Adopting and incorporating crowdsourced traffic data in advanced transportation management systems

The widespread availability of internet and mobile devices has made crowdsourced reports a considerable source of information in many domains. Traffic managers, among others, have started using crowdsourced traffic incident reports (CSTIRs) to complement their existing sources of traffic monitoring. One of the prominent providers of CSTIRs is Waze. In this dissertation, first a quantitative analysis was conducted to evaluate Waze data in comparison to the existing sources of Iowa Department of Transportation. The potential added coverage that Waze can provide was also estimated. Redundant CSTIRs of the same incident were found to be one of the main challenges of Waze and CSTIRs in general. To leverage the value of the redundant reports and address this challenge, a state-of-the-art cluster analysis was implemented to reduce the redundancies, while providing further information about the incident. The clustered CSTIRs indicate the area impacted by an incident and provide a basis for estimating the reliability of the cluster. Furthermore, the challenges with clustering CSTIRs were described and recommendations were made for parameter tuning and cluster validation. Finally, an open-source software package was offered to implement the clustering method in near real-time. This software downloads and parses the raw data, implements clustering, tracks clusters, assigns a reliability score to clusters, and provides a RESTful API for information dissemination portals and web pages to use the data for multiple applications within the DOT and for the general public. With emerging technologies such as connected vehicles and vehicle-to-infrastructure (V2I) communication, CSTIRs and similar type of data are expected to grow. The findings and recommendations in this work, although implemented on Waze data, will be beneficial to the analysis of these emerging sources of data

Facing-up Challenges of Multiobjective Clustering Based on Evolutionary Algorithms: Representations, Scalability and Retrieval Solutions

Aquesta tesi es centra en algorismes de clustering multiobjectiu, que estan basats en optimitzar varis objectius simultàniament obtenint una col•lecció de solucions potencials amb diferents compromisos entre objectius. El propòsit d'aquesta tesi consisteix en dissenyar i implementar un nou algorisme de clustering multiobjectiu basat en algorismes evolutius per afrontar tres reptes actuals relacionats amb aquest tipus de tècniques. El primer repte es centra en definir adequadament l'àrea de possibles solucions que s'explora per obtenir la millor solució i que depèn de la representació del coneixement. El segon repte consisteix en escalar el sistema dividint el conjunt de dades original en varis subconjunts per treballar amb menys dades en el procés de clustering. El tercer repte es basa en recuperar la solució més adequada tenint en compte la qualitat i la forma dels clusters a partir de la regió més interessant de la col•lecció de solucions ofertes per l’algorisme.Esta tesis se centra en los algoritmos de clustering multiobjetivo, que están basados en optimizar varios objetivos simultáneamente obteniendo una colección de soluciones potenciales con diferentes compromisos entre objetivos. El propósito de esta tesis consiste en diseñar e implementar un nuevo algoritmo de clustering multiobjetivo basado en algoritmos evolutivos para afrontar tres retos actuales relacionados con este tipo de técnicas. El primer reto se centra en definir adecuadamente el área de posibles soluciones explorada para obtener la mejor solución y que depende de la representación del conocimiento. El segundo reto consiste en escalar el sistema dividiendo el conjunto de datos original en varios subconjuntos para trabajar con menos datos en el proceso de clustering El tercer reto se basa en recuperar la solución más adecuada según la calidad y la forma de los clusters a partir de la región más interesante de la colección de soluciones ofrecidas por el algoritmo.This thesis is focused on multiobjective clustering algorithms, which are based on optimizing several objectives simultaneously obtaining a collection of potential solutions with different trade¬offs among objectives. The goal of the thesis is to design and implement a new multiobjective clustering technique based on evolutionary algorithms for facing up three current challenges related to these techniques. The first challenge is focused on successfully defining the area of possible solutions that is explored in order to find the best solution, and this depends on the knowledge representation. The second challenge tries to scale-up the system splitting the original data set into several data subsets in order to work with less data in the clustering process. The third challenge is addressed to the retrieval of the most suitable solution according to the quality and shape of the clusters from the most interesting region of the collection of solutions returned by the algorithm

Improving the efficiency of a clustering genetic algorithm

Finding optimal clusterings is a difficult task. Most clustering methods require the number of clusters to be specified in advance, and hierarchical methods typically produce a set of clusterings. In both cases, the user has to select the number of clusters. This paper proposes improvements for a clustering genetic algorithm that is capable of finding an optimal number of clusters and their partitions automatically, based upon numeric criteria. The proposed improvements were designed to enhance the efficiency of a clustering genetic algorithm. The modified algorithms are evaluated in several simulations