Estimation and Prediction of Mobility and Reliability Measures Using Different Modeling Techniques

Authors

Fatema Hoque Farzana, Florida International UniversityFollow

Document Type

Thesis

Degree

Master of Science (MS)

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. Yan Xiao

Third Advisor's Committee Title

Committee Member

Keywords

Mobility, Reliability, Demand

Date of Defense

11-9-2018

Abstract

The goal of this study is to investigate the predictive ability of less data intensive but widely accepted methods to estimate mobility and reliability measures. Mobility is a relatively mature concept in the traffic engineering field. Therefore, many mobility measure estimation methods are already available and widely accepted among practitioners and researchers. However, each method has their inherent weakness, particularly when they are applied and compared with real-world data. For instances, Bureau of Public Roads (BPR) Curves are very popular in static route choice assignment, as part of demand forecasting models, but it is often criticized for underperforming in congested traffic conditions where demand exceeds capacity. This study applied five mobility estimation methods (BPR Curve, Akcelic Function, Florida State University (FSU) Regression Model, Queuing Theory, and Highway Capacity Manual (HCM) Facility Procedures) for different facility types (i.e. Freeway and Arterial) and time periods (AM Peak, Mid-Day, PM Peak). The study findings indicate that the methods were able to accurately predict mobility measures (e.g. speed and travel time) on freeways, particularly when there was no congestion and the volume was less than the capacity. In the presence of congestion, none of the mobility estimation methods predicted mobility measures closer to the real-world measure. However, compared with the other prediction models, the HCM procedure method was able to predict mobility measures better. On arterials, the mobility measure predictions were not close to the real-world measurements, not even in the uncongested periods (i.e. AM Peak and Mid-Day). However, the predictions are relatively better in the AM and Mid-Day periods that have lower volume/capacity ration compared to the PM Peak period.

To estimate reliability measures, the study applied three products from the Second Strategic Highway Research Program (SHRP2) projects (Project Number L03, L07, and C11) to estimate three reliability measures; the 80^th percentile travel time index, 90th percentile travel time index, and 95^th percentile travel time index. A major distinction between mobility estimation process and reliability estimation process lies in the fact that mobility can be estimated for any particular day, but reliability estimation requires a full year of data. Inclusion of incident days and weather condition are another important consideration for reliability measurements. The study found that SHRP2 products predicted reliability measures reasonably well for freeways for all time periods (except C11 in the PM Peak). On arterials, the reliability predictions were not close to the real-world measure, although the differences were not as drastic as seen in the case of arterial mobility measures.

Identifier

FIDC007026

Recommended Citation

Farzana, Fatema Hoque, "Estimation and Prediction of Mobility and Reliability Measures Using Different Modeling Techniques" (2018). FIU Electronic Theses and Dissertations. 3880.
https://digitalcommons.fiu.edu/etd/3880