Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times

In ridepooling systems with electric fleets, charging is a complex decision-making process. Most electric vehicle (EV) taxi services require drivers to make egoistic decisions, leading to decentralized ad-hoc charging strategies. The current state of the mobility system is often lacking or not shared between vehicles, making it impossible to make a system-optimal decision. Most existing approaches do not combine time, location and duration into a comprehensive control algorithm or are unsuitable for real-time operation. We therefore present a real-time predictive charging method for ridepooling services with a single operator, called Idle Time Exploitation (ITX), which predicts the periods where vehicles are idle and exploits these periods to harvest energy. It relies on Graph Convolutional Networks and a linear assignment algorithm to devise an optimal pairing of vehicles and charging stations, in pursuance of maximizing the exploited idle time. We evaluated our approach through extensive simulation studies on real-world datasets from New York City. The results demonstrate that ITX outperforms all baseline methods by at least 5% (equivalent to $70,000 for a 6,000 vehicle operation) per week in terms of a monetary reward function which was modeled to replicate the profitability of a real-world ridepooling system. Moreover, ITX can reduce delays by at least 4.68% in comparison with baseline methods and generally increase passenger comfort by facilitating a better spread of customers across the fleet. Our results also demonstrate that ITX enables vehicles to harvest energy during the day, stabilizing battery levels and increasing resilience to unexpected surges in demand. Lastly, compared to the best-performing baseline strategy, peak loads are reduced by 17.39% which benefits grid operators and paves the way for more sustainable use of the electrical grid

<scp>ReSurveyEurope</scp>: A database of resurveyed vegetation plots in Europe

AbstractAimsWe introduce ReSurveyEurope — a new data source of resurveyed vegetation plots in Europe, compiled by a collaborative network of vegetation scientists. We describe the scope of this initiative, provide an overview of currently available data, governance, data contribution rules, and accessibility. In addition, we outline further steps, including potential research questions.ResultsReSurveyEurope includes resurveyed vegetation plots from all habitats. Version 1.0 of ReSurveyEurope contains 283,135 observations (i.e., individual surveys of each plot) from 79,190 plots sampled in 449 independent resurvey projects. Of these, 62,139 (78%) are permanent plots, that is, marked in situ, or located with GPS, which allow for high spatial accuracy in resurvey. The remaining 17,051 (22%) plots are from studies in which plots from the initial survey could not be exactly relocated. Four data sets, which together account for 28,470 (36%) plots, provide only presence/absence information on plant species, while the remaining 50,720 (64%) plots contain abundance information (e.g., percentage cover or cover–abundance classes such as variants of the Braun‐Blanquet scale). The oldest plots were sampled in 1911 in the Swiss Alps, while most plots were sampled between 1950 and 2020.ConclusionsReSurveyEurope is a new resource to address a wide range of research questions on fine‐scale changes in European vegetation. The initiative is devoted to an inclusive and transparent governance and data usage approach, based on slightly adapted rules of the well‐established European Vegetation Archive (EVA). ReSurveyEurope data are ready for use, and proposals for analyses of the data set can be submitted at any time to the coordinators. Still, further data contributions are highly welcome.</jats:sec

Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques

Public road authorities and private mobility service providers need information on and derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area state (occupancy, in- and out-flux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real-time data sources. Based on a case study in a real-life scenario in the city of Arnhem, a Neural Network-based approach outperforms a Random Forrest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naïve, seasonal random walk model. Although the performance degrades with increasing the prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naïve model. Furthermore, it is shown that predicting the in- and out-flux is a far more difficult task (i.e. performance gains of 30%), which needs more training data, not based exclusively on occupancy rate. However, the performance of predicting in- and out-flux is less sensitive for the prediction horizon. In addition, it is shown that real-time information of current occupancy rate is the independent variable with the highest contribution to the performance, although time, traffic flow and weather variables also deliver a significant contribution. During real-time deployment, the model performs 3 times better than the naïve model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers

Predicting parking occupancy via machine learning in the web of things

The Web of Things (WoT) enables information gathered by sensors deployed in urban environments to be easily shared utilizing open Web standards and semantic technologies, creating easier integration with other Web-based information, towards advanced knowledge. Besides WoT, an essential aspect of understanding dynamic urban systems is artificial intelligence (AI). Via AI, data produced byWoT-enabled sensory observations can be analyzed and transformed into meaningful information, which describes and predicts current and future situations in time and space. This paper examines the impact of WoT and AI in smart cities, considering a real-world problem, the one of predicting parking availability. Traffic cameras are used as WoT sensors, together with weather forecasting Web services. Machine learning (ML) is employed for AI analysis, using predictive models based on neural networks and random forests. The performance of the ML models for the prediction of parking occupancy is better than the state of the art work in the problem under study, scoring an MSE of 7.18 at a time horizon of 60 minutes.This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy