Calibration of an Infrared-based Automatic Counting System for Pedestrian Traffic Flow Data Calibration of an Infrared-based Automatic Counting System for Pedestrian Traffic Flow Data CollectionCollection

Step 3: Calibration ModelsCalibration models were built using the relationship between raw sensor counts and the group patterns. The calibration models were proposed as :

(a) Group 1 (b) Group 2 (c) Group 3

Parameters including β20, β21, β30, and β31 were estimated using the bootstrap regression method. 10,000 bootstraps, each of size 100, were conducted. The estimated coefficients for each replication were presented in the following scatter plots:

This study used the estimated mean coefficients to establish the final calibration models.

For 15-minute intervals, the estimated calibration models are:

For 1-hour intervals, the estimated calibration models are:

Step 4: Validation TestNew datasets collected at three different sites were tested. The first dataset was collected on April 10 (10:30am to10:30pm) at the same trail (site 1) where training data were collected. The second dataset was collected on October 19 (9:00am to 10:00pm) at a trail on Rutgers Busch campus (site 2). And the third dataset was collected on May 22 (9:00am to 5:00pm) at an intersection nearby Trenton transit center, NJ (site 3).

Site 1 Site 2 Site 3

Step 3: Calibration ModelsCalibration models were built using the relationship between raw sensor counts and the group patterns. The calibration models were proposed as :

(a) Group 1 (b) Group 2 (c) Group 3

Parameters including β20, β21, β30, and β31 were estimated using the bootstrap regression method. 10,000 bootstraps, each of size 100, were conducted. The estimated coefficients for each replication were presented in the following scatter plots:

This study used the estimated mean coefficients to establish the final calibration models.

For 15-minute intervals, the estimated calibration models are:

For 1-hour intervals, the estimated calibration models are:

Step 4: Validation TestNew datasets collected at three different sites were tested. The first dataset was collected on April 10 (10:30am to10:30pm) at the same trail (site 1) where training data were collected. The second dataset was collected on October 19 (9:00am to 10:00pm) at a trail on Rutgers Busch campus (site 2). And the third dataset was collected on May 22 (9:00am to 5:00pm) at an intersection nearby Trenton transit center, NJ (site 3).

Site 1 Site 2 Site 3

ResultsThe results of validation tests were presented in the following figure. Visually, the calibrated infrared counts were more close to the ground truth than the raw counts.

Statistical comparison results were presented in the following table. Wilcoxon matched=pairs signed-ranks test showed that the there is no significant difference between the calibrated results and the ground truth. The overall errors reduced by about 20 percent.

Conclusions•This study proposed a calibration procedure so that the raw data can be calibrated to reflect the ground truth.•It established a statistical relationship between the pedestrian arrival patterns and the infrared counter performance.•The calibration approach performed better at the high volume trails.•More factors should be considered to calibrate intersection counts as the pedestrian arrival patterns are more complex than trails.

AcknowledgmentsThe authors would like to thank the NJDOT for providing guidance and suggestions at various stages of this study. We also appreciate Mr. Ranjit Walla and Robert Williams of the Alan M. Voorhees Transportation Center at Rutgers University for the initial work of this project.

For detailed information contact: yanghong@eden.rutgers.edu

ResultsThe results of validation tests were presented in the following figure. Visually, the calibrated infrared counts were more close to the ground truth than the raw counts.

Statistical comparison results were presented in the following table. Wilcoxon matched=pairs signed-ranks test showed that the there is no significant difference between the calibrated results and the ground truth. The overall errors reduced by about 20 percent.

Conclusions•This study proposed a calibration procedure so that the raw data can be calibrated to reflect the ground truth.•It established a statistical relationship between the pedestrian arrival patterns and the infrared counter performance.•The calibration approach performed better at the high volume trails.•More factors should be considered to calibrate intersection counts as the pedestrian arrival patterns are more complex than trails.

AcknowledgmentsThe authors would like to thank the NJDOT for providing guidance and suggestions at various stages of this study. We also appreciate Mr. Ranjit Walla and Robert Williams of the Alan M. Voorhees Transportation Center at Rutgers University for the initial work of this project.

For detailed information contact: yanghong@eden.rutgers.edu

AbstractConventional data collection methods such as manual counting hardly satisfy the requirements of long-term pedestrian studies. Technological advancements accelerated the development of automatic pedestrian counting devices. Practices show that none of automatic counter performs perfectly. There is need to improve the automatic counting performance.

This paper attempts to propose a novel calibration procedure to estimate more reliable counts using the raw sensor outputs. It focuses on the relationship between pedestrian arrival patterns and pedestrian counter performance. Lab experiments and field tests were conducted to establish and validate the statistical relationships between actual counts, sensor counts and arrival patterns. Statistical tests illustrated that there wee no statistically significant differences between the “calibrated” and actual counts.

