Probabilistic Forecasts of Bike-Sharing Systems for Journey Planning

International audienceWe study the problem of making forecasts about the future availability of bicycles in stations of a bike-sharing system (BSS). This is relevant in order to make recommendations guaranteeing that the probability that a user will be able to make a journey is sufficiently high. To do this we use probabilistic predictions obtained from a queuing theoretical time-inhomogeneous model of a BSS. The model is parametrized and successfully validated using historical data from the Vélib ' BSS of the City of Paris. We develop a critique of the standard root-mean-square-error (RMSE), commonly adopted in the bike-sharing research as an index of the prediction accuracy, because it does not account for the stochasticity inherent in the real system. Instead we introduce a new metric based on scoring rules. We evaluate the average score of our model against classical predictors used in the literature. We show that these are outperformed by our model for prediction horizons of up to a few hours. We also discuss that, in general, measuring the current number of available bikes is only relevant for prediction horizons of up to few hours

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

Bike Renting Data Analysis: The Case of Dublin City

Public bike renting is more and more popular in cities to incentivise a reduction in car journeys and to boost the use of green transportation alternatives. One of the challenges of this application is to effectively plan the resources usage. This paper presents some analysis of Dublin bike renting scheme based on statistics and data mining.It provides available bike patterns at the most interesting bike stations, that is, the busiest and the quietest stations. Consistency checking with new data reinforces confidence in the patterns obtained. Identifying available bike patterns helps to better address user needs such as organising the rebalancing of the bike numbers between stations in advance of demand

Temporal decomposition and semantic enrichment of mobility flows

Mobility data has increasingly grown in volume over the past decade as loc- alisation technologies for capturing mobility ows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the back end of a new generation of space-time GIS systems. This data has become increasingly important as GIS is now an essen- tial decision support platform in many domains that use mobility data, such as eet management, accessibility analysis and urban transportation planning. This thesis applies the machine learning method of probabilistic topic mod- elling to decompose and semantically enrich mobility ow data. This process annotates mobility ows with semantic meaning by fusing them with geograph- ically referenced social media data. This thesis also explores the relationship between causality and correlation, as well as the predictability of semantic decompositions obtained during a case study using a real mobility dataset

Accuracy and Uncertainty in Traffic and Transit Ridership Forecasts

Investments of public dollars on highway and transit infrastructure are influenced by the anticipated demands for highways and public transportations or traffic and transit ridership forecasts. The purpose of this study is to understand the accuracy of road traffic forecasts and transit ridership forecasts, to identify the factors that affect their accuracy, and to develop a method to estimate the uncertainty inherent in those forecasts. In addition, this research investigates the pre-pandemic decline in transit ridership across the US metro areas since 2012 and its influence on the accuracy of transit forecasts. The sample of 1,291 road projects from the United States and Europe compiled for this research shows that measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Similarly for 164 large-scale transit projects, the observed ridership was about 24.6% lower than forecasts on average. The accuracy depends on the mode, length of the project, year the forecast was produced as well as socio-economic and demographic changes from the production to observation year. In addition, we have found evidence of recent changes in transit demand to be affecting the transit ridership forecast accuracy. From 2012 to 2018, bus ridership decreased by almost 15% and rail ridership decreased by about 4% on average across the metropolitan areas in the United States. This decline is unexpected, because it coincided with the period of economic and demographic growth: indicators typically associated with rising transit ridership. We found that the advent of new mobility options in ride hailing services, bike and scooter shares as well as declining gas prices and increasing transit fares have the highest impact on ridership decline. Adjusting the ridership forecasts for these factors in a hypothetical scenario saw an improved transit ridership forecast performance. Despite the advances in modeling techniques and the availability of rich travel data over the years, expecting perfect forecasts (where observations are equal to the forecasts), may not be prudent because of its forward-facing nature. Forecasts need to convey their inherent uncertainty so that planners and policymakers can take that into account when they are making any decision about a project. The existing methods to quantify the uncertainty rely on flawed assumptions regarding input variability and interaction and are significantly resource intensive. An alternate method is one that considers the uncertainty inherent in the travel demand models themselves based on empirical evidence. In this research, I have developed a tool to quantify the uncertainty in traffic and transit ridership forecasts through a retrospective evaluation of the forecast accuracy from the two largest available databases of traffic and transit ridership forecasts. The factors associated with the accuracy and the recent decline in transit ridership lead the formulation of quantile regression as a new method to quantify the uncertainty in forecasts. Together with a consideration of decision intervals or breakpoints where a project decision might change, such ranges can be used to quantify project risk and produce better forecasts