Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems

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

Deadline for manuscript submissions: 25 April 2024 | Viewed by 12613

Special Issue Editors

Transport Studies Unit, University of Oxford, Oxford OX1 3QY, UK
Interests: GIS; spatial data science; transport geography; mobility analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A sustainable transport system plays a vital role in a successful society, the construction and operation of which require the support of considerable amounts of transport data from a variety of sources to inspect road asset conditions; track traffic flow dynamics; monitor transport emergencies; analyse traffic safety, equality, and accessibility, etc. With the advancement of new and scalable data sources, robust acquisition methodologies, and transmission techniques, unprecedented amounts of traffic information are being generated and collected from various data sources, such as roadside sensors, very-high-resolution (VHR)/HR satellite imagery, streetscape observations, sensor-rich mobile devices, and connected and autonomous vehicles (CAVs). Compared with conventional data sources, these emerging geospatial big datasets are massive in size, spatiotemporally fine-scaled, and high-dimensional (e.g., multivariate and multivalued), providing researchers with rich and timely information to effectively manage transport systems and gain new insights into different transport challenges (e.g., crashes, congestion, emissions, mobility inequality). However, managing and analysing these big complex datasets expose new problems and challenges in terms of strategies to promote multiple aspects, including (1) data integration, enrichment, storage, archiving, and sharing; (2) data quality control (e.g., reducing data uncertainty and redundancy); (3) data security, integrity, and privacy; and (4) data processing, analysis, and visualization.

This Special Issue invites the submission of original research papers and review articles that showcase the latest developments, innovations, and applications of emerging transport data in sustainable transport management and operations. Topics of interest include but are not limited to:

  • Application of emerging geospatial data in road asset recognition, digitization, and inventorization;
  • Application of emerging geospatial data in transport equity and accessibility, including environmental policy assessments;
  • Application of new geo-visualization methods and platforms for exploring big transport data;
  • Strategies for encouraging transport data sharing and protecting data privacy and security;
  • Multi-source data fusion challenges and considerations in transport applications;
  • Examining the role of geospatial big data in enhancing resilience and emergency responses for transport systems in the face of natural disasters, pandemics, and other crises.

You may choose our Joint Special Issue in Remote Sensing.

Dr. Xiao Li
Dr. Xiao Huang
Dr. Zhenlong Li
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 submissions that pass pre-check are 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 1700 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 transport system
  • emerging transport data
  • big data analytics
  • novel sensing technologies
  • road asset recognition and digitization
  • transport monitoring and assessment

Published Papers (7 papers)

