Models and algorithms for the optimal design of bus routes in public transportation systems

In this thesis we study models and algorithms for the optimal design of bus routes in urban public transportation systems. The problem known as TNDP (Transit Network Design Problem) consists in determining the number and itinerary of public transportation lines and their corresponding frequencies, in terms of a given infrastructure of streets and stops. The solutions should satisfy a given origin-destination demand and should take into account the interests of users and operators and a given set of physical, policy and budgetary constraints. We propose an explicit mixed integer linear programming formulation which incorporates the waiting time and the existence of multiple lines in the behavior of the passengers.Then, we discuss the impact in the structure of the model of adding transfer, infrastructure and bus capacity constraints. We apply the model (using a standard solver) to very small test cases as well as to a real one, related to a small-sized city comprising 13 bus lines. In order to deal with cases of larger sizes, we propose a greedy constructive algorithm that produces a set of routes that are convenient for both users and operators, taking into account constraints related to transfers. By using a real test case, we show that the proposed algorithm improves results from the state of the art.As a further extension, we represent the existence of the conflicting objectives of users and operators using a multi-objective combinatorial optimization model for the TNDP. This new model is solved by a metaheuristic that exploits the multi-objective nature of the problem in order to solve it eficiently. By using a benchmark test case and a real one, we show that the proposed algorithm improves results from the state of the art and produces solutions with characteristics comparable to the real one. Objective values of both constructive and metaheuristic algorithms are compared with values corresponding to reference solutions; for the first one we compare against optimal solutions obtained with the mathematical formulation, while for the second one we compare with the solution operating the public transportation system of the city corresponding to the real test case. Finally we discuss the relationships between the diferent contributions of this thesis and we comment several issues related to the application of the proposed methodologies to real cases. We also give some opinions and recommendations concerning future developments in this research field.En esta tesis se estudian modelos y algoritmos para el diseño óptimo de recorridos de buses en sistemas de transporte público urbano colectivo. El problema conocido como TNDP (Transit Network Design Problem) consiste en determinar el número y el itinerario de líneas de transporte público y sus correspondientes frecuencias, en términos de una infraestructura dada de calles y paradas. Las soluciones deben satisfacer una demanda origen-destino dada y deben tener en cuenta los intereses de los usuarios y de los operadores y un conjunto dado de restricciones físicas, políticas y de presupuesto. Se propone una formulación explícita de programación lineal entera mixta, que incorpora el tiempo de espera y la existencia de múltiples líneas en el comportamiento de los pasajeros. Seguidamente se discute el impacto en la estructura del modelo, al agregar restricciones de transbordos y de capacidad de la infraestructura y de los buses. El modelo se aplica (usando un solver estándar) a casos de prueba muy pequeños, así como a uno real relativo a una ciudad pequeña que consta de 13 líneas de buses. Con el propósito de atacar casos de mayor tamaño, se propone un algoritmo constructivo ávido que produce un conjunto de recorridos que son convenientes tanto para los usuarios como para los operadores, teniendo en cuenta restricciones de transbordos. Utilizando un caso de prueba real, se muestra que el algoritmo propuesto mejora resultados del estado del arte. Como una extensión del algoritmo constructivo, se representa la existencia de los objetivos en conflicto de usuarios y operadores usando un modelo de optimización combinatoria multi-objetivo para el TNDP.Este nuevo modelo se resuelve con una metaheurística que explota la naturaleza multi-objetivo del problema para resolverlo eficientemente. Utilizando un caso de prueba de referencia existente en la literatura y uno real, se muestra que el algoritmo propuesto mejora resultados del estado del arte y produce soluciones de características comparables a las del sistema real. Los valores objetivo del algoritmo constructivo y de la metaheurística se comparan con valores correspondientes a soluciones de referencia; en el primer caso se compara contra soluciones óptimas obtenidas con la formulación matemática, mientras que para el segundo se compara contra la solución que opera el sistema de transporte público de la ciudad correspondiente al caso de prueba real. Finalmente se discuten las relaciones entre las diferentes contribuciones de esta tesis y se comentan varias cuestiones relacionadas a la aplicación de las metodologías propuestas a casos reales. También se formulan algunas opiniones y recomendaciones en relación a futuros desarrollos de éste tópico de investigación

Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots

In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for real-world imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers

