Special Issue "Geospatial Big Data and Transport"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2017).

Special Issue Editors

Prof. Dr. Bin Jiang
Website
Guest Editor
Faculty of Engineering and Sustainable Development, Division of GIScience, University of Gävle, SE-801 76 Gävle, Sweden
Interests: geospatial analysis and modeling; structure and dynamics of urban systems; geoinformatics and computational geography
Special Issues and Collections in MDPI journals
Prof. Dr. Constantinos Antoniou
Website
Guest Editor
Chair of Transportation Systems Engineering, Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich

Special Issue Information

Dear Colleagues,

The emerging geospatial big data, such as massive GPS traces, smart cards, cell phones, and social media users’ locations and trajectories, constitute a fundamental new instrument for transport and mobility research, likely to replace conventional transport data collection for many applications in the near future. The data are usually accurately measured, individual based, and often available in real-time, differing significantly from conventional transport data that are often estimated, aggregated, and delayed in time. These data characteristics make geospatial big data extremely powerful and instrumental in transport and mobility research. Not only being large in size, but also complex in structure, such as social connections, makes such big data unique. How to develop new insights into the big data for better understanding transport systems, and human travel behaviors accordingly is a major challenge for researchers in the fields of geographic information science and transport. We believe that data-intensive computational methods, such as big data analytics, complex networks, visual analytics, and agent-based simulations are among the powerful tools for uncovering the underlying structure and dynamics of transport system and human travel behaviors. After an era of initial exploration, one can argue that today we have the tools, the data and the maturity to embark into a second generation of exploiting rich geospatial data, in a way that will transform how we measure, model, and improve transport and human mobility applications. We therefore call for papers (both review and research) that exploit emerging big data for developing new insights into transport systems and human travel behaviors. Suggested topics include, but are not limited to:

  • Reviews on the state of the art in geospatial big data and transport
  • Human mobility patterns derived from social media data
  • GPS traces for understanding human mobility
  • Smart cards for uncovering human travel behavior
  • Transport structure and dynamics based on topological and scaling analyses
  • Agent-based simulations of human travel behaviors

Read more please click: https://www.researchgate.net/publication/291333584_ISPRS_International_Journal_of_Geo-Information_Special_Issue_on_Geospatial_Big_Data_and_Transport

Prof. Dr. Bin Jiang
Prof. Dr. Constantinos Antoniou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Editorial

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Open AccessEditorial
Spatial Heterogeneity, Scale, Data Character and Sustainable Transport in the Big Data Era
ISPRS Int. J. Geo-Inf. 2018, 7(5), 167; https://doi.org/10.3390/ijgi7050167 - 28 Apr 2018
Cited by 7
Abstract
In light of the emergence of big data, I have advocated and argued for a paradigm shift from[...] Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Research

