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.

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.

Validation and Calibration of Freeway Reliability Methodology in the Highway Capacity Manual: Method and Case Studies

2018 ◽  
Vol 2672 (15) ◽  
pp. 93-104 ◽  
Author(s):  
Nabaruna Karmakar ◽  
Seyedbehzad Aghdashi ◽  
Nagui M. Rouphail ◽  
Billy M. Williams
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Practical Implementation ◽  
Travel Time Reliability ◽  
Time Data ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Reliability Performance ◽  
The Impact

Traffic congestion costs drivers an average of $1,200 a year in wasted fuel and time, with most travelers becoming less tolerant of unexpected delays. Substantial efforts have been made to account for the impact of non-recurring sources of congestion on travel time reliability. The 6th edition of the Highway Capacity Manual (HCM) provides a structured guidance on a step-by-step analysis to estimate reliability performance measures on freeway facilities. However, practical implementation of these methods poses its own challenges. Performing these analyses requires assimilation of data scattered in different platforms, and this assimilation is complicated further by the fact that data and data platforms differ from state to state. This paper focuses on practical calibration and validation methods of the core and reliability analyses described in the HCM. The main objective is to provide HCM users with guidance on collecting data for freeway reliability analysis as well as validating the reliability performance measures predictions of the HCM methodology. A real-world case study on three routes on Interstate 40 in the Raleigh-Durham area in North Carolina is used to describe the steps required for conducting this analysis. The travel time index (TTI) distribution, reported by the HCM models, was found to match those from probe-based travel time data closely up to the 80th percentile values. However, because of a mismatch between the actual and HCM estimated incident allocation patterns both spatially and temporally, and the fact that traffic demands in the HCM methods are by default insensitive to the occurrence of major incidents, the HCM approach tended to generate larger travel time values in the upper regions of the travel time distribution.

scholarly journals Traffic Congestion Quantification for Urban Heterogeneous Traffic Using Public Transit Buses as Probes

2018 ◽  
Vol 47 (4) ◽  
pp. 257-267
Author(s):  
Selvaraj Vasantha Kumar ◽  
Ramaswamy Sivanandan
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Public Transit ◽  
Heterogeneous Traffic ◽  
Time Data ◽  
Transit Buses ◽  
Wide Range ◽  
Acceleration And Deceleration ◽  
Bus Stops

Understanding congestion in space-time domain using performance measures is essential prior to suggesting improvement schemes to reduce congestion. With technological advances like Global Positioning System (GPS), many metropolitan planning organizations give more emphasis on travel time based performance measures to quantify congestion, than on traditional way of using volume-to-capacity (V/C) ratios. In India, often it may not be possible to use personal vehicles as probes for travel time data collection. However, the public transit buses fitted with GPS devices could be used as cheap and effective probes to estimate the congestion status of other types of vehicles in the stream. The present study is an attempt in this direction. Two bus transit routes in Chennai, India were considered as case studies in order to cover the wide range of geometric and traffic conditions on urban arterials. GPS-fitted buses on these routes were used as probes in congestion quantification. As the dwell time at bus stops is a unique characteristic of transit buses when compared to other vehicles in the stream, a methodology has been proposed to find the dwell times including acceleration and deceleration times based on the approaching and departing speeds at bus stops. Regression models were then developed to predict the Congestion Index (CI) for various types of vehicles using bus CI, weighted carriageway width and the presence or absence of signalized intersection as independent variables. The results are promising and could be considered for real-time display of congestion levels for Advanced Traveler Information System (ATIS) applications.

scholarly journals Comparative Evaluation of Technologies and Data Sources to Capture Travel Time at Section-Level on Urban Streets

2015 ◽  
Author(s):  
Srinivas S. Pulugurtha ◽  
Rahul C. Pinnamaneni ◽  
Venkata R. Duddu ◽  
R.M. Zahid Reza
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Network Characteristics ◽  
Urban Streets ◽  
Urban Street ◽  
Percentage Difference ◽  
Travel Time Data ◽  
Data Source ◽  
Street Segments ◽  
Section Level

This paper focuses on capturing section-level (a signalized intersection to the next) travel times on urban street segments using Bluetooth detectors as well as from INRIX data source and comparing it with manual and Global Positioning System (GPS) floating test car methods (test car with a trained technician and GPS unit to capture travel time between selected points) for each travel time run. Results obtained indicate that section-level travel time data captured using Bluetooth detectors on urban street segments are less accurate and not dependable when compared with GPS unit and INRIX. The role of various on-network characteristics on the percentage difference in travel time from GPS unit, INRIX, and Bluetooth detectors was also examined.

Depth solution from borehole and travel time data using three-variable hypersurface splines

2001 ◽  
Vol 46 (3) ◽  
pp. 201-211 ◽  
Author(s):  
P.F. Xu ◽  
Z.W. Yu ◽  
H.Q. Tan ◽  
J.X. Ji
Keyword(s):  
Travel Time ◽  
Time Data ◽  
Travel Time Data

Crustal structure in the Transvaal*

1956 ◽  
Vol 46 (4) ◽  
pp. 293-316
Author(s):  
P. G. Gane ◽  
A. R. Atkins ◽  
J. P. F. Sellschop ◽  
P. Seligman
Keyword(s):  
Travel Time ◽  
Crustal Structure ◽  
Time Data ◽  
Lower Velocity ◽  
Travel Time Data ◽  
The Mean

abstract Travel-time data are given at 25 km. intervals between 50 and 500 km. for traverses west, south, east, and north of Johannesburg. These derive from numerous seismograms of Witwatersrand earth tremors taken by means of a triggering technique. The only phases considered to be consistent are those mentioned below, and few signs of a change of velocity with depth were discovered. There were no great differences in the results for the various directions, and the mean results were: P 1 = + 0.24 + Δ / 6.18 sec . S 1 = + 0.37 + Δ / 3.66 sec . P n = + 7.61 + Δ / 8.27 sec . S n = + 11.4 + Δ / 4.73 sec . which give crustal depths of 35.1 and 33.3 km. from P and S data respectively. These depths include about 1.3 km. of superficial material of lower velocity.

Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data Using Attribute Fusion Based on Deep Learning

2021 ◽  
Author(s):  
Hongyu Zhou ◽  
Rui Guo ◽  
Deqiang Tao ◽  
Guojun Deng ◽  
Maokun Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Travel Time ◽  
Joint Inversion ◽  
Time Data ◽  
Travel Time Data

Analyzing distributions for travel time data collected using radio frequency identification technique in urban road networks

2018 ◽  
Vol 62 (1) ◽  
pp. 106-120 ◽  
Author(s):  
JianHua Guo ◽  
ChangGuang Li ◽  
Xiao Qin ◽  
Wei Huang ◽  
Yun Wei ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Travel Time ◽  
Radio Frequency Identification ◽  
Road Networks ◽  
Time Data ◽  
Urban Road ◽  
Travel Time Data ◽  
Identification Technique ◽  
Frequency Identification
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