The impact of travel time variability and travelers’ risk attitudes on the values of time and reliability

2016 ◽  
Vol 93 ◽  
pp. 207-224 ◽  
Author(s):  
Mickael Beaud ◽  
Thierry Blayac ◽  
Maïté Stéphan
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Risk Attitudes ◽  
The Impact ◽  
Travel Time Variability

Uncertainty Aversion and Its Role in Travel Decision Making Under Uncertainty

2017 ◽  
Vol 8 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zheng Li
Keyword(s):  
Decision Making ◽  
Travel Time ◽  
Model Performance ◽  
Time Variability ◽  
Decision Making Under Uncertainty ◽  
Uncertainty Aversion ◽  
Choice Data ◽  
Data Set ◽  
The Impact ◽  
Travel Time Variability

Travel time variability is a random phenomenon, and within the presence of it, uncertainty is associated with decision making. When a choice is made in an uncertain situation, the probability distribution is based on the subjective judgments of a decision maker. This paper introduces a psychological perspective to the concept of travel time variability, by embedding a belief-based weighting, so as to better understand decision making under uncertainty. This research argues that a subjective probability approach accounting for degrees of belief should be addressed in order to capture the impact of travel time variability on decision making. Using a simulated choice data set, the author provides an example of modelling uncertainty aversion, and illustrate its impacts on model performance.

Road Pricing under Travel Time Variability

2019 ◽  
Author(s):  
Gege Jiang ◽  
Hong Kam LO ◽  
Zheng LIANG
Keyword(s):  
Travel Time ◽  
Road Pricing ◽  
Time Variability ◽  
Travel Time Variability

scholarly journals Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

scholarly journals Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Time Of Day ◽  
Weather Data ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Time Data ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

scholarly journals Modeling distributions of travel time variability for bus operations

2015 ◽  
Vol 50 (1) ◽  
pp. 6-24 ◽  
Author(s):  
Zhenliang Ma ◽  
Luis Ferreira ◽  
Mahmoud Mesbah ◽  
Sicong Zhu
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Variability
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