Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

Abstract

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

Data analytics approach for travel time reliability pattern analysis and prediction

“…The maximum (and average) PTIs on weekends of these two segments are 1.28 (1.10) and 1.24 (1.08), respectively. The results indicate that traffic congestion on weekends becomes less frequent and also travel demand on weekends is perhaps much lower than that on weekdays, which is consistent with previous studies [18,34]. Case 2 For the segments showing the a.m. peak characteristics, the travel time on Tuesday is the least reliable.…”

“…Basically, TTR can be analyzed using travel time distribution data, including both single mode distribution [16] and multimode distribution [17,18]. Eliasson [19] investigated the relationship between travel time distribution and different TOD periods.…”

“…In addition, the researchers also noticed that the new reliability estimation method showed higher sensitivity to geographical locations. Chen et al [18] explored the travel time distribution and variability patterns for different road types in different time periods. The authors compared the goodness-of-fit results of several distribution types and analyzed TTR patterns with BI and COV.…”

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

“…Most existing studies on the travel time distribution (TTD) have put significant efforts into identifying the best fitting model. The fitted distributions can be classified as single-mode distributions [11][12][13][14][15][16][17][18] and other distributions (multi-mode distribution and truncated distribution) [19][20][21]. Herman and Lam [4] suggested gamma or log-normal distribution for TTD.…”

“…Results of calculations for finding characteristics of available routes connecting the origin-Comparison of total travel time for the routes connecting the[1][2][3][4][5][6][7][8][9][10][11][12] origin-destination pair at 95% confidence level.…”