Quiet paths for people : developing routing analysis and Web GIS application

Altistuminen saasteille saattaa vähentää merkittävästi aktiivisten liikkumismuotojen, kuten kävelyn ja pyöräilyn terveyshyötyjä. Yksi liikenteestä johtuvista saasteista on melu, joka voi aiheuttaa terveyshaittoja, kuten kohonnutta verenpainetta ja stressiä. Aikaisemmissa tutkimuksissa ja selvityksissä melulle altistumista on arvioutu yleensä kotipaikan suhteen ja liikkumisen aikana tapahtuva altistus on jäänyt vähemmälle huomiolle. Koska liikkumisen aikainen (dynaaminen) melualtistus saattaa muodostaa merkittävän oan kaupunkilaisten päivittäisestä kokonaismelualtistuksesta, tarvitaan kehittyneempiä menetelmiä dynaamisen melualtistuksen arvioimiseen ja vähentämiseen. Tässä tutkielmassa kehitin kävelyn reititysmenetelmän ja sovelluksen, jolla voi 1) etsiä lyhimmän reitin, 2) mallintaa kävelyn aikaisen melualtistuksen ja 3) löytää vaihtoehtoisia, hiljaisempia reittejä. Sovellus hyödyntää OpenStreetMap-tieverkostoaineistoa ja mallinnettua aineistoa tieliikenteen tyypillisistä päiväajan melutasoista. Reitinetsintä perustuu kehittämääni melukustannusfunktioon ja alhaisimman kustannuksen reititysanalyysiin. Melukustannukset lasketaan sovelluksessa lukuisilla eri meluherkkyyskertoimilla, minkä ansiosta sovellus löytää useita vaihtoehtoisia (hiljaisempia) reittejä. Jotta eri reittien meluisuutta (melualtistuksia) voidaan vertailla, kehitin sarjan melualtistusindeksejä. Tapaustutkimuksessa tutkin Helsingistä tehtävien työmatkojen aikaisia melualtistuksia; selvitin rekistereihin perustuvien työmatkojen mukaiset joukkoliikennereitit ja tutkin reittien kävelyosuuksien aikaisia melualtistuksia reitittämällä kävelyreitit uudestaan kehittämälläni reitityssovelluksella. Lisäksi tutkin hiljaisempien reittivaihtoehtojen mahdollistamia vähennyksiä melualtistuksissa tapaustutkimuksessa mallinnetuilla kävelyreiteillä. Tapaustutkimuksen tulokset indikoivat, että tyypilliset dynaamiset melualtistukset vaihtelevat huomattavasti eri asuinpaikkojen välillä. Toisaalta merkittävä osa melulle altistumisesta on mahdollista välttää hiljaisemmilla reittivaihtoehdoilla; tilanteesta riippuen, hiljaisemmat reitit tarjoavat keskimäärin 12–57 % vähennyksen altistuksessa yli 65 dB melutasoille ja 1.6–9.6 dB keskimääräisen vähennyksen reittien keskimääräisessä melutasossa. Altistuksen mahdolliseen vähennykseen näyttäisivät vaikuttavan ainakin 1) melualtistuksen suuruus lyhimmällä (ts. verrokki) reitillä, 2) lyhimmän reitin pituus, eli etäisyys lähtö- ja kohdepisteen välillä reititysgraafissa ja 3) hiljaisemman reitin pituus lyhimpään reittiin verrattuna. Julkaisin hiljaisten kävelyreittien reitityssovelluksen avoimena web-rajapintapalveluna (API - Application Programming Interface) ja kehitin hiljaisten kävelyreittien reittioppaan mobiilioptimoituna web-karttasovelluksena. Kaikki tutkielmassa kehitetyt menetelmät ja lähdekoodit ovat avoimesti saatavilla GitHub-palvelussa. Yksilöiden ja kaupunkisuunnittelijoiden tietoutta dynaamisesta altistuksesta melulle (ja muille saasteille) tulisi lisätä kehittämällä altistusten arviointiin ja vähentämiseen kehittyneempiä analyyseja ja sovelluksia. Tässä tutkielmassa kehitetty web-karttasovellus havainnollistaa hiljaisten reittien reititysmenetelmän toimivuutta tosielämän tilanteissa ja voi näin ollen auttaa jalankulkijoita löytämään hiljaisempia, ja siten terveellisempiä, reittivaihtoehtoja. Kun ympäristöllisiin altistuksiin perustuvaa reitinetsintää kehitetään pidemmälle, tulisi pyrkiä huomioimaan useampia erillisiä altistuksia samanaikaisesti ja siten reitittämään yleisesti ottaen terveellisempiä reittejä.It is likely that journey-time exposure to pollutants limit the positive health effects of active transport modes (e.g. walking and cycling). One of the pollutants caused by vehicular traffic is traffic noise, which is likely to cause various negative health effects such as increased stress levels and blood pressure. In prior studies, individuals’ exposure to community noise has usually been assessed only with respect to home location, as required by national and international policies. However, these static exposure assessments most likely ignore a substantial share of individuals’ total daily noise exposure that occurs while they are on the move. Hence, new methods are needed for both assessing and reducing journey-time exposure to traffic noise as well as to other pollutants. In this study, I developed a multifunctional routing application for 1) finding shortest paths, 2) assessing dynamic exposure to noise on the paths and 3) finding alternative, quieter paths for walking. The application uses street network data from OpenStreetMap and modeled traffic noise data of typical daytime traffic noise levels. The underlying least cost path (LCP) analysis employs a custom-designed environmental impedance function for noise and a set of (various) noise sensitivity coefficients. I defined a set of indices for quantifying and comparing dynamic (i.e. journey-time) exposure to high noise levels. I applied the developed routing application in a case study of pedestrians’ dynamic exposure to noise on commuting related walks in Helsinki. The walks were projected by carrying out an extensive public transport itinerary planning on census based commuting flow data. In addition, I assessed achievable reductions in exposure to traffic noise by taking quieter paths with statistical means by a subset of 18446 commuting related walks (OD pairs). The results show significant spatial variation in average dynamic noise exposure between neighborhoods but also significant achievable reductions in noise exposure by quieter paths; depending on the situation, quieter paths provide 12–57 % mean reduction in exposure to noise levels higher than 65 dB and 1.6–9.6 dB mean reduction in mean dB (compared to the shortest paths). At least three factors seem to affect the achievable reduction in noise exposure on alternative paths: 1) exposure to noise on the shortest path, 2) length of the shortest path and 3) length of the quiet path compared to the shortest path. I have published the quiet path routing application as a web-based quiet path routing API (application programming interface) and developed an accompanying quiet path route planner as a mobile-friendly web map application. The online quiet path route planner demonstrates the applicability of the quiet path routing method in real-life situations and can thus help pedestrians to choose quieter paths. Since the quiet path routing API is open, anyone can query short and quiet paths equipped with attributes on journey-time exposure to noise. All methods and source codes developed in the study are openly available via GitHub. Individuals’ and urban planners’ awareness of dynamic exposure to noise and other pollutants should be further increased with advanced exposure assessments and routing applications. Web-based exposure-aware route planner applications have the potential to help individuals to choose alternative, healthier paths. When developing exposure-based routing analysis further, attempts should be made to enable simultaneously considering multiple environmental exposures in order to find overall healthier paths

