Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

Patterns of mobility in a smart city

Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponíveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo município. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python. Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Urban traffic flow prediction, a spatial-temporal approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesCurrent advances in computational technologies such as machine learning combined with traffic data availability are inspiring the development and growth of intelligent transport Systems (ITS). As urban authorities strive for efficient traffic systems, traffic forecasting is a vital element for effective control and management of traffic networks. Traffic forecasting methods have progressed from traditional statistical techniques to optimized data driven methods eulogised with artificial intelligence. Today, most techniques in traffic forecasting are mainly timeseries methods that ignore the spatial impact of traffic networks in traffic flow modelling. The consideration of both spatial and temporal dimensions in traffic forecasting efforts is key to achieving inclusive traffic forecasts. This research paper presents approaches to analyse spatial temporal patterns existing in networks and goes on to use a machine learning model that integrates both spatial and temporal dependency in traffic flow prediction. The application of the model to a traffic dataset for the city of Singapore shows that we can accurately predict traffic flow up to 15 minutes in advance and also accuracy results obtained outperform other classical traffic prediction methods