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Research

20 pages, 7218 KiB  
Article
Assessing the Transformative Potential: An Examination of the Urban Mobility Impact Based on an Open-Source Microscopic Traffic Simulator for Autonomous Vehicles
by Liliana Andrei and Oana Luca
ISPRS Int. J. Geo-Inf. 2024, 13(1), 16; https://doi.org/10.3390/ijgi13010016 - 03 Jan 2024
Viewed by 1380
Abstract
Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using [...] Read more.
Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using a complex methodology encompassing various software types such as Simulation of Urban Mobility (SUMO 1.17.0) and STREETMIX, the article explores the results of a simulation that anticipates the implementation of AVs through different market penetration scenarios. We investigate how AVs could enhance the efficiency of transportation networks, reducing congestion and potentially increasing the throughput. However, we also acknowledge the dynamic nature of the scenarios, as new mobility patterns emerge in response to this technological shift. Furthermore, we propose innovative urban design approaches that could harness the full potential of AVs, fostering the development of sustainable and resilient cities. By exploring these design strategies, we hope to provide valuable guidance for urban planners and policymakers as they navigate the challenges and opportunities presented by the integration of these advanced technologies. Full article
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16 pages, 3958 KiB  
Article
Bibliometric Insights into the Implications of Urban Built Environment on Travel Behavior
by Chao Gao, Xinyi Lai, Shasha Li, Zhiwei Cui and Zhiyou Long
ISPRS Int. J. Geo-Inf. 2023, 12(11), 453; https://doi.org/10.3390/ijgi12110453 - 06 Nov 2023
Cited by 1 | Viewed by 1558
Abstract
With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades [...] Read more.
With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades (1997–2023), utilizing 1745 publications from the Web of Science database through network analysis and content analysis. It provides a comprehensive quantitative analysis encompassing publication trends, national and institutional collaborations, and keyword evolution clustering perspectives. The results reveal that (1) academic interest in exploring the implications of the urban built environment on travel behavior has grown markedly, especially in the past decade, with emerging technological approaches and research perspectives; (2) the USA, P.R.CHINA, and the United Kingdom are major research forces in this field, with notable contributions from research institutions in P.R.CHINA and the USA; (3) the “Transportation Research Part” series journals demonstrate extensive influence both in terms of publication count and citation count; (4) through keyword co-occurrence network analysis, three development stages along with five major clusters were identified: travel behavior modeling and public health, active transportation and sustainable development, urban development and carbon emissions, land use and transportation integration, and urban transportation systems and machine learning. Overall, sustained research remains warranted within this field, particularly focusing on selecting new built environment metrics while integrating emerging technologies into travel behavior modeling frameworks. The insights from this study have implications for urban transportation planning and policy, offering guidance on future research directions and policymaking. Full article
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19 pages, 10853 KiB  
Article
Urban Resident Travel Survey Method Based on Cellular Signaling Data
by Junzhuo Li, Wenyong Li and Guan Lian
ISPRS Int. J. Geo-Inf. 2023, 12(8), 304; https://doi.org/10.3390/ijgi12080304 - 28 Jul 2023
Viewed by 932
Abstract
A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify [...] Read more.
A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term. Full article
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27 pages, 3528 KiB  
Article
Exploring the Impact of Built Environment Factors on the Relationships between Bike Sharing and Public Transportation: A Case Study of New York
by Baohua Wei and Lei Zhu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 293; https://doi.org/10.3390/ijgi12070293 - 20 Jul 2023
Cited by 1 | Viewed by 1773
Abstract
Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship [...] Read more.
Bike sharing offers a usable form of feeder transportation for connecting to public transportation and effectively meets unmet travel demands, alleviating the pressure on public transportation systems by diverting urban commuters. To advance the comprehension of how the built environment shapes the relationship between bike-sharing systems and public transport modes, we implement a categorization framework that divides bike-sharing data into three distinct patterns: competition, integration, and complementation, based on their coordination with public transportation. The SLM model is employed to investigate the complex correlations between the relationship patterns and four key groups of environmental factors encompassing land use, transportation systems, urban design, and social economy. We find a strong correlation between four groups of environmental factors and three relationship patterns. Furthermore, the built environment variables exhibit significant variations across the three patterns. Users in the competitive mode prefer the flexibility of shared bikes and place a higher value on the sightseeing and leisure benefits. Instead, users in the integration and complementation modes tend to prefer shared bikes to supplement unmet travel demand and place a higher value on commuting benefits. These findings can benefit urban planners seeking to encourage greater diversity in transportation modes and incentivize more commuting. Full article
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16 pages, 4831 KiB  
Article
Black Carbon Concentration Estimation with Mobile-Based Measurements in a Complex Urban Environment
by Minmeng Tang, Tri Dev Acharya and Deb A. Niemeier
ISPRS Int. J. Geo-Inf. 2023, 12(7), 290; https://doi.org/10.3390/ijgi12070290 - 20 Jul 2023
Cited by 2 | Viewed by 1419
Abstract
Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC [...] Read more.
Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC concentrations in Oakland, CA. Random Forest was the best-performing model, with regression coefficient (R2) values of 0.701 on the train set and 0.695 on the validation set with a root mean square error (RMSE) of 0.210 mg/m3. Vehicle speed and local road systems were the most sensitive variables in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where highways and truck routes are significant emission sources. Using the land use method to estimate BC concentrations may lead to underestimating some localized hotspots. This work can improve air quality exposure assessment for vulnerable populations and help emphasize potential environmental justice issues. Full article
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25 pages, 6840 KiB  
Article
Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach
by Raihanah Adawiyah Shaharuddin and Md Yushalify Misro
ISPRS Int. J. Geo-Inf. 2023, 12(6), 226; https://doi.org/10.3390/ijgi12060226 - 31 May 2023
Cited by 1 | Viewed by 2647
Abstract
Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road [...] Read more.
Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver’s behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users. Full article
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15 pages, 3503 KiB  
Article
Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network
by Ke Shang, Zeyu Wan, Yulin Zhang, Zhiwei Cui, Zihan Zhang, Chenchen Jiang and Feizhou Zhang
ISPRS Int. J. Geo-Inf. 2023, 12(5), 208; https://doi.org/10.3390/ijgi12050208 - 22 May 2023
Viewed by 1218
Abstract
The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking [...] Read more.
The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale. Full article
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