AN INTERSECTION TRAFFIC DATA COLLECTION DEVICE
UTILIZING LOGGING CAPABILITIES OF TRAFFIC CONTROLLERS
AND CURRENT TRAFFIC SENSORS
Final Report
November 2008
UI Budget KLK134
NIATT Report Number N08-13
Prepared by
National Institute for Advanced Transportation Technology
University of Idaho
Ahmed Abdel-Rahim
Brian k. Johnson
DISCLAIMER
The contents of this report reflect the views of the authors,
who are responsible for the facts and the accuracy of the
information presented herein. This document is disseminated
under the sponsorship of the Department of Transportation,
University Transportation Centers Program, in the interest of
information exchange. The U.S. Government assumes no
liability for the contents or use thereof.
1. Report No. 2. Government Accession
No.
3. Recipient’s Catalog No.
4. Title and Subtitle
An Intersection Traffic Data Collection Device Utilizing Logging
Capabilities of Traffic Controllers and Current Traffic Sensors
5. Report Date
November 2008
6. Performing Organization Code
KLK134
7. Author(s)
Abdel-Rahim, Dr. Ahmed; Johnson, Dr. Brian
8. Performing Organization
Report No. N08-13
9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)
National Institute for Advanced Transportation Technology
University of Idaho
PO Box 440901; 115 Engineering Physics Building
Moscow, ID 83844-0901
11. Contract or Grant No.
DTRS98-G-0027
12. Sponsoring Agency Name and Address
US Department of Transportation
Research and Special Programs Administration
400 7th Street SW
Washington, DC 20509-0001
13. Type of Report and Period
Covered
Final Report: November
2006 – August 2008
14. Sponsoring Agency Code
USDOT/RSPA/DIR-1
15. Supplementary Notes:
16. Abstract
The project presents a high-resolution data logging device that can be used in real-time traffic monitoring at signalized
intersections. The data logging device can be connected to traffic cabinets using different connection modes. The data
logging device logs the status of all input and output communication channels and updates their status continuously. This
data can be accessed remotely through an Ethernet port over IP based communication. The data logging device presented in
this project provides an opportunity for high-resolution real-time performance monitoring of intersection operations.
The project presents two applications in which the data logging device was used to monitor intersection performance. In the
first application, the device was used to plot continuous time-occupancy and signal indication graphs for different
movements. Such plots provide system operators with the information needed to assess the efficiency of phase operations
and to continuously monitor the level of green time utilization for different phases. Two applications to demonstrate how
the data logging device can be used to monitor intersection operations are presented in this report. The first application is
microscopic time-occupancy and signal indication status plots for different movements. Such plots provide system operators
with the information needed to assess the efficiency of phase operations and to continuously monitor the level of green time
utilization for each phase. The research project examined the validity of using this microscopic detector occupancy and
signal indication status data to obtain traffic counts, identify heavy vehicles in the traffic stream, and determine the
percentage of stopped and non-stopped vehicles. The second application is macroscopic in nature and is intended to show
how the data logging device can be used to estimate average values of different performance measures based on detector
and signal indication status information. The delay and speed results estimated using the proposed approach are compared to
speed and delay data obtained from a VISSIM microscopic simulation model. The comparisons show that the data logger
device can reliably and accurately estimate average delay and speed values for signalized intersection approaches using
detector occupancy and signal indication data.
17. Key Words
Traffic data, traffic signal controllers, signalized
intersections
18. Distribution Statement
Unrestricted; Document is available to the public through the
National Technical Information Service; Springfield, VT.
19. Security Classif. (of
this report)
Unclassified
20. Security Classif. (of
this page)
Unclassified
21. No. of
Pages
51
22. Price
…
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
An Intersection Data Collection Device Utilizing Logging Capabilities . . . i
TABLE OF CONTENTS
TABLE OF CONTENTS ........................................................................................................... I
LIST OF TABLES .................................................................................................................... II
LIST OF FIGURES .................................................................................................................. II
1. INTRODUCTION ............................................................................................................ 1
1.1 BACKGROUND ............................................................................................................ 1
1.2 PROJECT OVERVIEW AND OBJECTIVES ....................................................................... 2
1.3 REPORT ORGANIZATION ............................................................................................. 3
2. REAL-TIME MONITORING OF CONTROLLER OPERATION ................................. 4
2.1 OVERVIEW ................................................................................................................. 4
2.2 DATA LOGGING DEVICE: COMPONENTS AND COMMUNICATION ARCHITECTURE ........ 4
2.3 TRAFFIC CABINETS: AN OVERVIEW ........................................................................... 5
2.4 CONNECTING THE DATA LOGGING DEVICE TO DIFFERENT CABINET ASSEMBLIES ...... 8
2.5 DATA LOGGING DEVICE OUTPUT FILES .................................................................... 12
2.6 MOES REPORTING CAPABILITIES OF TRAFFIC CONTROL SOFTWARE: STATE OF THE
PRACTICE ....................................................................................................................... 14
3. ESTIMATING DELAY AND SPEED USING DETECTOR DATA ............................. 16
3.1 DEFINITIONS ............................................................................................................ 16
3.2 MOE ESTIMATION APPROACH ................................................................................. 17
3.2.1 Estimating Average Delay using Detector Data .............................................. 18
3.2.2 Estimating Average Speed using Detector Data .............................................. 20
4. RESEARCH METHODOLOGY AND EXPERIMENTAL DESIGN ........................... 22
4.1 PROPOSED DELAY ESTIMATION METHOD ................................................................. 22
4.2 PROPOSED SPEED ESTIMATION APPROACH ............................................................... 25
4.3 HARDWARE-IN-THE-LOOP SIMULATION MODEL ....................................................... 27
5. ANALYSIS AND RESULTS .......................................................................................... 30
5.1 INTRODUCTION......................................................................................................... 30
5.2 MICROSCOPIC TIME-OCCUPANCY AND SIGNAL INDICATION PLOTS .......................... 30
5.2.1 Estimation of Vehicle Count and Vehicle Type ................................................. 33
5.2.2 Estimation of Stopped and Non-Stopped Vehicles ........................................... 38
5.3 DELAY AND SPEED ESTIMATION ............................................................................... 39
An Intersection Data Collection Device Utilizing Logging Capabilities . . . ii
5.3.1 Delay Estimation – Webster Formulation (Method1) ...................................... 39
5.3.2 Delay Estimation – Method 2 .......................................................................... 42
5. 3.3 Speed Estimation ............................................................................................. 44
5.4 SUMMARY ................................................................................................................ 46
6. CONCLUSIONS AND FURTHER RESEARCH .......................................................... 47
6.1 SUMMARY ................................................................................................................ 47
6.2 CONCLUSIONS .......................................................................................................... 48
6.3 FURTHER RESEARCH ................................................................................................ 48
7. REFERENCES ............................................................................................................... 50
LIST OF TABLES
TABLE 1: MOES REPORTING CAPABILITIES OF DIFFERENT CONTROL SOFTWARE PACKAGES
................................................................................................................................... 15
LIST OF FIGURES
FIGURE 1 DATA LOGGING DEVICE COMPONENTS ................................................................ 5
