travel time data collection and spatial information … · 2014-03-13 · travel time data...

TRAVEL TIME DATA COLLECTION AND SPATIAL INFORMATION TECHNOLOGIES FOR RELIABLE

TRANSPORTATION SYSTEMS PLANNING

Srinivas S. Pulugurtha, Ph.D., P.E. Venkata R. Duddu, Ph.D., E.I.

The University of North Carolina at Charlotte (UNC Charlotte)

TRB Sensing Technologies for Transportation Applications Workshop #148 January 12, 2014

Civil & Environmental Engineering Department

http://www.uncc.edu/

Research Team


• Dr. Edd Hauser - Director, Ctr. for Transp. Policy Studies • Dr. Xiaoyu Wang - Research Assistant Prof., Charlotte Vis. Ctr. • Graduate Students

– Rahul Pinnamaneni – R. M. Zahid Reza – Sai Venkata Nallamalli – Vinay Thokala – Vishnu Payyavula – Md. Shah Imran – Ravi Kiran Puvvala – Pooya Najaf


Key Research Tasks

• Compare and evaluate travel time data from different technologies / non-connected devices

• Develop data tools and query applications • Assess transportation systems reliability

– Compute reliability measures • Link- or corridor-level?

– Evaluate correlation between different reliability measures – Identify thresholds and level-of-service (LOS) categories

• Model the effect of incidents on reliability • Develop DSS tools using visualization techniques



Comparative Evaluation of Travel Time from Different Technologies and

Sources



Travel Time Data Collection & Technologies / Sources

• Study area: Charlotte, North Carolina • Technologies / sources:

– Manual & GPS – Bluetooth devices – Private sources such as INRIX – Automatic Vehicle Location (AVL) units on buses



Data Collection

• Manual, GPS & Bluetooth data collection – 6 corridors – Peak and off-peak hours on

two consecutive weekdays for each corridor


Route Number Route Name Type No. of Lanes AADT Bus Availability Speed Limit

(mph) Weekdays Weekends 11 North Tryon Major Arterial 3 25,000-30,000 Yes Yes 55 12 South Blvd Arterial 2 20,000-25,000 Yes Yes 40 14 Providence Road Arterial 2 30,000-40,000 Yes Yes 45 20 Sharon Road Local 2 14,000-20,000 Yes No 35 22 Graham Street Rd Arterial 2 14,000-20,000 Yes Yes 45

I-85 Interstate 85 Freeway 4 30,000-60,000 No No 70

Study Corridors for Data Collection


Data Collection (Cont.)


Run 1- Start Point Time: 06:55 AM

Run 1 - End Point Time: 07:15 AM



Study Corridors for Manual, GPS & Bluetooth Data Collection


Data Processing

• Manual data – Preparing travel time worksheet – Recording elapsed time data between signalized intersections and bus-

stop locations – Recording delay at signals

• GPS data – GPS device installed in a test vehicle – Travel time data from GPS was directly recorded into a Laptop – PC-Travel Software was used to process the GPS data

• Bluetooth data – Collection of raw data from USB flash drives connected to the devices – Data filtering techniques were applied based on minimum and maximum

travel speeds • Travel time based on minimum and maximum possible speeds




Comparison of Travel Time by Travel Run during Off-peak (Mid-Day) and Peak (Evening) Periods along South Blvd (Top) & I-85 (Bottom)

ID Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX

(%) Bluetooth (%)

5/29/2013 Run 1 (Time) 11:15 AM Run 2 (Time) 11:49 AM Run 3 (Time) 12:17 PM

1 82.5 0.6 8.7 89.2 91.1 1.0 11.8 77.2 90.0 1.1 31.3 144.4 2 128.3 0.5 1.9 54.7 115.8 0.2 15.0 77.4 137.5 0.4 -7.1 98.5 3 323.4 0.2 -40.4 -40.1 323.8 0.1 -35.0 -18.4 246.7 0.5 -14.6 -7.9 4 126.6 0.3 -24.2 -27.1 123.9 0.9 -17.2 19.2 119.8 -2.3 -22.4 -18.4

5/29/2013 Run 1 (Time) 4:46:50 PM Run 2 (Time) 5:28:00 PM Run 3 (Time) 6:20:10 PM

1 150.5 16.3 -36.5 -12.6 184.0 -3.8 -49.7 -12.3 173.0 1.2 -46.5 -6.7 2 146.3 36.7 5.0 46.4 225.8 -0.4 -22.9 -3.0 211.1 0.4 -32.4 -5.7 3 244.2 -40.2 -11.1 -13.3 319.8 0.1 -46.0 -26.8 380.2 -0.1 -31.8 -43.2 4 157.9 -43.6 -16.8 -50.0 163.1 0.6 -37.1 -27.0 146.5 0.3 -50.9 -26.2

ID Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX (%) Bluetooth (%) Manual (Sec) GPS (%) INRIX

(%) Bluetooth (%)

6/25/2013 Run 1 (Time) 11:03:27 AM Run 2 (Time) 11:27:57 AM Run 3 (Time) 11:51:30 AM

1 91.2 -0.2 28.1 -2.0 91.5 -1.6 114.4 -2.7 91.7 -0.8 28.0 16.2 2 92.6 -0.6 -7.6 -34.1 92.6 0.4 -57.9 -27.1 91.2 -0.2 -8.6 -45.0 3 48.6 -1.2 -17.3 11.0 48.7 -3.5 -19.9 5.3 50.6 -1.2 -20.6 4.9 4 104.3 -0.3 -14.9 -24.0 102.3 -0.3 -15.0 -22.3 105.4 -0.4 -16.9 -30.6

6/25/2013 Run 1 (Time) 4:07:20 PM Run 2 (Time) 4:32:50 PM Run 3 (Time) 5:13:08 PM

1 90.7 0.3 -0.1 21.0 90.8 0.2 29.3 12.9 90.1 1.0 30.3 -- 2 94.1 1.0 -10.1 -29.3 90.3 -0.3 -5.6 -43.2 81.4 2.0 4.7 -18.0 3 53.4 3.0 -27.0 0.1 49.3 -0.6 -19.7 12.7 50.1 1.8 -22.2 -6.0 4 104.5 -1.4 -15.0 -32.7 102.3 -1.3 -12.6 -33.2 103.6 -0.6 -15.6 -28.7


Sample Size by Time-of-the-day



Effect of Sample Size and Link-length / Spacing on Data Quality


South Blvd – Inbound Direction Time-of-the-

day Run

Link1 (1.3 miles) Link 2 (1.3 miles) Link 3 (1.9 miles) Link 4 (0.8 miles) Sample

Size Percent

Error Sample

Size Percent

Error Sample

Size Percent

Error Sample

Size Percent

Error

Mid-Day

1 4 89.2 7 54.7 12 -40.1 2 -27.1 2 6 77.2 2 77.4 24 -18.4 6 19.2 3 4 144.4 8 98.5 12 -7.9 5 -18.4 4 9 40.3 6 67.1 15 -23.4 5 -10.2

PM

1 6 -12.6 4 46.4 8 -13.3 5 -50.0 2 9 -12.3 1 -3.0 12 -26.8 5 -27.0 3 5 -6.7 4 -5.7 6 -43.2 2 -26.2 4 7 104.6 2 13.1 9 -44.3 16 0.4



Relation between Bluetooth Detector Spacing and % Difference

• Pearson correlation between spacing and % difference is -0.07.


Civil & Environmental Engineering Department Percentage Difference in Travel Time for Different Segments



Percentage Difference in Travel Time by Data Collection Period for All the Runs on Selected Arterial Streets


Comparison of Technologies – Key Findings

• Ability (sample size) to detect differs for freeways and arterial streets – More noise / disturbances lowering detection rate

• Detection rate varies by time-of-the-day – f(traffic volume)? – Weather & environmental conditions?

• Travel time data from both Bluetooth detectors and INRIX are reasonably close to manually captured travel time data along the freeway segment than when compared to arterials segments

• For arterial streets, travel times from INRIX are more promising when compared to the travel times from the Bluetooth detectors – Role of network characteristics?



Assessing Transportation System Reliability



Data Collection (Cont.)

• Inrix data – Over 200 routes – Data

downloaded for years 2008 to 2012



Data Processing & Performance Measures

• AVL & Inrix data – Data processing and mining - performed using Microsoft SQL Server – Data tools & query applications were developed to compute:

• Minimum • Average • Maximum • Median • 85th Percentile • 95th Percentile (Planning Time – PT)

– Factors considered • Time-of-day & day-of-week • For each run

– Computation of reliability measures



• Measures of Reliability


Index Equation Index Equation

NCHRP Definition SD of travel time λSkew

AASHTO Definition and TranSystems

Definition

Probability on-time performance

Buffer Time (BT) Variability TT85-TT15 Buffer Time Index

(BTI) Variability TT80-TT20

First worst travel times over a month

Variability TT70-TT30

Second worst travel times over a month

Acceptable Travel Time Variation Index

P(Tavg+ATTV)

Planning Time (PT) Desired Travel Time

Reduction Index P(Tave-DTTR)

Planning Time Index (PTI)

Travel Time Index (TTI)

Travel Time Variability (TTV)

