Principal Investigator

Sean Qian

Status

Completed

Start Date

Sept. 1, 2017

End Date

Dec. 31, 2018

Project Type

Research Applied

Grant Program

Private Funding

Grant Cycle

2017 Smart Mobility Challenge

Visibility

Public

Abstract

A CSX intermodal rail terminal is planned to open in late 2017 on a parcel of land located immediately north of the McKees Rocks Bridge in the Borough of McKees Rocks and Stowe Township, PA.  The development will consist of an intermodal facility that will accommodate approximately 50,000 lifts per year opening year (2018) and 136,000 lifts per year at full buildout (2023).  Access to the terminal is proposed via an improved Michael Alley to Island Avenue (SR 0051). It is expected to generate a significant number of trucks in the Borough of McKees Rocks, which adds additional burdens on the existing roadway in the Borough. The terminal may bring in heavy congestion to individual roadway drivers.  A traffic impact study was conducted indicating a minor congestion increase with the new infrastructure. This research project conducts an in-depth analysis of the potential traffic impact in high temporal and spatial resolutions. Using the data collected in the traffic impact study along with other relevant data sets possessed by CMU Mobility Data Analytics Center, we simulate individual cars and trucks, and model their route choices, travel time and mixed traffic flow conditions. The result includes the travel time, travel delay, vehicle-mile-traveled and emissions for each road segment and intersection by time of day. We will also examine the effectiveness of potential traffic management strategies, specifically West Carson Street Extension and truck routing.    

Description

Task 1: Identify various data sources for in-depth data analytics
1.	Request GIS models.
2.	Establish a refined GIS model for McKees Rocks and its surrounding areas. A stand-alone version of Borough/Township GIS with the following data is necessary for this study, which should include street names, street levels (highway, major arterials, minor streets, alleys, etc.), the number of lanes, and speed limit. 
3.	Obtain historical travel time data for main road segments from INRIX. 
4.	Obtain traffic data used in the traffic impact analysis by CSX.
5.	Obtain the number of trucks to be generated by the rail terminal and their approximate destinations.
Task 2:  Modeling of the existing traffic conditions without the terminal
We will use a mesoscopic network analysis methodology to conduct this research. CMU Mobility Data Analytics Center uses a dynamic network analysis tool (MAC-POSTS) capable of estimating network-wide traffic impact for any general networks consisting of freeway, arterials and local streets. It has the capacity of modeling dynamic traffic evolution for both trucks and cars, with the consideration of travel control and demand management. It adopts state-of-the-art traffic models and is much more computationally efficient than other microscopic models that are extremely labor intensive to build.
We first model the existing traffic conditions using MAC-POSTS. The estimated travel time and flow rates are expected to match those observations collected in the Task 1.
Task 3:  Modeling of the future traffic conditions with the presence of the terminal
In this task, we extend and apply the model calibrated in Task 2 to forecast the future traffic conditions. We first add the roadway and signalized intersection extensions that are planned as the CSX facility becomes operational. We then load the additional trucks generated by the terminal to the network model. The traffic impact can be measured by time-of-day performance metrics at both the street level and the regional level, such as total traffic delay, average travel time, emissions, energy use, vehicle-miles traveled, etc. 
Task 4: Modeling the potential benefits of traffic mitigation plans
In addition, we examine the effectiveness of two potential traffic management strategies. First, West Carson Street may be expanded to mitigate traffic impacts. We will work with the Borough managers and engineers to identify extension plans, and encode them into the MAC-POSTS model established in Tasks 2 and 3. We can run the model and compare the results to those obtained in Task 3. Second, we will fully examine truck routing plans that include time-of-day truck access restrictions and truck routing advisory information. Again the results will be obtained by the MAC-POSTS and compared to the results obtained in Task 3. 

Timeline

Oct 1-Oct 31: Task 1: Identify various data sources for in-depth data analytics
Nov 1-Dec 31: Task 2:  Modeling of the existing traffic conditions without the terminal
Jan 1-Jan 31: Task 3:  Modeling of the future traffic conditions with the presence of the terminal
Feb 1-Aug 31: Task 4: Modeling the potential benefits of traffic mitigation plans

Strategic Description / RD&T

Deployment Plan

We will work closely with the Borough of McKees Rocks throughout this project. While we focus on several particular applications (trucks and roadway) to demonstrate the method and leverage our resources, the methodology can be broadly applicable and scalable to other cities or regions. This generality will attract attentions from various groups interested in smart infrastructure, green design, and environmental policies. In the future, potential funding agencies/collaborators include the state DOT, the City of Pittsburgh, SPC, local governments, communities and foundations.

Expected Outcomes/Impacts

The desired results are a large-scale multi-modal transportation network model built for the Borough of McKees Rocks in the Pittsburgh region that supports decision making for municipalities.  

Expected Outputs

TRID

Individuals Involved

Email	Name	Affiliation	Role	Position
seanqian@cmu.edu	Qian, Sean	Carnegie Mellon University	PI	Faculty - Untenured, Tenure Track

Budget

Amount of UTC Funds Awarded

$60000.00

Total Project Budget (from all funding sources)

$60000.00

Documents

Type	Name	Uploaded
Publication	N/A	March 25, 2018, 12:07 p.m.
Presentation	Mobility Data Analytics	March 25, 2018, 12:07 p.m.
Presentation	Mobility Data Analytics	March 25, 2018, 12:07 p.m.
Progress Report	60_Progress_Report_2018-03-30	March 25, 2018, 12:07 p.m.
Presentation	Car-truck modeling for McKees Rocks	Sept. 23, 2018, 7:15 p.m.
Progress Report	60_Progress_Report_2018-09-30	Sept. 23, 2018, 7:15 p.m.
Final Report	60-final.pdf	Feb. 12, 2019, 9:55 a.m.
Publication	On the variance of recurrent traffic flow for statistical traffic assignment.	Dec. 2, 2020, 9:06 a.m.
Publication	A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity.	Dec. 2, 2020, 9:06 a.m.
Publication	A Low Rank Dynamic Mode Decomposition Model for Short-Term Traffic Flow Prediction	Dec. 2, 2020, 9:32 a.m.

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