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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 Doctoral Thesis in Information and Communication Technology 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 framläg- ges till offentlig granskning för avläggande av doktorsexamen i informations- och kommunikationsteknik 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 re- quires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of re- sources 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 land- scape 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) para- digm in which citizens use their mobile communication and/or sensing de- vices 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 pro- vide 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 challenges in MCS-driven route planning applica- tions. We aim to demonstrate a logical research progression over time, start- ing from fundamentals of human-in-the-loop sensing 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 prob- lems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures. Keywords: smart cities, smart mobility, urban mobility, mobile crowd- sensing, route/journey planning, route optimisation, heuristic algorithms v Sammanfattning 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 stä- der runt om i världen är att stödja medborgarnas rörlighet inom stadsmiljöer medan samtidigt minska trafikstockningar, olyckor och föroreningar. Att ut- veckla en effektivare och grönare (eller med ett ord; smartare) stadsrörlighet är en av de svåraste problemen att bemöta för stora metropoler. I denna av- handling 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. Mobile Crowdsensing (MCS), paradigmen i vilken befolkningen exploa- terar sin mobilkommunikation och/eller mobilasensorer med syftet att frivil- ligt samla, distribuera, lokalt processera och analysera geospecifik informa- tion. 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 data- behandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rör- lighetsmöjliggörare, eng. Smart Mobility Enablers, nämligen MCS och re- se/ruttplanering. Vi kan därmed till viss utsträckning sammanföra forsknings- utmaningar 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. Nyckelord: smarta städer, smart rörlighet, rörlighetsavkänning, mobil publikmätning, ruttplanering, ruttoptimering, heuristiska algoritmer vii Resumen La movilidad urbana es considerada una de las principales desencadenan- tes 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 con- taminación. 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 georrefe- renciada, 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 propor- cionar 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 mo- vilidad inteligente: MCS y la planificación de viajes/rutas, uniendo en cierta medida los distintos desafíos de investigación. Hemos dividido nuestros objeti- vos de investigación en dos etapas: (1) Desafíos arquitectónicos en el diseño de sistemas MCS y (2) Desafíos algorítmicos en la planificación de rutas aprove- chando la información del MCS. Nuestro objetivo es demostrar una progresión lógica de la investigación a lo largo del tiempo, comenzando desde los fun- damentos 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. Palabras clave: Ciudades inteligentes, movilidad inteligente, mobile crowd- sensing, planificación de viajes/rutas, optimización de rutas, algoritmos heu- rísticos ix Acknowledgements This work has been supported by the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission under the FPA 2012-0030. I am deeply thankful to the following people, without the help and support of whom, I would not have managed to complete this thesis: My supervisors Mihhail Matskin and Josep-Lluís Larriba-Pey for their guidance, constant feedback and encouragement throughout this work. EMJD-DC programme coordinators, Vladimir Vlassov and Leandro Navarro, for their guidance through this joint doctorate journey. My co-authors, Bahaeddin Eravci, Burcu Kolbay, Elif Eser, Hakan Ferhatos- manoğlu, and Nima Dokoohaki. Working with them has been a great pleasure and invaluable learning experience. My friends and colleagues, Edward Tjörnhammar and Pedro Herruzo, for trans- lating the summary of this thesis in Swedish and Spanish. My friends, colleagues, and fellow PhD students at UPC and KTH, especially Ariel Duarte, Arnau Prat-Pérez, Burcu Kolbay, Edward Tjörnhammar, Erol
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