FIGURE 2 NEMA TS2 TYPE 1 TRAFFIC CONTROL CABINET ................................................. 6
FIGURE 3 NEMA TS2 TYPE 2 TRAFFIC CONTROL CABINET ................................................. 7
FIGURE 4 PROPOSED DATA LOGGING DEVICE CONNECTION TO NEMA TS1 CABINETS ....... 9
FIGURE 5 TWO PROPOSED DATA LOGGING DEVICE CONNECTION OPTIONS TO TS2 TYPE 1
CABINET .................................................................................................................... 10
FIGURE 6 DATA LOGGING DEVICE PROPOSED CONNECTION TO TS2 TYPE 2 CABINET ........11
FIGURE 7 SAMPLE OF DATA LOGGING DEVICE OUTPUT FILES ............................................ 13
FIGURE 8 DELAY COMPONENTS AT A SIGNALIZED INTERSECTION APPROACH .................... 17
FIGURE 9 TIME DISTANCE DIAGRAM AT A SINGLE SIGNAL (SKABARDONIS ET AL., 2005) .. 24
FIGURE 10 ASSUMED FLOW DENSITY DIAGRAM (SKABARDONIS ET AL., 2005) ................. 25
FIGURE 11 HARDWARE-IN-THE-LOOP SIMULATION MODEL ............................................... 28
FIGURE 12 VISSIM SIMULATION NETWORK ...................................................................... 29
FIGURE 13 EXAMPLES OF TIME-OCCUPANCY PLOTS FOR TWO CONFLICTING PHASES ........ 31
FIGURE 14 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
(1ST
VEHICLE IN THE QUEUE) ...................................................................................... 34
FIGURE 15 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
An Intersection Data Collection Device Utilizing Logging Capabilities . . . iii
(2ND
VEHICLE IN THE QUEUE) ..................................................................................... 35
FIGURE 16 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
(3RD
VEHICLE IN THE QUEUE) ..................................................................................... 35
FIGURE 17 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
(4TH
VEHICLE IN THE QUEUE) ..................................................................................... 36
FIGURE 18 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
(5TH
VEHICLE IN THE QUEUE) ..................................................................................... 36
FIGURE 19 ESTIMATED BOUNDARY LINES OF TIME-OCCUPANCY BETWEEN CARS AND HVS
(6TH
VEHICLE IN THE QUEUE) ..................................................................................... 37
FIGURE 20 ESTIMATED BOUNDARY VALUES OF OCCUPANCY TIME FOR CARS AND HVS .. 37
FIGURE 21 A SAMPLE OF DISCHARGE TIME-HEADWAY IN A CYCLE ................................... 39
FIGURE 22 COMPARISON OF SIMULATED AND ESTIMATED DELAY (METHOD 1) ................. 40
FIGURE 23 MEAN ABSOLUTE ERROR AND MEAN ABSOLUTE PERCENT ERROR - DELAY
ESTIMATION (METHOD 1) .......................................................................................... 41
FIGURE 24 COMPARISON OF SIMULATED AND ESTIMATED DELAY ESTIMATION (METHOD 2)
................................................................................................................................... 42
FIGURE 25 MEAN ABSOLUTE ERROR AND MEAN PERCENT ABSOLUTE ERROR - DELAY
ESTIMATION (METHOD 2) .......................................................................................... 43
FIGURE 26 COMPARISON OF SIMULATED AND ESTIMATED SPEED ESTIMATION .................. 44
FIGURE 27 MEAN ABSOLUTE ERROR AND MEAN PERCENT ABSOLUTE ERROR OF SPEED
ESTIMATION ............................................................................................................... 45
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 1
1. INTRODUCTION
1.1 Background
Delay and speed are the primary measures of effectiveness (MOEs) used to evaluate the
performance of traffic signal systems. Delay and speed values, measured in the field, are
extremely valuable to system operators because they provide accurate information
regarding the quality of service for different movements at signalized intersections.
Several methods have been developed and employed to measure delay in the field.
However, most of these methods rely on manually collected traffic counts and require
intensive data collection efforts. Detector and signal indication information, available in
the traffic controller and cabinet infrastructure, can be effectively used to estimate delay
and speed values as well as other signalized intersections MOEs.
Real-time monitoring of traffic signal system operations can also be accomplished
through the central or closed loop software that communicates with traffic controllers in
the field through a network of communication devices. These control software tools use
detector and signal status data to estimate different performance measures such as
detector occupancy, volume, delay, speed, and the level of green-time utilization for each
movement and for the intersection. There are a few significant issues with this approach.
First, most of these control software tools report only average values over a specific time
interval that ranges from one minute to fifteen minutes. This is a huge limitation when
one wants to evaluate second-by-second performance of either the control logic or
detection technology used. Second, the only data available are those explicitly collected
by the vendor of the closed loop or central control software. In addition, these data are
typically not easily accessible and may require complicated direct database access within
the software. Finally, the type and accuracy of the measures obtained are highly
dependent on the detection configuration used in the intersection.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 2
1.2 Project Overview and Objectives
This research project presents an alternative approach to achieve real-time monitoring of
signalized intersections operations using instrumentation at each signalized intersection
cabinet. A logging device is used to collect high-resolution detector and signal status
data. The data logging device can be embedded in the cabinets and connected to the input
and output (I/O) communication channels. The device logs the status of each I/O channel
for every time interval, which can be as low as ten milliseconds (0.01 second), and stores
it into a data file. This data file can be remotely accessed through an Internet Protocol
(IP) based communication. This device allows for real-time high-resolution data
collection and monitoring of signalized intersections operations independent of the
control software used and, thus, has several potential advantages. The data items that can
be monitored and reported are not limited by what data items are collected or by the
frequency at which the vendor of the closed loop or central system software collects
them. The interface device can be accessed from the district office over any
communication channel available in the field. The proposed data collection device has
several potential advantages:
1. The data items that can be monitored are not limited by what data items are polled
by or the frequency they are polled, by the vendor of the closed loop or central
system software, if any.
2. Intelligent data acquisition devices can be embedded in the signal cabinet that
execute data tabulation logic and are accessible via web browsers over an IP
based communication.
3. The system will be completely isolated from the ITD operations and will not
impact the operation of the ITD signal systems.
Two applications to demonstrate how the data logging device can be used to monitor
intersection operations are presented in this report. The first application is microscopic
time-occupancy and signal indication status plots for different movements. Such plots
provide system operators with the information needed to assess the efficiency of phase
operations and to continuously monitor the level of green time utilization for each phase.
The research project examined the validity of using this microscopic detector occupancy
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 3
and signal indication status data to obtain traffic counts, identify heavy vehicles in the
traffic stream, and determine the percentage of stopped and non-stopped vehicles. The
second application is macroscopic in nature and is intended to show how the data logging
device can be used to estimate average values of different performance measures based
on detector and signal indication status information. The delay and speed results
estimated using the proposed approach are compared to speed and delay data obtained
from a VISSIM microscopic simulation model. The comparisons show that the data
logger device can reliably and accurately estimate average delay and speed values for
signalized intersection approaches using detector occupancy and signal indication data.
This project has the followings four objectives: 1) review and document the state of the
practice of real-time monitoring of traffic signal system operations; 2) test the validity of
using the data logging device to monitor and report the status of different I/O channels; 3)
develop a procedure to use detector occupancy and signal indication data, reported by the
data logging device, to estimate approach and intersection performance measures; and 4)
validate the procedures to estimate performance measures and test their accuracy using a
hardware-in-the-loop simulation model.
