Crowdsensing-driven route optimisation algorithms for smart urban mobility

Urban rörlighet anses ofta vara en av de främsta möjliggörarna för en hållbar statsutveckling. Idag skulle det dock kräva ett betydande skifte mot renare och effektivare stadstransporter vilket skulle stödja ökad social och ekonomisk koncentration av resurser i städerna. En viktig prioritet för städer runt om i världen är att stödja medborgarnas rörlighet inom stadsmiljöer medan samtidigt minska trafikstockningar, olyckor och föroreningar. Att utveckla en effektivare och grönare (eller med ett ord; smartare) stadsrörlighet är en av de svåraste problemen att bemöta för stora metropoler. I denna avhandling närmar vi oss problemet från det snabba utvecklingsperspektivet av ITlandskapet i städer vilket möjliggör byggandet av rörlighetslösningar utan stora stora investeringar eller sofistikerad sensortenkik. I synnerhet föreslår vi utnyttjandet av den mobila rörlighetsavkännings, eng. Mobile Crowdsensing (MCS), paradigmen i vilken befolkningen exploaterar sin mobilkommunikation och/eller mobilasensorer med syftet att frivilligt samla, distribuera, lokalt processera och analysera geospecifik information. Rörlighetavkänningssdata (t.ex. händelser, trafikintensitet, buller och luftföroreningar etc.) inhämtad från frivilliga i befolkningen kan ge värdefull information om aktuella rörelsesförhållanden i stad vilka, med adekvata databehandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rörlighetsmöjliggörare, eng. Smart Mobility Enablers, nämligen MCS och rese/ruttplanering. Vi kan därmed till viss utsträckning sammanföra forskningsutmaningar från dessa två delar. Vi väljer att separera våra forskningsmål i två delar, dvs forskningssteg: (1) arkitektoniska utmaningar vid design av MCS-system och (2) algoritmiska utmaningar för tillämpningar av MCS-driven ruttplanering. Vi ämnar att visa en logisk forskningsprogression över tiden, med avstamp i mänskligt dirigerade rörelseavkänningssystem som MCS och ett avslut i automatiserade ruttoptimeringsalgoritmer skräddarsydda för specifika MCS-applikationer. Även om vi förlitar oss på heuristiska lösningar och algoritmer för NP-svåra ruttproblem förlitar vi oss på äkta applikationer med syftet att visa på fördelarna med algoritm- och infrastrukturförslagen.La movilidad urbana es considerada una de las principales desencadenantes de un desarrollo urbano sostenible. Sin embargo, hoy en día se requiere una transición hacia un transporte urbano más limpio y más eficiente que soporte una concentración de recursos sociales y económicos cada vez mayor en las ciudades. Una de las principales prioridades para las ciudades de todo el mundo es facilitar la movilidad de los ciudadanos dentro de los entornos urbanos, al mismo tiempo que se reduce la congestión, los accidentes y la contaminación. Sin embargo, desarrollar una movilidad urbana más eficiente y más verde (o en una palabra, más inteligente) es uno de los temas más difíciles de afrontar para las grandes áreas metropolitanas. En esta tesis, abordamos este problema desde la perspectiva de un panorama TIC en rápida evolución que nos permite construir movilidad sin la necesidad de grandes inversiones ni sofisticadas tecnologías de sensores. En particular, proponemos aprovechar el paradigma Mobile Crowdsensing (MCS) en el que los ciudadanos utilizan sus teléfonos móviles y dispositivos, para nosotros recopilar, procesar y analizar localmente información georreferenciada, distribuida voluntariamente. Los datos de movilidad recopilados de ciudadanos que voluntariamente quieren compartirlos (por ejemplo, eventos, intensidad del tráfico, ruido y contaminación del aire, etc.) pueden proporcionar información valiosa sobre las condiciones de movilidad actuales en la ciudad, que con el algoritmo de procesamiento de datos adecuado, pueden utilizarse para enrutar y gestionar el flujo de gente en entornos urbanos. Por lo tanto, en esta tesis combinamos dos prometedoras fuentes de movilidad inteligente: MCS y la planificación de viajes/rutas, uniendo en cierta medida los distintos desafíos de investigación. Hemos dividido nuestros objetivos de investigación en dos etapas: (1) Desafíos arquitectónicos en el diseño de sistemas MCS y (2) Desafíos algorítmicos en la planificación de rutas aprovechando la información del MCS. Nuestro objetivo es demostrar una progresión lógica de la investigación a lo largo del tiempo, comenzando desde los fundamentos de los sistemas de detección centrados en personas, como el MCS, hasta los algoritmos de optimización de rutas diseñados específicamente para la aplicación de estos. Si bien nos centramos en algoritmos y heurísticas para resolver problemas de enrutamiento de clase NP-hard, utilizamos ejemplos de aplicaciones en el mundo real para mostrar las ventajas de los algoritmos e infraestructuras propuestas

Optimizing Vaccine Supply Chains with Drones in Less-Developed Regions: Multimodal Vaccine Distribution in Vanuatu

