Analysis of Travel Patterns from Cellular Network Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Analysis of Travel Patterns from Cellular Network Data PDF full book. Access full book title Analysis of Travel Patterns from Cellular Network Data by Nils Breyer. Download full books in PDF and EPUB format.
Author: Nils Breyer
Publisher: Linköping University Electronic Press
ISBN: 9176850552
Category :
Languages : en
Pages : 32
Get Book Here
Book Description
Traffic planners are facing a big challenge with an increasing demand for mobility and a need to drastically reduce the environmental impacts of the transportation system at the same time. The transportation system therefore needs to become more efficient, which requires a good understanding about the actual travel patterns. Data from travel surveys and traffic counts is expensive to collect and gives only limited insights on travel patterns. Cellular network data collected in the mobile operators infrastructure is a promising data source which can provide new ways of obtaining information relevant for traffic analysis. It can provide large-scale observations of travel patterns independent of the travel mode used and can be updated easier than other data sources. In order to use cellular network data for traffic analysis it needs to be filtered and processed in a way that preserves privacy of individuals and takes the low resolution of the data in space and time into account. The research of finding appropriate algorithms is ongoing and while substantial progress has been achieved, there is a still a large potential for better algorithms and ways to evaluate them. The aim of this thesis is to analyse the potential and limitations of using cellular network data for traffic analysis. In the three papers included in the thesis, contributions are made to the trip extraction, travel demand and route inference steps part of a data-driven traffic analysis processing chain. To analyse the performance of the proposed algorithms, a number of datasets from different cellular network operators are used. The results obtained using different algorithms are compared to each other as well as to other available data sources. A main finding presented in this thesis is that large-scale cellular network data can be used in particular to infer travel demand. In a study of data for the municipality of Norrköping, the results from cellular network data resemble the travel demand model currently used by the municipality, while adding more details such as time profiles which are currently not available to traffic planners. However, it is found that all later traffic analysis results from cellular network data can differ to a large extend based on the choice of algorithm used for the first steps of data filtering and trip extraction. Particular difficulties occur with the detection of short trips (less than 2km) with a possible under-representation of these trips affecting the subsequent traffic analysis.
Author: Nils Breyer
Publisher: Linköping University Electronic Press
ISBN: 9176850552
Category :
Languages : en
Pages : 32
Get Book Here
Book Description
Traffic planners are facing a big challenge with an increasing demand for mobility and a need to drastically reduce the environmental impacts of the transportation system at the same time. The transportation system therefore needs to become more efficient, which requires a good understanding about the actual travel patterns. Data from travel surveys and traffic counts is expensive to collect and gives only limited insights on travel patterns. Cellular network data collected in the mobile operators infrastructure is a promising data source which can provide new ways of obtaining information relevant for traffic analysis. It can provide large-scale observations of travel patterns independent of the travel mode used and can be updated easier than other data sources. In order to use cellular network data for traffic analysis it needs to be filtered and processed in a way that preserves privacy of individuals and takes the low resolution of the data in space and time into account. The research of finding appropriate algorithms is ongoing and while substantial progress has been achieved, there is a still a large potential for better algorithms and ways to evaluate them. The aim of this thesis is to analyse the potential and limitations of using cellular network data for traffic analysis. In the three papers included in the thesis, contributions are made to the trip extraction, travel demand and route inference steps part of a data-driven traffic analysis processing chain. To analyse the performance of the proposed algorithms, a number of datasets from different cellular network operators are used. The results obtained using different algorithms are compared to each other as well as to other available data sources. A main finding presented in this thesis is that large-scale cellular network data can be used in particular to infer travel demand. In a study of data for the municipality of Norrköping, the results from cellular network data resemble the travel demand model currently used by the municipality, while adding more details such as time profiles which are currently not available to traffic planners. However, it is found that all later traffic analysis results from cellular network data can differ to a large extend based on the choice of algorithm used for the first steps of data filtering and trip extraction. Particular difficulties occur with the detection of short trips (less than 2km) with a possible under-representation of these trips affecting the subsequent traffic analysis.
Author: David Gundlegård
Publisher: Linköping University Electronic Press
ISBN: 9176851729
Category :
Languages : en
Pages : 76
Get Book Here
Book Description
Cellular networks of today generate a massive amount of signalling data. A large part of this signalling is generated to handle the mobility of subscribers and contains location information that can be used to fundamentally change our understanding of mobility patterns. However, the location data available from standard interfaces in cellular networks is very sparse and an important research question is how this data can be processed in order to efficiently use it for traffic state estimation and traffic planning. In this thesis, the potentials and limitations of using this signalling data in the context of estimating the road network traffic state and understanding mobility patterns is analyzed. The thesis describes in detail the location data that is available from signalling messages in GSM, GPRS and UMTS networks, both when terminals are in idle mode and when engaged in a telephone call or a data session. The potential is evaluated empirically using signalling data and measurements generated by standard cellular phones. The data used for analysis of location estimation and route classification accuracy (Paper I-IV in the thesis) is collected using dedicated hardware and software for cellular network analysis as well as tailor-made Android applications. For evaluation of more advanced methods for travel time estimation, data from GPS devices located in Taxis is used in combination with data from fixed radar sensors observing point speed and flow on the road network (Paper V). To evaluate the potential in using cellular network signalling data for analysis of mobility patterns and transport planning, real data provided by a cellular network operator is used (Paper VI). The signalling data available in all three types of networks is useful to estimate several types of traffic data that can be used for traffic state estimation as well as traffic planning. However, the resolution in time and space largely depends on which type of data that is extracted from the network, which type of network that is used and how it is processed. The thesis proposes new methods based on integrated filtering and classification as well as data assimilation and fusion that allows measurement reports from the cellular network to be used for efficient route classification and estimation of travel times. The thesis also shows that participatory sensing based on GPS equipped smartphones is useful in estimating radio maps for fingerprint-based positioning as well as estimating mobility models for use in filtering of course trajectory data from cellular networks. For travel time estimation, it is shown that the CEP-67 location accuracy based on the proposed methods can be improved from 111 meters to 38 meters compared to standard fingerprinting methods. For route classification, it is shown that the problem can be solved efficiently for highway environments using basic classification methods. For urban environments the link precision and recall is improved from 0.5 and 0.7 for standard fingerprinting to 0.83 and 0.92 for the proposed method based on particle filtering with integrity monitoring and Hidden Markov Models. Furthermore, a processing pipeline for data driven network assignment is proposed for billing data to be used when inferring mobility patterns used for traffic planning in terms of OD matrices, route choice and coarse travel times. The results of the large-scale data set highlight the importance of the underlying processing pipeline for this type of analysis. However, they also show very good potential in using large data sets for identifying needs of infrastructure investment by filtering out relevant data over large time periods.
Author: Santi Phithakkitnukoon
Publisher: Springer Nature
ISBN: 9811967148
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
This book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors’ research studies. Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The book’s chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.
Author: Mariem Fekih (Doctor of Transportation Sciences)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Fei Yang
Publisher: Springer Nature
ISBN: 9811680086
Category : Computers
Languages : en
Pages : 235
Get Book Here
Book Description
This book is devoted to the technology and methodology of individual travel behavior analysis and refined travel information extraction. Traditional resident trip surveys are characterized by many shortcomings, such as subjective memory errors, difficulty in organization and high cost. Therefore, in this book, a set of refined extraction and analysis techniques for individual travel activities is proposed. It provides a solid foundation for the optimization and reconstruction of traffic theoretical models, urban traffic planning, management and decision-making. This book helps traffic engineering researchers, traffic engineering technicians and traffic industry managers understand the difficulties and challenges faced by transportation big data. Additionally, it helps them adapt to changes in traffic demand and the technological environment to achieve theoretical innovation and technological reform.
Author: Dongyuan Yang
Publisher: Springer Nature
ISBN: 9811933383
Category : Science
Languages : en
Pages : 349
Get Book Here
Book Description
This book chiefly focuses on urban traffic, an area supported by massive amounts of data. The application of big data to urban traffic provides strategic and technical methods for the multi-directional and in-depth observation of complex adaptive systems, thus transforming conventional urban traffic planning and management methods. Sharing valuable insights into how big data can be applied to urban traffic, it offers a valuable asset for information technicians, traffic engineers and traffic data analysts alike.
