Urban commuters’ valuation of travel time reliability based on stated preference survey: A case study of Beijing

2017 ◽  
Vol 95 ◽  
pp. 372-380 ◽  
Author(s):  
Weibin Kou ◽  
Xumei Chen ◽  
Lei Yu ◽  
Yi Qi ◽  
Ying Wang
Keyword(s):  
Travel Time ◽  
Stated Preference ◽  
Travel Time Reliability ◽  
Stated Preference Survey

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Commuter Bicyclist Route Choice: Analysis Using a Stated Preference Survey

2003 ◽  
Vol 1828 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Monique A. Stinson ◽  
Chandra R. Bhat
Keyword(s):  
Travel Time ◽  
Stated Preference ◽  
Empirical Models ◽  
Policy Implications ◽  
Facility Planning ◽  
Quality Factors ◽  
Stated Preference Survey ◽  
Factors Affecting ◽  
Choice Analysis ◽  
Using Data

The importance of factors affecting commuter bicyclists’ route choices was evaluated. Both route-level (e.g., travel time) and link-level (e.g., pavement quality) factors are examined. Empirical models are estimated using data from a stated preference survey conducted via the Internet. The models indicate that, for commuter bicyclists, travel time is the most important factor in choosing a route. Presence of a bicycle facility (especially a bike lane or separate path), the level of automobile traffic, pavement or riding surface quality, and presence of a bicycle facility on a bridge are also very important determinants. Furthermore, there are policy implications of these results for bicycle facility planning.

Quantifying the Impact of the COVID-19 Pandemic on Passenger Vehicle Drivers’ Willingness to Pay for Travel Time Savings and Reliability

2021 ◽  
pp. 036119812110388
Author(s):  
Tristan Cherry ◽  
Mark Fowler ◽  
Claire Goldhammer ◽  
Jeong Yun Kweun ◽  
Thomas Sherman ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Travel Time ◽  
Travel Behavior ◽  
Local Governments ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Travel Time Reliability ◽  
Travel Time Savings ◽  
Time Savings ◽  
The Impact

The COVID-19 pandemic has fundamentally disrupted travel behavior and consumer preferences. To slow the spread of the virus, public health officials and state and local governments issued stay-at-home orders and, among other actions, closed nonessential businesses and educational facilities. The resulting recessionary effects have been particularly acute for U.S. toll roads, with an observed year-over-year decline in traffic and revenue of 50% to 90% in April and May 2020. These disruptions have also led to changes in the types of trip that travelers make and their frequency, their choice of travel mode, and their willingness to pay tolls for travel time savings and travel time reliability. This paper describes the results of travel behavior research conducted on behalf of the Virginia Department of Transportation before and during the COVID-19 pandemic in the National Capital Region of Washington, D.C., Maryland, and Northern Virginia. The research included a stated preference survey to estimate travelers’ willingness to pay for travel time savings and travel time reliability, to support forecasts of traffic and revenue for existing and proposed toll corridors. The survey collected data between December 2019 and June 2020. A comparison of the data collected before and during the pandemic shows widespread changes in travel behavior and a reduction in willingness to pay for travel time savings and travel time reliability across all traveler types, particularly for drivers making trips to or from work. These findings have significant implications for the return of travelers to toll corridors in the region and future forecasts of traffic and revenue.

scholarly journals Metro Train Operation Plan Analysis Based on Station Travel Time Reliability

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ruihua Xu ◽  
Fangsheng Wang ◽  
Feng Zhou
Keyword(s):  
Travel Time ◽  
Clustering Algorithm ◽  
Algorithm Design ◽  
Service Level ◽  
Travel Time Reliability ◽  
Plan Analysis ◽  
Train Operation ◽  
Metro Systems ◽  
Transportation Organization

The train operation plan plays an essential role in metro systems and directly affects transportation organization efficiency and passenger service level. In metro systems, passengers have paid more attention to the travel time reliability (TTR), reflecting the reliability of metro operation management. This article proposes an analysis method of train operation plan based on TTR in the station dimension. First, an automated fare collection (AFC) data-driven framework is established to calculate the station travel time reliability (STTR) and analyze the train operation plan at different periods. The framework structure consists of four steps: AFC data preprocessing, STTR calculation and assignment, clustering algorithm design based on SOM neural network, and train operation plan analysis and optimization. Second, the proposed method is applied to the Beijing metro network as a case study. Several promising results are analyzed that allow the optimization of the existing train operation plan. Our research shows that STTR is a good supplement for the existing metro operation assignment studies, which can help analyze and optimize the train operation plan effectively. This study is also applicable to other metro networks with AFC systems.

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