5

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

25 / 44

Surrogate Measures of Safety

Step 5: Safety Indicators 4.0

Expected evolution Motion pattern prediction Normal adaptation prediction Constant velocity prediction

Time-to-collision (TTC) in seconds

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.0

´ N. Saunier, Polytechnique Montreal

0.5

1.0 1.5 2.0 Interaction-instant time in seconds

2.5

3.0 +1.02e3 March 19rd 2015

25 / 44

Surrogate Measures of Safety

Step 6: Interpretation

Collision Probability

For each interaction, we have 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

2

3

4 Time (second)

5

6

2

3

4 Time (second)

5

6

TTC (second)

3.0 2.5 2.0 1.5 1.0 0.5

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

Collision Probability

How should data be aggregated? 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

2

3

4 Time (second)

5

6

2

3

4 Time (second)

5

6

TTC (second)

3.0 2.5 2.0 1.5 1.0 0.5

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

Maximum Collision Probability 140 120 100 80 60 40 20 00.1 70 60 50 40 30 20 10 00.1

Traffic Conflicts

0.2

0.3

0.4

0.5 0.6 0.7 Collision Probability

0.8

0.9

1.0

60 50 40 30 20 10 00.0

1.0

30 25 20 15 10 5 00.0

Collisions

0.2

0.3

0.4

0.5 0.6 0.7 Collision Probability

´ N. Saunier, Polytechnique Montreal

0.8

0.9

Minimum TTC Traffic Conflicts

0.5

1.0

1.5

2.0 2.5 TTC (second)

3.0

3.5

4.0

4.5

2.0 2.5 TTC (second)

3.0

3.5

4.0

4.5

Collisions

0.5

1.0

1.5

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

Histogram of Before-and-After TTC Frequency of traffic events

600 TTC Before TTC After

500 400 300 200 100 0

0

2

4

6

8 TTC

min

10 12 in seconds

14

16

18

20

Histogram of Before-and-After PET affic events

3000 2500

PET Before PET After

2000

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation Traffic Conflicts 72 64 56 48 40 32 24 16 8 0 ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Motion pattern

Normal adaptation

Cumulative Observations (%)

Constant velocity

Step 6: Interpretation 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 _cl_1 _cl_3 0.6 _cl_2 0.4 _cl_5 _cl_4 0.2 _cl_6 0.00 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 All instants Unique pair: minimum value Unique pair: 15th centile

TTC observations (seconds)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

26 / 44

Surrogate Measures of Safety

Step 6: Interpretation

Cycle Track on Right Cycle Track on Left Bicycle Flow for 5s before to 5s after Turning-Vehicle Flow for 5s before to 5s after Number of Lanes on the Main Road Number of Lanes on the Turning Road Cut-off 1 Cut-off 2 Cut-off 3 Number of Observations Log likelihood

Model I. Cycle track on the right vs. no cycle track

Model II. Cycle track on the left vs. no cycle track

Coef.

Std. Err.

Sig.

Coef.

Std. Err.

Sig.

Coef.

Std. Err.

Sig.

0.395 -

0.181 -

0.03 -

-

-

-

-0.513

0.131

0.00

Not Significant

-2.771

0.132

0.00

-0.151

0.078

0.05

Not Significant

-6.599 -4.233 -3.150

´ N. Saunier, Polytechnique Montreal

0.353 0.273 0.256 2880 -804

0.00 0.00 0.00

Not Significant

Model III. Cycle track on the right vs. cycle track on the left

0.088

0.038

0.02

0.066

0.034

0.05

-3.265

0.090

0.00

-3.131

0.080

0.00

Not Significant

Not Significant

0.324

0.146

0.03

0.457

0.178

0.01

-7.372 -3.807 -2.102

0.301 0.223 0.211 4803 -1876

0.00 0.00 0.00

-7.621 -4.125 -2.479

0.323 0.265 0.258 6567 -2330

0.00 0.00 0.00

March 19rd 2015

26 / 44

Surrogate Measures of Safety

´ N. Saunier, Polytechnique Montreal

Dist (m)

Dist (m)

50 Cluster 1 - 23.1%(28/121) 50 Cluster 2 - 42.7%(35/82) 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Cluster 3 0 . 0%(0 / 8) Cluster 4 - 42.1%(8/19) 50 50 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Cluster 5 38 . 5%(5 / 13) Cluster 6 - 11.5%(6/52) 50 50 40 40 30 30 20 20 10 10 0 2 0 2 4 6 8 10 12 14 0 2 0 2 4 6 8 10 12 14 Time (s) Time (s) Dist (m)

