A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies.

Simulating the Impacts of Hybrid Campus and Autonomous Electric Vehicles as GHG Mitigation Strategies: A Case Study for a Mid-Size Canadian Post-Secondary School

This paper presents how a post-secondary institution like University of British Columbia’s Okanagan (UBCO) campus can reduce its carbon footprint and be aligned with the government’s target through promoting virtual campus and autonomous electric vehicles (AEVs). Different virtual campus scenarios are developed: online classes only, working-from-home only, and a hybrid of both. In the case of AEVs, alternative penetration rates for levels 2 and 5 are considered. A total of 50 scenarios are tested using a sub-area transport simulation model for UBCO, which is extracted from the regional travel demand forecasting model. The results suggest that a 40% AEV penetration rate coupled with fully in-person classes reduces GHG by ~36% compared to the 2018-level, which will help UBCO to achieve their 2030 emission reduction target and be aligned with the provincial target. The 50% AEV and 10% hybrid virtual campus reduces emissions by ~48%, which is aligned with the 2040 provincial target. A fully virtual campus will help to reach the 2050 provincial target by reducing GHG by ~76%. The results further demonstrate that level 5 AEVs produce lesser emissions than level 2 at a lower AEV penetration rate for the fully in-person campus scenario. At higher penetration rates, level 5 performs better only if it is coupled with 10% of students, faculties and staffs attending virtual campus scenario.

Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia

This study investigated whether indices for socioeconomic, demographic and urban form characteristics can reflect the overall effect of each category in a demand forecasting model. Regression equations were developed for trip generation of the land use of long day care centres (LDCC) in the metropolitan region of Hobart, Australia, to estimate the morning peak hourly private car trip generation of the centres. The independent variables for the model were functions of socioeconomic, demographic and urban form related indices, while the dependent variable was private car trip generation per number of staff or children. Findings show that using indices for socioeconomic, demographic and urban form characteristics enhances overall model performance, while the models based on the commonly used method for estimating trip generation present acceptable results in just some specific sites. The use of socioeconomic, demographic and urban form indices can reflect differences in these characteristics across suburbs when estimating trip generation.