Jump to: Editorial

Open AccessArticle
Improving Destination Choice Modeling Using Location-Based Big Data
ISPRS Int. J. Geo-Inf. 2017, 6(9), 291; https://doi.org/10.3390/ijgi6090291 - 20 Sep 2017
Cited by 3
Abstract
Citizens are increasingly sharing their location and movements through “check-ins” on location based social networks (LBSNs). These services are collecting unprecedented amounts of big data that can be used to study how we travel and interact with our environment. This paper presents the [...] Read more.
Citizens are increasingly sharing their location and movements through “check-ins” on location based social networks (LBSNs). These services are collecting unprecedented amounts of big data that can be used to study how we travel and interact with our environment. This paper presents the development of a long distance destination choice model for Ontario, Canada, using data from Foursquare to model destination attractiveness. A methodology to collect and process historical check-in counts has been developed, allowing the utility of each destination to be calculated based on the intensity of different activities performed at the destination. Destinations such as national parks and ski areas are very strong attractors of leisure trips, yet do not employ many people and have few residents. Trip counts to such destinations are therefore poorly predicted by models based on population and employment. Traditionally, this has been remedied by extensive manual data collection. The integration of Foursquare data offers an alternative approach to this problem. The Foursquare based destination choice model was evaluated against a traditional model estimated only with population and employment. The results demonstrate that data from LBSNs can be used to improve destination choice models, particularly for leisure travel. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Public Transit Route Mapping for Large-Scale Multimodal Networks
ISPRS Int. J. Geo-Inf. 2017, 6(9), 268; https://doi.org/10.3390/ijgi6090268 - 26 Aug 2017
Cited by 4
Abstract
For the simulation of public transport, next to a schedule, knowledge of the public transport routes is required. While the schedules are becoming available, the precise network routes often remain unknown and must be reconstructed. For large-scale networks, however, a manual reconstruction becomes [...] Read more.
For the simulation of public transport, next to a schedule, knowledge of the public transport routes is required. While the schedules are becoming available, the precise network routes often remain unknown and must be reconstructed. For large-scale networks, however, a manual reconstruction becomes unfeasible. This paper presents a route reconstruction algorithm, which requires only the sequence and positions of the public transport stops and the street network. It uses an abstract graph to calculate the least-cost path from a route’s first to its last stop, with the constraint that the path must contain a so-called link candidate for every stop of the route’s stop sequence. The proposed algorithm is implemented explicitly for large-scale, real life networks. The algorithm is able to handle multiple lines and modes, to combine them at the same stop location (e.g., train and bus lines coming together at a train station), to automatically reconstruct missing links in the network, and to provide intelligent and efficient feedback if apparent errors occur. GPS or OSM tracks of the lines can be used to improve results, if available. The open-source algorithm has been tested for Zurich for mapping accuracy. In summary, the new algorithm and its MATSim-based implementation is a powerful, tested tool to reconstruct public transport network routes for large-scale systems. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals
ISPRS Int. J. Geo-Inf. 2017, 6(3), 62; https://doi.org/10.3390/ijgi6030062 - 24 Feb 2017
Cited by 7
Abstract
The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The [...] Read more.
The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities). Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
How They Move Reveals What Is Happening: Understanding the Dynamics of Big Events from Human Mobility Pattern
ISPRS Int. J. Geo-Inf. 2017, 6(1), 15; https://doi.org/10.3390/ijgi6010015 - 12 Jan 2017
Cited by 1
Abstract
The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has [...] Read more.
The context in which a moving object moves contributes to the movement pattern observed. Likewise, the movement pattern reflects the properties of the movement context. In particular, big events influence human mobility depending on the dynamics of the events. However, this influence has not been explored to understand big events. In this paper, we propose a methodology for learning about big events from human mobility pattern. The methodology involves extracting and analysing the stopping, approaching, and moving-away interactions between public transportation vehicles and the geographic context. The analysis is carried out at two different temporal granularity levels to discover global and local patterns. The results of evaluating this methodology on bus trajectories demonstrate that it can discover occurrences of big events from mobility patterns, roughly estimate the event start and end time, and reveal the temporal patterns of arrival and departure of event attendees. This knowledge can be usefully applied in transportation and event planning and management. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique
ISPRS Int. J. Geo-Inf. 2016, 5(11), 218; https://doi.org/10.3390/ijgi5110218 - 22 Nov 2016
Cited by 13
Abstract
Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and [...] Read more.
Finding a method to evaluate people’s emotional responses to urban spaces in a valid and objective way is fundamentally important for urban design practices and related policy making. Analysis of the essential qualities of urban space could be made both more effective and more accurate using innovative information techniques that have become available in the era of big data. This study introduces an integrated method based on geographical information systems (GIS) and an emotion-tracking technique to quantify the relationship between people’s emotional responses and urban space. This method can evaluate the degree to which people’s emotional responses are influenced by multiple urban characteristics such as building shapes and textures, isovist parameters, visual entropy, and visual fractals. The results indicate that urban spaces may influence people’s emotional responses through both spatial sequence arrangements and shifting scenario sequences. Emotional data were collected with body sensors and GPS devices. Spatial clustering was detected to target effective sampling locations; then, isovists were generated to extract building textures. Logistic regression and a receiver operating characteristic analysis were used to determine the key isovist parameters and the probabilities that they influenced people’s emotion. Finally, based on the results, we make some suggestions for design professionals in the field of urban space optimization. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Belgium through the Lens of Rail Travel Requests: Does Geography Still Matter?
ISPRS Int. J. Geo-Inf. 2016, 5(11), 216; https://doi.org/10.3390/ijgi5110216 - 15 Nov 2016
Cited by 8
Abstract
This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by train in Belgium. Travel requests [...] Read more.
This paper uses on-line railway travel requests from the iRail schedule-finder application for assessing the suitability of that kind of big data for transportation planning and to examine the temporal and regional variations of the travel demand by train in Belgium. Travel requests are collected over a two-month period and consist of origin-destination flows between stations operated by the Belgian national railway company in 2016. The Louvain method is applied to detect communities of tightly-connected stations. Results show the influence of both the urban and network structures on the spatial organization of the clusters. We also further discuss the implications of the observed temporal and regional variations of these clusters for transportation travel demand and planning. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory
ISPRS Int. J. Geo-Inf. 2016, 5(11), 207; https://doi.org/10.3390/ijgi5110207 - 09 Nov 2016
Cited by 17
Abstract
Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real [...] Read more.
Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale
ISPRS Int. J. Geo-Inf. 2016, 5(6), 78; https://doi.org/10.3390/ijgi5060078 - 01 Jun 2016
Cited by 16
Abstract
Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk [...] Read more.
Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Open AccessArticle
Characterizing Traffic Conditions from the Perspective of Spatial-Temporal Heterogeneity
ISPRS Int. J. Geo-Inf. 2016, 5(3), 34; https://doi.org/10.3390/ijgi5030034 - 10 Mar 2016
Cited by 15
Abstract
Traffic conditions are usually characterized from the perspective of travel time or the average vehicle speed in the field of transportation, reflecting the congestion degree of a road network. This article provides a method from a new perspective to characterize traffic conditions; the [...] Read more.
Traffic conditions are usually characterized from the perspective of travel time or the average vehicle speed in the field of transportation, reflecting the congestion degree of a road network. This article provides a method from a new perspective to characterize traffic conditions; the perspective is based on the heterogeneity of vehicle speeds. A novel measurement, the ratio of areas (RA) in a rank-size plot, is included in the proposed method to capture the heterogeneity. The proposed method can be performed from the perspective of both spatial heterogeneity and temporal heterogeneity, being able to characterize traffic conditions of not only a road network but also a single road. Compared with methods from the perspective of travel time, the proposed method can characterize traffic conditions at a higher frequency. Compared to methods from the perspective of the average vehicle speed, the proposed method takes account of the heterogeneity of vehicle speeds. The effectiveness of the proposed method has been demonstrated with real-life traffic data of Shenzhen (a coastal urban city in China), and the advantage of the proposed RA has been verified by comparisons to similar measurements such as the ht-index and the CRG index. Full article
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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