Dist (m)

Dist (m)

Dist (m)

Step 6: Interpretation

March 19rd 2015

26 / 44

Surrogate Measures of Safety

4.5 Cluster 2 - 38.2%(55/144) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

Cluster 3 - 33.3%(3/9) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

Cluster 4 - 5.0%(1/20) 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

´ N. Saunier, Polytechnique Montreal

TTC (s)

TTC (s)

TTC (s)

4.5 Cluster 1 - 19.4%(13/67) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.01 2 3 4 5 6 7 8 9 Time (s)

TTC (s)

Step 6: Interpretation

March 19rd 2015

26 / 44

Case Studies

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

27 / 44

Case Studies

Lane-Change Bans at Urban Highway Ramps 86 Ramp: A20-E-E56-3 Treatment: Yes

Region(s): UPreMZ, PPreMZ Analysis length: 50 m

Treated site (with lane marking) [St-Aubin et al., 2012, St-Aubin et al., 2013a]

All conflicts 0.06

Type A conflicts Type C conflicts All unique pair conflicts

0.05

Type A unique pair conflicts

Frequency (% per 0.5 increment)

Type C unique pair conflicts All unique individual conflicts

0.04

Type A unique individual conflicts Type C unique individual conflicts 0.03

0.02

0.01

0

5

10

15

20

25

30

35

40

45

50

Measured TTC (s)

Figure 37 – Conflict analysis Cam20-16-Dorval (Treated).

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

28 / 44

Case Studies

Lane-Change Bans at Urban Highway Ramps 70 Ramp: A20-E-E56-3 Treatment: No

Region(s): UPreMZ Analysis length: 50 m

All conflicts Type A conflicts Type C conflicts All unique pair conflicts Type A unique pair conflicts Type C unique pair conflicts All unique individual conflicts Type A unique individual conflicts Type C unique individual conflicts

0.09

Frequency (% per 0.5 increment)

0.08 0.07 0.06 0.05

Untreated site (no lane marking) [St-Aubin et al., 2012, St-Aubin et al., 2013a]

0.04 0.03 0.02 0.01 0 5

10

15

20 Measured TTC (s)

25

30

35

Figure 27 – Conflict analysis Cam20-16-Dorval (Untreated).

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

28 / 44

Case Studies

´ Big Data: Roundabout Safety in Quebec

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

29 / 44

Case Studies

Speed Fields in Roundabouts

[St-Aubin et al., 2013b] ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

30 / 44

Case Studies

K-means cluster profile for TTC regression # 1 2

3 4 5

6

Description Small single and double lane residential collectors Single-lane regional highways and arterials with speed limits of 70-90 km/h and mostly polarized flow ratios 2-lane arterials with very high flow ratios Hybrid lane 1− >2 2− >1 arterials with very low flow ratios Traffic circle converted to roundabout (2 lanes, extremely large diameters, tangential approach angle) Single-lane regional highway with largeangle quadrants (140 degrees) and mixed flow ratios

´ N. Saunier, Polytechnique Montreal

Nzones 11

Nobs 4,200

16

26,243

5 3

13,307 4,809

4

10,295

2

2,235

March 19rd 2015

31 / 44

Case Studies

TTC Distribution Comparison by Cluster

Cumulative Observations (%)

1.0

_cl_1 _cl_3 _cl_2 _cl_5 _cl_4 _cl_6

0.8

0.6

0.4 0.2

0.00 ´ N. Saunier, Polytechnique Montreal

2

4

6

TTC observations (seconds)

8

10 March 19rd 2015

32 / 44

Case Studies

Analysis of Bicycle Facilities in Montreal

Bicycle boxes (only 4 in Montreal)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

33 / 44

Case Studies

Analysis of Bicycle Facilities in Montreal

Bicycle boxes (only 4 in Montreal) video data collected at 2 sites, before and after the installation of a bicycle box, and 2 control sites without

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

33 / 44

Case Studies

Analysis of Bicycle Facilities in Montreal

Bicycle boxes (only 4 in Montreal) video data collected at 2 sites, before and after the installation of a bicycle box, and 2 control sites without

Cycle tracks: 650 km of bicycle network in 2015

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

33 / 44

Case Studies

Model of Dangerous Interactions at Bicycle Boxes

Explanatory variables Constant Bicycle Flow during 30s before Vehicle Flow 1 during 30s before Vehicle Flow 2 during 30s before Presence of Bicycle Box Observations Percentage of positive obs. Log-likelihood Pseudo R2 *

Interaction Type 1 Dangerous Interaction (PET < 1.5s) Elas. Coef. p-val. Elas.

