A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings from the City of Austin, TX

E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous operational and safety concerns. This study analyzes e-scooter and dockless e-bike sharing system user behavior. We investigate how average trip speed change depending on the day of the week and the time of the day. We used a dataset from the city of Austin, TX from December 2018 to May 2019. Our results generally show that the trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar usage pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different usage pattern for the average speed of e-bikes and e-scooters over the hours of the day. We found that users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes. This study is a building block in this field, which serves as a first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users.Comment: Submitted to the International Journal of Sustainable Transportatio

Evaluating the Effectiveness of Bike Sharing Programs in Encouraging Sustainable Transportation in Urban Areas

Bike sharing programs have emerged as a popular solution to promote sustainable transportation in urban areas. This research abstract presents five key points that highlight the effectiveness of bike sharing programs in encouraging sustainable transportation. Firstly, these programs facilitate a modal shift by providing convenient access to bicycles, encouraging individuals to choose cycling as a sustainable transportation option and reducing reliance on private motorized vehicles, thereby decreasing carbon emissions. Secondly, bike sharing programs effectively address the last-mile problem by offering bicycles at strategic locations near transit hubs, providing a convenient and efficient mode of transportation for short-distance trips and complementing existing public transit systems. Thirdly, these programs enhance transportation accessibility by offering affordable rental options, enabling a broader range of people, including those without access to private vehicles or unable to afford their upkeep, to access transportation, thus promoting inclusivity and reducing transportation inequality. Moreover, bike sharing programs promote public health by encouraging regular cycling as a mode of transportation. This promotes physical activity and helps individuals meet recommended activity guidelines, resulting in a reduced risk of non-communicable diseases such as obesity, cardiovascular diseases, and diabetes, contributing to the overall sustainability and well-being of urban populations. Lastly, bike sharing programs generate valuable data that can inform urban transportation planning and infrastructure development. By analyzing usage patterns, trip durations, and popular routes, city planners can identify areas with high demand for cycling infrastructure, leading to the efficient allocation of resources and the optimization of urban transportation systems

multimodal choice model for e mobility scenarios

Abstract The paper focuses on the definition, calibration and testing of a simulation model that is able to represent multimodal choice behaviours for electric vehicles. Taking into account the interchange between public transport and electric private mobility, the model estimates the parking demand at the Park & Ride sites equipped with charging stations. The model is based on a data-driven approach, in which mainly Floating Car Data and open data of public transport have derived the explanatory variables. Specifically, a machine learning method (Random Forest) has been used to calibrate and test the model in the real case of the metropolitan area of Rome (Italy). We first perform a stability analysis, letting the parameters of the model vary. We then carry out a sensitivity analysis on the variables that can affect the user propensity to adopt the Park & Ride. Finally, we profile and test an incentive policy to boost the choice of Park & Ride. Results suggest that the model succeeds in simulating Park & Ride by electric vehicles and, therefore, it can be extremely valuable for planning financial support to the multimodal travel choice and forecasting vehicle-to-grid scenarios

Modeling bike counts in a bike-sharing system considering the effect of weather conditions

The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.Comment: Published in Case Studies on Transport Policy (Volume 7, Issue 2, June 2019, Pages 261-268

Predicting bicycle arrivals in a Bicycle Sharing System network: A data science driven approach grounded in Zero-Inflated Regression

The adoption of bicycle sharing systems (BSS) is growing in order to improve the way people move around cities, but also to stimulate the development of a more sustainable urban mobility. For the proper functioning of a BSS, it is important to have bicycles permanently available at the stations for users to start their trips, so the literature has undertaken efforts, from the perspective of the service operator, to improve the process of redistribution of bicycles and thus ensure their availability at the different stations. Since the guarantee of available bicycles cannot be assured, this work proposes to develop, from the cyclist's perspective, a proof of concept on the feasibility of informing the user about the possibility of starting a trip in a pre-defined time interval. The main contributions of this work are: (i) the ability to predict how many bicycles will arrive at a given station is a feasible improvement for BSS, (ii) the models developed through the Zero-Inflated Regression approach are a path that can be explored to improve prediction and (iii) unprecedented methodological contribution to the literature on BSS focusing on the end-user's decision power about whether or not it will soon be possible to start a trip.A adoção de sistemas de bicicletas partilhadas (BSS) vem crescendo com o objetivo de melhorar a forma como as pessoas se deslocam pelas cidades, mas também para estimular o desenvolvimento de uma mobilidade urbana mais sustentável. Para o bom funcionamento de um BSS é importante que haja bicicletas permanentemente disponíveis nas estações para os utilizadores iniciarem as suas viagens, pelo que a literatura tem empreendido esforços, sob a ótica do operador do serviço, para melhorar o processo de redistribuição das bicicletas e assim garantir a sua disponibilidade nas diferentes estações. Como a garantia de bicicletas disponíveis não pode ser assegurada, este trabalho propõe-se desenvolver, sob a ótica do ciclista, uma prova de conceito sobre a viabilidade de informar o utilizador acerca da possibilidade de iniciar uma viagem num intervalo de tempo pré-definido. As principais contribuições deste trabalho são: (i) a capacidade de previsão de quantas bicicletas chegarão a uma determinada estação é uma melhoria viável para os BSS, (ii) os modelos desenvolvidos através da aproximação Zero-Inflated Regression são um caminho que pode ser explorado para melhorar a previsão e (iii) contributo metodológico inédito à literatura sobre os BSS com foco no poder decisório do utilizador final sobre se será, ou não, possível iniciar uma viagem em breve

Passively generated big data for micro-mobility: state-of-the-art and future research directions

The sharp rise in popularity of micro-mobility poses significant challenges in terms of ensuring its safety, addressing its social impacts, mitigating its environmental effects, and designing its systems. Meanwhile, micro-mobility is characterised by its richness in passively generated big data that has considerable potential to address the challenges. Despite an increase in recent literature utilising passively generated micro-mobility data, knowledge and findings are fragmented, limiting the value of the data collected. To fill this gap, this article provides a timely review of how micro-mobility research and practice have exploited passively generated big data and its applications to address major challenges of micro-mobility. Despite its clear advantages in coverage, resolution, and the removal of human errors, passively generated big data needs to be handled with consideration of bias, inaccuracies, and privacy concerns. The paper also highlights areas requiring further research and provides new insights for safe, efficient, sustainable, and equitable micro-mobility

Modeling bike availability in a bike-sharing system using machine learning

This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.</p