Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

Authors

Samaneh Khazraeian, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Civil Engineering

First Advisor's Name

Dr. Mohammed Hadi

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Dr. Albert Gan

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Dr. L. David Shen

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Dr. Xia Jin

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Dr. Zhenmin Chen

Fifth Advisor's Committee Title

Committee Member

Sixth Advisor's Name

Dr. Yan Xiao

Sixth Advisor's Committee Title

Committee Member

Keywords

Connected Vehicle Technology, ITS Investment Support, AHP, Return on Investment, Bottleneck Management, Incident Detection, Queue Estimation and Warning

Date of Defense

10-27-2017

Abstract

The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology.

Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection.

In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management.

The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data.

Identifier

FIDC004061

Recommended Citation

Khazraeian, Samaneh, "Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management" (2017). FIU Electronic Theses and Dissertations. 3496.
https://digitalcommons.fiu.edu/etd/3496