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Special Issue "Neo-Geography and Crowdsourcing Technologies for Sustainable Urban Transportation"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 November 2017

Special Issue Editor

Guest Editor
Dr. Mohamed Bakillah

1. Senior Advisor of GIS Governmental Center, RAK, United Arab Emirates
2. Associated Senior Researcher, Department of Geomatic Engineering, Laval University, Canada
3. Associated Senior Researcher, GIScience research group, Heidelberg University, Germany
Website | E-Mail
Interests: GIScience; Big Data; volunteered deographic information; semantic interoperability and standards; mobiles; sensor networks; ad hoc networks; data mining; spatiotemporal analysis; reasoning

Special Issue Information

Dear Colleagues,

The ongoing trend of urbanization has led to the accommodation of more than half of the world’s population in urban areas, and this percentage is predicted to rise to about 70% by 2050. This urban population growth would, in turn, have great impact on human activities, mainly urban transportation, which, in turn, impacts the Earth’s ecosystem.
Geo-information science and Earth observation provides valuable data and technologies for understanding and enhancing transportation processes. Within this context, and under the umbrella of neo-geography, geo-crowd sourcing, Location Based Social Networks (LBSN) and Volunteered Geographic Information (VGI) have recently became interesting sources for technologies that could potentially improve former urban systems and processes through providing up-to-date and detailed information. We welcome scholars to share their research on challenges and solutions of neo-geography and crowdsourcing technologies for Sustainable Urban Transportation.

Dr. Mohamed Bakillah
Guest Editor

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. Sustainability 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 1400 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.

 

Keywords

  • Sustainable urban traveling
  • Intelligent and Efficient Transportation Systems
  • Smart city
  • GI solution for Transportation and health sustainability
  • Crowdsourcing and Volunteered Geographic Information
  • Location-based social networks (LBSN)
  • Big Data
  • Spatial-temporal analysis

Published Papers (4 papers)

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Research

Open AccessArticle Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users
Sustainability 2017, 9(6), 1060; doi:10.3390/su9061060
Received: 20 March 2017 / Revised: 15 June 2017 / Accepted: 16 June 2017 / Published: 19 June 2017
Cited by 1 | PDF Full-text (4385 KB) | HTML Full-text | XML Full-text
Abstract
As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the
[...] Read more.
As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station. Full article
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Open AccessArticle Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility
Sustainability 2017, 9(6), 997; doi:10.3390/su9060997
Received: 22 February 2017 / Revised: 4 May 2017 / Accepted: 16 May 2017 / Published: 9 June 2017
Cited by 1 | PDF Full-text (2519 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As
[...] Read more.
Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most famous crowdsourced mapping platforms, we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. We assess the completeness of OSM data regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods, we present the results of fitness for use of OSM data for routing services of disabled persons. Based on empirical results, it is concluded that OSM data of relatively large spatial extents inside all studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed. Finally, the article ends with an outlook for future research study in this area. Full article
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Open AccessArticle Siting of Carsharing Stations Based on Spatial Multi-Criteria Evaluation: A Case Study of Shanghai EVCARD
Sustainability 2017, 9(1), 152; doi:10.3390/su9010152
Received: 25 November 2016 / Revised: 11 January 2017 / Accepted: 16 January 2017 / Published: 20 January 2017
Cited by 5 | PDF Full-text (3895 KB) | HTML Full-text | XML Full-text
Abstract
Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai,
[...] Read more.
Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai, called EVCARD, has also developed rapidly with very large demand and supply. Aiming to determine the optimal locations of future stations of the EVCARD, this research employs a novel method combining the analytic hierarchy process (AHP) and geographical information system (GIS) with big data. Potential users, potential travel demand, potential travel purposes, and distance from existing stations are selected as the decision criteria. A siting decision system is established, consisting of 15 evaluation indicators which are calculated from multi-source data on mobile phones, taxi trajectory, point of interests (POI), and the EVCARD operation. The method of the AHP is used to determine the indicator weights, and the “Spatial Analyst” tool of ArcGIS is adopted to generate the indicator values for every 1 km × 1 km decision unit. Finally, synthetic scores are calculated to evaluate the candidate sites of EVCARD stations. The results of the case study verify the effectiveness of the proposed method, which can provide a more scientific and feasible method for carsharing operators to site stations, avoiding aimless and random decisions. Full article
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Open AccessArticle A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning
Sustainability 2017, 9(1), 134; doi:10.3390/su9010134
Received: 27 November 2016 / Revised: 3 January 2017 / Accepted: 13 January 2017 / Published: 18 January 2017
Cited by 1 | PDF Full-text (8712 KB) | HTML Full-text | XML Full-text
Abstract
Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering
[...] Read more.
Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners. Full article
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