Frequency of Congestion

Percent of days/periods that are

congested


Link-level Travel Time and Reliability Measures


North Tryon St Corridor during Weekdays

Link

Off-peak Hours (10:00 PM - 11:00 PM) Peak Hours (8:00 AM - 9:00 AM)

Travel Time (Minutes) Travel Time Percentile BTI (85)

BTI (95)

Travel Time (Minutes) Travel Time Percentile BTI (85)

BTI (95) Min Max Avg 15 85 95 Min Max Avg 15 85 95

1 2.20 2.99 2.58 2.57 2.57 2.70 -0.31 4.66 2.03 11.98 2.95 2.70 3.08 3.48 4.26 17.70

2 0.44 1.40 0.49 0.49 0.49 0.49 -1.03 -1.03 0.43 2.04 0.56 0.49 0.59 0.86 5.21 53.88

3 1.50 2.61 1.56 1.54 1.54 1.63 -1.04 4.28 1.63 4.89 2.53 2.19 2.68 3.62 5.83 42.64

4 1.73 2.52 2.12 2.13 2.13 2.13 0.50 0.50 1.18 5.01 2.10 1.67 2.61 3.17 24.41 50.64

5 1.00 1.27 1.06 1.06 1.06 1.06 -0.13 -0.13 0.34 0.92 0.50 0.44 0.55 0.63 9.89 26.96

6 0.12 0.18 0.13 0.13 0.13 0.13 0.21 0.21 0.04 0.39 0.14 0.09 0.18 0.28 25.96 97.12

7 0.07 0.11 0.07 0.07 0.07 0.07 -0.38 -0.38 0.95 2.17 1.19 1.08 1.21 1.33 1.86 12.30

8 0.01 0.03 0.01 0.01 0.01 0.02 -3.99 10.78 0.12 0.52 0.15 0.13 0.16 0.23 3.72 50.33

9 0.40 0.55 0.41 0.41 0.41 0.41 -0.28 -0.28 0.01 0.11 0.03 0.02 0.02 0.04 -5.32 73.57


27

Reliability During Weekday Peak (Left) & Off-peak (Right) Hours


28

Reliability During Weekend Peak (Left) & Off-peak (Right) Hours


Correlation Matrix For Travel Times and Reliability Indices For Weekday (All Day)



Correlation Matrix For Travel Times and Reliability Indices For Weekday (All Day) (Cont.)



Role of Reliability Measures Performance Measure Role

Avg. Travel Time (TT) Reports a nominal level of congestion as opposed to providing any information on the variation of travel rates. Helps in evaluating unique reliability measures through correlation.

TT -10th Percentile Evaluate variance in travel times TT - 15th Percentile Evaluate variance in Travel Times TT - 50th Percentile Evaluate Skewness TT - 85th Percentile These are upper percentiles of travel time distributions and can be

used as performance indicators. Can be used for before-and-after studies for comparison.

TT - 90th Percentile TT - 95th Percentile (PT) TTV - 90 (TT90-TT10)

These measures indicate variability in travel times or unpredictability of travel times from the users’ viewpoint. Can be used for before-and-after studies for comparison.

TTV - 85 (TT85-TT15) TTV - 95 (TT95-TT15)

BT BTI

BTI and PTI help track reliability over time and evaluate the condition of the facility. All these measures can be used for ranking and prioritization by agencies. These measures can also be used to compare the performance of one segment with another.

PTI λ Skew λ Var TTI * Performance measures in the bold are not correlated with the average travel times



Assessing Reliability – Key Findings

• Identification of specific locations for improvements will be difficult if corridor-level reliability measures are computed and used instead of link-level reliability measures

• Using corridor-level measures for prioritization and ranking may also lead to unnecessary and additional expenditures

• Use of 85th or 95th percentile travel times to compute reliability and assess performance should depend on user’s acceptance levels in the region

• Percent of links or lane miles with poor reliability scores by time-of-the-day could be used for assessment of transportation network performance (area-level)



33

Temporal Variation in Travel Time over Space - Crash Scenario


34


Acknowledgements

• This presentation is based on information collected and research performed for a research project funded by the United States Department of Transportation – Research and Innovative Technology Administration (USDOT/RITA) under Cooperative Agreement Number RITARS-12-H-UNCC.

• USDOT/RITA Program Manager: Mr. Caesar Singh



Disclaimer

• Views, opinions, findings, and conclusions reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT/RITA, or any State, or the University of North Carolina at Charlotte or other entity. The authors are responsible for the facts and the accuracy of the data presented herein. This presentation does not constitute a standard, specification, or regulation.



travel time data collection and spatial information … · 2014-03-13 · travel time data...

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