Characterizing client usage patterns and service demand for car-sharing systems

The understanding of the mobility on urban spaces is useful for the creation of smarter and sustainable cities. However, getting data about urban mobility is challenging, since only a few companies have access to accurate and updated data, that is also privacy-sensitive. In this work, we characterize three distinct car-sharing systems which operate in Vancouver (Canada) and nearby regions, gathering data for more than one year. Our study uncovers patterns of users’ habits and demands for these services. We highlight the common characteristics and the main differences among car-sharing systems. Finally, we believe our study and data is useful for generating realistic synthetic workloads

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

Usage Trend Analysis and Forecasting for Ride Sharing: A case of Bildeleringen : An empirical approach using the car-specific data

Car-sharing is gaining a lot of popularity amongst users, as more people are finding various instances and benefits to use this service. With this development, there is increasing number of companies setting up car-sharing platforms to satisfy this growing demand. As is characteristic of highly competitive industries, the players win market share by effective planning and efficient operations. One aspect of effective planning is ensuring that the carsharing fleet of cars is suitable to the needs of the target customers. The goal of this paper is to use past data to analyse the car features that are affecting the demand of cars and propose a model to predict the future demand of cars using these features. To achieve this, we obtained the ride data from Bildeleringen, the leading car sharing operator in Bergen (Norway). We analysed all of the data tables and picked the variables that were essential to our study. After cleaning up the data, we created a new dataset that gave car level information on the car type, car features, the availability period, and the usage variable. We obtained two measures of usage from the data – time driven and kilometres driven. Based on the business model of Bildeleringen where more of the cost of usage is attributed to the driven time, we chose the time driven as the more appropriate usage measure. Also, we noticed that some cars were available on the platform for way longer than others, hence we went a step further to define the measure of usage as the kilometre driven as a ratio of the time available on the platform. Using charts, histograms, and box plots, we investigated the possible relation in the car features and the usage of these cars on first glance. We then proceeded to run a multiple linear regression on our data set. We then used 10 data prediction methods to model the car usage and tested the predictive performance of the models using cross validation. The models used belonged to the Linear regression, Ensembles, Decision tree, Bagging and Boosting. The results of the show that are the car level features that affect the demand are transmission type, wheel drive system, baby pillow availability, child seat installed, and roof box installed. Based on the Mean Squared Error comparison, we also found that the Decision tree is the best model to use for the prediction.nhhma

A Comparison of Modelling Approaches for the Long-term Estimation of Origin Destination Matrices in Bike Sharing Systems

Micro-mobility services have gained popularity in the last years, becoming a relevant part of the transportation network in a plethora of cities. This has given rise to a fruitful research area, covering from the impact and relationships of these transportation modes with preexisting ones to the different ways for estimating the demand of such services in order to guarantee the quality of service. Within this domain, docked bike sharing systems constitute an interesting surrogate for understanding the mobility of the whole city, as origin-destination matrices can be obtained straightforward from the information available at the docking stations. This work elaborates on the characterization of such origin-destination matrices, providing an essential set of insights on how to estimate their behavior in the long-term. To do so, the main non-mobility features that affect mobility are studied and used to train different machine learning algorithms to produce viable mobility patterns. The case study performed over real data captured by the bike sharing system of Bilbao (Spain) reveals that, by virtue of a properly selected set of features and the adoption of specialized modeling algorithms, reliable long-term estimations of such origin-destination matrices can be effectively achieved

Does Car Sharing Contribute to Urban Sustainability from User-Motivation Perspectives?

Funding Information: Funding: The paper was funded by Latvian Council of Science, the project ”The Impact of COVID-19 on Sustainable Consumption Behaviours and Circular Economy” (Nr. lzp-2020/2-0317). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Mobility, its current state and development perspectives in the future creates challenges with respect to sustainability, the first of which is the uncontrolled increase in greenhouse gas emissions in the last few decades, while road transport is one of the “sinners” creating long-term negative impact. The second is the dominance of car travel and car usage in the passenger transportation segment before the latest COVID-19 pandemic accelerated environmental problems. Although recent trends show new, greener patterns in consumption, there is still a relatively low share of consumers acknowledging the importance of sustainable and green preferences. This research study aims to investigate car sharing from users’ perspectives and to determine the most significant factors influencing their choice of sharing services to ensure upscaling of car sharing and, thus, contribute to urban sustainability. This research study contributes to the overall scientific discussion on car sharing and its role within urban sustainability, particularly with the following: (1) deeper investigation of car sharing and its users motivation perspectives in Latvia; (2) analyses of the most significant motivational factors for car-sharing users and aspects of sustainability; and (3) the insight into the generational differences triggering a number of car-sharing users. The existing and potential users of car sharing were surveyed in order to determine the motivational factors for its usage and attitudes towards it. Socio-demographic variables in statistical analysis were used to identify economic and environmental factors that meaningfully influence the choice of car-sharing services. The results of this study can support further development in new car-sharing business models and the value proposition for consumers in Latvia, as well as preparing policy recommendations on the promotion of sustainable transport. These findings are also useful to academics for the investigation of recent trends in car sharing during the COVID-19 pandemic.publishersversionPeer reviewe

Identifying the Leaders: Applying Diffusion of Innovation Theory to Use of a Public Bikeshare System in Vancouver, Canada

Public bike share programs are growing in popularity globally with increasing recognition of their potential and accrued benefits for mobility, health, and the environment. Any city planning to launch a program will be keenly interested in understanding who may use it, in order to enable strategic marketing that will facilitate quick uptake and adoption. We applied the Diffusion of Innovation Theory to data from a population-based telephone survey to characterize who is most likely to use a new public bike share program. The telephone survey of 901 Vancouver residents was conducted prior to the launch of Vancouver\u27s public bike share program. Results showed that a majority (n=614/901, 69.1%, 95% CI: 66.3%/72.7%) of respondents thought that public bike share was a good idea, however, only a quarter (n=217/901, 24.2%, 95% CI: 21.1%, 27.3%) said they would be either likely or very likely to use the program. Logistic regression identified characteristics associated with greater and lower likelihood of use. These characteristics were used to create an adoption curve that defines population segments anticipated to be the leaders in adopting the program. The theory was used to develop implementation recommendations to maximize program uptake including ensuring that the program has tangible advantages over driving or transit; is affordable and easy to try out; integrates with transit and car share opportunities; and appeals to social trends such as environmental responsibility. These results can assist planning and promotion in cities set to launch public bike share programs

On the potential for one-way electric vehicle car-sharing in future mobility systems

This research was carried out as part of the ESPRIT project, which was funded under grant agreement no. 653395 of the European Union’s Horizon 2020 research and innovation programme.Peer reviewedPublisher PD