scholarly journals Visualizations of Travel Time Performance Based on Vehicle Reidentification Data

2017 ◽  
Vol 2646 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Stanley Ernest Young ◽  
Elham Sharifi ◽  
Christopher M. Day ◽  
Darcy M. Bullock
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Control ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Traffic Patterns ◽  
Before And After ◽  
Vehicle Reidentification ◽  
Arterial Performance

This paper provides a visual reference of the breadth of arterial performance phenomena based on travel time measures obtained from reidentification technology that has proliferated in the past 5 years. These graphical performance measures are revealed through overlay charts and statistical distribution as revealed through cumulative frequency diagrams (CFDs). With overlays of vehicle travel times from multiple days, dominant traffic patterns over a 24-h period are reinforced and reveal the traffic behavior induced primarily by the operation of traffic control at signalized intersections. A cumulative distribution function in the statistical literature provides a method for comparing traffic patterns from various time frames or locations in a compact visual format that provides intuitive feedback on arterial performance. The CFD may be accumulated hourly, by peak periods, or by time periods specific to signal timing plans that are in effect. Combined, overlay charts and CFDs provide visual tools with which to assess the quality and consistency of traffic movement for various periods throughout the day efficiently, without sacrificing detail, which is a typical byproduct of numeric-based performance measures. These methods are particularly effective for comparing before-and-after median travel times, as well as changes in interquartile range, to assess travel time reliability.

Improving Interstate Freeway Travel Time Reliability Analysis by Clustering Travel Time Distributions

2021 ◽  
pp. 036119812110120
Author(s):  
Xiaoxiao Zhang ◽  
Mo Zhao ◽  
Justice Appiah ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Clustering Methods ◽  
Time Data ◽  
Dissimilarity Matrix ◽  
Model Based ◽  
Probe Vehicle ◽  
Vehicle Data

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

Developing and Implementing a Port Fluidity Performance Measurement Methodology using Automatic Identification System Data

2018 ◽  
Vol 2672 (11) ◽  
pp. 30-40 ◽  
Author(s):  
C. James Kruse ◽  
Kenneth N. Mitchell ◽  
Patricia K. DiJoseph ◽  
Dong Hun Kang ◽  
David L. Schrank ◽  
...  
Keyword(s):  
Supply Chain ◽  
Travel Time ◽  
Performance Measures ◽  
Automatic Identification ◽  
Identification System ◽  
Army Corps ◽  
Supply Chain Performance ◽  
Travel Time Reliability ◽  
Automatic Identification System ◽  
The U.S

The U.S. Army Corps of Engineers (USACE) is responsible for the maintenance of federally authorized navigation channels and associated infrastructure. As such, USACE requires objective performance measures for determining the level of service being provided by the hundreds of maintained navigation projects nationwide. To this end, the U.S. Army Engineer Research and Development Center partnered with Texas A&M Transportation Institute to develop a freight fluidity assessment framework for coastal ports. The goal was to use archival automatic identification system (AIS) data to develop and demonstrate how ports can be objectively compared in relation to fluidity, or the turnaround time reliability of oceangoing vessels. The framework allows USACE to evaluate maintained navigation project conditions alongside port system performance indices, thereby providing insight into questions of required maintained channel dimensions. The freight fluidity concept focuses on supply chain performance measures such as travel time reliability and end-to-end shipping costs. Although there are numerous research efforts underway to implement freight fluidity, this is the first known application to U.S. ports. This paper covers AIS data inputs, quality control, and performance measures development, and also provides a demonstration application of the methodology at the Port of Mobile, Alabama, highlighting travel time and travel time reliability operating statistics for the overall port area. This work provides foundational knowledge to practitioners and port stakeholders looking to improve supply chain performance and is also valuable for researchers interested in the development and application of multimodal freight fluidity performance measures.

scholarly journals Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

2020 ◽  
Vol 2674 (6) ◽  
pp. 85-97 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Estimate Travel Time ◽  
The Difference ◽  
Travel Time Variability

The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.