Human centric routing algorithm for urban cyclists and the influence of street network spatial configuration

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesUnderstanding wayfinding behavior of cyclist aid decision makers to design better cities in favor of this sustainable active transport. Many have modelled the physical influence of building environment on wayfinding behavior, with cyclist route choices and routing algorithm. Incorporating cognitive wayfinding approach with Space Syntax techniques not only adds the human centric element to model routing algorithm, but also opens the door to evaluate spatial configuration of cities and its effect on cyclist behavior. This thesis combines novel Space Syntax techniques with Graph Theory to develop a reproducible Human Centric Routing Algorithm and evaluates how spatial configuration of cities influences modelled wayfinding behavior. Valencia, a concentric gridded city, and Cardiff with a complex spatial configuration are chosen as the case study areas. Significant differences in routes distribution exist between cities and suggest that spatial configuration of the city has an influence on the modelled routes. Street Network Analysis is used to further quantify such differences and confirms that the simpler spatial configuration of Valencia has a higher connectivity, which could facilitate cyclist wayfinding. There are clear implications on urban design that spatial configuration with higher connectivity indicates legibility, which is key to build resilience and sustainable communities. The methodology demonstrates automatic, scalable and reproducible tools to create Human Centric Routing Algorithm anywhere in the world. Reproducibility self-assessment (https://osf.io/j97zp/): 3, 3, 3, 2, 1 (Input data, Preprocessing, Methods, Computational Environment and Results)

Human Centric Routing Algorithm for Urban Cyclists and the Influence of Street Network Spatial Configuration

Treball Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Codi: SBA032. Curs: 2019/2020Understanding wayfinding behavior of cyclist aid decision makers to design better cities in favor of this sustainable active transport. Many have modelled the physical influence of building environment on wayfinding behavior, with cyclist route choices and routing algorithm. Incorporating cognitive wayfinding approach with Space Syntax techniques not only adds the human centric element to model routing algorithm, but also opens the door to evaluate spatial configuration of cities and its effect on cyclist behavior. This thesis combines novel Space Syntax techniques with Graph Theory to develop a reproducible Human Centric Routing Algorithm and evaluates how spatial configuration of cities influences modelled wayfinding behavior. Valencia, a concentric gridded city, and Cardiff with a complex spatial configuration are chosen as the case study areas. Significant differences in routes distribution exist between cities and suggest that spatial configuration of the city has an influence on the modelled routes. Street Network Analysis is used to further quantify such differences and confirms that the simpler spatial configuration of Valencia has a higher connectivity, which could facilitate cyclist wayfinding. There are clear implications on urban design that spatial configuration with higher connectivity indicates legibility, which is key to build resilience and sustainable communities. The methodology demonstrates automatic, scalable and reproducible tools to create Human Centric Routing Algorithm anywhere in the world. Reproducibility self-assessment (https://osf.io/j97zp/): 3, 3, 3, 2, 1 (Input data, Preprocessing, Methods, Computational Environment and Results)

Acquisition of business intelligence from human experience in route planning

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 2015, available online at:http://www.tandfonline.com/10.1080/17517575.2012.759279The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e., plan the routes that the shippers have to follow to deliver the goods. In this paper we present an AI-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimized routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimizes the delivery process. The solution uses Data Mining to extract knowledge from the company information systems and prepares it for analysis with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a Genetic Algorithm (GA) that, given the processed information, optimizes the routes following several objectives, such as minimize the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, in average, the routes made by the human experts.This work has been partially supported by the SpanishMinistry of Science and Innovation under the projects ABANT (TIN 2010-19872) and by Jobssy.com company under Project FUAM-076913

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

Dynamic trip planner for public transport using genetic algorithm

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper