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.

Evaluating Travel Time Reliability Based on Fuzzy Logic

2011 ◽  
Vol 97-98 ◽  
pp. 952-955
Author(s):  
Xiong Fei Zhang ◽  
Rui Min Li ◽  
Min Liu ◽  
Qi Xin Shi
Keyword(s):  
Fuzzy Logic ◽  
Travel Time ◽  
Time Distribution ◽  
Travel Time Reliability ◽  
Statistical Measures ◽  
Travel Time Distribution ◽  
Network Operations ◽  
Deviation Coefficient ◽  
Measure Of Performance ◽  
Stochastic Properties

Travel time reliability, as a measure of performance, is attracting more and more attention because unreliable transportation information hinders travelers’ decision making and creates difficulties for authorities to manage network operations. Since travel time reliability is closely related to the stochastic properties of the day-to-day travel time distribution, several statistical measures have been proposed, including standard deviation, coefficient of variation, buffer index, misery index and so on. Each of these measures is derived from travel time distribution but captures only one or two characteristics of travel time. In this paper, an effort is made to evaluate travel time reliability incorporating as many characteristics of travel time as possible based on fuzzy logic. The basic rules are: (1) the larger the variance is, the more unreliable the travel time is; (2) the larger the travel times of unlucky travelers are, the more unreliable the travel time is; (3) the larger the distribution skews to the left, the more unreliable the travel time is. The proposed methodology has been tested and analyzed with field data.

Research on Link Travel Time Reliability Model Based on Massive Expressway Toll Data

2014 ◽  
Vol 505-506 ◽  
pp. 719-726 ◽  
Author(s):  
Tao Wen ◽  
Chang Cheng Li ◽  
Chun Jiang Che ◽  
Lian De Zhong ◽  
Xin Xin
Keyword(s):  
Normal Distribution ◽  
Travel Time ◽  
Estimation Method ◽  
Model Parameters ◽  
Reliability Model ◽  
Travel Time Reliability ◽  
Time Data ◽  
Moment Estimation ◽  
Travel Time Data ◽  
Link Travel Time

Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

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.

scholarly journals Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Shaopeng Zhong ◽  
Lihui Zhang ◽  
Max Bushell
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Marginal Cost ◽  
Demand Uncertainty ◽  
Travel Demand ◽  
Stochastic Network ◽  
Reliability Measures ◽  
Distribution Method ◽  
Travel Time Distribution ◽  
Marginal Cost Pricing

Focusing on the first-best marginal cost pricing (MCP) in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP) model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1) the effect of stochastic perception error (SPE) on the perceived travel time distribution and the components of road toll; (2) the effect of road toll on the actual travel time distribution and its reliability measures; (3) the effect of road toll on the total network travel time distribution and its statistics; and (4) the effect of travel demand level and the value of reliability (VoR) level on the components of road toll.

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.

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