1) The document discusses travel time estimation from an intelligent transportation systems perspective. 2) It aims to compare literature-based travel time estimates to field measurements and develop models to predict travel time and congestion. 3) The methodology involves collecting Bluetooth and sensor data from the Nebraska Department of Transportation on traffic along various road segments, then using this data to model travel time and congestion through linear regression and logistic regression techniques.

1. Travel Time Estimation: An ITS
Perspective
Sunil
Gyawali
Tim

2. Outline
• Introduction
• Objective
• Methodology
• Collection of Information
• NDOR’s Sensors Deployment
• Travel Time based on Bluetooth Data
• Time Vs. Velocity Plot based on NDOR Sensor
Data

3. Introduction
Why Travel Time?
• To asses operational management and planning
of network
Indicator : LOS of road link
Parameter: Congestion
• As appreciated information for road users

4. Objective
• Compare literature based Travel Time
estimation to the field measured Travel Time
• Develop models for predicting Travel Time and
Congestion and assess their performance.

5. Methodology

6. Collection of Information (Day1)

7. Collection of Information (Day 2)

8. NDOR Sensors within the
Study Segments

9. Travel Time based on
Bluetooth Data

10. Time Vs. Velocity Plot based on
NDOR Sensor Data
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
20
40
60
80
100
Time
Velocity
WB 114 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
85
90
95
100
105
Time
Velocity
WB 156 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
70
80
90
100
110
Time
Velocity
WB 144 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
40
60
80
100
120
Time
Velocity
WB 132 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
20
40
60
80
100
120
Time
Velocity
WB 127 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
90
95
100
105
110
115
Time
Velocity
WB 204 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
80
85
90
95
100
105
110
Time
Velocity
WB 192 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
70
80
90
100
110
Time
Velocity
WB 168 Street
0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76
80
90
100
110
120
Time
Velocity
WB ON Ramp Light Pole
5PM-6PM Congestion

11. Instantaneous Model and Sensor
Data
•

12. Travel Time (Bluetooth Data Vs.
Instantaneous Model based on
Sensor Data)
Section RMS
Subsection1 0.152
Subsection2 0.087
Whole Section 0.109

13. Multiple Linear Regression for
Travel Time

14. Travel Time Model Performance

15. Logistic Regression for
Congestion

16. Congestion Model Performance

17. Findings from the Study
• The Travel Time estimated with Instantaneous Model
validates with the Field Measured (Bluetooth based) Travel
Time.
• The Travel Time is highly correlated with independent
variables as velocities at beginning and end of the section,
segment length, and the earlier travel time (5 minute before)
as evident in multiple linear regression modeling.
• Similarly, the situation of the segment being congested or not
is also explained by above mentioned variables along with
number entry/exit points along the segment.
• Since from the Time Vs. Velocity plot, the congestion at the
period 5 PM- 6 PM was seemingly high at the upstream