TS58-1098.pdf

7/27/2019 TS58-1098.pdf

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USE OF BLUETOOTH BASED TRAVEL TIME INFORMATION FOR TRAFFIC

OPERATIONS

Michael Wieck

Associate Vice President, Transportation Systems

Iteris, Inc.1700 Carnegie Avenue, Suite 100

Santa Ana, CA

82705-5551

+1-303-905-7008

[email protected]

ABSTRACT

In 2009, the Minnesota Department of Transportation selected an Arterial Travel Time

Measurement System using Bluetooth Technology as part of its 2009-2010 ITS Innovative

Idea Program. The project deploys 8 Bluetooth readers along CSAH 81 together with a

central web based analysis software. One key goal of this project is to demonstrate how the

travel time information may be used as a performance measure for arterial traffic

management and operations. This paper provides the results of this deployment and draws

conclusions for the use of Bluetooth based data collection for arterial traffic operations and

performance management.

KEY WORDS

Bluetooth based data collection, travel times, traffic signal operations, performance

management

INTRODUCTION

Accurate measurements of travel time – provided in “real time” and collected for statistical

analysis – can be very beneficial in the management and operation of an arterial network. As

summarized in a recent study in Denver (1), measuring and monitoring arterial travel times

can provide:

• Traffic signal operators with the tools to better monitor their signal systems and the data

necessary to make informed and timely operational decisions (e.g., development and

selection of optimum timing plans);

• Planning information to other stakeholders such as City and regional transportation

planners, transportation boards or City Councils;

• Useful traveler information disseminated to the public.

One of the key attributes of ‘Good Performance Measures’ is that they should be accurate and

easy to collect and analyze. This attribute has proved challenging in arterial environments due

to the significant variability of traffic flow and speeds, which is caused by the presence of

multiple signalized intersections along the arterial, differing distances between signals, signal

phasing and timing, ‘side friction’ from driveways and curb parking, and the fact that

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vehicles can enter and leave the arterial at numerous locations almost at will. A promising

approach to collect traffic flow data is to identify and track vehicles using unique

“signatures” while they are traveling on the arterial network. A vehicle may be identified at a

point along the arterial at time = T1 - and then again further downstream (at distance D) at

time = T2, with T2-T1 representing the travel time for that vehicle along the link of length D.

Tracking multiple vehicles as they traverse the link provides an estimate of the average traveltime as well as a basis for statistically relevant samples. Whereas several technical solutions

have appeared in recent years (using license plate, toll tag and cell phone tracking, as well as

the matching of electromagnetic signatures), Bluetooth technology is more and more being

used to obtain reliable and accurate travel time information in a cost effective manner. While

the majority of deployments to date have been on freeway networks, initial work done (2)

shows that despite an increased variance compared with monitoring Interstate traffic, travel

time trends can be easily identified on an arterial network.

One key area of interest is whether the information gathered on travel times through the use

of Bluetooth technology can also be used for traffic signal operations. If this question can be

answered positively, City traffic operators will have a new set of relatively inexpensive toolsat their disposal to improve their operations.

THE PROJECT

Eight Bluetooth readers have been installed at 6 intersections along CSAH 81 in Hennepin

County, MN. The CSAH 81 corridor is an excellent choice for testing a travel time data

collection system. Traffic volumes range from around 10,000 vehicles/day to over 25,000

vehicles/day. The corridor runs through several suburban communities and has signal

infrastructure throughout its length. The roadway’s characteristics change over its length and

this provides a vigorous test area for a system studying a variety of different arterial

environments.

At each intersection, the Bluetooth information is sent back to the central office via 3G

wireless communication. At the beginning and end of the corridor, two readers have been

installed at those intersections at opposite sites communicating among each other via WiFi,

whereas the other intersections are equipped with one reader only at alternate sides. The

reason for this layout is to test the sensitivity of the signal reception to distance from the

vehicles.

At the central office, a web based application stores and processes the data, calculating the

travel times between pairs of Bluetooth readers, as well as other information, e.g. the number of times a specific MAC address has been read in the vicinity of one Bluetooth reader, and

the number of devices read at one location during a certain interval.

The project was operational for several months in 2010 and finished in March 2011. The

central software did collect data and processed the resulting information, and a Final Report

has been delivered.

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Figure 1: Corridor Layout

ANALYSIS

Key parameters that can be used for performance measurements are:

Location Specific Measurements

• Number of measurements (counts) of unique devices

• Number of measurements (counts) of individual devices

Location specific measurements provide information on the number of vehicles that are being

detected at a specific location, but also on how long those vehicles are within detection range

of the Bluetooth reader – i.e. at or close to the intersection. This latter information can be

used to assess the performance of the intersection – comparing this with cycle times etc. can

help identify the development of bottlenecks and be used to develop trending information.

Travel Times

• Travel Times between two locations• Number of devices scanned at both locations

Travel time information between two locations can be used to analyze trends, and to identify

changes in traffic patterns. Of particular relevance is the fact that a vehicle is typically

detected several times at one location. This allows to compare travel times from the first time

a vehicle has been detected at the first intersection to the last time it has been detected at the

second one (which effectively includes the time traversing the intersections) with the travel

time between the last time the vehicle was detected at the first intersection and the first time it

was detected at the second one (effectively measuring the travel time between intersections).

(First-First and Last-Last measurements also are possible and effectively add the time

traversing one intersection to the travel time between the intersections).

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The remainder of this paper provides a description of the process used in collecting and

evaluating the data, and the data analysis of the data collected in the second half of 2010, as

well as an assessment of the benefits of this information for traffic signal operations and

planning agencies.

BLUETOOTH SYSTEM DATA COLLECTION AND EVALUATION PROCESS

The process used for collecting system data and evaluating the performance of the Arterial

Bluetooth Travel Time Monitoring System along CSAH 81 consisted of the following tasks:

• Review of existing traffic information along CSAH 81 provided by Hennepin County.

• Collection of traffic data, including volume and travel times along the CSAH 81

corridor, prior to system deployment and during system deployment, in order to compare

manually collected data against data collected from Bluetooth devices traveling through

the CSAH 81 corridor.

• Collection of device data, including individual MAC addresses from known Bluetooth

devices traveling along the CSAH 81 corridors, as well as time-stamped information on

the first and the last time a particular device was detected within the range of one

Bluetooth receiver.

• Analysis of compared data in a number of ways, including average speed, number of

Bluetooth matches, standard deviation and percentage of speed differential between the

two sets of data. Other anomalies related to corridor conditions (i.e. construction along

the corridor, traffic incidents, etc.) were also noted as they occurred.

Reviewing travel time summaries indicated this corridor experiences a relatively large

difference in travel time. This can be attributed to the nature of this arterial, the dailyfluctuations in mainline and side street demand or mid block driveways along a segment of

the corridor, which can have substantial impact on the travel time within the corridor.

As an initial step, Bluetooth devices with known MAC addresses were successfully tracked

along the corridor, verifying the accuracy of using the underlying technology. The central

office software provided for data filtering and smoothing: the filtering allowed to select

travel times below a user definable ‘max time’ (between 1 and 360 minutes), and to eliminate

the last 5% of travel times within a distribution of the frequency of occurrence of a particular

travel time to eliminate outliers. Data smoothing was performed by modifying the average

time interval within which data is averaged, between 1 and 30 minutes.

RESULTS

It is important that the data collected is of a sufficient sample size to represent the conditions

to a desired degree of accuracy. Using standard statistical analysis techniques, the sample size

can be calculated from the following parameters: confidence level desired, margin of error,

and population size. Assuming a 95% confidence level with a 5 % margin of error, and using

the volume (~18,000 vehicles) identified from tube counts between 6:30 AM and 6:30 PM on

a single day, a sample size of 375 “hits” or “matches” would be necessary. A majority of the

segments exceed this sample size, a few segments are slightly below but are well within a

90% confidence of 267 samples.

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Average Travel Times

The following tables show average calculated travel times for October 2010.

From To Travel Time (sec) Average Speed (mph)

No. of Bluetooth Matches Standard Deviation

36th Ave. 42nd Ave. 121.8 33.0 72 8.85

42nd Ave. Bass Lake 194.3 35.6 22 6.50

Bass Lake 63rd Ave. 89.8 49.1 69 11.18

63rd Ave. 71st Ave. 117.6 36.6 100 10.53

71st Ave. Greenhaven 132.1 40.6 117 12.09



36th Ave. 42nd Ave. 120.3 32.3 326 7.96

42nd Ave. Bass Lake 211.9 35.0 132 8.19

Bass Lake 63rd Ave. 91.8 49.0 257 12.37

63rd Ave. 71st Ave. 106.7 40.7 313 10.84




36th Ave. 42nd Ave. 122.5 32.6 245 8.19

42nd Ave. Bass Lake 224.3 32.7 164 7.92

Bass Lake 63rd Ave. 98.9 45.1 434 12.67

63rd Ave. 71st

Ave. 110.0 39.1 285 10.98


October Travel Times



Northbound AM Peak Period Travel Times

Northbound Off Peak Period Travel Times

Northbound PM Peak Period Travel Times

TABLE 1: TRAVEL TIME DATA – NORTHBOUND

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From To

Travel Time (sec)

Average Speed (mph)

No. of Bluetooth Matches

Standard Deviation

Greenhaven 71st Ave. 128.7 40.0 173 10.08

71st Ave. 63rd Ave. 89.7 48.1 147 10.17

63rd Ave. Bass Lake Rd. 80.3 53.0 116 9.49

Bass Lake Rd. 42nd Ave. 239.4 33.3 51 10.27

42nd Ave. 36th Ave. 103.7 37.5 96 8.25

From To

Travel Time (sec)

Average Speed (mph)

