Complaints Analysis as an Opportunity to Counteract Social Transport Exclusion in Shared Mobility Systems

Turoń, Katarzyna

doi:10.3390/smartcities5030044

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…This finding also supports the conclusion in Ref. [38] that the main areas of transportation social exclusion are system infrastructure and operation.…”

Section: Discussionsupporting

confidence: 91%

Shared Cycling Demand Prediction during COVID-19 Combined with Urban Computing and Spatiotemporal Residual Network

Cao

Wang

2022

Sustainability

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The regularity and demand predictions of shared cycling are very necessary and challenging for the management and development of urban pedestrian and bicycle traffic. The bicycle-sharing system has the problem of spatial and temporal demand fluctuations and presents a very complex nonlinear regularity. The demand for shared bicycles is affected by many factors, including time, space, weather and the situation of COVID-19. This study proposes a new bicycle-sharing demand forecasting model (USTARN) based on the impact of COVID-19, which combines urban computing and spatiotemporal attention residual network. USTARN consists of two parts. In the first part, a spatiotemporal attention residual network model is established to learn the temporal correlation and spatial correlation of shared bicycle demand. The temporal characteristic branches of each spatial small region are trained, respectively, to predict the shared bicycle demand in batches in different regions and periods according to the historical data. In order to improve the prediction accuracy of the model, the second part of the model adjusts and redistributes the prediction results of the first part by learning other information of the city, such as the severity of COVID-19, weather, temperature, wind speed and holidays. It can predict the demand for shared bicycles in different urban areas in different periods and different severities of COVID-19. This study uses the order data of shared bicycles during the period of COVID-19 in 2020 obtained from the open data platform of Shenzhen municipal government as verification, analyzes the spatiotemporal regularity of the system demand and discusses the impact of the number of newly diagnosed patients and the daily minimum temperature on the demand for shared bicycles. The results show that USTARN can fully reflect time, space, the epidemic situation, weather and temperature, and the prediction results of the impact of wind speed and other factors on the demand for shared bicycles are better than the classical methods.

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“…This finding also supports the conclusion in Ref. [38] that the main areas of transportation social exclusion are system infrastructure and operation.…”

Section: Discussionsupporting

confidence: 91%

Shared Cycling Demand Prediction during COVID-19 Combined with Urban Computing and Spatiotemporal Residual Network

Cao

Wang

2022

Sustainability

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“…Techniques from the field of growth hacking [43,44] can be helpful. Moreover, provision of additional accessories, such as helmets, is also important to increase the safety perception [45].…”

Section: Insights For Policymakersmentioning

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

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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.

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Do we all need shared E-scooters? An accessibility-centered spatial equity evaluation approach

Abouelela,

Durán-Rodas,

Antoniou

2024

Transportation Research Part A: Policy and Practice

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Complaints Analysis as an Opportunity to Counteract Social Transport Exclusion in Shared Mobility Systems

Cited by 6 publications

References 48 publications

Shared Cycling Demand Prediction during COVID-19 Combined with Urban Computing and Spatiotemporal Residual Network

Shared Cycling Demand Prediction during COVID-19 Combined with Urban Computing and Spatiotemporal Residual Network

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

Do we all need shared E-scooters? An accessibility-centered spatial equity evaluation approach

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