662 research outputs found

    Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

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    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

    Predicting passenger origin-destination in online taxi-hailing systems

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    Because of transportation planning, traffic management, and dispatch optimization importance, passenger origin-destination prediction has become one of the most important requirements for intelligent transportation systems management. In this paper, we propose a model to predict the next specified time window travels' origin and destination. To extract meaningful travel flows, we use K-means clustering in four-dimensional space with maximum cluster size limitation for origin and destination zones. Because of the large number of clusters, we use non-negative matrix factorization to decrease the number of travel clusters. Also, we use a stacked recurrent neural network model to predict travel count in each cluster. Comparing our results with other existing models shows that our proposed model has 5-7% lower mean absolute percentage error (MAPE) for 1-hour time windows, and 14% lower MAPE for 30-minute time windows.Comment: 25 pages, 20 figure

    A Survey on Urban Traffic Anomalies Detection Algorithms

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    © 2019 IEEE. This paper reviews the use of outlier detection approaches in urban traffic analysis. We divide existing solutions into two main categories: flow outlier detection and trajectory outlier detection. The first category groups solutions that detect flow outliers and includes statistical, similarity and pattern mining approaches. The second category contains solutions where the trajectory outliers are derived, including off-line processing for trajectory outliers and online processing for sub-trajectory outliers. Solutions in each of these categories are described, illustrated, and discussed, and open perspectives and research trends are drawn. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of all the kinds of representations in urban traffic data, including flow values, segment flow values, trajectories, and sub-trajectories. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier urban traffic detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case

    Fuel consumption analysis of driven trips with respect to route choice

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    Advances in technology equip traffic domain with instruments to gather and analyse data for safe and fuel-efficient traveling. In this article, we elaborate on the effects that taxi drivers' route selection has on fuel efficiency. For this purpose, we fuse real driving behaviour data from taxi cabs, weather, digital map, and traffic situation information to gain understanding of how the routes are selected and what are the effects in terms of fuel-efficiency. Analysis of actually driven trips and their quickest and shortest counterparts is conducted to find out the fuel-efficiency consequences on route selection. The judgments are used for developing a fuel-consumption model, exploring further the route characteristics and external factors affecting fuel consumption.Peer reviewe

    An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

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    Origin-destination~(OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.Comment: 49 pages, 6 figure

    Investigations of outdoor mobility patterns of taxicabs in urban scenarios

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    This thesis investigates various outdoor mobility patterns of taxicabs in urban environments based on open-data real traces and it proposes a suitable outdoor mobility model to fit the provided measurement data. This thesis is processing user traces of taxicabs of two major cities: Rome and San Francisco downloaded from CRAWDAD open-source repository, which is responsible for sharing data from real networks and real mobile users across the various research communities around the world. There are numerous sources of collecting traces of users in a city, such as mobile devices, vehicles, smart cards, floating sensors etc. This thesis presents a comparative analysis of the mobility patterns of various taxicabs from Rome and San Francisco cities based on data collected via GPS-enabled mobile devices. Finding suitable mobility models of taxicabs to represent the travelling patterns of users moving from one location to another with respect to their varying time, location and speed can be quite helpful for the advanced researches in the diverse fields of wireless communications, such as better network planning, more efficient smart city design, improved traffic flows in cities. Also other applications such as weather forecasting, cellular coverage planning, e-health services, prediction of tourist areas, intelligent transport systems can benefit from the information hidden in user traces and from being able to find out statistically valid mobility models. The work here focused on extracting various mobility parameters from the crowdsourced open-source data and trying to model them according to various mobility models existing in the literature. The measurement analysis of this thesis work was completed in Matlab

    Investigations of outdoor mobility patterns of taxicabs in urban scenarios

    Get PDF
    This thesis investigates various outdoor mobility patterns of taxicabs in urban environments based on open-data real traces and it proposes a suitable outdoor mobility model to fit the provided measurement data. This thesis is processing user traces of taxicabs of two major cities: Rome and San Francisco downloaded from CRAWDAD open-source repository, which is responsible for sharing data from real networks and real mobile users across the various research communities around the world. There are numerous sources of collecting traces of users in a city, such as mobile devices, vehicles, smart cards, floating sensors etc. This thesis presents a comparative analysis of the mobility patterns of various taxicabs from Rome and San Francisco cities based on data collected via GPS-enabled mobile devices. Finding suitable mobility models of taxicabs to represent the travelling patterns of users moving from one location to another with respect to their varying time, location and speed can be quite helpful for the advanced researches in the diverse fields of wireless communications, such as better network planning, more efficient smart city design, improved traffic flows in cities. Also other applications such as weather forecasting, cellular coverage planning, e-health services, prediction of tourist areas, intelligent transport systems can benefit from the information hidden in user traces and from being able to find out statistically valid mobility models. The work here focused on extracting various mobility parameters from the crowdsourced open-source data and trying to model them according to various mobility models existing in the literature. The measurement analysis of this thesis work was completed in Matlab

    A statistical approach for studying urban human dynamics

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    A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThis doctoral dissertation proposed several statistical approaches to analyse urban dynamics with aiming to provide tools for decision making processes and urban studies. It assumed that human activity and human mobility compose urban dynamics. Initially, it studied geolocated social media data and considered them as a proxy for where and when people carry out what it is defined as the human activity. It employed techniques associated with generalised linear models, functional data analysis, hierarchical clustering, and epidemic data, to explain the spatio-temporal distribution of the places where people interact with their social networks. Afterwards, to understand the mobility in urban environments, data coming from an underground railway system were used. The information was considered repeated daily measurements to capture the regularity of human behaviour. By implementing methods from functional principal components data analysis and hierarchical clustering, it was possible to describe the system and identify human mobility patterns
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