Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

Ceccato, Riccardo; Chicco, Andrea; Diana, Marco

doi:10.1080/03081060.2020.1868084

Cited by 14 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparison of the cross-validation accuracy of the logit and the random forest models show that the latter performs only slightly better, which is also observed in Ceccato et al [29]. Hence, the presence of non-linear interactions is very low or nil, sup-porting the underlying linear assumptions of the binary logit model.…”

Section: Insights For Modellerssupporting

confidence: 66%

“…On the other hand, based on an exploration of mode choice between car-sharing system and traditional modes, Ceccato et al [29] conclude that a binary logit model results in a more reliable prediction than a random forest classifier. Nevertheless, the significant variables in both models are found to be same.…”

Section: Existing Comparisons Between Logit Models and Random Forest ...mentioning

confidence: 99%

See 1 more Smart Citation

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

et al. 2023

View full text Add to dashboard Cite

The implementation of bike-sharing systems (BSSs) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSSs based on stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety-related aspects on the mode shift, through which behavioural insights are derived. For example, car users are found to be twice as sensitive to the cost of BSSs than to that of car. Similarly, they are highly sensitive to BSS travel time. Based on the behavioural findings, policy measures are suggested under the following categories: (i) finance, (ii) regulation, (iii) infrastructure, (iv) campaigns and (v) customer targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach, as the latter is slowly gaining prominence in the field of transport. For the comparison, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur when using mathematically more powerful models, such as the random forest classifier.

show abstract

Section: Insights For Modellerssupporting

confidence: 66%

Section: Existing Comparisons Between Logit Models and Random Forest ...mentioning

confidence: 99%

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The difference in spatio-temporal travel characteristics of the elderly and young adults has been reported (e.g., Szeto et al, 2017;Shao et al, 2019). Apart from the individual perspective, households with more than one member have a lower propensity to switch toward car-sharing than people living alone or without relatives (Ceccato et al, 2021), whereas the presence of children and the addition of a car increase the propensity to transition from transit to car (Fatmi and Habib, 2016).…”

Section: Socio-economic Statusmentioning

confidence: 98%

Impacts of the walking environment on mode and departure time shifts in response to travel time change: Case study in the multi-layered Hong Kong metropolis

Chan

Chen

et al. 2022

Travel Behaviour and Society

View full text Add to dashboard Cite

“…At the same time, they also present some drawbacks with respect to traditional econometric models, such as their limited interpretability [17,18], the delivery of elasticities that are incongruent with the existing behavioural literature [19] or the difficulty to consistently adapt ML algorithms to discrete choice data [20]. These limitations have been addressed in the literature by comparing different ML methodologies [21][22][23][24] or comparing the performance and variable importance of ML algorithms with different logit models [18,25,26].…”

Section: Machine Leaning and Transport Modellingmentioning

confidence: 99%

Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling

Vega-Gonzalo

Christidis

2022

Sustainability

View full text Add to dashboard Cite

The increasing use of new data sources and machine learning models in transport modelling raises concerns with regards to potentially unfair model-based decisions that rely on gender, age, ethnicity, nationality, income, education or other socio-economic and demographic data. We demonstrate the impact of such algorithmic bias and explore the best practices to address it using three different representative supervised learning models of varying levels of complexity. We also analyse how the different kinds of data (survey data vs. big data) could be associated with different levels of bias. The methodology we propose detects the model’s bias and implements measures to mitigate it. Specifically, three bias mitigation algorithms are implemented, one at each stage of the model development pipeline—before the classifier is trained (pre-processing), when training the classifier (in-processing) and after the classification (post-processing). As these debiasing techniques have an inevitable impact on the accuracy of predicting the behaviour of individuals, the comparison of different types of models and algorithms allows us to determine which techniques provide the best balance between bias mitigation and accuracy loss for each case. This approach improves model transparency and provides an objective assessment of model fairness. The results reveal that mode choice models are indeed affected by algorithmic bias, and it is proven that the implementation of off-the-shelf mitigation techniques allows us to achieve fairer classification models.

show abstract

Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

Cited by 14 publications

References 37 publications

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

Can Bike-Sharing Reduce Car Use in Alexandroupolis? An Exploration through the Comparison of Discrete Choice and Machine Learning Models

Impacts of the walking environment on mode and departure time shifts in response to travel time change: Case study in the multi-layered Hong Kong metropolis

Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling

Contact Info

Product

Resources

About