No. of Bluetooth Matches

Standard Deviation


71st Ave. 63rd Ave. 89.9 46.9 274 10.17



42nd Ave. 36th Ave. 118.2 33.9 273 8.81

From ToTravel Time

(sec)Average Speed

(mph)No. of Bluetooth

MatchesStandard Deviation


71st Ave. 63rd Ave. 94.3 45.6 211 10.94



42nd Ave. 36th Ave. 129.7 33.4 177 10.43

Southbound AM Peak Period Travel Times


Southbound PM Peak Period Travel Times



Southbound Off Peak Period Travel Times

TABLE 2: TRAVEL TIME DATA – SOUTHBOUND

Verification of Results

Manual travel time runs were compared with the calculated travel times using Bluetooth

MAC address matches. The following table shows the results of the comparison. The

calculated differences were relatively small. Larger differences can be associated with the

position of the Bluetooth reader relative to the center of the intersection which was used to

mark each segment for the field measurements. In theory, the field vehicle could be stopped

and measured by the Bluetooth and not have reached the center of the intersection. This could

potentially account for different travel times for the field measurements. Also, the travel

patterns of the travel time runs and the average travel patterns of the overall set of measured

vehicles could be different, due to platoon and other effects.

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Last Seen at 36th Ave.

Last Seen at Greenhaven Ave.

Total Travel Time (Bluetooth

measurement)

Collected Travel Times Difference (Sec.)

6:53:07 AM 7:05:58 AM 771 726 45

7:27:53 AM 7:38:02 AM 609 630 218:01:21 AM 8:12:57 AM 694 743 49

8:47:07 AM 8:58:31 AM 684 750 66

9:17:01 AM 9:28:21 AM 680 706 26

9:47:11 AM 9:58:09 AM 658 602 56

10:18:08 AM 10:28:04 AM 596 700 104

10:46:19 AM 10:58:08 AM 709 677 32

11:25:12 AM 11:37:25 AM 733 764 31

11:54:27 AM 12:05:53 PM 686 698 12

1:04:44 PM 1:16:19 PM 695 791 96

1:36:45 PM 1:51:01 PM 856 892 36

2:10:48 PM 2:23:37 PM 769 778 9

2:43:35 PM 2:54:07 PM 632 666 34

3:15:54 PM 3:27:38 PM 704 777 73

3:48:30 PM 3:59:42 PM 672 728 56

4:20:22 PM 4:33:44 PM 802 787 15

4:51:17 PM 5:06:10 PM 893 945 52

5:24:09 PM 5:38:08 PM 839 851 12

5:58:53 PM 6:11:34 PM 761 785 24

October 13th Travel Time Comparison

TABLE 3: TRAVEL TIME COMPARISON Trend Analysis

One analysis focused on identifying traffic flow trends. An opportunity arose due to

construction taking place on the corridor beginning August 10, 2010. The following graph

summarizes average travel time for AM, OFF, and PM peak periods for the typical travel

days (Tuesday, Wednesday, and Thursday) for the week in advance, the first week of

construction and the second week of construction along the segment of 63rd Street and Bass

Lake Road. Temporary traffic control started August 10, 2010; as shown the remainder of the

week, all periods experienced increased travel times. Travel times started to balance in the

second week as travelers adjusted to the traffic control or potentially moved to alternateroutes, but it still remained higher than pre‐construction travel times.

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Figure 3: Travel Times during Construction Period

OUTLOOK

The project has provided Mn/DOT and Hennepin County with a large volume of data, which,

once synthesized and analyzed, could be of value for a number of traffic operations and

transportation system planning purposes. Based on the data that was collected during this

project, Hennepin County Public Works staff identified a number of ways in which the data

could be used, as follows:

• Public travel time information purposes

The County could provide pre‐trip travel times on specific routes, route delays or other

traveler information as determined appropriate by the County.

• Assist in the prioritization of signal timing projects

Of particular interest to Hennepin County is assistance in determining how often and when to retime arterial corridor traffic signals. Presently the County reviews

approximately one third of its signalized corridors each year and prioritizes signal

retiming activities based on needs and available budget. One enhancement that could be

made to the system in this regard would be in the “automation” of the system in terms of

notifying the agency when certain thresholds have been met along a corridor based on

collected travel time/speed information.

• Post-construction review of corridor travel speeds

• Transportation system planning

As part of this project, tube counts were compiled to confirm ADT and verify speeds

while Bluetooth equipment was in use. Using data gathered by the Bluetooth readerscould either reduce the need for other data collection activities required by County staff,

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or augment those efforts and streamline data collection processes, currently in place.

• Integration with regional/county traveler information systems

Data collected by the system would have to be “automated” and streamed into the

Regional Traffic Management Center (RTMC) in Roseville and/or Hennepin County’s

Public Works Facility in Medina

• Performance MeasurementPotential performance measures could include:

- The affects of a widening project along an arterial could be monitored

- The placement and use of changeable message boards to move traffic from a

congested arterial could be evaluated with before and after travel time information

REFERENCES

(1) Performance Measures for Traffic Signal Operations, Final Report , Denver Regional

Council of

Governments,

November

2008.

(2) Real ‐Time Travel Time Estimates Using MAC Address Matching, J.S. Wasson, J.R.

Sturdevant, D.M. Bullock, ITE Journal Vol. 78 No.6,June 2008.

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