摘要
Travel time reliability(TTR)is an important measure which has been widely used to represent the traffic conditions on freeways.The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor.A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte,North Carolina.A number of influential factors are considered when analyzing TTR,which include,but are not limited to,time of day,day of week,year,and segment location.A time series model is developed and used to predict the TTR.Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions.The research results can provide insightful and objective information on the traffic conditions along freeway segments,and the developed data-driven models can be used to objectively predict the future TTRs,and thus to help transportation planners make informed decisions.
Travel time reliability(TTR) is an important measure which has been widely used to represent the traffic conditions on freeways. The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor. A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte, North Carolina. A number of influential factors are considered when analyzing TTR, which include, but are not limited to,time of day, day of week, year, and segment location. A time series model is developed and used to predict the TTR. Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions. The research results can provide insightful and objective information on the traffic conditions along freeway segments, and the developed data-driven models can be used to objectively predict the future TTRs, and thus to help transportation planners make informed decisions.
基金
the financial support by the United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133)
