Stay Time Prediction for Individual Stay Behavior

Chen, Jie; Xiao, Zhu; Long, Wangchen; Bai, Jian; Havyarimana, Vincent

doi:10.1109/access.2019.2940545

Cited by 11 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers used statistical methods and Poisson and negative binomial regressions to analyze the impact of various factors on urban roadside parking maneuvering time [26]. Some researchers proposed the use of private car trajectory data to achieve fuzzy logic-based dwell behavior detection and dwell time inference [27]. Some proposed an airport curbside simulation model that calculates the optimal length of an airport curbside based on traffic characteristics, such as speed, dwell time, and parking demand [1].…”

Section: ) Traffic Behavior Analysismentioning

confidence: 99%

Analyzing the Features of Passenger Drop-Off Behavior at Airport Curbsides: A Case Study From Guangxi Province, China

Jianfeng,

Qian,

Qianqian

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Vehicles often face congestion at airport curbsides because private vehicles, taxis, and ridehailing vehicles compete for limited space while dropping off passengers. This competition may lead to blockages and long dwell times, thus worsening the congestion. To solve this issue, management strategies such as first-in-first-out queuing or fines for excessive dwell times have been suggested. However, there is a lack of reliable video data and analysis of drop-off behavior at airport curbsides. In this study, we empirically analyzed the key characteristics of passenger drop-off behavior at the Airport T2 terminal in Guangxi Province, China, which handles approximately 18 million passengers per year. First, we extracted the relevant features of both passengers and vehicles by deep learning algorithm, such as the direction of passenger movement, vehicle type, vehicle location, trunk state, and passenger drop-off time. Subsequently, we constructed new features, such as driver behavior and passenger behavioral complexity, based on the original features. We used least-squares regression and logistic regression to analyze the data. Our analysis reveals that the drop-off time of passengers primarily depends on the complexity of their behavior during the dropoff process. Additionally, we observed that specific features, such as driver behavior and vehicle type, could be employed to estimate passenger drop-off behavior. These findings have practical implications in providing valuable insights into the design and management of airport curbside areas and future strategies for connected vehicles.

show abstract

Section: ) Traffic Behavior Analysismentioning

confidence: 99%

Analyzing the Features of Passenger Drop-Off Behavior at Airport Curbsides: A Case Study From Guangxi Province, China

Jianfeng,

Qian,

Qianqian

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As a result, we set hidden units to 32 in our experiments. To avoid overfitting, a normalization term is added in loss computation as shown in Eq (12).…”

Section: Hyperparameters Settingmentioning

confidence: 99%

“…Research groups and companies analyze this data using big data and machine learning to improve people’s living standards [ 5 ]. Researchers have used data from multiple sources to improve traffic-related operations with applications in traffic congestion prediction [ 6 ], traffic flow prediction [ 7 ], traffic speed estimation [ 8 ], traffic demand prediction [ 9 ], traffic signal control [ 10 ], parking space forecasting [ 11 ], stay point detection [ 12 ], traffic accident prediction [ 13 ], accident severity analysis [ 14 ], and many others.…”

Section: Introductionmentioning

confidence: 99%

An attention-based recurrent learning model for short-term travel time prediction

2022

View full text Add to dashboard Cite

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

show abstract

Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data

Yuan,

Sho,

Eom

et al. 2024

Habitat International

View full text Add to dashboard Cite

Stay Time Prediction for Individual Stay Behavior

Cited by 11 publications

References 52 publications

Analyzing the Features of Passenger Drop-Off Behavior at Airport Curbsides: A Case Study From Guangxi Province, China

Analyzing the Features of Passenger Drop-Off Behavior at Airport Curbsides: A Case Study From Guangxi Province, China

An attention-based recurrent learning model for short-term travel time prediction

Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data

Contact Info

Product

Resources

About