1.3 Report Organization
This report is organized in six chapters. After the introduction, chapter 2 presents a
background on real-time monitoring of controller operation and the state of the practice
in traffic signal systems real-time monitoring. Chapter 3 includes a review of different
methods used to estimate speed and delay based on detector data. Chapter 4 covers the
analysis methodology and experimental design. Chapter 5 documents the results of the
analysis. Finally, chapter 6 presents the conclusions and proposed ideas for future
research.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 4
2. REAL-TIME MONITORING OF CONTROLLER OPERATION
2.1 Overview
In integrated traffic signal systems, real-time monitoring can be accomplished through
the central or closed loop software that provides control decisions and continuously
communicates with the traffic controllers and cabinets. The type and quality of the MOEs
reported depend on the control software and on the configuration of the detection system
used in the field. The only MOEs available are those explicitly collected by the vendor of
the closed loop or central system. In addition, only average values are usually collected
and reported. Furthermore, data retrieved by the closed loop or central system are not
typically accessible to users. For traffic signal systems that have no control software,
real-time monitoring can only be done by accessing the MOEs collected and stored in the
traffic controller. System operators have to access these controllers and manually
download this data every time. This is a huge limitation considering the limited resources
available for operators of such small traffic signal systems.
An alternative to achieve real-time monitoring is to use instrumentation at each cabinet
that is connected to actuator and detector signals. The intelligent data acquisition device
provides real-time high-resolution data logging and performance monitoring for
signalized intersections. It can be embedded in the signal cabinet and executes data
tabulation logic and writes the status of all I/O channels to a data file that is remotely
accessible through IP based communication. The data that can be monitored are not
limited by what data are collected or by the frequency at which they are collected by the
closed loop, central system software, or traffic controllers.
2.2 Data Logging Device: Components and Communication Architecture
Figure 1 shows the proposed data logging instrumentation and its major components.
This instrumentation is based upon the “Opto 22” family of ultimate I/O brains (item 10
in Figure 1) and “SNAP IDC 5” modules (4 channel10-24 VDC inputs- item 3 in Figure
1).
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 5
Figure 1: Data logging device components.
2.3 Traffic Cabinets: An Overview
A traffic cabinet is essentially a platform within which modular components can be added
to serve a variety of applications at the intersection. It provides the communications
infrastructure between the various subsystems, as well as a system to monitor their
operation. Further, the cabinet provides power supplies suitable for the various electronic
subassemblies mounted throughout the cabinet.
Cabinet assemblies consist of a controller cabinet, controller unit, back panel,
malfunction management unit, bus interface unit, switches, and connectors. The National
Electrical Manufacturers Association (NEMA) family of cabinets include: NEMA TS 1,
NEMA TS2 Type 1, and NEMA TS2 Type 2 cabinets. NEMA TS1 cabinets include a
controller along with the conflict monitor, detectors’ connection matrix, load switches,
other peripheral equipment, and the necessary internal wiring. NEMA TS2 standard
defines two types of controllers and cabinet architectures, the TS2 Type 1 and TS2 Type
2. The NEMA TS2 controller assembly is nearly identical to the TS 1. The two primary
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 6
differences are the change in controller unit and the conflict monitor being replaced by a
malfunction management unit (MMU). The NEMA TS2 Type 1 cabinet is unique in the
sense that it uses a RS-485/SDLC data link connection to the peripheral devices, with a
separate power connector. The TS2 Type 2 provides the same connectors as the TS1 but
includes the data link connector. The TS2 cabinet also uses a bus interface unit (BIU) for
communication between the various control components and detectors. The BIU provides
simplification in cabinet wiring as well as flexibility and power. The TS2 assembly
contains a shelf-mounted power supply unit that provides the appropriate power to each
of the controller devices. The detectors in the TS2 cabinet are rack-mounted. The TS2
standard defines advanced traffic signal operations, such as coordination and preemption,
and developed standards for pre-timed operations and advanced cabinet monitoring and
diagnostics. Details of NEMA TS2 Type 1 and NEMA TS2 Type 2 cabinets are shown in
Figure 2 and Figure 3, respectively.
Figure 2: NEMA TS2 Type 1 traffic control cabinet.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 7
Figure 3: NEMA TS2 Type 2 traffic control cabinet.
In terms of communication with the traffic controllers, the NEMA TS1 and NEMA TS2
Type 2 standards use the four connectors A, B, C and D on the front of the controller.
The “A” connector provides power to the controller as well as inputs and outputs with the
cabinet. The “B” and “C” connectors provide various inputs and outputs for control. The
A, B, and C connector pin outs are standardized by NEMA and are interchangeable
among all manufacturers. Each connector is different, preventing cables from being
inserted in the wrong connection port. The “D” connector provides communication,
preemption, and expanded detection capabilities that are used in more advanced systems.
Typical controllers have eight available detection inputs. The D connector provides input
for eight additional detectors. The D connector pin out is not standardized by NEMA;
therefore, it may not be interchangeable.
In NEMA TS2 cabinets, the BIU links the controller to the cabinet input/output (I/O)
elements. It can also be used as a detector interface device. The BIU is responsible for
controlling load switches, receiving and isolating pedestrian calls, analyzing detector
faults, time-stamping detector calls, and providing detector resets. By design, the BIU is
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 8
free of operator controls. The BIU performs its I/O functions based upon a pre-wired card
rack address. The MMU is a more advanced device, not only monitoring all of the
conflict voltages, but also communicating with the controller, providing an additional
element of monitoring. The type-16 MMU is usually used in a NEMA TS2 standard
cabinet that monitors up to 16 traffic signal channels for conflicting inputs, improper
sequencing, incorrect timing, and invalid signal voltage levels. The MMU is also capable
of operating in older TS1 type cabinets and is compatible with 12-channel conflict
monitor units conforming to the TS 1 standard. All connectors, indicators, and operator
controls are located on the front panel of the MMU. Channel and control input signals
and relay output connections are made through two connectors. Indicators on the front of
the MMU provide status and fault information. The MMU performs continuous
diagnostic tests during all operating modes.
2.4 Connecting the Data Logging Device to Different Cabinet Assemblies
In a standard NEMA TS1 style cabinet, the connections to the controller are made
through the connection matrix on the back panel of the cabinet. These terminals are the
only available connection points for the data logging device. The proposed connection is
shown in Figure 4. The data logging device cables should have non-locking fork
terminals that can be connected to the matrix. The connection is done by loosening the
screws on the back panel then connecting the data logging device cable terminals. This
should not interfere with the cabinet operations and should not cause any malfunction
within the cabinet.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 9
Figure 4: Proposed data logging device connection to NEMA TS1 cabinets.
In a NEMA TS2 Type 1 style cabinet, the data logging device connection is rather
challenging as the cabinet assembly does not have a connection matrix. The controller
communicates with the cabinet using a serial connection through the cabinet’s BIUs. The
communication link from the controller uses the RS-485 serial communication format or
synchronous data link control (SDLC) in combination with the NEMA standard TS2
command frames. There are two possible connection options. The first option is to
connect via the data logging device to the terminals on the back panel of the cabinet. The
second option is to connect the cabinets’ BIUs that are hardwired to this back panel. This
will likely require cooperation with the cabinet vendors as details of BIU wiring
mechanism are needed. The first mode of connection is represented uses a solid line and
the second mode is represented uses dashed lines in Figure 5.
CMU
DLD
Load Switch Detector Auxiliary
Devices
Controller
A B C D
Connection Matrix MS Connector
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 10
Figure 5: Two proposed data logging device connection options to TS2 Type 1
cabinet.
As shown in Figure 6a and 6b, there are two options to connect the data logging device in
a NEMA TS2 Type 2 style cabinet since this cabinet combines standards for the TS1 and
TS2 Type 1. The first is to connect the device to the connection matrix on the back panel,
like that of the NEMA TS1 cabinet. The second is through a serial connection through
either the cabinet’s back panel or the BIUs similar to that for TS2 Type1 cabinets.
