Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration

Rebalancing techniques for station-based bike sharing systems

With bike-sharing systems that utilize fixed rent and drop off stations becoming popular in cities and metropolitan areas worldwide, the issue of station fill balance becomes apparent. It is important for the user experience and the organization's bottom line that bikes are available at the stations where they are needed and that stations do not become too crowded and thus prevent easy returns. There is not, however, a clear solution of how to perform this rebalancing. Considerations include how to determine the stations that most need to be rebalanced, how frequently to do this rebalancing in the system, and how many resources to expend doing it. Methodologies answering some of these questions have been proposed, but many do not provide all of the answers necessary to fully implement a real-world solution. Additionally, there is no benchmarking tool to fairly compare these rebalancing approaches on a given system. This thesis proposes exactly this kind of tool in the form of a station-based bike-sharing system simulator. The simulator is modular and provides several parameters to allow the comparison of different systems, historical data, workloads, and rebalancing strategies. To demonstrate its capabilities, experiments were run comparing the effects of individual parameter changes and various combinations of parameter configurations on various metrics, including gross revenue and lost revenue from missed demand. Analysis of these experimental results gives not only a look into the simulation's uses as a comparative tool, but also provides information on alternatives to common predictive rebalancing strategies

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

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.Comment: 21 pages, 10 figure

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality