CASSI: Designing a Simulation Environment for Vehicle Relocation in Carsharing

Simulations offer an efficient solution to comprehensive represent operational services and to track the impact of changing systematic factors and business constraints. Carsharing services provide users with mobility services on demand. Although research has introduced strategies to optimize efforts to set up and operate such a system, they lack reusable and flexible simulation environments. For instance, carsharing research applies simulations to better understand and solve the problem of balancing vehicle supply and demand, which operators need to solve to prevent operational inefficiencies and ensure customer satisfaction. Hence, one cannot feasibly test new balancing mechanisms directly in a real-world environment. As for now, researchers have implemented simulations from scratch, which results in high development efforts and a limited ability to compare results. In this paper, we address this gap by designing a versatile carsharing simulation tool that researchers can easily use and adapt. The tool simplifies the process of modeling a carsharing system and developing operation strategies. Furthermore, we propose various system performance measures to increase the developed solutions’ comparability

Estimación del impacto ambiental y social de los nuevos servicios de movilidad

El transporte es fuente de numerosas externalidades negativas, como los accidentes de tráfico, la congestión en las zonas urbanas y la falta de calidad del aire. El transporte también es un sector que contribuye sustancialmente a la crisis climática con más del 16% de las emisiones globales de gases de efecto invernadero como resultado de las actividades de transporte. Muchos creen que la introducción de nuevos servicios de movilidad podría ayudar a reducir esas externalidades. Sin embargo, con cada introducción de un nuevo servicio de movilidad podemos observar factores que podrían contribuir negativamente a la sostenibilidad del sistema de transporte: una cadena de cambios de comportamiento causados por la introducción de posibilidades completamente nuevas. El objetivo de esta tesis es investigar cómo los nuevos servicios de movilidad, habilitados por la electrificación, la conectividad y la automatización, podrían impactar en las externalidades causadas por el transporte. En particular, el objetivo es desarrollar y validar un marco de modelado capaz de capturar la complejidad del sistema de transporte y aplicarlo para evaluar el impacto potencial de los vehículos automatizados.Transport is a source of numerous negative externalities, such as road accidents, congestion in urban areas and lacking air quality. Transport is also a sector substantially contributing to climate crisis with more than 16% of global greenhouse gas emissions being a result of transport activities. Many believe that the introduction of new mobility services could help reduce those externalities. However, with each introduction of a new mobility service we can observe factors that could negatively contribute to the sustainability of the transport system – a chain of behavioural changes caused by introduction of entirely new possibilities. The aim of this thesis is to investigate how the new mobility services, enabled by electrification, connectivity and automation, could impact the externalities caused by transport. In particular the objective is to develop and validate a modelling framework able to capture the complexity of the transport system and to apply it to assess the potential impact of automated vehicles.This work was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement N035297. Moreover, this research has been partially funded by the Spanish Ministry of Science and Innovation through the project: AUTONOMOUS – InnovAtive Urban and Transport planning tOols for the implementation of New mObility systeMs based On aUtonomouS driving”, 2020-2023, ERDF (EU) (PID2019-110355RB-I00)

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems

Electric Free Floating Car Sharing systems offer a convenient and environmentally-friendly way to move in cities. However, their design and deployment is not a trivial task. In this work, we focus on fleet charging management, aiming at maximizing the number of trips of users, while minimizing the cost of relocating cars for charging. In particular, we compare two different car charging infrastructures: a centralised charging hub in a highly dynamic zone of the city, and a distributed set of charging poles around the most-used zones, where users can eventually contribute to plug cars. For this scope, we build a data-driven mobility demand model and a simulator that we use to study the performance and costs of fleet charging management. As a case study, we first consider the city of Turin. Then, we extend the results to three other cities (Milan, New York City and Vancouver). Results show that, given enough charging capacity, a distributed infrastructure is superior in terms of both satisfied trips and charging relocation cost. Additionally, with the contribution of users, the relocation cost might decrease even further

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

Electric Revolution and Free Floating Car Sharing: A Data Driven Methodology for System Design

Nowadays, the increase in traffic congestions, land consumption, and pollution emission due to private car ownership makes the rise of shared mobility possible. One of the most spread implementations of shared mobility is Free Floating Car Sharing (FFCS). It is a car rental model where the users can pick and release the car everywhere within an operative area. The customers can reserve (and return) the vehicle using a web-based application. With just a simple tap, the users can unlock and lock the smart vehicle. Usually, the provider bills the users only for the time spend driving, with time-minute based fares. All the other costs, like petrol, insurance, and maintenance, are in charge of the provider. This service’s flexibility fills the urban mobility gap between public transport’s relative cheapness and the comfort and capillarity of private car ownership. Indeed, FFCS allows people to travel and commute faster than the standard public bus but avoiding all the fixed and variable costs related to private car ownership. Given the recent electric cars market increase and all the benefits those vehicles carry, replacing FFCS fleet with electric-powered cars may still improve urban centers’ quality of life. The setup and management of an electric FFCS require ingenuity to minimize the users’ discomfort due to car plugging procedures. In my thesis, I present a methodology to address, in different cases of studies, all the challenges related to the conversion of combustion engine cars to electric vehicles in FFCS. In particular, my research’s main driver is to propose a methodology to build a profitable and technically sustainable system setup, able to guarantee a flexible and appealing mobility service to an increasing customer audience. In the first part of my thesis, I describe the software I developed to scrape from the web real combustion engine FFCS, from two providers: car2go and Enjoy. The car2go data collection lasted from December 2016 to January 2018, collecting more than 27 million users’ bookings spread in 23 cities. The Enjoy data collection phase started in May 2017 and lasted until June 2019, recording about 6 million bookings in 6 cities. Then, I characterize both datasets in Turin, one of the cities in which both FFCS providers work. I detect the outliers, filter them out from the dataset, and extract geotemporal users’ travel patterns. After that, I compare the car2go customer’s pattern with the one-way and two-way car-sharing system. The results show how users prefer more flexible services like FFCS or one-way car sharing. Once the data are consolidated, I develop: A methodology to place a charging station in a city by looking at users’ patterns. System policies to manage the fleet when the vehicle state of charge may not guarantee a trip. Via an event-based trace-driven simulator able to replicate the recorded trips in an electrified scenario evaluating each configuration’s feasibility. Via accurate simulation in Berlin, Milan, Turin, and Vancouver, I study different electric FFCS setup. By placing the charging station in the most frequented areas, by offering an incentive to the users to plug the car when the battery state of charge is below a safety threshold, and balancing the spread of poles, it is possible to obtain a sustainable system covering with charging station only the 8-10 % of zones. To reduce the number of charging stations to have a sustainable electric FFCS, I compare several optimization algorithms. The results show how a Genetic Algorithm can find a better solution to shrink the minimum amount of resources to sustain the same mobility demand. After that, I move my attention to the users’ rentals’ demand predictability. The main goal is to understand how different open-data sources could impact the recorded FFCS users’ rental. Initially, I compare several time-series forecasts to predict the users’ demand in the short and medium-term. Random Forest regression produces better accuracy and results in terms of interpretability. Then I correlate the socio-economics features characterizing each city neighborhood to FFCS demand, and again, the Random Forest regression outperforms other algorithms. Finally, I question the system scalability figuring out several scenarios having increasing demand. I use a model to synthesize users’ demand by looking only at the geospatial users’ rentals. By varying the electric FFCS setup and simulating the new scenario, I point out how a linear increase in the demand intensity requires a fleet sublinear increase. Finally, I project those considerations in euros, proofing how electric FFCS has room for economic growth