Investigating the capacity of continuous household travel surveys in capturing the temporal rhythms of travel demand

El-Assi, Wafic; Morency, Catherine; Miller, Eric J.; Habib, Khandker Nurul

doi:10.1007/s11116-019-09981-x

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the logit function helped in capturing the unobserved heterogeneity caused by repeated measures from the same individuals because the same individual may fill more than one scenario. In line with recent studies (30)(31)(32), R-project was used as an analytical instrument with its lme4 package (33) to conduct GLMM-based analysis. The package lme4 and its function ''glmer'' helped implement logit function to capture heterogeneity among the repeated measures.…”

Section: Modeling Instrumentsmentioning

confidence: 99%

The Impacts of Incentives on Seat Yielding Behavior on the Bus

Baig

Zhang

Chen

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The global target to achieve inclusive public transportation includes the availability of courtesy seating for vulnerable individuals such as elderly people, people with disabilities, pregnant women, and children. The frequent usage of courtesy seats by passengers who are not vulnerable individuals is an issue pertaining to public transportation. It is important to encourage the travelers to yield seats in the presence of vulnerable individuals because the latter might get seriously injured by falling if they stay on a moving vehicle. This study explores the influence of incentives and situational factors affecting individuals’ decisions to yield seats to vulnerable people. Through an online questionnaire survey, 404 valid responses were obtained. The generalized linear mixed model (GLMM) with repeated measures was used to identify the influential factors affecting the self-reported decisions of yielding seats to vulnerable individuals. The results indicate that the situational factors (health, crowd, vulnerable passenger type, standing time after yielding seats), incentive types, and incentive amounts are significant factors affecting the decisions to yield seats. The study’s findings also indicate that travelers are more sensitive to people with disabilities and pregnant women than to elderly people and children. The practical implications, including campaigning, education interventions, and incentive-based mechanism, were suggested to encourage travelers to yield seats to vulnerable individuals.

show abstract

Section: Modeling Instrumentsmentioning

confidence: 99%

The Impacts of Incentives on Seat Yielding Behavior on the Bus

Baig

Zhang

Chen

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Although gathering OD demand information from direct-interview surveys (household and roadside) or travel diaries is a costly and time-consuming process but also vulnerable to becoming outdated and encountering sampling bias problems. Despite its limitations, valuable data can be extracted from travel surveys to estimate route and transportation mode choice models (Cascetta 1984;Bierlaire and Toint 1995;Cascetta 2009;Scheffer et al 2017;El-Assi et al 2020). Subsequently, the use of vehicular count data to estimate OD demand has become attractive with the new development of traffic detection technologies.…”

Section: Introductionmentioning

confidence: 99%

Time-dependent estimation of origin-destination matrices using partial path data and link counts

Vahidi

Shafahi

2022

Preprint

View full text Add to dashboard Cite

The precise estimation of time-varying demand matrices using traffic data is an essential step for planning, scheduling, and evaluating advanced traffic management systems (ATMS). This paper presents an innovative method (based on the least squares approach) to deal with the inherent complexities in estimating the dynamic characteristics of changing demand flow over time and considering congestion conditions. The time-dependent Origin-Destination (OD) demand matrices of the network are estimated by exploitation of the received partial paths data from an automated vehicle identification (AVI) system, and vehicle counts data from loop detectors on a subset of the links. A traffic assignment approach based on partial paths is embedded into the measurement equations of the least squares model. For all time intervals, the relation between the variable aspects of congestion (the temporal and spatial distribution of the OD traffic flows) is established by their variance-covariance matrices. The LSQR algorithm, an iterative algorithm that is logically equivalent to the conjugate gradient method, is employed for solving the proposed least squares problem. Numerical examples performed on three different approaches (only link counts data, only partial path flows data, and both of them) show that using the variance-covariance matrices is more precise for estimating time-dependent OD matrices. The Sioux Falls network is presented to examine the solution algorithm’s effectiveness and the model’s main ideas. This paper reports the features of the discussed model based on synthetic data as proof of concept that using partial path flows significantly improves the results for solving time-dependent OD matrices estimation problems.

show abstract

Time-dependent estimation of origin–destination matrices using partial path data and link counts

Vahidi,

Shafahi

2023

Transportation

View full text Add to dashboard Cite

Investigating the capacity of continuous household travel surveys in capturing the temporal rhythms of travel demand

Cited by 5 publications

References 10 publications

The Impacts of Incentives on Seat Yielding Behavior on the Bus

The Impacts of Incentives on Seat Yielding Behavior on the Bus

Time-dependent estimation of origin-destination matrices using partial path data and link counts

Time-dependent estimation of origin–destination matrices using partial path data and link counts

Contact Info

Product

Resources

About