Exploratory Analysis of the Relationships between Congestion, Travel Time Reliability, and Freight-Related Performance Management Measures and Their Associativity with the Roadway Attributes

2020 ◽  
Vol 2674 (10) ◽  
pp. 571-582
Author(s):  
Chowdhury Siddiqui ◽  
Kwanpyo Ko
Keyword(s):  
South Carolina ◽  
Performance Management ◽  
Travel Time ◽  
Performance Measures ◽  
Urban Areas ◽  
Exploratory Analysis ◽  
Travel Time Reliability ◽  
Negatively Associated ◽  
Management Measures ◽  
Highway System

The purpose of this study is to investigate the performance management measures related to highway system reliability, freight, and traffic congestion in light of the federal rulemaking that establishes these performance measures. The study conducts an exploratory analysis to understand their inter-relationships and examines their (un)common underlying attributes to discover their associativity with each of the performance measures. In doing so several traffic and roadway related characteristics of each reporting segment of the National Highway System (NHS) of South Carolina were processed and modeled for the travel time reliability and peak hour excessive delay using generalized linear models with a log-link function. The results from the study indicate that the unreliable Interstate segments contributed to about 87% of the excessive delays on the entire Interstate. It was also found that more than half of the non-Interstate NHS segments that experienced excessive delay, were reliable and they contributed to approximately 52% of the entire peak hour excessive delay of the non-Interstate NHS. The results from the model indicate that the directional annual average daily traffic (AADT) and the urban areas are the two most important attributes positively associated with all three performance measures, while the number of through lanes was found to be negatively associated with all three performance measures. The length of the reporting segments was positively associated with the excessive delays but negatively associated with the travel time reliabilities. The percentage of single trucks was unique to the Interstate delays and positively associated.

Vehicle Reidentification as Method for Deriving Travel Time and Travel Time Distributions: Investigation

2003 ◽  
Vol 1826 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Carlos Sun ◽  
Glenn Arr ◽  
Ravi P. Ramachandran
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Performance Test ◽  
Time Window ◽  
Time Windows ◽  
Travel Times ◽  
Travel Time Distribution ◽  
Vehicle Reidentification ◽  
And Control

Vehicle reidentification was investigated as a method for deriving travel time and travel time distributions with loop and video detectors. Vehicle reidentification is the process of tracking vehicles anonymously from site to site to produce individual vehicle travel times and overall travel time distribution. Travel time and travel time distribution are measures of the performance and reliability of the transportation system and are useful in many transportation applications such as planning, operations, and control. Findings from the investigation included ( a) results from a platoon reidentification algorithm that improved upon a previous indvidual vehicle reidentification algorithm, ( b) sensitivity analysis on the effect of time windows in deriving travel times, and ( c) derivation and goodness of fit of travel time distributions using vehicle reidentification. Arterial data from Southern California were used in testing the algorithm’s performance. Test results showed that the algorithm can reidentify vehicles with an accuracy of greater than 95.9% with 92.4% of total vehicles; can calculate individual travel times with approximately 1% mean error with the most effective time window; and can derive travel time distributions that fit actual distributions at a 99% confidence level.

Monitoring and Predicting Freeway Travel Time Reliability

2005 ◽  
Vol 1917 (1) ◽  
pp. 54-62 ◽  
Author(s):  
J. W. C. van Lint ◽  
H. J. van Zuylen
Keyword(s):  
Travel Time ◽  
Discrete Choice Models ◽  
Time Of Day ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Explanatory Variables ◽  
Travel Time Distribution ◽  
Mean And Variance ◽  
Context Specific

Generally, the day-to-day variability of route travel times on, for example, freeway corridors is considered closely related to the reliability of a road network. The more that travel times on route r are dispersed in a particular time-of-day (TOD) and day-of-week (DOW) period, the more unreliable travel times on route r are conceived to be. In the literature, many different aspects of the day-to-day travel time distribution have been proposed as indicators of reliability. Mean and variance do not provide much insight because those metrics tend to obscure important aspects of the distribution under specific circumstances. It is argued that both skew and width of this distribution are relevant indicators for unreliability; therefore, two reliability metrics are proposed. These metrics are based on three characteristic percentiles: the 10th, 50th, and 90th percentile for a given route and TOD-DOW period. High values of either metric indicate high travel time unreliability. However, the weight of each metric on travel time reliability may be application- or context-specific. The practical value of these particular metrics is that they can be used to construct so-called reliability maps, which not only visualize the unreliability of travel times for a given DOW-TOD period but also help identify DOW-TOD periods in which congestion will likely set in (or dissolve). That means identification of the uncertainty of start, end, and, hence, length of morning and afternoon peak hours. Combined with a long-term travel time prediction model, the metrics can be used to predict travel time (un)reliability. Finally, the metrics may be used in discrete choice models as explanatory variables for driver uncertainty.

