Travel Time Reliability Estimation: Use of Median Travel Time as Measure of Central Tendency

2011 ◽  
Author(s):  
M. Arezoumandi ◽  
G. H. Bham
Keyword(s):  
Travel Time ◽  
Reliability Estimation ◽  
Travel Time Reliability ◽  
Central Tendency

Replication of Daily and Monthly Freeway Demand Variations for Travel Time Reliability Procedures

2020 ◽  
Vol 2674 (9) ◽  
pp. 727-741
Author(s):  
Amjad Dehman ◽  
Tom Brijs ◽  
Alexander Drakopoulos
Keyword(s):  
Travel Time ◽  
Reliability Assessment ◽  
Reliability Estimation ◽  
Assessment Procedure ◽  
Travel Time Reliability ◽  
Peak Period ◽  
Traffic Demand ◽  
Time Index ◽  
The Mean ◽  
Demand Variation

The sixth edition of the Highway Capacity Manual (HCM) incorporates a travel time reliability assessment procedure for freeways and urban streets. Several demand adjustment factors, referred to by demand multipliers, are used to capture traffic demand variation across different days and months. These factors are currently produced by referencing the average daily traffic volume of each day-month combination to a base daily volume. However, practitioners usually perform traffic analyses during specific times of the day, for example, peak periods, off-peak periods, or even peak hours, demand multipliers may therefore replicate demand variation more accurately if they are based on traffic volumes concurred in time intervals narrower than a day. This paper investigates six criteria or periods to derive demand multipliers: full-day, pre AM-peak, AM peak-period, midday, PM peak-period, and post PM-peak. The study explores how these periods affect the scale of demand multipliers and the travel time reliability assessment. It was found that the main statistics of demand multipliers, that is, the mean, range, and standard deviation, greatly differ across the different multiplying periods. If analyzing peak periods on oversaturated corridors, the adoption of daily-volume multipliers was found to significantly overestimate the mean travel time index and planning time index during both the AM and PM peak periods, the accuracy of the travel time reliability estimation was considerably influenced. The study concludes with major findings and recommendations for possible enhancements to the HCM travel time reliability procedure.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

2017 ◽  
Vol 2652 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Zhenliang Ma ◽  
Sicong Zhu ◽  
Haris N. Koutsopoulos ◽  
Luis Ferreira
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Central Tendency ◽  
Automatic Vehicle Location ◽  
Conditional Mean ◽  
Vehicle Location ◽  
Travel Time Variability

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

Travel Time Reliability

2021 ◽  
pp. 109-121
Author(s):  
Sharmili Banik ◽  
Anil Kumar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Travel Time Reliability

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

2021 ◽  
pp. 036119812110130
Author(s):  
S M A Bin Al Islam ◽  
Mehrdad Tajalli ◽  
Rasool Mohebifard ◽  
Ali Hajbabaie
Keyword(s):  
Travel Time ◽  
Market Share ◽  
Signal Control ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Traffic Demand ◽  
Adaptive Signal Control ◽  
Loop Detector ◽  
Traffic Performance ◽  
Adaptive Signal

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

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