Coef.

p-val.

Coef.

Interaction Type 2 Dangerous Interaction (PET < 1.5s) p-val. Elas. Coef. p-val. Elas.

-0.559

0.00

-

-1.954

0.00

-

-2.994

0.00

-

-4.354

0.00

-

0.423

0.00

7.7 %

0.434

0.00

2.1 %

-

-

-

-

-

-

0.091

0.00

1.6 %

0.040

0.04

0.2 %

0.063

0.00

0.4 %

-

-

-

-0.086

0.00

-1.6 %

-0.082

0.01

-0.4 %

0.117

0.00

0.8 %

0.097

0.00

0.1 %

-0.739

0.00

-14 %*

-1.226

0.00

-7 %*

-0.726

0.00

-5 %*

-2.050

0.00

-2 %*

Interaction (PET < 5s)

Interaction (PET < 5s)

1054

1054

27.6 %

7.5 %

9.8 %

1.3 %

-544.00 0.133

-251.48 0.109

-299.85 0.117

-66.44 0.110

Elasticity for discrete change of dummy variable from 0 to 1

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

34 / 44

Case Studies

Turning Vehicle Interactions with Cycle Tracks

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

35 / 44

Case Studies

Site Selection

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

36 / 44

Case Studies

Site Selection

Cycle track on the right Cycle track on the left No cycle track Total

# intersections 8 intersections 7 intersections 8 intersections 23 intersections

Duration 37 h 22 h 31 h 90 h

Videos were collected on weekdays during the evening peak period from 3pm to 7pm

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

36 / 44

Case Studies

Road User Selection

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

37 / 44

Case Studies

Three PET Ordered Logit Models

Cycle Track on Right Cycle Track on Left Bicycle Flow for 5s before to 5s after Turning-Vehicle Flow for 5s before to 5s after Number of Lanes on the Main Road Number of Lanes on the Turning Road Cut-off 1 Cut-off 2 Cut-off 3 Number of Observations Log likelihood

Model I. Cycle track on the right vs. no cycle track

Model II. Cycle track on the left vs. no cycle track

Coef.

Std. Err.

Sig.

Coef.

Std. Err.

Sig.

Coef.

Std. Err.

Sig.

0.395 -

0.181 -

0.03 -

-

-

-

-0.513

0.131

0.00

Not Significant

-2.771

0.132

0.00

-0.151

0.078

0.05

Not Significant

-6.599 -4.233 -3.150

´ N. Saunier, Polytechnique Montreal

0.353 0.273 0.256 2880 -804

0.00 0.00 0.00

Not Significant

Model III. Cycle track on the right vs. cycle track on the left

0.088

0.038

0.02

0.066

0.034

0.05

-3.265

0.090

0.00

-3.131

0.080

0.00

Not Significant

Not Significant

0.324

0.146

0.03

0.457

0.178

0.01

-7.372 -3.807 -2.102

0.301 0.223 0.211 4803 -1876

0.00 0.00 0.00

-7.621 -4.125 -2.479

0.323 0.265 0.258 6567 -2330

0.00 0.00 0.00

March 19rd 2015

38 / 44

Perspectives

Outline

1

Introduction

2

Automated Video Analysis

3

Traffic Intelligence

4

Surrogate Measures of Safety

5

Case Studies

6

Perspectives

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

39 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations 4 highway sites: 2-3 lanes, with exit and merging behaviour

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations 4 highway sites: 2-3 lanes, with exit and merging behaviour Calibration of car-following and lane change models

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations 4 highway sites: 2-3 lanes, with exit and merging behaviour Calibration of car-following and lane change models Fit distributions of microscopic measures (headway, number of lane changes)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations 4 highway sites: 2-3 lanes, with exit and merging behaviour Calibration of car-following and lane change models Fit distributions of microscopic measures (headway, number of lane changes) Use derivative-free (“black box”) optimization software NOMAD

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Framework for the Calibration and Validation of Traffic Micro-simulation

Terrible state of practice (MULTITUDE project) Propose proper definitions Automated calibration and validation of traffic micro-simulation based on video observations 4 highway sites: 2-3 lanes, with exit and merging behaviour Calibration of car-following and lane change models Fit distributions of microscopic measures (headway, number of lane changes) Use derivative-free (“black box”) optimization software NOMAD

Partnership with WSP (consulting company)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

40 / 44

Perspectives

Night-time Safety Study of the link between lighting and safety [Nabavi Niaki et al., 2014] Night-time observations: video data from thermal camera

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

41 / 44

Perspectives

Open Questions for Surrogate Measures of Safety

How can we aggregate indicators over time and space (and severity), without hiding information?