Controller
MMU
DLD
SD
LC
Load Switch BIU BIU Detector BIU Auxiliary
Devices
Back Panel
SDLC
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 11
a. Connection Via the Back Panel and/or BIUs
b. Connection Via Connection Matrix
Figure 6: Data logging device proposed connection to TS2 Type 2 cabinet.
MMU
DLD
Connection Matrix
BIU Detector BIU Auxiliary
Devices
Controller
A B C D
MS Connector
Load Switch
MMU
DLD
New
SD
LC
Connection Matrix
BIU Detector BIU Auxiliary
Devices
Controller
A B C D
MS Connector
Load Switch
Back Panel
SDLC
BIU
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 12
2.5 Data Logging Device Output Files
The data logging device monitors and records the communication exchanged between the
detector and the controller and between the controller and signal heads. It also records
any other special calls sent to the controller such as pre-emption calls. In essence, the
device monitors activities in all input and output communication channels to and from the
controllers. In each sampling interval, it scans the status of all input/output channels and
records the state of each channel (on or off). The data are then stored in a log file which
can be accessed through the Ethernet port. The sampling interval for data logging can be
as small as 10ms. However, since most cabinets update the communication channel status
every 300 ms, a resolution time ranging from 300 ms to 1000 ms can more easily be used
in traffic signal system monitoring applications.
Data recorded by the data logging device include date, time, and the status of each
communication channel on the sampling interval. Figure 7 shows a sample of the data
logging device files for the status of detector and signal indication I/O communication
channels. A value of “-1” represents when the communication channel is “On”; a value of
“0” represents when the communication channel is “Off.”
Figure 7a shows the status of different vehicle detectors using a 100 ms resolution.
Detector occupancy and vehicle count can be directly calculated from these raw detector
data principally based on the discontinuity distribution of occupancy time followed by
un-occupancy time. Figure 7b shows the signal indication status for different phases. The
average cycle length and the duration of green, red, and yellow intervals can be directly
calculated from the raw signal state and timing data.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 13
a. Status of Detector Input Channels
b. Status of Signal Indication Output Channels
Figure 7: Sample of data logging device output files.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 14
2.6 MOEs Reporting Capabilities of Traffic Control Software: State of the Practice
Performance measures reporting capabilities for different controller and control software
tools were reviewed and documented in this section. Two controller software tools:
Econolite (used in Econolite TS2 controllers) and Nextphase (used in 170 and 2070 type
controllers) and two centralized control software packages QuicNet/4 and IconsTM
are
reviewed. Their MOEs reporting capabilities are listed in Table 1.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 15
Table 1: MOEs Reporting Capabilities of Different Control Software Packages
MOEs
Reporting
Controller Software Centralized control Software
Econolite Nextphase QuicNet Icons TM
Volume When system detector is
enabled, volume is
reported in the detector
events logs.
Reports actual counts
of the detector during
the most recent
reporting period for all
detectors
Reports
volume counts
for system
detectors only.
Reports actual counts of
the detector during the
most recent reporting
period for all detectors.
Reports volume for each
link
Delay Average delay
based on
detector
occupancy
Average delay for each
link based on detector
occupancy
Speed Speed are calculated
based on average vehicle
and detector length
(single speed detector); or
based on effective
distance between the
leading edges of start and
end detectors (speed trap
length) and the time (used
in two-detector).
Reports average speed
for system detectors
only. Speed samples
are registered at the
end of each actuation.
It is shown in system
detector status of
submenu of Status
Reports
average speed
for system
detectors only.
Calculated speed value
using a measured
volume and occupancy
in a specific time period,
it depends on detection
zone length and vehicle
length. Reports average
link speed.
Occupancy When system detector is
enabled, occupancy is
reported in the detector
events logs.
Reports the percentage
time each detector was
occupied during the
most recent reporting
period for all
detectors.
Reports
average
occupancy for
system
detectors only.
Reports detector
occupancy during the
most recent reporting
period for all detectors.
Reports volume for each
link
Green Split Actual green split is
reported. It is calculated
via phase split minuses
clearance time.
Reports minimum
green split, nominal
green split and
maximum green split
for each phase.
Provides real
time split
monitoring
Reports real-time green
split display for each link
Time-Space
Diagram
Displays a
real-time space
diagram.
Shows green, yellow
and red times and
progression of vehicle
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 16
3. ESTIMATING DELAY AND SPEED USING DETECTOR DATA
3.1 Definitions
Control delay at a signalized intersection approach is defined by the Highway Capacity
Manual (HCM2000) as “the additional travel time experienced by a vehicle affected by
intersection control, relative to conditions where the vehicle is unaffected by intersection
control.” The following definitions of delay and speed are used in this project (Figure 8).
Control delay ( td ) is the portion of the total delay attributed to traffic signal
operation for signalized intersections. Control delay includes four components:
1) initial deceleration delay ( dd ), 2) queue move-up time, 3) stopped delay ( sd ),
and 4) acceleration delay ( ad ).
Approach delay (apd ) includes stopped time, but also includes the time lost
when a vehicle decelerates from its original speed to a stop, as well as
accelerating from the stop back to its original speed.
Stopped delay ( sd ) is the time that a vehicle is stopped while waiting to pass
through the intersection. It includes only the time that a vehicle is actually
stopped waiting at the red signal.
Deceleration delay ( dd ) is defined as the time needed by a vehicle to reduce its
speed.
Acceleration delay ( ad ) is defined as the time taken by a vehicle to resume its
desired speed from a stop. This delay can begin before or at the stop bar,
depending on the vehicle’s queue position. The acceleration delay consists of
two components: acceleration before the stop bar ( 1ad ) and acceleration after
the stop bar ( 2ad ).
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 17
Queue delay (qd ) is defined in HCM as the delay experienced by queued
vehicles. This delay consists of two control delay components sd and 1ad .
Figure 8: Delay components at a signalized intersection approach.
The following speed definitions are used in this study:
Average Speed(s) is the summation of the instantaneous or spot-measured speeds
at a specific location of vehicles divided by the number of vehicles observed.
Average Running Speed (sr) is defined as the length of the segment divided by the
average running time of vehicles to traverse the segment. "Running time" includes
only time that vehicles spend in motion.
Average Travel Speed (st) is defined as the length of the segment divided by the
average travel time of vehicles traversing the segment, including all stopped delay
times.
3.2 MOE Estimation Approach
A variety of methods to estimate different MOEs for an intersection approach have been
developed using data collected through loop or video detectors. The following sections
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 18
document several delay and speed estimation methods using detector and signal
indication data.
3.2.1 Estimating Average Delay using Detector Data
Skabardonis et al. (2005) proposed an analytical model to estimate the total control delay
of an intersection approach in real-time based on flow, occupancy measurements, and
signal status data. The model is based on the kinematic wave theory that considers the
temporal and spatial formation of the queue and the assumption of a linear flow-density
relationship. This delay was considered as the sum of 1) the delay because of a traffic
signal, 2) the delay because of the queue, and 3) the over-saturation delay. The detectors
were placed approximately 300 feet upstream of the intersection stop-line, and detector
data were collected and stored every 30 seconds. The model was applied in two arterial
sites, and the predicted results were compared with the simulated data from COSSIM and
the field data. This project uses this method and it will be described in detail in chapter 4.