In recent years, many less-developed countries (LDCs) have been exploring new opportunities provided by drones, such as the capability to deliver items with minimal infrastructure, fast speed, and relatively low cost, especially for high value-added products such as lifesaving medical products and vaccines. This dissertation optimizes the delivery network and operations for routine childhood vaccines in LDCs. It analyzes two important problems using mathematical programming, with an application in the South Pacific nation of Vanuatu. The first problem is to optimize the nation-wide multi-modal vaccine supply chain with drones to deliver vaccines from the national depot to all health zones in an LDC. The second problem is to optimize vaccine delivery using drones within a single health zone while considering the synchronization of drone deliveries with health worker outreach trips to remote clinics. Both problems consider a cold chain time limit to ensure vaccine viability. The two research problems together provide a holistic solution at the strategic and operational levels for the vaccine supply chain network in LDCs. Results from the first problem show that drones can reduce cost and delivery time simultaneously by replacing expensive and/or slow modes. The use of large drones is shown to save up to 60% of the delivery cost and the use of small drones is shown to save up to 43% of the delivery cost. The research highlights the tradeoff between delivery cost and service, with tighter cold chain limits providing faster delivery to health zones at the expense of added cost. Results from the second problem show that adding drones to delivery plans can save up to 40% of the delivery cost and improve the service time simultaneously by resupplying vaccines when the cold chain and payload limit of health workers are reached. This research contributes to both literature and practice. It develops innovative methodologies to model drone paths with relay stations and to optimize synchronized multi-stop drone trips with health worker trips. The models are tested with real-world data for an island nation (Vanuatu), which provides data for a geographic setting new to the literature on drone delivery and vaccine distribution

Airline schedule punctuality management

Airline schedule punctuality is a complex problem and one of the major concerns of the airline top management. Flight schedule disturbances may occur as delays and/or cancellations. There are many internal and external reasons for delays. These delays may propagate in the aircraft cycles and cause a large schedule disturbance. This may influences passenger satisfaction and airline resources. The objective of this research is to formulate a systematic approach for schedule punctuality which supports management decision making. The punctuality management system is structured to combine all schedule punctuality components, input and output variables. Five models are incorporated in this system. The first model is the disturbance model which generates random delays based on an estimated Lognormal delay distribution function. The delay analysis is carried out from a one year sample of delay statistics in which general, original , reactionary and other delay types are classified. The second model is the recovery model which incorporates the disturbance model with management strategies to determine delay propagation. A PC based simulation model (SKDMOD) is developed as a prototype which integrates disturbance and recovery models using SIMSCRIPT 11.5. 18 management strategies are simulated covering ground times (30, 40 and 50 minutes), maximum delay times to assign spare aircraft (1, 2, 3, 4, 5, and 6 hours) and spare aircraft using part of the domestic network of Saudi Arabia. The third model is the passengers' attitude model which determines the delay impact functions and the maximum passenger revenue loss based on 262 responses from a passenger interview survey. The fourth model is the revenue model which estimates the passengers' revenue loss. The fifth model is the cost model which estimates the extra cost resulting from implementation of the management strategies. All strategies are evaluated to determine the optimum based on profit and profit margin. OPTIM is the optimization program developed to find the optimum strategy(ies). This approach provides a guidelines for the management of punctuality. It integrates all the tools developed in a decision support system framework

Network Constrained Wind Integration: An Optimal Cost Approach

Planning electricity supply is important because power demand continues to increase while there is a concomitant desire to increase reliance on renewable sources. Extant research pays particular attention to highly variable, low-carbon energy sources such as wind and small-scale hydroelectric power. Models generally employ only a simple load levelling technique, ensuring that generation meets demand in every period. The current research considers the power transmission system as well as load levelling. A network model is developed to simulate the integration of highly variable non-dispatchable power into an electrical grid that relies on traditional generation sources, while remaining within the network’s operating constraints. The model minimizes a quadratic cost function over two periods of 336 hours, with periods representing low (summer) and high (winter) demand, subject to various linear constraints. The model is numerically solved using Matlab and GAMS software environments. Results indicate that, even for a grid heavily dependent on hydroelectricity, the addition of wind power can create difficulties, with system costs increasing with wind penetration, sometimes significantly.Electric networks, optimal power flow, wind power, intermittent sources

The commuter rail circulator network design problem: formulation, solution methods, and applications

Commuter rail is increasingly popular as a means to introduce rail transportation to metropolitan transportation systems. The long-term benefits of commuter rail include the addition of capacity to the transportation system, providing a quality commute alternative, and shifting land use toward transit-oriented development patterns. The success of a commuter rail system depends upon cultivating a ridership base upon which to expand and improve the system. Cultivating this ridership is dependent upon offering a quality transportation option to commuters. Characteristics of commuter rail systems in the United States present challenges to offering quality service that must be overcome. Commuter rail has been implemented only on existing rail right-of-way (ROW) and infrastructure (depending upon condition) in the United States. Existing rail ROW does not often coincide with current commercial and residential demand centers and necessitates the use of a circulator system to expand the service boundary of commuter rail to reach these demand centers. The commuter rail circulator network design problem (CRCNDP) addresses a particular aspect of the commuter rail trip, seeking to improve the performance of the entire system through accurately modeling the portion of the trip from rail station to the final destination. This final leg includes both the trip on the circulator vehicle and the walking trip from the circulator stop to the final destination. This dissertation seeks to provide an innovative mathematical programming formulation and solution methodology for the CRCNDP and apply this method to a case study.Civil, Architectural, and Environmental Engineerin