Rental Bikes Research Articles

Visualization of explainable artificial intelligence for GeoAI

Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations. While there are different plots available to visualize Shapley values, there is a lack of suitable visualization for geospatial use cases, resulting in the loss of the geospatial context in traditional plots. This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrate its feasibility through an exemplary use case—predicting bike activity in a rental bike system. The visualizations show that visualizing Shapley values on geographic maps can provide valuable insights that are not visible in traditional plots for Shapley additive explanations. Geovisualizations are recommended for explaining machine learning models in geospatial applications or for extracting knowledge about real-world applications. Suitable visualizations for the considered use case are a proportional symbol map and a mapping of computed Voronoi values to the street network.

A predictive analytics approach for forecasting bike rental demand

The demand for rental bikes in urban areas fluctuates, leading to localized surpluses and shortages. To address this challenge, effective bike relocation strategies are essential for ensuring equitable distribution and maximizing customer satisfaction. This study aims to employ advanced machine learning techniques to forecast bike rental demand in urban areas, thereby enhancing the efficiency and accessibility of bike rental services and contributing to sustainable urban mobility. The study comprehensively analyzes various influencing factors using machine learning models, including Ordinary Least Squares regression, MLP Regression, Gradient Boosting Regression, Random Forest Regression, Polynomial Regression, and Decision Tree Regression. The primary objective is to identify the most accurate predictor by comparing key metrics such as R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient. Insights gained from this analysis aid in identifying influential variables and ensure the development of resource-efficient and adaptable models, leading to more informed decision-making for rental bike businesses. Additionally, future research directions involve the implementation of artificial intelligence technology to predict overall bike demand based on urban cities’ criteria, including the number of national and international tourists. By addressing these objectives, this study seeks to provide valuable insights and tools for rental bike businesses to optimize operations, make strategic decisions, and enhance customer experience in competitive urban markets.

Causal inference of Seoul bike sharing demand

The global surge in environmental consciousness has significantly boosted the demand for rental bikes, particularly in metropolitan areas such as Seoul. This study delves into the causal relationships affecting this demand using a dataset from Seoul’s bike-sharing system. Unlike previous research focusing predominantly on predictive analytics, this work innovatively applies multiple linear regression models to uncover causal inferences, offering insights that extend beyond mere forecasting. The challenges addressed include dealing with non-linear relationships and heteroscedasticity by employing the logarithmic transformation of rental counts. This approach not only aids in normalizing the data but also enhances the interpretability of the regression outcomes, emphasizing the changes in demand as a function of various environmental and temporal variables. Recent developments in causal inference methodologies have allowed for more robust and detailed analysis, paving the way for this study’s contribution to the field. The findings underscore the significant influence of factors such as hour of the day, humidity, and seasonal changes on bike rental volumes, which can inform policy-making and operational strategies in urban transport planning.

A comprehensive data analysis of e-bike mobility and greenhouse gas emissions in a higher education community: IPBike study of case

Sustainable mobility is a goal for several countries. This kind of mobility depends not only on personal motivation but also on government actions. Encouraging people, particularly children and younger, to raise awareness of the importance and benefits of using sustainable transport, like bicycles, is crucial to developing a sustainable society. This work presents the IPBike project, a Portuguese project applied at the Polytechnic Institute of Bragança. This project aims to promote the use of sustainable transport in the academic community through a rental bike program. This paper presents the results of the over three years of the IPBike project, as well as the user’s impressions and suggestions to improve the project. Moreover, a greenhouse gas emission reduction is estimated, comparing the displacement using bikes or only by cars. In general, the results achieved are promising to promote sustainable cities and plan the future since the bikes of the project are constantly rented and the user’s positive feedback, which makes the IPBikes a popular community asset. Moreover, the results demonstrate a bike-sharing program’s capacity to impact people’s lives, improve their health, reduce travel expenses, and impact society and the environment. Besides, according to the estimates, the IPBikes provided a reduction of 7% per year in the greenhouse gases emitted by the users, which means a significant reduction all over the project implantation.

Utilizing Machine Learning to Predict the Number of Bikes in an Area

Machine learning is a type of Artificial Intelligence that uses data to make predictions and improve the accuracy of its outcomes. In this article, the problem discussed is classified as supervised learning and the technique utilized is Logistic Regression. After a description detailing what supervised learning and logistic regression are, using a data set to develop a model which predicts the number of bikes a rental bike company should provide based on certain conditions is discussed. The accuracy of this model is also communicated and the challenges and how the final estimations were reached are covered.

