Performance Ranking of Arterial Corridors Using Travel Time and Travel Time Reliability Metrics

Abstract

Performance measures are important for managing transportation systems and demonstrating accountability. Probe vehicle data has emerged as a means of gathering vast amounts of information about highway networks. This paper presents a scalable methodology for analyzing arterial travel times, taking into account both the central tendency of the travel time and its reliability. A pilot analysis is carried out for 28 arterials with a total of 341 signalized intersections across the state of Indiana. Starting from individual minute-by-minute speed records, the data are converted into travel times and aggregated into time series cohorts that correspond to typical traffic signal time-of-day periods, reflecting different time-of-day behavior characteristics of traffic control in arterials. The data is normalized with respect to the ideal travel time (based on the speed limits on each route) to account for individual route lengths and speeds. Data is compiled for all Wednesdays from January through July 2014 to investigate arterial characteristics. The data shows that a greater density of traffic signals on a route loosely corresponds to higher average travel times and less reliability. A composite index incorporating both the average values and reliability characteristics of travel time is developed, and used to rank the arterials according to their performance. • Data must be normalized because routes are of different length. DATA QUALITY ABSTRACT SURVEY OF ARTERIALS• Travel Rate method expresses travel time as amount of time per mile.• Normalization To Ideal Travel Time expresses travel time as percentage of the ideal travel time • This analysis uses speed limit travel time as surrogate for ideal travel time.• This analysis uses normalization to speed limit travel time in order to compare multiple roadways with different travel speed expectations.

Understanding Factors Affecting Arterial Reliability Performance Metrics

“…Standard deviation of travel times can also be used to characterize the variability in travel times (Day et al, 2015). Day et al suggest that standard deviations should also be normalized with the speed limit travel times to account for varying lengths and speed limits.…”

“…The likeliness is large if either or or both are large. Day et al (2015) studied travel times on arterial routes in Indiana using measures of central tendency (speed-normalized travel time) and variability (normalized standard deviation). They created a composite index that included the average value of travel time and its unreliability and used the index to rank arterials.…”

“…There has been limited research on arterial corridors. While some research has focused on arterial travel time estimation and measurement (Liu et al, 2005;Kwong et al, 2009;Liu et al, 2006b), other researchers have studied arterial performance and ranking corridors for the purpose of traffic signal retiming (Day et al, 2015;Lavrenz et al, 2016).…”

Integrating Traffic Signal Performance Measures into Agency Business Processes

“…These new datasets have proven extremely useful for evaluation of freeway facilities and quantifying congestion on large scales [27][28][29][30][31]. That utility has also been demonstrated for arterials [32,33].…”

Assessing Longitudinal Arterial Performance and Traffic Signal Retiming Outcomes

“…Historically, studies examining longer-term microscopic trends in traffic performance have approached the topic from an external perspective, without active intervention on the system(s) in question. For instance, Day et al compiled a statewide, multi-year ranking of signalized corridors using measures of travel time and travel time reliability (3). The extent to which specific timing plan modifications impacted these travel times was not extensively considered.…”