Author: Yu-Jen Chen
Publisher:
ISBN:
Category : Structural equation modeling
Languages : en
Pages : 152
Get Book Here
Book Description
Research on travel behavior continues to be one of the most prominent areas in the transportation area. Planners and policymakers try to understand and manage travel behavior. Making and implementation of travel demand management (TDM) policies greatly rely on the understanding of the determinants of activity-travel patterns and travel demand. Among the activity-travel patterns, trip chaining and joint travel have received much research interest. Trip chaining is typically viewed as a home-based tour that connects multiple out-of-home activities. Joint travel is commonly defined as traveling with others. Travel demand is generally measured by trip generation and travel distances. Investigating different aspects of travel behavior helps us better understand the links between activity participation and mobility, and improves the evaluation of the transportation infrastructure investments and policies such as high occupancy vehicle (HOV) lanes and vehicle miles traveled (VMT) reduction programs. Several studies have regarded trip chaining, joint travel, trip generation, and travel distances as different dimensions of travel behavior to be examined in terms of various socio-demographics and urban form factors. However, limited work has been done to use activity-travel patterns as mediating variables and analyze how trip chaining and joint travel shape the resulting travel demand. Furthermore, relationships between travel behavior and urban form factors at out-of-home activity locations remain unclear. Based on the 2012 travel survey data from the Cleveland Metropolitan Area, this study first investigates the relationships among trip chaining, joint travel, home-based tour generation, and travel distances at three different levels: tour, individual, and household levels. Second, the influences of socio-demographics and urban form factors at tour origins and destinations on travel behavior are examined simultaneously. Lastly, while using trip chaining and joint travel as mediating variables, this study estimates the mediating effects of socio-demographics and urban form via activity-travel patterns on travel demand. The Structural Equation Modelling (SEM) approach is applied. The study reveals the existence of significant relationships between activity-travel patterns and travel demand. Trip chaining is negatively associated with joint travel. While it increases travel distances, this effects gets weakened through its indirect effect via decreased tour generation. Joint travel appears to increase tour generation but decrease the travel distances. Most socio-demographics have significant effects with expected signs on travel behavior. The analysis suggests that urban form factors at tour origins and destinations play important roles on the resulting travel demand. Some urban form factors may not have direct effects on travel demand but have significant indirect effects on tour generation or travel distances through activity-travel patterns. This research presents how activity-travel patterns shape travel demand and concludes that trip chaining and joint travel should be taken into consideration while analyzing travel demand. The findings on socio-demographics and urban form factors can be used as inputs to improve the future evaluation of transportation projects and help planners integrate land-use strategies as tools to change people’s travel behavior. This will further mitigate the negative externalities associated with our travel patterns.
Author: Dewan Masud Karim
Publisher: CRC Press
ISBN: 1003822797
Category : Computers
Languages : en
Pages : 451
Get Book Here
Book Description
In the face of resource depletion, environmental changes, lifestyle changes, demographic and digital adaptation, old ideologies of city building and expensive and complex automobility solutions are in freefall. These changes are creating severe friction between the old and new paradigms. This book provides new perspectives through the process of ideological disassociation and concepts of human mobility code. The basic premise of the book, human mobility is an essential component of our creativity that comes from our unconscious desire to become a part of a community. Several new concepts in the book starts with the hallmark of new discovery of human mobility code and its implications of urban mobility boundary systems to stay within safe planetary zone. A new discovery of human mobility code from comprehensive research finding prove that each individual develops a unique mobility footprint and become our mobility identity. Beyond individual hallmarks, human develops collective mobility codes through interaction with the third space on which entire mobility systems lie and are created by the fundamentals of city planning and the design process. Readers are introduced to an innovative mobility planning process and reinvention of multimodal mobility approaches based on new mobility code while formulating new concepts, practical solutions and implementation techniques, tools, policies, and processes to reinforce low-carbon mobility options while addressing social equity, environmental, and health benefits. Finally, the book arms us with knowledge to prevent the disaster of full technological enlightenment against our natural human mobility code.
Author: Shaopeng Zhong
Publisher: Springer Nature
ISBN: 9811680167
Category : Business & Economics
Languages : en
Pages : 296
Get Book Here
Book Description
This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.
Author: Gao Cong
Publisher: Springer
ISBN: 3319691791
Category : Computers
Languages : en
Pages : 879
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.
