How accurate is the regional travel demand model in mimicking real-world travel times?. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Pulugurtha, Srinivas S.; Duvvuri, Sarvani V.; Jain, Ravina N.; Venigalla, Mohan
• Is Part Of: Urban, planning and transport research. Volume 7:Issue 1(2019)
• Page Start: 53
• Page End: 73
• Publication Date: 2019-01-01
• Subjects: Travel time -- regional travel demand model -- speed limit -- time of the day -- area type; City planning -- Periodicals; Cities and towns -- Growth -- Periodicals; Urban renewal -- Periodicals; Cities and towns -- Research -- Periodicals; Urban transportation -- Periodicals; Cities and towns -- Growth; Cities and towns -- Research; City planning; Urban renewal; Urban transportation; Periodicals; 307.116
• DOI: 10.1080/21650020.2019.1661790
• Languages: English
• ISSNs: 2165-0020

Abstract

ABSTRACT: This paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85 th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10 th to 50 th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to mid-day and night-time period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.