Freeway Network Travel Time Reliability Analysis Methodology and Software Tool Development

2021 ◽  
pp. 036119812110184
Author(s):  
Wei Sun ◽  
Scott S. Washburn
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Assignment ◽  
Software Tool ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Analysis Methodology ◽  
Weather Events

Applications in the field often require analysis of freeway travel time reliability (TTR) at the network level. While micro-simulation is suitable for performing network-level analysis, the computational burden can become unreasonable when TTR analysis is factored in. As for macro-simulation, most network analysis studies often use simplified link performance functions to represent the travel time and flow relationship. Such functions are not generally sensitive to the range of geometric and traffic conditions that can influence freeway facility operations. This research extends the Highway Capacity Manual (HCM) freeway TTR analysis methodology to the network level. The proposed freeway network TTR analysis methodology generates scenarios that represent the impacts of origin–destination (OD) demand variations, weather events, incident events, and work zone events on freeway network travel time. For each scenario, the methodology performs user equilibrium (UE) traffic assignment and the HCM freeway facility core methodology is applied to represent the travel time and flow relationship. The method of successive average approach is applied to solve the UE traffic assignment. Finally, scenario travel times (and/or other performance measures) are aggregated into various distributions of interest, such as the network-, facility-, and OD-level distributions, and TTR performance measures are calculated at the three different levels. A software tool is developed using C# language on the .NET Framework. The software tool provides a convenient and efficient approach for transportation planners and researchers to conduct the freeway network TTR analysis, which helps to bridge the gap between research and practice.

scholarly journals Uncovering the influence of commuters’ perception on the reliability ratio

10.32866/8330 ◽  
2019 ◽  
Author(s):  
Carlos Carrion ◽  
David Levinson
Keyword(s):  
Travel Time ◽  
Discrete Choice ◽  
Revealed Preference ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Discrete Choice Modeling ◽  
Loop Detectors ◽  
Travel Time Savings ◽  
Time Savings ◽  
Gps Devices

The dominant method for measuring values of travel time savings (VOT), and values of travel time reliability (VOR) is discrete choice modeling. Studies using revealed preference have tended to use travel times measured by devices such as loop detectors, and thus the perception error of travelers has been largely ignored. In this study, the influence of commuters’ perception error is investigated on data collected of commuters recruited from previous research. The subjects’ self-reported travel times from surveys, and the subjects’ travel times measured by GPS devices were collected. The results indicate that the subjects reliability ratio is greater than 1 in the models with self- reported travel times. In contrast, subjects reliability ratio is smaller than 1 in the models with travel times as measured by GPS devices.

scholarly journals Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yong-Sheng Zhang ◽  
En-Jian Yao
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Estimation Model ◽  
Forecasting Accuracy ◽  
Normal Distributions ◽  
Proposed Model ◽  
Truncated Normal ◽  
Metro System ◽  
Probability Measurement

The walking, waiting, transfer, and delayed in-vehicle travel times mainly contribute to route’s travel time reliability in the metro system. The automatic fare collection (AFC) system provides huge amounts of smart card records which can be used to estimate all these times distributions. A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. Finally, based on AFC data in Guangzhou Metro, the estimations show that all parameters can be estimated endogenously and identifiably. Meanwhile, the truncated property of the travel time is significant and the threshold tested by the surveyed data is reliable. Furthermore, the superiority of the proposed model over the existing model in estimation and forecasting accuracy is also demonstrated.

Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time

2005 ◽  
Vol 1917 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Mecit Cetin ◽  
George F. List ◽  
Yingjie Zhou
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Travel Time Reliability ◽  
System State ◽  
Travel Times ◽  
State Estimate ◽  
Factors Affecting ◽  
Minimum Number ◽  
Time Information ◽  
Detection Technologies

Using probe vehicles rather than other detection technologies has great value, especially when travel time information is sought in a transportation network. Even though probes enable direct measurement of travel times across links, the quality or reliability of a system state estimate based on such measurements depends heavily on the number of probe observations across time and space. Clearly, it is important to know what level of travel time reliability can be achieved from a given number of probes. It is equally important to find ways (other than increasing the sample size of probes) of improving the reliability in the travel time estimate. This paper provides two new perspectives on those topics. First, the probe estimation problem is formulated in the context of estimating travel times. Second, a method is introduced to create a virtual network by inserting dummy nodes in the midpoints of links to enhance the ability to estimate travel times further in a way that is more consistent with the processing that vehicles receive. Numerical experiments are presented to illustrate the value of those ideas.

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