Travel Time Estimation: An ITS Perspective

Sunil Gyawali

Tim Melichar

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

IntroductionWhy Travel Time?•To asses operational management and

planning of networkIndicator : LOS of road linkParameter: Congestion

•As appreciated information for road users

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.

Methodology

Collection of Information (Day1)

Collection of Information (Day 2)

NDOR Sensors within the Study Segments

Travel Time based on Bluetooth Data

Time Vs. Velocity Plot based on NDOR Sensor Data

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7620

40

60

80

100

Time

Vel

ocity

WB 114 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7685

90

95

100

105

Time

Vel

ocity

WB 156 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7670

80

90

100

110

Time

Vel

ocity

WB 144 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7640

60

80

100

120

Time

Vel

ocity

WB 132 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7620

40

60

80

100

120

Time

Vel

ocity

WB 127 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7690

95

100

105

110

115

Time

Vel

ocity

WB 204 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7680

85

90

95

100

105

110

Time

Vel

ocity

WB 192 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7670

80

90

100

110

Time

Vel

ocity

WB 168 Street

0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.7680

90

100

110

120

Time

Vel

ocity

WB ON Ramp Light Pole

5PM-6PM Congestion

Instantaneous Model and Sensor Data

The INSTANTANEOUS MODEL referenced from the literature by Daiheng and Haizhong (2008) calculates the travel time through the following equation:

Where Vf = final velocity, Vo= Initial Velocity

Travel Time (Bluetooth Data Vs. Instantaneous Model based on Sensor Data)

Section RMSSubsection1 0.152Subsection2 0.087

Whole Section 0.109

Multiple Linear Regression for Travel Time

 

Travel Time Model Performance

 

Logistic Regression for Congestion

Congestion Model Performance

 

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 location from S 144 Street, our study segment being on downstream location may not have captured that effect fully.