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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.
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- 1. Travel Time Estimation: An ITS Perspective Sunil Gyawali Tim
- 2. Outline Introduction Objective Methodology Collection of Information NDORs 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
- 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