Origin–Destination-Based Travel Time Reliability

Abstract

Travel time reliability (TTR) is an important performance indicator for transportation systems. TTR can be generally categorized as either segment based or origin–destination (O-D) based. A primary difference between the two TTR estimations is that route information is implied in segment-based TTR estimations. Segment-based TTR estimations have been widely studied in previous research; however, O-D–based TTR estimations are used infrequently. This paper provides detailed insight into O-D–based TTR estimations and raises three new issues: ( a) How many routes do travelers usually take and what are the TTR values associated with these routes? ( b) Do statistical differences exist between route-specific and non-route-specific (NRS) TTR values? ( c) How can O-D–based TTR information be delivered? Two processes were proposed to address the issues. Three TTR measures—standard deviation, coefficient of variation, and buffer index—were calculated. The bootstrapping technique was used to measure the accuracy of the TTR measures. Approximate confidence intervals were used to investigate statistically the differences between route-specific and NRS TTR measures. A large quantity of taxicab GPS-based data provided data support for estimating O-D–based TTR measures. The results of O-D–based TTR measures showed that no statistically significant differences existed between route-specific and NRS TTR measures for most of the time periods examined. Statistically significant differences could still be found in some time periods. Travelers may take advantage of these differences to choose a more reliable route. Access to both numeric TTR values and route preference, instead of just to TTR information on segments of interest, can be beneficial to travelers in planning an entire trip.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…In order to evaluate OD, it is necessary to set various indexes to evaluate passengers' behaviors quantitatively. Yang S used taxi GPS data to estimate OD travel time, which helps to analyze the route preference of passengers [14]. Margaretic P applied the spatial economic flow model and characteristic function spatial filtering method to air transportation, and discussed the impact of departure destination on air passenger flow [15].…”

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data

“…Comparison studies are limited, though. In [13], the authors concluded that there were no significant differences between O-D-based and route-based estimates in most part of the studied time periods. In [14], the researchers found that adding an alternative path tends to decrease the O-D-based TTR.…”

“…), there are some other TTR measures, such as travel time variability (TTV), planning travel time index (PI), and buffer index (BI). Among these measures, BI has been widely utilized in existing literature [7,8,13,27,28] and as concluded in [29] has a high consistency with the coefficient of variation and thus is best suitable for the measure of TTR. e authors adopted the idea and took BI as the primary TTR measure.…”

Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm

“…Each hidden traffic state was represented by a Gaussian distribution. Using Gaussian mixture distributions to model travel-time distribution is reasonable because previous studies (e.g., Yang et al 2017;Yang and Wu 2016;Jenelius and Koutsopoulos 2015;Feng et al 2014;Guo et al 2011) have shown that they can effectively determine travel-time distributions when traffic is heterogeneous. Hofleitner and Bayen's algorithm (2011) then used numerical optimization methods to achieve optimized TTD.…”