Travel Time Performance Measures for Passenger Cars and Trucks by Road Facility Type

2021 ◽  
Author(s):  
Sarvani V. Duvvuri ◽  
Raghuveer Gouribhatla ◽  
Raunak Mishra ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Passenger Cars ◽  
Time Performance ◽  
Facility Type

scholarly journals Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

2021 ◽  
Author(s):  
Sarvani Duvvuri ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Pearson Correlation ◽  
Time Data ◽  
Network Characteristics ◽  
Time Index ◽  
Time Performance ◽  
Travel Time Data ◽  
Day Of The Week

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis

2020 ◽  
Vol 14 (12) ◽  
pp. 1524-1533 ◽  
Author(s):  
Xinzhi Zhong ◽  
Yajie Zou ◽  
Zhi Dong ◽  
Shaoxin Yuan ◽  
Muhammad Ijaz
Keyword(s):  
Travel Time ◽  
Survival Model ◽  
Finite Mixture ◽  
Passenger Cars ◽  
Urban Arterial

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 Travel Time Differences between Cargo Cycles and Cars in Commercial Transport Operations

2019 ◽  
Vol 2673 (8) ◽  
pp. 623-637 ◽  
Author(s):  
Johannes Gruber ◽  
Santhanakrishnan Narayanan
Keyword(s):  
Travel Time ◽  
Real Life ◽  
Time Difference ◽  
Significant Variable ◽  
Factors Affecting ◽  
Traffic Conditions ◽  
Time Performance ◽  
Trip Distance ◽  
Business Sectors ◽  
Lack Of Knowledge

Cargo cycles are gaining more interest among commercial users from different business sectors, and they compete with cars in urban commercial transport. Though many studies show the potential of cargo cycles, there is still a reluctance to deploy them. One possible reason for this is the lack of knowledge regarding their suitability in relation to travel time. Therefore, this study aims to explore cargo cycles’ travel time performance by quantifying the travel time differences between them and conventional vehicles for commercial trips. The authors compare real-life trip data from cargo cycles with Google’s routed data for cars. By doing this, the authors explore the factors affecting the travel time difference and propose a model to estimate this difference. The attributes for the model were selected keeping in mind the ease of obtaining values for the variables. Results indicate cycling trip distance to be the most significant variable. The study shows that expected travel time difference for trips with distances between 0 and 20 km (12.4 mi) ranges from -5 min (cargo cycle 5 min faster) to 40 min with a median of 6 min. This value can decrease if users take the optimal cycling route and the traffic conditions are worse for cars. Although what is an acceptable amount of travel time difference depends on the user, practitioners can be certain of the travel time difference they can expect, which enables them to assess the suitability of cargo cycles for their commercial operations.

Performance Measures for Multimodal Transportation Systems

1996 ◽  
Vol 1518 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Richard H. Pratt ◽  
Timothy J. Lomax
Keyword(s):  
Land Use ◽  
Performance Measurement ◽  
Travel Time ◽  
Performance Measures ◽  
System Performance ◽  
Transportation Systems ◽  
Regional Networks ◽  
Multimodal Transportation ◽  
Decision Context ◽  
Multimodal Systems

Transportation systems analyses have been evolving as the decision context for improvement projects and programs has changed. The increased emphasis on the movement of persons and goods, and a recognition of the importance of system performance measures that address the needs and interests of the audiences for mobility information, will result in a very different set of procedures for evaluating transportation and land use infrastructure and policies. Some of the key underlying concerns of performance measurement for multimodal systems are presented. Definitions are included for congestion, mobility, and accessibility that are used to guide the development of performance measures. Travel time–based measures are seen as the most readily understandable quantities, and examples are used to show how mobility can be measured for locations, corridors, transit analyses, and regional networks.

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.

Travel Time Reliability Measures Accommodating Scheduling Preferences of Travelers

2019 ◽  
Vol 2673 (4) ◽  
pp. 708-721 ◽  
Author(s):  
Mojtaba Rajabi-Bahaabadi ◽  
Afshin Shariat-Mohaymany ◽  
Shu Yang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Travel Time Reliability ◽  
Time Data ◽  
Reliability Measures ◽  
Time Performance ◽  
Early Arrival ◽  
Travel Time Distribution ◽  
Travel Time Data ◽  
Schedule Adherence

Existing travel time reliability measures fail to accommodate scheduling preferences of travelers and cannot distinguish between the variability associated with early and late arrivals. This study introduces two new travel time reliability measures based on concepts from behavioral economics. The first proposed measure is an indicator of the width of travel time distribution. It considers scheduling preferences of travelers and can distinguish between early arrival and late arrival. The second measure determines the skewness of travel time distribution. To estimate the proposed measures, travel time is modeled by mixture models and closed-form expressions are derived for the expected values of early and late arrivals. In addition, real travel time data from a freeway segment is used to compare the proposed measures with the existing travel time reliability measures. The results suggest that, although there exist significant correlations between travel time reliability measures, travelers’ preferences have considerable effects on the travel time reliability as perceived by them. Furthermore, four measures are developed based on the notions of early and late arrivals to assess the on-time performance (schedule adherence) of transit vehicles at stop level. The results of this study show that the four measures can serve as complementary to the existing on-time performance indices.

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.

Sign in / Sign up

Export Citation Format

Share Document