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Crowdsensing-Driven Route Optimisation Algorithms for Smart Urban Mobility

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Crowdsensing-Driven Route Optimisation Algorithms for Smart Urban Mobility Crowdsensing-driven route optimisation algorithms for smart urban mobility Petar Mrazović ADVERTIMENT La consulta d’aquesta tesi queda condicionada a l’acceptació de les següents condicions d'ús: La difusió d’aquesta tesi per mitjà del repositori institucional UPCommons (http://upcommons.upc.edu/tesis) i el repositori cooperatiu TDX ( http://www.tdx.cat/ ) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d’investigació i docència. No s’autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d’un lloc aliè al servei UPCommons o TDX. No s’autoritza la presentació del seu contingut en una finestra o marc aliè a UPCommons (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. 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Crowdsensing-driven Route Optimisation Algorithms for Smart Urban Mobility PETAR MRAZOVIĆ School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm, Sweden 2018 Computer Architecture Department UPC Polytechnic University of Catalonia Barcelona, Spain 2018 KTH School of Electrical Engineering and Computer Science TRITA-EECS-AVL-2018:65 SE-164 40 Kista ISBN 978-91-7729-947-9 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till of- fentlig granskning för avläggande av doktorsexamen i informationsoch kommunikationstek- nik måndagen den 22 oktober 2018 kl. 11:00 i Sal C, Electrum, Kungliga Tekniska högskolan, Kistagången 16, Kista. © Petar Mrazović, september 2018 Tryck: Universitetsservice US AB iii Abstract Urban mobility is often considered as one of the main facilitators for greener and more sustainable urban development. However, nowadays it requires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of resources in cities. A high priority for cities around the world is to support residents’ mobility within the urban environments while at the same time reducing congestions, accidents, and pollution. However, developing a more efficient and greener (or in one word, smarter) urban mobility is one of the most difficult topics to face in large metropolitan areas. In this thesis, we approach this problem from the perspective of rapidly evolving ICT landscape which allow us to build mobility solutions without the need for large investments or sophisticated sensor technologies. In particular, we propose to leverage Mobile Crowdsensing (MCS) paradigm in which citizens use their mobile communication and/or sensing devices to collect, locally process and analyse, as well as voluntary distribute geo-referenced information. The mobility data crowdsensed from volunteer residents (e.g., events, traffic intensity, noise and air pollution, etc.) can provide valuable information about the current mobility conditions in the city, which can, with the adequate data processing algorithms, be used to route and manage people flows in urban environments. Therefore, in this thesis we combine two very promising Smart Mobility enablers – MCS and journey/route planning, and thus bring together to some extent distinct research challenges. We separate our research objectives into two parts, i.e., research stages: (1) architectural challenges in designing MCS systems and (2) algorithmic chal- lenges in MCS-driven route planning applications. We aim to demonstrate a logical research progression over time, starting from fundamentals of human-in-the-loop sens- ing systems such as MCS, to route optimisation algorithms tailored for specific MCS applications. While we mainly focus on algorithms and heuristics to solve NP-hard routing problems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures. Keywords: smart cities, smart mobility, urban mobility, mobile crowdsensing, route/journey planning, route optimisation, heuristic algorithms v Sammanfattning Urban rörlighet anses ofta vara en av de främsta möjliggörarna för en hållbar stats- utveckling. Idag skulle det dock kräva ett betydande skifte mot renare och effektivare stadstransporter vilket skulle stödja ökad social och ekonomisk koncentration av re- surser i städerna. En viktig prioritet för städer runt om i världen är att stödja medbor- garnas 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 den- na avhandling närmar vi oss problemet från det snabba utvecklingsperspektivet av IT landskapet i städer vilket möjliggör byggandet av rörlighetslösningar utan stora stora investeringar eller sofistikerad sensortenkik. I synnerhet föreslår vi utnyttjandet av den mobila rörlighetsavkännings, eng. Mobi- le Crowdsensing (MCS), paradigmen i vilken befolkningen exploaterar sin mobilkom- munikation och/eller mobilasensorer med syftet att frivilligt samla, distribuera, lo- kalt 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 vil- ka, med adekvata databehandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rörlighets- möjliggörare, 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) ar- kitektoniska 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 mänsk- ligt dirigerade rörelseavkänningssystem som MCS och ett avslut i automatiserade rut- toptimeringsalgoritmer 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 in- frastrukturförslagen. Nyckelord: smarta städer, smart rörlighet, rörlighetsavkänning, mobil publikmät- ning, ruttplanering, ruttoptimering, heuristiska algoritmer vii Resumen 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 ciudada- nos 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 efi- ciente 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 pro- blema 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 vo- luntariamente. Los datos de movilidad
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