AbstractConventional data collection methods such as manual counting hardly satisfy the requirements of long-term pedestrian studies. Technological advancements accelerated the development of automatic pedestrian counting devices. Practices show that none of automatic counter performs perfectly. There is need to improve the automatic counting performance.

This paper attempts to propose a novel calibration procedure to estimate more reliable counts using the raw sensor outputs. It focuses on the relationship between pedestrian arrival patterns and pedestrian counter performance. Lab experiments and field tests were conducted to establish and validate the statistical relationships between actual counts, sensor counts and arrival patterns. Statistical tests illustrated that there wee no statistically significant differences between the “calibrated” and actual counts.

IntroductionPedestrian counts are important for decision making in pedestrian facility planning, signal timing, and pedestrian safety modeling. However, the quality of existing pedestrian data is considered quite poor and the priority for more accurate pedestrian traffic collection is high. Researchers have been developing new counting tools to improve efficiency and quality of pedestrian data.

Infrared counters are one of the frequently used pedestrian counting devices. Infrared counters yield high accuracy with single pedestrians, but have accuracy concerns with simultaneous arrivals.

The objective of this study was to calibrate an infrared-based automatic pedestrian counter deployed at locations with relatively high pedestrian volume. The relationship between counter errors and actual pedestrian traffic patterns were investigated.

Infrared CounterEcoCounter, a dual sensor pyroelectric infrared counter, was selected for this study.

Typical features of EcoCounter are: •Two lenses sensitive to human body infrared radiation,•Avoid false counts caused by plant movement, rain or sun,•Dual-direction count by double-direction vertical technology,•Work properly in all weather conditions,•Internal battery life is up to 10 years,•Minimum data integration: 15-minute,•Data logger capacity: up to 1 year,•Easy to install.

IntroductionPedestrian counts are important for decision making in pedestrian facility planning, signal timing, and pedestrian safety modeling. However, the quality of existing pedestrian data is considered quite poor and the priority for more accurate pedestrian traffic collection is high. Researchers have been developing new counting tools to improve efficiency and quality of pedestrian data.

Infrared counters are one of the frequently used pedestrian counting devices. Infrared counters yield high accuracy with single pedestrians, but have accuracy concerns with simultaneous arrivals.

The objective of this study was to calibrate an infrared-based automatic pedestrian counter deployed at locations with relatively high pedestrian volume. The relationship between counter errors and actual pedestrian traffic patterns were investigated.

Infrared CounterEcoCounter, a dual sensor pyroelectric infrared counter, was selected for this study.

Typical features of EcoCounter are: •Two lenses sensitive to human body infrared radiation,•Avoid false counts caused by plant movement, rain or sun,•Dual-direction count by double-direction vertical technology,•Work properly in all weather conditions,•Internal battery life is up to 10 years,•Minimum data integration: 15-minute,•Data logger capacity: up to 1 year,•Easy to install.

Research MethodologyOur research methodology can be summarized as follows:•Step 1: Conduct pilot lab tests•Step 2: Conduct field tests•Step 3: Develop calibration approach •Step 4: Test calibration approach.

Step 1: Pilot Lab Tests

Step 2: Field TestsA 6-day data collection was scheduled in Piscataway, NJ. The selection of the test sites was based on criteria such as pedestrian volume, availability of mounting facility, accessibility, and the recommendation of the NJDOT.

Research MethodologyOur research methodology can be summarized as follows:•Step 1: Conduct pilot lab tests•Step 2: Conduct field tests•Step 3: Develop calibration approach •Step 4: Test calibration approach.

Step 1: Pilot Lab Tests

Step 2: Field TestsA 6-day data collection was scheduled in Piscataway, NJ. The selection of the test sites was based on criteria such as pedestrian volume, availability of mounting facility, accessibility, and the recommendation of the NJDOT.

Paper: 10-3574 Kaan OZBAY, Ph.D., Hong YANG, M.Sc. and Bekir BARTIN, Ph.D. Rutgers, The State University of New Jersey

FIG 2. Controlled Pedestrian Arrival Patterns

TABLE 2. Pedestrian Arrival Patterns

TABLE 4. Wilcoxon Matched-pairs Signed-Ranks Test

FIG 7. Comparisons of Validation Results

FIG 1. Configuration of Infrared EcoCounter

TABLE 1. Pilot Tests Results

TABLE 3. Correlations : Total Flow vs.. Counts of Each Group

FIG 3. Field tests at a Pedestrian Trail

FIG 4. Example of Potential Infrared Sensor Counting Types

FIG 5. Bootstrap Replications of Regression Coefficients

FIG 6. Validation Tests at Different SitesMounting screwsDual lens

Activation KeyCables

Sensor logger

CountedCountedMissed

CountedMissed

Rutgers Intelligent Transportation Systems (RITS) Laboratory Department of Civil & Environmental Engineering