Estimation of daily bicycle traffic using machine and deep learning techniques

Miah, Md. Mintu; Hyun, Kate; Mattingly, Stephen P.; Khan, Hannan

doi:10.1007/s11116-022-10290-z

Cited by 10 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning models were applied in four selected studies and three of them were published in 2022. Note that two studies (Kwigizile et al, 2019;Miah, Hyun, Mattingly, & Khan, 2022) applied multiple machine learning models and selected the one with the best performance and Table 4 only listed the best model. Kwigizile et al (2019) compared random forest, K nearest neighbors, regression tree, neural network, and support vector machine, and found that random forest performed best in terms of RMSE (root mean squared error).…”

Section: Methods For Estimationmentioning

confidence: 99%

See 1 more Smart Citation

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Tao,

Lindsey,

Stern

et al. 2024

JTLU

View full text Add to dashboard Cite

To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.

show abstract

Section: Methods For Estimationmentioning

confidence: 99%

“…Two studies reported the relative importance of the crowdsourced variables considered in their machine learning models. Miah, Hyun, Mattingly and Khan (2022) reported the relative importance of Strava bicycle trip count in their two machine learning models.…”

Section: The Role Of Crowdsourced Variablesmentioning

confidence: 99%

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Tao,

Lindsey,

Stern

et al. 2024

JTLU

View full text Add to dashboard Cite

show abstract

“…Accurate anomaly perception and detection are prerequisites for the normal execution of railway transportation process control and operation planning (Bababeik et al, 2019). With the development of novel technologies such as machine learning, most current anomaly detection are based on data-driven deep learning methods (Sabih et al, 2022;Tang et al, 2022;Miah et al, 2023). As labelling large datasets is challenging, anomaly detection is often considered an unsupervised deep learning problem (Jerez et al, 2023;.…”

Section: Literature Reviewmentioning

confidence: 99%