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

42 / 44

Perspectives

Open Questions for Surrogate Measures of Safety

How can we aggregate indicators over time and space (and severity), without hiding information? How can we compare the various methods and indicators?

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

42 / 44

Perspectives

Open Questions for Surrogate Measures of Safety

How can we aggregate indicators over time and space (and severity), without hiding information? How can we compare the various methods and indicators? How do we validate the methods? With respect to what?

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

42 / 44

Perspectives

Open Questions for Surrogate Measures of Safety

How can we aggregate indicators over time and space (and severity), without hiding information? How can we compare the various methods and indicators? How do we validate the methods? With respect to what? How do we account for exposure? Conflicts are, by definition, not exposure [Hauer, 1982]

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

42 / 44

Perspectives

Other Interests

Traffic monitoring, probe data Naturalistic driving studies Advanced Driver Assistance Systems and vehicle automation project for the Ministry of Transportation on experimentations on the safety impact of Intelligent Speed Adaptation and Speed Data Loggers Senior associate of the Canadian Automated Vehicles Centre of Excellence (CAVCOE)

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

43 / 44

Perspectives

Collaboration with Tarek Sayed (UBC), Karim Ismail (Carleton), Mohamed Gomaa Mohamed, Paul St-Aubin, Matin Nabavi Niaki ´ (Polytechnique Montreal), Luis Miranda-Moreno, Sohail Zangenehpour (McGill), Aliaksei Laureshyn (Lund) Funded by the Natural Sciences and Engineering Research ´ Council of Canada (NSERC), the Quebec Research Fund for ´ Nature and Technology (FRQNT) and the Quebec Ministry of Transportation (MTQ), City of Montreal

Questions? ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

44 / 44

Perspectives

Hauer, E. (1982). Traffic conflicts and exposure. Accident Analysis & Prevention, 14(5):359–364. Nabavi Niaki, M. S., Saunier, N., Miranda-Moreno, L. F., Amador-Jimenez, L., and Bruneau, J.-F. (2014). A method for road lighting audit and safety screening. Transportation Research Record: Journal of the Transportation Research Board, 2458:27–36. presented at the 2014 Transportation Research Board Annual Meeting. ¨ H., Jodoin, J.-P., Laureshyn, A., Nilsson, M., Saunier, N., Ardo, ˚ om, ¨ Svensson, A., Miranda-Moreno, L. F., Bilodeau, G.-A., and Astr K. (2014). Public video data set for road transportation applications. In Transportation Research Board Annual Meeting Compendium of Papers. ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

44 / 44

Perspectives

14-2379. St-Aubin, P., Miranda-Moreno, L., and Saunier, N. (2012). A surrogate safety analysis at protected freeway ramps using cross-sectional and before-after video data. In Transportation Research Board Annual Meeting Compendium of Papers. 12-2955. St-Aubin, P., Miranda-Moreno, L., and Saunier, N. (2013a). An automated surrogate safety analysis at protected highway ramps using cross-sectional and before-after video data. Transportation Research Part C: Emerging Technologies, 36:284–295. St-Aubin, P., Saunier, N., Miranda-Moreno, L., and Ismail, K. (2013b). Detailed driver behaviour analysis and trajectory interpretation at roundabouts using computer vision data. ´ N. Saunier, Polytechnique Montreal

March 19rd 2015

44 / 44

Perspectives

In Transportation Research Board Annual Meeting Compendium of Papers. 13-5255. St-Aubin, P., Saunier, N., and Miranda-Moreno, L. F. (2014). Large-scale automated proactive road safety analysis using video data. Transportation Research Part C: Emerging Technologies. Re-submitted in February 2015. ´ C. (2006). Svensson, A. and Hyden, Estimating the severity of safety related behaviour. Accident Analysis & Prevention, 38(2):379–385.

´ N. Saunier, Polytechnique Montreal

March 19rd 2015

44 / 44