Arterial Performance Measurement Using Multiple Traffic Data Sources_UTC Project InformationForm (2)

UTC Project Information Project Title Arterial Performance Measurement Using Multiple Traffic Data Sources University Rensselaer Polytechnic Institute Principal Investigator Xuegang Jeff

UTC Project Information

Project Title Arterial Performance Measurement Using Multiple Traffic Data Sources

University Rensselaer Polytechnic Institute

Principal

Investigator

Xuegang (Jeff) Ban, Ph.D

PI Contact

Information

Rensselaer Polytechnic Institute Civil and Environmental Engineering

4034 JEC Phone: 518-276-8043 Email: banx@rpi.edu Funding Source(s)

and Amounts

Provided (by each

agency or

organization)

TransINFO UTC: $212,175.00 Matching funds from RPI School of Engineering graduate student tuition cost share: $122,381 00

Total Project Cost $334,556.00

Agency ID or

Contract Number

N/A

Start and End

Dates

1/1/14 – 12/31/15

Brief Description of

Research Project

This research aims to develop methods on how to best mine the different data

elements in Urban Hybrid Traffic Data (U-HTD), how to protect privacy when

processing and using U-HTD, and how to develop novel methods that can best utilize U-HTD for critical urban transportation applications The key research areas are listed as follows:

 Develop big data analytics methods to mine U-HTD for urban traffic modeling applications

 Privacy implications when processing/using U-HTD for transportation applications and techniques that may alleviate privacy concerns

 Methods to understand travelers’ behavior, decision making process and the relationship with built environment

 Methods to evaluate urban freight performances (such as delivery times at stops, number of stops per tour, etc.)

 Methods to understand the behaviors of key urban freight system components, their interactions, and the relationship between transportation and land use

 Freight tour synthesis models that use analytical approaches to estimate aggregated freight demand using secondary data sources such as U-HTD

 Network optimization models for tour-based, multi-class (especially passengers and trucks) origin-destination demand estimation using secondary data sources such as U-HTD

Describe

Implementation of

Research

Outcomes (or why

not implemented)

Place Any Photos

Here

Impacts/Benefits

of Implementation

(actual, not

anticipated)

Web Links

 Reports

 Project

website

http://www.buffalo.edu/transinfo/Research/Arterialperformancemeasurement.ht

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