A Stacking-Based Ensemble Model for Prediction of Metropolitan Bike Sharing Demand

Due to the climate crisis and the improvement of public transportation networks, countries around the world are strongly advocating the low-carbon traveling mode. Shared bike as a new business model has a positive impact on the urban environment and transportation. The ability to estimate the hourly demand for bike sharing with high accuracy is essential for metropolis to offer stable bike rental services. Presently, data mining and predictive analysis technology can be utilized to realize the forecast of the hourly demand of shared bicycles. Data used in this article include the Seoul bike rented count dataset and weather information. This paper discusses various machine learning models for rental bike demand prediction, including Linear Regression, Ridge Regression, Lasso Regression, K-Nearest Neighbor, Random Forest, Decision Tree Regression, Support Vectors Machine, and Gradient Boosting Decision Tree. Different parameter tuning methods have been applied to improve the performance of basic predictive models. In addition, the redundant and irrelevant features have been removed to improve the performance of each basic model. After evaluating the individual basic predictors, several competent basic predictors are selected to compose a stacking-based ensemble model. Experimental results show that the stacking-based ensemble model outperforms the basic predictive models in all indicators.

Variable selection for quantile autoregressive model: Bayesian methods versus classical methods

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.

Multivariate Demand Forecasting for Rental Bike Systems Based on an Unobserved Component Model

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model.

Users’ Perception and Barriers to Using Self-Driven Rental Bikes

The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications.

Predicting Bike-Sharing Demand Using Random Forest

Being able to accurately predict bike-sharing demand is important for Intelligent Transport Systems and traveler information systems. These challenges have been addressed in a number of cities worldwide. This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall. The performance of the proposed RF model is evaluated using three statistical measurements: root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R). The results show that the RF model has high predictive accuracy with an RMSE of 210 cnt/h, an MAE of 121 cnt/h, and an R of 0.90. The performance of the RF model is also compared with a linear regression model and shows superior accuracy.

Predicting Bike-Sharing Demand Using Random Forest

Being able to accurately predict bike-sharing demand is important for Intelligent Transport Systems and traveler information systems. These challenges have been addressed in a number of cities worldwide. This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall. The performance of the proposed RF model is evaluated using three statistical measurements: root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R). The results show that the RF model has high predictive accuracy with an RMSE of 210 cnt/h, an MAE of 121 cnt/h, and an R of 0.90. The performance of the RF model is also compared with a linear regression model and shows superior accuracy.

Predicting Bike-Sharing Demand Using Random Forest

Being able to accurately predict bike-sharing demand is important for Intelligent Transport Systems and traveler information systems. These challenges have been addressed in a number of cities worldwide. This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall. The performance of the proposed RF model is evaluated using three statistical measurements: root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R). The results show that the RF model has high predictive accuracy with an RMSE of 210 cnt/h, an MAE of 121 cnt/h, and an R of 0.90. The performance of the RF model is also compared with a linear regression model and shows superior accuracy.

Rebalancing Docked Bicycle Sharing System with Approximate Dynamic Programming and Reinforcement Learning

The bicycle, an active transportation mode, has received increasing attention as an alternative in urban environments worldwide. However, effectively managing the stock levels of rental bicycles at each station is challenging as demand levels vary with time, particularly when users are allowed to return bicycles at any station. There is a need for system-wide management of bicycle stock levels by transporting available bicycles from one station to another. In this study, a bicycle rebalancing model based on a Markov decision process (MDP) is developed using a real-time dynamic programming method and reinforcement learning considering dynamic system characteristics. The pickup and return demands are stochastic and continuously changing. As a result, the proposed framework suggests the best operation option every 10 min based on the realized system variables and future demands predicted by the random forest method, minimizing the expected unmet demand. Moreover, we adopt custom prioritizing strategies to reduce the number of action candidates for the operator and the computational complexity for practicality in the MDP framework. Numerical experiments demonstrate that the proposed model outperforms existing methods, such as short-term rebalancing and static lookahead policies. Among the suggested prioritizing strategies, focusing on stations with a larger error in demand prediction was found to be the most effective. Additionally, the effects of various safety buffers were examined.

Prediction of Demand for Shared Bikes using Multiple Linear Regression Model

The bike-sharing system allows people to rent a bike at one of the rental stations, use them for their journey, and return them to any other or the same station. In recent years, such systems encourage people to use bikes as a good complement to travel using modes of transport. The main purpose of this article is to predict the demand for shared bikes using multiple linear regression. Moreover, the article presents data cleaning process and data visualization for better understanding and to get useful insights from data. The results we get after visualization is that count of bike sharing is least for spring, the number of bikes increased in year 2019 and count of total rental bikes including both casual and registered increases in summer and are less in holidays. To understand which attributes needs to get dropped we used RFE (Recursive Feature Elimination) and VIF (Variance Inflation Factor) to drop columns with high p-values and to check the multicollinearity respectively. Now the model is able to predict the future demand for shared bikes in a city.

Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

Multi-armed bandit with sub-exponential rewards

We consider a general class of multi-armed bandits (MAB) problems with sub-exponential rewards. This is primarily motivated by service systems with exponential inter-arrival and service distributions. It is well-known that the celebrated Upper Confidence Bound (UCB) algorithm can achieve tight regret bound for MAB under sub-Gaussian rewards. There has been subsequent work by Bubeck et al. (2013) [4] extending this tightness result to any reward distributions with finite variance by leveraging robust mean estimators. In this paper, we present three alternative UCB based algorithms, termed UCB-Rad, UCB-Warm, and UCB-Hybrid, specifically for MAB with sub-exponential rewards. While not being the first to achieve tight regret bounds, these algorithms are conceptually simpler and provide a more explicit analysis for this problem. Moreover, we present a rental bike revenue management application and conduct numerical experiments. We find that UCB-Warm and UCB-Hybrid outperform UCB-Rad in our computational experiments.

A Prediction of Bike Flow in Bike Renting Systems with the Tensor Model and Deep Learning

Rental bikes are popular in many urban areas to help people expand their mobility. It is important to make the rental bicycle usable and available to the general public at the appropriate time and place. Inevitably, providing the city with a steady supply of rental bicycles becomes a major concern. The most important aspect is the estimation of the number of bicycles required in each bicycle sharing station at any given hour. This paper gives an examination of human mobility as indicated by bicycle renting information of the bike sharing system. In this paper, we proposed a new approach for forecasting the bike inflow and outflow from one station to another during certain time slots. Our method analyses human mobility pattern by two steps: (1) Using Tuckers tensor decomposition to create a 3D tensor to model human mobility and extract latent temporal and spatial characteristics of various stations and time slots. (2) to use a Long-Short Term Memory Neural Network to model the relationship between mobility patterns and the derived latent spatial and temporal features in order to predict bike flow between stations. The main contribution of this study that with the extracted latent characteristics through Tucker’s factorization we improve the accuracy of prediction by 16% and decrease the amount of training data that used in prediction. Also, a root mean squared error of prediction is 1,5 bikes. We compare our model with baseline models as historical average, ARMA, the feed-forward neural network, and KNN. The proposed method showed the best results

Multi-armed Bandit with Sub-exponential Rewards

We consider a general class of multi-armed bandits (MAB) problems with sub-exponential rewards. This is primarily motivated by service systems with exponential inter-arrival and service distributions. It is well-known that the celebrated Upper Confidence Bound (UCB) algorithm can achieve tight regret bound for MAB under sub-Gaussian rewards. There has been subsequent work by Bubeck et al. (2013) extending this tightness result to any reward distributions with finite variance by leveraging robust mean estimators. In this paper, we present three alternative UCB based algorithms, termed UCB-Rad, UCB-Warm, and UCB-Hybrid, specifically for MAB with sub-exponential rewards. While not being the first to achieve tight regret bounds, these algorithms are conceptually simpler and provide a more explicit analysis for this problem. Moreover, we present a rental bike revenue management application and conduct numerical experiments. We find that UCB-Warm and UCB-Hybrid outperform UCB-Rad in our computational experiments.

Seoul bike trip duration prediction using data mining techniques

Trip duration is the most fundamental measure in all modes of transportation. Hence, it is crucial to predict the trip-time precisely for the advancement of Intelligent Transport Systems and traveller information systems. To predict the trip duration, data mining techniques are employed in this study to predict the trip duration of rental bikes in Seoul Bike sharing system. The prediction is carried out with the combination of Seoul Bike data and weather data. The data used include trip duration, trip distance, pickup and dropoff latitude and longitude, temperature, precipitation, wind speed, humidity, solar radiation, snowfall, ground temperature and 1-hour average dust concentration. Feature engineering is done to extract additional features from the data. Four statistical models are used to predict the trip duration. (a) Linear regression, (b) Gradient boosting machines, (c) k nearest neighbour and (d) Random Forest (RF). Four performance metrics root mean squared error, coefficient of variance, mean absolute error and median absolute error is used to determine the efficiency of the models. In comparison with the other models, the best model RF can explain the variance of 93% in the testing set and 98% (R 2) in the training set. The outcome proves that RF is effective to be employed for the prediction of trip duration.

Motor-vehicle drivers’ behavioral response to increased bicycle traffic

Introduction: This paper investigates whether motor-vehicle driver behavior changes when there are more bicycles on the road. Method: Data on trips on a rapidly expanding public bike share scheme in Chicago are combined with speed violations captured by a network of 79 cameras. Using weekly data from July 2014 to December 2016, violations at 26 sites where there was a considerable increase in bicycle traffic are compared with a control group of 53 locations where rental bicycles are not available. Results: An increase in rental bicycle usage is statistically related to a reduction in the number of speeding violations, with an estimated elasticity of −0.04. Conclusion: The increased presence of bicyclists makes at least some motorists drive more cautiously. Practical Application: This research provides some insight into the mechanism behind the observed reduction in crash rates as bicyclists become more numerous. Some motorists moderate their speeds allowing more time to avoid collisions and a reduction in the severity of the vehicle-bicyclist collisions that still occur.