4,698 research outputs found
Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version
Enhancing integrated transport planning: A spatial multi-criteria analysis approach to the MyCiTi integrated rapid transit system, South Africa
Since the birth of the automobile in 1886, its popularity amongst people has risen dramatically owing to the freedom, comfort, speed, safety and unique designs offered by this mode of transport. 2014 saw approximately 71.15 million units of new vehicle sales globally, showing that private car usage is still on the rise. Rapid degradation of the environment and slumped economic growth can be attributed to the automobilecentric transport system. Raised environmental and social awareness has driven campaigns to promote greener modes of transport instead, such as public and nonmotorised transportation. This has seen the introduction of BRT systems in South African cities however; fully integrated transport systems are yet to be achieved. Thus there is a dire need for a design support tool that is adequately capable of processing built environment characteristics in the development of a BRT feeder network that is fully appreciative of the influence of NMT and the urban fabric, and is thus appropriate to the needs of the community it is trying to serve. This study comprised the application of two Spatial Multi-Criteria based methodologies in which a list of built environment characteristics and public transport demand formed the inputs for the analyses. The analysis produced a composite suitability map for each approach, in which each pixel represented the appropriateness of having a BRT feeder route located in that respective pixel. Routes between O-D pairs identified were solved by carrying out a least cost path assessment based on the mean impedance values along the existing road network. The routes developed were compared to the MyCiTi feeder bus routes using Key Performance Indicators established in this research to determine whether this study was successful in producing an enhanced BRT feeder route planning tool. Apart from one route, the set of feeder routes developed for each approach were exactly the same with the second method producing lower average impedance values per kilometre thus it was deemed stronger. When compared to the MyCiTi feeder routes, similar operational efficiencies were achieved with respect to average travel time, coverage and directness. However; the study methodologies provided a greater level of NMT planning inclusion and consideration of environmental factors. Furthermore; it achieved this in a systematic and transparent manner, providing immensely powerful benefits for transportation planners in the public sector. This study was successful in demonstrating that SMCA combined with the Network Analyst tool in ArcGIS has the ability to enhance the quality and appropriateness of BRT feeder routes, whilst achieving acceptable operational efficiencies. The results could further be improved by incorporating more data on local NMT trends and behaviour. Furthermore; this tool can be applied to solving pedestrian, bicycle and other public transport routing problems
MOBILITY MANAGEMENT IN EUROPEAN PROJECTS. LESSONS LEARNED FOR ROMANIA
The purpose of this research is to study three initiatives of promoting mobility management (DELTA, EPOMM, SEE-MMS), in the context of European Union policies, and to present solutions that could be used in the practice of urban mobility management in Romania.mobility management, sustainable development
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
New Perspectives on Modelling and Control for Next Generation Intelligent Transport Systems
This PhD thesis contains 3 major application areas all within an Intelligent Transportation
System context.
The first problem we discuss considers models that make beneficial use of the large
amounts of data generated in the context of traffic systems. We use a Markov chain
model to do this, where important data can be taken into account in an aggregate form.
The Markovian model is simple and allows for fast computation, even on low end computers,
while at the same time allowing meaningful insight into a variety of traffic system
related issues. This allows us to both model and enable the control of aggregate, macroscopic
features of traffic networks. We then discuss three application areas for this model:
the modelling of congestion, emissions, and the dissipation of energy in electric vehicles.
The second problem we discuss is the control of pollution emissions in
eets of hybrid
vehicles. We consider parallel hybrids that have two power units, an internal combustion
engine and an electric motor. We propose a scheme in which we can in
uence the mix
of the two engines in each car based on simple broadcast signals from a central infrastructure.
The infrastructure monitors pollution levels and can thus make the vehicles
react to its changes. This leads to a context aware system that can be used to avoid pollution
peaks, yet does not restrict drivers unnecessarily. In this context we also discuss
technical constraints that have to be taken into account in the design of traffic control
algorithms that are of a microscopic nature, i.e. they affect the operation of individual
vehicles. We also investigate ideas on decentralised trading of emissions. The goal here
is to allocate the rights to pollute fairly among the
eet's vehicles.
Lastly we discuss the usage of decentralised stochastic assignment strategies in traffic
applications. Systems are considered in which reservation schemes can not reliably be
provided or enforced and there is a signifficant delay between decisions and their effect. In
particular, our approach facilitates taking into account the feedback induced into traffic
systems by providing forecasts to large groups of users. This feedback can invalidate the
predictions if not modelled carefully. At the same time our proposed strategies are simple
rules that are easy to follow, easy to accept, and significantly improve the performance
of the systems under study. We apply this approach to three application areas, the assignment
of electric vehicles to charging stations, the assignment of vehicles to parking
facilities, and the assignment of customers to bike sharing stations.
All discussed approaches are analysed using mathematical tools and validated through
extensive simulations
Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms
This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm
Championing and promoting innovation in UK megaprojects
In this article we explore the role of innovation champions and the ways innovation is championed and promoted in five selected UK megaprojects. To date, this research area is surprisingly under-explored in the megaproject management literature. The study is based on thirty interviews with innovation champions to capture their own perceptions and narratives on the ways innovation is stimulated and promoted in megaprojects, combined with relevant textual materials. Innovation was commonly defined as a step change or best practice that creates value that could be financial, environmental, societal etc. The data demonstrate the role of CEOs and their collaboration with innovation champions and academic partners in introducing innovation into the vocabulary of megaprojects, developing and formalising innovation strategy, and transferring experiences across megaprojects. The data show the ways different communication channels are used to promote innovations by champions such as forums, portals, platforms, campaigns, events etc
A Study of the Static Bicycle Reposition Problem with a Single Vehicle
The Bicycle Sharing System (BSS), a public service system operated by the government or a private company, provides the convenient use of a bicycle as a temporary method of transportation. More specifically, this system allows people to rent a bike from one location, use it for a short time period and then return it to either to the same or a different location for an inexpensive fee. With the development of IT technology in the 1990s, it became possible to balance the bicycle inventory among the various destinations. In fact, a critical aspect to maintaining a satisfactory BSS is effectively rebalancing bicycle inventory across the various stations. In this research, we focus on the static bicycle repositioning problem with a single vehicle which is abstracted from the operation issue in the bicycle sharing system. The mathematical model for the static bicycle reposition problem had been created and several variations had been analyzed. This research starts to solve the problem from a very restrictive and constrained model and relaxes the constraints step by step to approach the real world case scenario. Several realistic assumptions have been considered in our research, such as a limited working time horizon, multiple visit limitation for the same station, multiple trips used for the vehicle, etc. In this research, we use the variable neighborhood search heuristic algorithm as the basic structure to find the solution for the static bicycle reposition problem. The numeric results indicate that our algorithms can provide good quality result within short solving time. By solving such a problem well, in comparison to benchmark algorithms, this research provides a starting place for dynamic bicycle repositioning and multiple vehicle repositioning
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