Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Simulating communication in a service-oriented architecture for V2V networks

A framework based on the concept of service-oriented architectures (SOA) to Support the assessment of vehicular ad-hoc networks (VANET) is herein presented. Concepts related to SOA, as well as technologies that allow real-time data acquisition and dissemination within urban environments, and simulation tools to aid the simulation of VANET were preliminarily Studied. A two-layered architecture wits specified oil the basis of the requirements for our simulation framework resulting in the specification of a multi-agent system formed of vehicle entities that are able to communicate and interact with each other and with their surrounding environment as well. A prototypical application was implemented, which Was used to demonstrate the feasibility of the approach presented through experimental results

A Case Study on Vestibular Sensations in Driving Simulators

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequat

Development and Performance Evaluation of Urban Mobility Applications and Services

L'abstract è presente nell'allegato / the abstract is in the attachmen

The effects of urbanization on the avian gut microbiome

The gut microbiome influences and is influenced by the host, and can affect the host organism by contributing to health, development and immunity. Similarly, the host can influence this community; it’s makeup can vary with host species, locality, diet, social stressors, and environmental stressors. Some of these environmental stressors have arisen due to human-induced rapid environmental change, like urbanization. The physiology and behaviors of organisms that are able to persist in urban environments are often different from their non-urban congeners. Nutrition, development, and immunity—all of which are affected by the gut microbiome—are important factors that can determine survival in urban environments. Ecologists are therefore asking new questions about how an urban environment shapes gut microbial communities, and how the numerous services gut fauna provide affect host success in an urban context. My dissertation research demonstrated that urbanization changes the bacterial communities of birds as well as provided correlational and experimental evidence for the biotic and abiotic traits driving these changes. Urban birds differed from rural ones by multiple measures. I also found evidence that noise pollution explains some variation in alpha diversity among urban and rural birds. Building upon this finding, I experimentally showed that the gut microbiome changes with exposure to noise, as does food intake and plasma corticosterone. However, contrary to my hypothesis, food intake and corticosterone were not the mediating factors between noise and the gut microbiome. All of this work was accomplished using noninvasive cloacal swabs to measure the gut microbiome, which my dissertation research found are reflective of the large intestine and capture individual variation in the microbiome. The work that comprised my dissertation will impact methods decisions in future microbiome studies in both free-living and captive birds. It will also contribute to the way we look at the relationships between host environment, host, and the gut microbiome, as well as influence how we think about urban ecology as a whole. Altogether, my dissertation research accomplished my goal to work in an emerging field at the interface of urban and microbial ecology

Driving cycle tracking device big data storing and management

Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing

Evaluation of content dissemination strategies in urban vehicular networks

The main drivers for the continuous development of Vehicularad-hoc Networks (VANETs) are safety applications and services. However, in recent years, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g., videos, ads, sensing and touristic information) dissemination. However, there is a lack of real studies considering content dissemination strategies to understand when and to whom the content should be disseminated using real vehicular traces gathered from real vehicular networks. This work presents a realistic study of strategies for dissemination of non-urgent contente with the main goal of improving contente delivery as well as minimizing network congestion and resource usage. First, we perform an exhaustive network characterization. Then, several content strategies are specified and evaluated in different scenarios (city center and parking lot). All the obtained results show that there are two content distribution strategies that clearly set themselves apart due to their superior performance: Local Rarest Bundle First and Local Rarest Generation First.info:eu-repo/semantics/publishedVersio

On the Integration of Unmanned Aerial Vehicles into Public Airspace

Unmanned Aerial Vehicles will soon be integrated in the airspace and start serving us in various capacities such as package delivery, surveillance, search and rescue missions, inspection of infrastructure, precision agriculture, and cinematography. In this thesis, motivated by the challenges this new era brings about, we design a layered architecture called Internet of Drones (IoD). In this architecture, we propose a structure for the traffic in the airspace as well as the interaction between the components of our system such as unmanned aerial vehicles and service providers. We envision the minimal features that need to be implemented in various layers of the architecture, both on the Unmanned Aerial Vehicle (UAV)'s side and on the service providers' side. We compare and contrast various approaches in three existing networks, namely the Internet, the cellular network, and the air traffic control network and discuss how they relate to IoD. As a tool to aid in enabling integration of drones in the airspace, we create a traffic flow model. This model will assign velocities to drones according to the traffic conditions in a stable way as well as help to study the formation of congestion in the airspace. We take the novel problem posed by the 3D nature of UAV flights as opposed to the 2D nature of road vehicles movements and create a fitting traffic flow model. In this model, instead of structuring our model in terms of roads and lanes as is customary for ground vehicles, we structure it in terms of channels, density and capacities. The congestion is formulated as the perceived density given the capacity and the velocity of vehicles will be set accordingly. This view removes the need for a lane changing model and its complexity which we believe should be abstracted away even for the ground vehicles as it is not fundamentally related to the longitudinal movements of vehicles. Our model uses a scalar capacity parameter and can exhibit both passing and blocking behaviors. Furthermore, our model can be solved analytically in the blocking regime and piece-wise analytically solved when in the passing regime. Finally, it is not possible to integrate UAVs into the airspace without some mechanism for coordination or in other words scheduling. We define a new scheduling problem in this regard that we call Vehicle Scheduling Problem (VSP). We prove NP-hardness for all the commonly used objective functions in the context of Job Shop Scheduling Problem (JSP). Then for the number of missed deadlines as our objective function, we give a Mixed Integer Programming (MIP) formulation of VSP. We design a heuristic algorithm and compare the quality of the schedules created for small instances with the exact solution to the MIP instance. For larger instances, these comparisons are made with a baseline algorithm

Implementation and experimental evaluation of Cooperative Awareness Basic Service for V2X Communications

A key aspect of Vehicle-to-Everything (V2X) communication is the concept of cooperative awareness, wherein the periodic exchange of status information allows vehicles to become aware of their surroundings for increased traffic safety and efficiency. This project aimed to implement the Cooperative Awareness (CA) basic service through the development of a low-cost, open-source On-board Unit (OBU)/Roadside Unit (RSU) that periodically broadcasts Cooperative Awareness Messages (CAM) using the 5.9 GHz band. Its proper operation and interoperability were verified by testing it with a commercial V2X device. This project also aimed to evaluate the effectiveness of the CA basic service through the development of an IEEE 802.11p-based V2X system simulator. The simulations were executed with varying vehicle traffic load (by changing the vehicle speed and the number of lanes) and CAM transmit frequency. The performance was then assessed by analyzing the Packet Reception Ratio (PRR), position error and Neighborhood Awareness Ratio (NAR) metrics. The presence of more vehicles in the slow speed and high lane count scenarios caused higher packet losses due to increased interference and collision probability, leading to low PRR and NAR values. Despite losing more CAMs, the slow speed scenarios had lower position errors since the displacement of vehicles was small. When the CAM transmit frequency was increased, the PRR decreased due to packet collisions. However, the position error was kept low as it benefited from the more frequent CAM transmissions and local database updates. Increasing the transmit frequency also increased the NAR, at least until a certain frequency threshold, beyond which the NAR started to worsen due to the dominant effect of interference in high message traffic situations