Liu et al. (2005) proposed a method to estimate average stopped delay of an intersection
approach using flow measurements and arrival timings from the two loop detectors at the
beginning and end points of the approach segment. Hellinga et al. (2000) proposed a
regression-based approach to estimate the total control delay of an intersection approach
using occupancy data from detectors located at different distances relative to the
approach’s stopbar. Loop detectors were modeled at four different locations (5, 30, 100
and 250 m upstream from the stopbar), and three different data aggregation intervals (100
seconds, 300 seconds, 900 seconds) were considered. Simulation models were used to
generate data needed to calibrate the regression model parameters. These regression
models were then used to estimate the average delay under different traffic volume
conditions based on detector occupancy data. One major limitation of this method is that
it did not consider signal timing parameters, such as average cycle length and green time
to cycle length ratio, which could greatly influence delay.
Li et al. (2008) proposed a formulation for average control delay estimation by cycle for
signalized intersections. The delay is expressed as a function of saturation flow rate, start
of green indication, lost time, duration of green interval for each cycle, queue clearance
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 19
time, arrival count, and free flow travel time from the advance loop to the stopbar, as
shown in Equation (1).
j
j
jCQ
j
LostLost
c
ct
FF
t
ct
jFFjjj
CQ
jj
CQ
j
TtA
ctTtAcTgtTgt
d
1
1
11
1
1
)(
)()()]1(2)()][([2
(1)
where
d j = the average control delay for cycle j;
µ
tCQj = the clearance queue time for cycle j;
g j = the green start time for cycle j;
T Lost = the lost time;
c j = the end time for cycle j;
c j-1
= the end time for cycle j-1;
A(t) =the instant arrival count at advance loops at time t;
T FF
= the free flow travel time from advance loop to the stop line.
Kebab et. al (2007) proposed to estimate the total control delay by collecting individual
vehicle’s timestamps at three locations along the intersection approach. The three data
collection points are: 1) at a point beyond the maximum queue length, 2) at a point where
the turning movements are fully developed, and 3) at the approach’s stopbar. The delay is
treated as the sum of the differences between the actual and free flow travel time at two
segments among the three points. Tung (2007) applied this method to estimate field delay
using video detection. The results of his study showed that the automated delay
measurement procedure produces accurate and reliable delay estimates. When compared
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 20
against delay values measured using the procedure proposed in the Highway Capacity
Manual, the proposed automated delay measurements produced more accurate results.
Lin et al. (2004) proposed an approach to estimate the control delay at signalized
intersections. The approach reduces the delay at each intersection, a non-negative
continuous variable, into two distinctive states, a state of zero-delay and a state of
nominal delay, coupled with a one-step probability transition matrix that relates the delay
to a vehicle to its delay at the adjacent upstream intersection. The calibration of the
parameters in the one-step probability transition matrix is based on the flow level, the
flow composition, and the degree of signal coordination along the path of a trip.
3.2.2 Estimating Average Speed using Detector Data
Son et al. (1998) classified the vehicles in a cycle into two categories according to
discharge headways: vehicles in the queue with saturation flow headways and vehicles
after the dissipation of the queue with departure headways equal to arrival headways. The
average speeds for the two types of vehicles can be calculated principally using detector
occupancy and vehicle count data. Finally, on the basis of the two speeds and signal
timing, the average speed for each cycle is estimated. This method is used as the speed
estimation method in this project and will be described in detail in chapter 4.
Zhang (1999) proposed a model to estimate the average speed for arterial traffic by
combining two speeds: one is estimated based on the approach’s volume/capacity ratio
and the other is based on volume counts and detector occupancy data. Weighting factors
are chosen to combine the two speeds. The average speed for each approach is a weighted
average of the two speeds. Weighting factors are determined and calibrated based on field
measurements of speed and according to the traffic volume level on the approach.
Wang et al. (2000) proposed a simplified equation to estimate arterial speed by isolating
the effect of speed variance. They conducted a study on the speed variance for different
volume levels and found that the variance is inversely proportional to volume levels.
They also found high correlation between speed variance and the mean effective vehicle
length. They established a log regression model to improve the accuracy of speed
estimation based on detectors occupancy data. Zhang et al. (2006) applied the catastrophe
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 21
theory to estimate average speed for the relationship among the traffic variables
associated with speed, occupancy, and flow. These variables could be extrapolated from
the data obtained from a single loop detector using three simple linear transformations.
Bermejo et al. (2003) used the extended Kalman filter method to linearize the
measurement equation of a general Kalman Filter model for estimating average speed
based on detector data. There are two phases in this method: the time update phase is
operated to “predict” a new state, and the measurement update phase is operated to
“correct” any new state. Lucas et al. (2004) proposed an approach to estimate average
speed on arterial based on second-by-second data from upstream and downstream
detectors. The detector data are first used to identify platoons of vehicles and then a
matching algorithm compares the platoons identified at the upstream and downstream
locations. The average speed estimate is based on the travel time of the median vehicles
in the platoons as determined at both the upstream and downstream locations.
Sun et al. (1999) proposed a model to estimate average speed using single loop inductive
waveforms. This model uses signal processing and statistical methods to extract speeds
and involves two main procedures. The first is the extraction of the vehicle slew rate from
the inductive vehicle waveform signal from the detector. The second is the estimation of
the vehicle speed based on slew rate of each vehicle. While this method yielded high
accurate results, it requires special instrumentation for each detector and is highly
sensitive to the accuracy of the detector’s inductive signal.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 22
4. RESEARCH METHODOLOGY AND EXPERIMENTAL DESIGN
4.1 Proposed Delay Estimation Method
Two different delay estimation methods are used in this project. The first is based on the
Webster delay equation (Liu, et al., 2005), a commonly used model to determine the
average control delay of an intersection approach. The Webster delay equation has three
terms. The first term presents the average delay for a particular approach assuming
uniform arrivals at a fixed-time signal-controlled intersection and can be easily derived
using deterministic queuing theory. The second term is added to account for random
arrivals. The third term is subtracted from the first two terms and varies from zero to a
value equal to the second term. The Webster delay equation is given as follows:
)52(2/1
2
22
)(65.0)1(2)1(2
)1(x
q
C
xq
xCD (2)
where
D = Average control delay (seconds/vehicle);
C = signal cycle length (seconds);
x = degree of saturation;
q = volume (vps); and
λ= effective green proportion.
Parameters C and λ are obtained from the signal timing data. Volume, q, is directly
obtained from the detector measurement. The degree of saturation, x, should be
calculated based on detector occupancy and signal timing data. A stop-line detector is
required to collect flow and occupancy measurement.
The second delay estimation method examined in this study uses the analytical model
proposed by Skabardonis and Geroliminis (2005). In this model, the delay is the sum of
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 23
two types of delay: delay caused by the traffic signal and delay caused by the queue
present in the intersection approach.
The first part of the model assumes that each vehicle has no interaction with other
vehicles in the traffic stream. Under this assumption, all queued vehicles are considered
stopped at the stop line (vertical queue). The delay, (d (t)), of a single vehicle as a
function of arriving time, t, is given by Equation (3):
a
f
d
f ut
uTrtd
22)( (3)
where
r = the effective red time;
T = the driver’s reaction time;
uf = free flow speed;
γd = the vehicle’s deceleration rate;
γa = the vehicle’s acceleration rate; and
t = the time a vehicle starts to decelerate.
The parameters for γd, γa, uf and T are assumed constant. Their values are determined
according to the Institute of Transportation Engineers (ITE) guidelines. The effective red
time, “r”, is directly obtained from signal timing data reported in the data logging device
output files. The parameters for uf and t are obtained from detector measurements.
The delay in the second part of the Skabardonis and Geroliminis model is the result of the
queue present at the traffic signal approach. It is estimated based on the kinematic wave
theory considering the temporal and spatial formation of the queue and assuming a
relationship between linear flow and density. The queue delay, dq, of the n-th vehicle
arriving at the signal from the beginning of the red time is the sum of three types of
delays (dq1, dq2, dq3) as illustrated in Figure 9.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 24
Figure 9: Time distance diagram at a single signal (Skabardonis et al., 2005).
The delay values can be determined using the following equations:
f
sqmq
u
LNnd )1),(min(1
(4)
w
sqmqqmq
u
LNNNnd ))),,( (max (min2
(5)
w
LNnd s
qq )1),(min(3
(6)
where
Ls = the effective length of a stopped vehicle;
uw = speed of the shockwave;
w = congested wave speed;
Nqm = number of the maximum queue; and
Nq = number of the maximum back of the queue.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 25
The effective length of a stopped vehicle, Ls, is assumed constant using the reciprocal of
the jam density (kj). The parameters, uw and w, are obtained from flow and occupancy
measurements based on an assumed linear flow-density relationship shown in Figure 10.
Figure 10: Assumed flow density diagram (Skabardonis et al., 2005).
The parameters of Nqm and Nq can be calculated using Equations (7) and (8).
wf
wf
qmuu
uurL
s
qm
qmL
LN (7)
w
w
quw
wurL
s
q
qL
LN (8)
4.2 Proposed Speed Estimation Approach
The average approach speed is estimated using the model proposed by Son and Oh
(1998). Vehicles in each cycle are classified into two categories: vehicles in the queue
with saturation flow headways and vehicles arriving and departing after the dissipation of
the queue with arrival headways. The average speed of a cycle (Vcycle) is determined using
the following equation:
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 26
AS
ANAsNS
cycleNN
NvNvtR
l
V
)1(0
(9)
where
t0 = the time of the first vehicle’s arrival during red time;
R = the red time;
vNS = the average speed of vehicles discharging at the saturation flow rate;
vNA = the average speed of vehicles discharging at the arrival flow rate;
NS = the number of vehicles crossing the stop line with saturation headway in a
cycle;
NA= the number of vehicles crossing the stop line with arrival headway in a cycle;
l = the average length of the sum of vehicles and detectors.
Red time, R, is directly obtained from signal timing data reported in the data logging
device output file. The parameters, t0, NS, NA and NC, are obtained from detector data. The
parameter, vNA , can be calculated using the following equations:
iocc
it
lv
)( (10)
A
N
i
i
NAN
v
v
A
1 (11)
vNS is calculated using the equation,
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 27
A
NA
cycleocc
s
NS
Nv
ltRT
lNv
)()( 0
(12)
In the three equations,
Vi = the speed of vehicle i;
(Tocc) cycle = the total occupancy time for a cycle; and
(tocc)i = the occupancy time of vehicle i.
The parameters, (Tocc) cycle and (tocc)i , are obtained directly from detector data.
4.3 Hardware-in-the-Loop Simulation Model
The data logging device was tested and validated in the lab using a hardware-in-the-loop
simulation model, in which the control of the intersection in the simulation model was
done by an actual traffic controller. A controller interface device (CID) was used to
facilitate the information exchange between the microscopic simulation model and an
actual traffic controller. Detector actuation information was sent from the simulation
model to the controller. Signal status information was sent back from the controller to the
simulation model. This data exchange was done in every simulation time step. In this
experiment, VISSIM microscopic simulation was used along with a NEMA TS2 traffic
controller. The data logging device was connected to the controller through an interface
connected to the A, B, C, and D connectors in the traffic controller. The data flow in the
hardware-in-the-loop simulation model used in the analysis is shown in Figure 11. The
simulation time step was set to 100 ms (0.1 second).
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 28
Figure 11: Hardware-in-the-loop simulation model.
The intersection used in the analysis is an isolated intersection located in the city of
Moscow, Idaho. The VISSIM simulation network is shown in Figure 12. This
intersection was run using standard eight-phase NEMA operation. Data presented in this
project focused on the eastbound approach through traffic (phase 4). The approach has
two lanes with a stop bar and an advanced detector placed approximately 180 feet
upstream of the intersection stop bar. Each simulation ran for a total simulation time of
20 minutes; data were collected for the last 15 minutes only. The average value of five
runs was used for each case.
The measure of effectiveness (MOE) chosen for this experiment was average delay and
speed of the eastbound through movement. The objective of the hardware-in-the-loop
simulation model experiment was to determine whether the average delay and speed for
the approach can be estimated with an acceptable level of accuracy using the high-
resolution data logging device output data.
PC
VISSIM
Controller
CID
DLD
Detector
actuations
Detector actuations
Phase status Phase status
Detecto
r actuatio
ns
Ph
ase status
Record data
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 29
Figure 12: VISSIM simulation network.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 30
5. ANALYSIS AND RESULTS
5.1 Introduction
This chapter presents the results and analysis of two applications to demonstrate how the
data logging device can be used to monitor intersection operations. The first application
uses microscopic time occupancy plots for different detectors. Validity of using detector
occupancy and signal indication date to estimate vehicle count, vehicle type, and the
numbers of stopped and non-stopped vehicles is examined as part of this analysis. The
second application is macroscopic and involves using the characteristics of detector
occupancy, headway, and signal indications to estimate average delay and speed values
using the methods identified in chapter 3. Delay and speed values reported by the
VISSIM hardware-in-the-loop simulation model were assumed to be the true delay and
speed values. They were compared against values estimated using the data logging device
output files. Two measures - mean absolute error and mean absolute percent error, were
used to compare the accuracy of the estimated measurements to true values.
5.2 Microscopic Time-Occupancy and Signal Indication Plots
Figure 13 shows an example of continuous time-occupancy plots for two detectors
located on the stop bar of an intersection approach. Signal indication for the approach is
shown along the x-axis. The plots are updated at 300ms intervals, a typical rate for a
standard cabinet to update detector and signal status information. These plots can provide
system operators with useful information regarding the efficiency of the phase operations.
Information such as average detector un-occupancy time and green time utilization can be
obtained directly for these graphs.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 31
Figure 13: Examples of time-occupancy plots for two conflicting phases.
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295
Time (0.3s)
Detector Occupancy (Phase 4)
Red Green Yellow
Phase 3(Green & Yellow)
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295
Time (0.3s)
Detector Occupancy (Phase 2)
Red Green Yellow
Phase 1(Green & Yellow)
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295 309 323
Time (0.3s)
Detector Occupancy (Phase 4)
Red Green Yellow
Phase 3(Green & Yellow)
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 32
Figure 13(Cont.): Examples of time-occupancy plots for two conflicting phases.
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281 295 309 323
Time (0.3s)
Detector Occupancy (Phase 2)
Red Green Yellow
Phase 1(Green & Yellow)
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281
Time (0.3s)
Detector Occupancy (Phase 4)
Red Green Yellow
Phase 3(Green & Yellow)
1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253 267 281
Time (0.3s)
Detector Occupancy (Phase 2)
Red Green Yellow
Phase 1(Green & Yellow)
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 33
5.2.1 Estimation of Vehicle Count and Vehicle Type
A set of time-occupancy data over 130 cycles was analyzed to estimate the vehicle count
and vehicle type. Although the data were gathered with different flow rates, the time
occupancy generally reached a relative constant state after the sixth vehicle in the queue.
So in the following analysis, the characteristics of time occupancy from the first to the
sixth vehicle in the queue are analyzed. The occupancy data were grouped on the basis of
vehicle queue position. Various characteristics were observed as follows:
The occupancy time for the first vehicle was greater than that for the following
vehicles because it included the driver’s reaction time calculated from the
beginning of the green interval to the beginning of the successive un-occupancy
time.
The distributions of time occupancy tended to gradually scatter from the first
vehicle to the sixth. For example, time occupancy of the first vehicle fluctuated
within a much smaller range than the time occupancy of the second or third
vehicle. The standard deviation of the time occupancy decreases as the vehicle
queue position increased.
The time occupancy values for few vehicles (outliers) were significantly greater
than for the others in each group.
The data became distinctly discontinuous for the 5th
and 6th
vehicles; during very
low rates, no vehicles were in these locations.
The traffic of the study approach included two types of vehicles, passenger cars and
heavy vehicles (HV). The boundary lines for each group were predicted and drawn with a
line of black dashes shown in Figure 14 through Figure 19, in which the points for heavy
vehicle are distinguished from those of cars with circles. The areas between cars and HVs
could be distinctively separated by a horizontal line (boundary line) as shown in Figures
14, 15, 18 and 19. The occupancy for passenger cars is defined below the line; the
occupancy for trucks occurs above it. However, in Figure 16 and 17, the areas for car and
trucks are not distinctly divided by a horizontal line since the occupancy values of some
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 34
cars are greater than those of heavy vehicles. The basic rule used to identify the
occupancy boundary between two vehicle types was to set the upper time limit of car
occupancy. In Figure 14 through 19, the points highlighted with a circle denote
occupancy time for HVs and all other point denotes occupancy time for car; the dash line
denotes estimated boundary line of time-occupancy between car & HV; the point
highlighted with dashes and a circle denotes a vehicle estimated in error.
Figure 14: Estimated boundary lines of time-occupancy between cars and HVs (1st
vehicle in the queue).
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the First Queued Vehicle
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 35
Figure 15: Estimated boundary lines of time-occupancy between cars and HVs (2nd
vehicle in the queue).
Figure 16: Estimated boundary lines of time-occupancy between cars and HVs (3rd
vehicle in the queue).
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the Second Queued Vehicle
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the Third Queued Vehicle
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 36
Figure 17: Estimated boundary lines of time-occupancy between cars and HVs (4th
vehicle in the queue).
Figure 18: Estimated boundary lines of time-occupancy between cars and HVs (5th
vehicle in the queue).
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the Fourth Queued Vehicle
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the Fifth Queued Vehicle
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 37
Figure 19: Estimated boundary lines of time-occupancy between cars and HVs (6th
vehicle in the queue).
Figure 20: Estimated boundary values of occupancy time for cars and HVs.
The HV occupancy area includes some passenger car points for the 3rd
and 4th
vehicle
figures. The car points in the HV area may have been caused by two vehicles occupying
the same detector and hence being detected as a single vehicle with a large value of
occupancy time. Though this situation affected the accuracy of vehicle count and type,
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
0 20 40 60 80 100 120 140
Occu
pa
ncy (
s)
Vehicle
Occupancy Time of the Sixth Queued Vehicle
3.5
2.0 1.9 1.8 1.7 1.6
0
1
2
3
4
5
6
1 2 3 4 5 6
Occu
pa
ncy (
s)
Queued Vehicle Position
Estimated Occupancy Boundary Values for Car & HV
Heavy Vehicle
Passage Car
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 38
the data suggested that the possibility of the error’s occurrence had a small impact on the
accuracy of estimation. Using a higher frequency data update rate (less than 300 ms) will
certainly improve the quality of traffic volume counts and HV identification output. The
occupancy boundary values for cars and HVs are summarized in Figure 20.
5.2.2 Estimation of Stopped and Non-Stopped Vehicles
The numbers of stopped and non-stopping vehicles in a cycle are two major parameters in
the proposed delay and speed estimation methods. They were determined based on the
size and distribution of the discharge time-headways and the status of the signal
indication for the phase. The headway between vehicles was computed on the basis of the
number of times the front bumpers of vehicles passed over the upstream end of the
detector. In general, the distribution of time headway in a cycle by vehicle number
showed the following tendency: the headways tended to gradually decrease and then start
to increase abruptly once the queue fully dissipates. The first increased values were
considered the boundary between the last stopped vehicle and the first non-stopped
vehicle. A typical sample of the headways in a cycle is shown in Figure 21, with the first
non-stopped vehicle being the 9th
one. In addition, another characteristic that can be used
to determine the first non-stopped vehicle is the of time-occupancy value. This value
depends on the length of the stopbar detector used and the free flow speed on the
approach. For the approach used in this analysis, it was determined that the time
occupancy for non-stopped may be equal to or less than 0.5 second. This characteristic
was also used to identify the first non-stopped vehicle in the traffic stream.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 39
Figure 21: A sample of discharge time-headway in a cycle.
5.3 Delay and Speed Estimation
Delay and speed values reported by the VISSIM simulation model were compared
against values estimated using the data logging device output files and following the
procedure described in chapter 3.The delay and speed values of each cycle were
calculated. Cycle-delays were then aggregated over the 15-minute simulation time.
Absolute error and percent absolute error values for each simulation run were used to
compare the accuracy of the estimated measurements to true values obtained from
VISSIM simulation output. Flow rates used in the experiments ranged from 400 vph to
800 vph.
5.3.1 Delay Estimation – Webster Formulation (Method1)
Figure 22 shows the comparison of the true delay (simulated) and delay values estimated
using the Webster formulation over 25 runs. Simulation runs with five different traffic
volume levels were performed: 400 vph, 500 vph, 600 vph, 700 vph, and 800 vph. Five
20-minute VISSIM simulation runs were conducted for each traffic flow rate using
different random number seeds to account for traffic variability. The first 5 minutes of the
simulation run was considered a start-up initialization period. Output from the last 15
minutes of the simulation runs were included in the analysis.
2.4
2.0 2.0 1.91.7
1.5 1.2
2.8
3.2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1 2 3 4 5 6 7 8 9
He
ad
wa
y (
s)
Vehicle Pairs
Discharge Time-Headway (10 Runs)
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 40
Figure 22: Comparison of simulated and estimated delay (method 1).
The accuracy of estimated delays was analyzed using the mean absolute error and mean
absolute percent error shown in Figure 23a and Figure 23b. The results indicate that the
maximum absolute errors for flow rates of 400 to 700 vph are less than 5.0
seconds/vehicle and that the average error value is approximately 2.0 seconds/vehicle.
The maximum percent errors are less than 20.0 percent with an average value of
approximately 6.0 percent. The error increased significantly as demand increased to 800
vph (near or over saturation). This is expected as Webster equation is primarily used to
determine delay values for under-saturated conditions (volume to capacity ration of less
than 0.8).
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
De
lay (
s)
Simulation Run
Delay Estimation ( Method1) Estimated
Simulated
400vph
500vph600vph
700vph800vph
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 41
a. Absolute Error
b. Percent Absolute Error
Figure 23: Mean absolute error and mean absolute percent error - delay estimation
(method 1).
0
4
8
12
16
400 500 600 700 800
Err
or
(se
c/v
eh)
Volume (vph)
15-Minute Absolute Error (Average Delay)
Ave.
Max.
Min.
0
8
16
24
32
400 500 600 700 800
Err
or
Perc
ent
Volume (vph)
15-Minute Absolute %Error (Average Delay)
Ave.
Max.
Min.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 42
5.3.2 Delay Estimation – Method 2
The second method to estimate delay in this study is based on the analytical model
proposed by Skabardonis and Geroliminis (2005). One limitation of this method is that it
requires the maximum queue length not to extend to the upstream detector location. In
this study, the detector was placed approximately 180 feet upstream of the stop bar.
Initial investigatory simulation runs showed that, when the traffic volume exceeds 500
vph, the maximum queue length exceeded the upstream detector location. Accordingly,
only three traffic flow levels: 300 vph, 400 vph and 500 vph were used in the analysis. As
in the previous experiment, five 20-minute runs were simulated for each flow rate with
different random number seed numbers. A sample of comparisons of simulated and
estimated average delay is presented in Figure 24.
Figure 24: Comparison of simulated and estimated delay estimation (method 2).
The maximum absolute error for 300 vph is less than 1.0 second/vehicle. The maximum
error values for volumes of 400 vph and 500 vph are less than 2.0 seconds/vehicle
(Figure 25a). All percent absolute errors are within 6.5 percent (Figure 25b). This
approach predicts delay with higher accuracy than the Webster method.
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
De
lay (
s)
Simulation Run
Delay Estimation (Method 2 )
Simulated
Estimated
300vph
400vph
500vph
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 43
a. Mean Absolute Error
b. Mean Absolute Percent Error
Figure 25: Mean absolute error and mean percent absolute error - delay estimation
(method 2).
0.0
0.6
1.2
1.8
2.4
300 400 500
Err
or
(se
c/v
eh)
Volume (vph)
15-Minute Absolute Error (Average Delay)
Ave.
Max.
Min.
0
2
4
6
8
300 400 500
Err
or
Pe
rce
nt
Volume (vph)
15-Minute Absolute %Error (Average Delay)
Ave.
Max.
Min.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 44
5. 3.3 Speed Estimation
The speed estimated in this study is the average spot-measured speeds at the approach
stopbar. The required data were obtained from the stopbar detectors. The mean speed of
all stop and non-stop vehicles in a cycle was estimated, and then all cycle-speeds are
aggregated into 15-minute intervals. Figure 26 compares the true speed values
(simulated) and the speed values estimated using the data logging device output files
following the procedures described in section 4.2.
Figure 26: Comparison of simulated and estimated speed estimation.
The accuracy of the estimated speed was analyzed using the mean absolute error and
mean percent absolute error. The results are shown in Figure 27a and Figure 27b. The
most accurate predicted value appeared at flow rate of 600 vph, with a maximum error of
only 0.7 feet/second/vehicle. As the volume increases or decreases, the error of estimated
speed tends to increase. In all cases, the maximum difference between the predicted and
the simulated speed is always less than 2.0 feet/second/vehicle, with the average values
ranging from 0.5 feet/second/vehicle to 1.2 feet/second/vehicle. The mean percent
absolute errors for all cases are less than 7.0 percent.
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Sp
ee
d (
ft/s
ec)
Simulation Run
Speed EstimationEstimated
Simulated
400vph500vph
600vph700vph
800vph
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 45
a. Mean Absolute Error
b. Mean Absolute Percent Error
Figure 27: Mean absolute error and mean percent absolute error of speed
estimation.
0.0
0.5
1.0
1.5
2.0
400 500 600 700 800
Err
or
(ft/
se
c/v
eh)
Volume (vph)
15-Minute Absolute Error (Average Speed)
Ave.
Max.
Min.
0
3
6
9
12
400 500 600 700 800
Err
or
Perc
ent
Volume (vph)
15 - Minute Absolute %Error (Average Speed)
Ave.Max.Min.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 46
5.4 Summary
Comparing the estimated range and values of errors for average delay and speed
values over different traffic flow rates, the minimum error appears to happen at
moderate traffic flow conditions (flow rates 600 vph). The error increases
proportionately as flow rates increase. When the flow rate reaches or exceeds the
capacity (saturation or oversaturation condition), the error of predicted delay and
speed values greatly increase.
Under unsaturated flow conditions, the maximum delay error is approximately 5.0
seconds/vehicle, and the maximum speed error is approximately 2.0 feet/second.
The average delay error is approximately 2.0 seconds/vehicle, and the average
speed error is approximately 1.0 feet/second. From an operational perspective,
these variations are within acceptable ranges. These values show that average
delay and speed values can be accurately estimated using the high-resolution
output data reported by the data logger device.
Comparing the two delay estimation methods, the second method provided delay
estimations with higher accuracy level. However, it can be used only for low and
moderate traffic flow rates when the maximum queue length does not exceed the
upstream detector location.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 47
6. CONCLUSIONS AND FURTHER RESEARCH
6.1 Summary
The project presents a high-resolution data logging device that can be used in real-time
traffic monitoring at signalized intersections. The data logging device can be connected
to traffic cabinets using different connection modes. It records the communication
exchanged between the detector and the controller and between the controller and signal
heads. It can also record any other special calls such as preemption. The data logging
device main function is to log and store the status of all input and output communication
channels at every time step, which can be as small as 10 milliseconds. The data logging
device can be accessed remotely through an Ethernet port over IP based communication
protocols. It provides an opportunity for high-resolution real-time performance
monitoring of intersection operations.
The project presents two applications in which the data logging device was used to
monitor signalized intersections performance.
In the first application, the device was used to plot continuous time-occupancy
and signal indication graphs for different movements. Such plots provide system
operators with the information needed to assess the efficiency of phase operations
and to continuously monitor the level of green time utilization for different
phases.
In the second application, the data logging device was used to estimate average
delay and speed values for signalized intersection approaches using detector
occupancy and signal indication data with a high level of accuracy.
The data logging device was tested and validated in the lab using a hardware-in-the-loop
simulation model. The simulation data were collected in this study from an isolated
intersection in the city of Moscow, Idaho. Webster delay equation and the analytical
model proposed by Skabardonis and Geroliminis (2005) were used to estimate the
average delay values for signalized intersection approaches. The speed estimation method
proposed by Son and Oh (1998) was used to estimate average speed values for each
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 48
approach. The accuracy of the estimated average delay and speed values were compared
against true delay and speed values obtained through microscopic simulation. The error
analysis was conducted using two measures: mean absolute error and mean absolute
percent error values.
6.2 Conclusions
Based on the results of the error analysis presented in this project, the data logging device
output can be used to accurately estimate average delay and speed values for signalized
intersection approaches. Results from the analysis validated the following four research
hypotheses:
the data logging device monitors and reports high-resolution detector and signal
indication status data that can be used to provide accurate detector occupancy and
signal-indication plots,
data reported by the data logging device can be used to estimate traffic counts
with a high degree of accuracy,
data reported by the data logging device can be used to identify heavy vehicles
(HVs) in the traffic stream,
data reported by the data logging device can be used to estimate average delay
and speed values for each approach with an acceptable level of accuracy.
6.3 Further Research
Future research tasks should involve field study at an isolated intersection to show
how the data logging device can be used to monitor intersection operation. The
data logging connection to NEMA TS2 Type 1 family of cabinets should be
developed, tested, and validated in the field.
Data reported by the data logging device can be applied to estimate other
performance measures, such as queue length, travel time, and mean stopped time.
The sampling interval of data logging has a significantly impact on the accuracy
of performance measures estimation. A best sampling interval should be
determined.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 49
A real-time performance measurement system based on the data logging device
can be developed to collect, store, and analyze data, not only for an isolated
intersection, but for a network of signalized intersections.
An Intersection Data Collection Device Utilizing Logging Capabilities . . . 50
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