Using Innovative Data in Transportation Planning and Modeling
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1 Using Innovative Data in Transportation Planning and Modeling presented at 2014 Ground Transportation Technology Symposium: Big Data and Innovative Solutions for Safe, Efficient, and Sustainable Mobility presented by Cambridge Systematics, Inc. Nikhil Puri November 19, 2014
2 Outline Context need for good data Applications in transportation planning and modeling Benefits and limitations of using (big) data Potential applications
3 Context Need for Good Data Transportation sector accounts for 25% of total commercial energy worldwide (2001 United Nations report) Annual cost of traffic congestion in the U.S. 100s of billions ($124 billion recent study by INRIX and the Centre for Economics and Business Research) Key infrastructure decisions based on travel demand models» Rely on (good) data 3 Traditional data collection intercept origin-destination, tube counters, household travel surveys» Still needed Innovative data collection sources anonymized cell phones, GPS probes, aircraft, Bluetooth devices and toll plaza, General Transit Feed Specification (GTFS)» Becoming more common» Not without limitations
4 Applications in Transportation Planning and Modeling Planning» Understanding O-D patterns, trip lengths, imputed trip purposes, external trip patterns» Performance measurement speeds» Travel behavior (infancy) Model Applications» Macroscopic and microscopic models» Calibration/validation of models trip lengths, trip distribution patterns, trip purposes, speeds, counts 4
5 Central Florida Person Trips: 2014 Note: Derived from AirSage Data 5
6 Central Florida Region Spatial Distribution of Daily Trips Sumter 2.5% Volusia 13.7% Marion 9.0% Brevard 11.4% Seminole 9.1% Flagler 2.6% Indian River 0.9% Polk, 2.7% Lake 7.6% Osceola 10.8% Central Florida Region Spatial Distribution (Destination) Percentage Trips 60.0% 50.9% 50.0% 40.0% 30.0% 20.0% Note: Partial counties shown in red. Orange 29.9% 26.7% 18.5% 10.0% 1.3% 2.6% 0.0% At Home Long Term Leaving the Within Visitor CFRPM CFRPM Region Region Through Trips Trip Type Note: Derived from AirSage Data 6
7 Temporal Distribution by Trip Purpose (Percentage & Absolute) 100% 80% 60% 16,000, % 34.1% 36.7% 14,000, % 19.1% 20.4% 44.1% 45.5% 12,000,000 40% 20% 42.5% 0% AM Peak Period PM Peak Period HBO HBW Daily NHB o Over 40 percent of trips are Home Based Other Trips o There is a large variation in Home Based Work trips between AM and PM periods o Approximately 22 percent of trips occur during the AM peak, whereas 26 percent occur during the PM peak 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 AM Peak Period PM Peak Period HBO HBW Daily NHB Note: Derived from AirSage Data 7
8 Disney World Patronage Interesting findings Green boundary represents Disney World area Summation of daily non-home-based work trips 189K trips. In comparison, observed 2013 counts was in the order of 207K annual visitors 8
9 Select Link Analysis Using Cell Phone Data Used anonymized cell phone data (2012) Select link analysis on bridges (O-Ds on bridges) 50.00% Clark 40.00% Floyd 30.00% Bullitt 20.00% Jefferson 10.00% Oldham 2000 HH Survey Attractions 0.00% Productions» O-D patterns compared reasonably well at county level 60.00% Attractions» Due to proximity of bridges, hard to distinguish which bridge traffic was on Source: Google Maps 70.00% Productions Findings Rest of KY (Externals) Rest of IN (Externals) 2012 Airsage Survey Comparison of County Level Distribution Patterns Household Travel Survey vs Cell Phone Data 9
10 Probe Speed Data Data tells us Bottleneck location and severity Duration of congestion Extents of queues Free-flow conditions Issues: Overlapping TMC links Heat map indicating congestion locations and time Some Applications: Performance Measures Macro and micro transportation model validation Toll diversion modeling 10 Example of overlapping TMC links
11 MTA (New York) BusTime Data Approximately 6,000 buses throughout NYC 8 Million daily data points GPS bus data every 30 seconds Currently comparing data against GTFS for reliability statistics Note: Not based on real data 11
12 Benefits and Limitations of Using (Big) Data Benefits» Often larger sample sizes» Easily collected; archived data available» Often cheaper with wider coverage Limitations» Spatial accuracy (cell phone data)» Currently unable to gain additional insights on trip behavior characteristics such as mode» Accuracy may be susceptible to weather» Privacy concerns 12
13 Potential Applications Using trajectory data route choice behavior Transit ridership Performance measurement for transit operations Travel behavior during events Emergency response Airport usage Tourism 13
14 Contact Information NIKHIL PURI Cambridge Systematics (646)
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