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Big Data Analytics in Sustainable Transport Planning and Management

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (23 October 2023) | Viewed by 5375

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Special Issue Editors

Prof. Dr. Agachai Sumalee

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Guest Editor

School of Integrated Innovation, Chulalongkorn University, Bangkok, Thailand
Interests: transit planning; intelligent transport system; network modeling; optimization and transport economics

Prof. Dr. Kenetsu Uchida

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Faculty of Engineering, Hokkaido University, North 13 West 8 Kita-ku, Sapporo, Japan
Interests: transportation planning; traffic engineering

Dr. Dong Ngoduy

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Guest Editor

Associate Professor, Institute of Transport Studies, Monash University, Melbourne, Australia
Interests: traffic flow theory and characteristics; traffic data fusion; urban network optimization; connected and autonomous vehicles

Special Issue Information

Dear Colleagues,

The issue of sustainable development in transportation has been a major concern in many cities in the past decades. The complex interaction between the demand and supply in the transportation network and related social context often leads to unresolvable problems. To understand this complexity, the traditional approach has relied on data collection exercises on household behavior, driver’s characteristics, traffic condition, etc. The scope of this data collection may not be able to represent the whole area and complexity of situations. The recent advancement of technology and telecommunication systems widens the potential data pool on travelers’ behavior and traffic characteristics, resulting in a potentially new way to analyze the root cause of transportation planning and management problems. This will eventually lead to a better strategy development for sustainable transport planning and management. This Special Issue aims to collect recent advanced research on the approach to analyze the potential big data available in the transport sector for the purpose of sustainable transport planning and management. The Special Issue welcomes the submission of original research papers on these topics (but not limited to them):

Travel behavior analysis based on big data or social network data;
Traffic condition and management strategy development from big data or from IOT data;
Intelligent transportation system development using big data or IOT data;
Traffic safety analysis using big data;
Transport and land use policy analysis based on large scale GIS data and citizens’ data;
Analysis of cellular phone network or other probe data for sustainable urban and transport planning;
Agent based simulation model using big data sources.

We look forward to receiving your contributions.

Prof. Dr. Agachai Sumalee
Prof. Dr. Kenetsu Uchida
Dr. Dong Ngoduy
Guest Editors

Manuscript Submission Information

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Keywords

transport data analytics
transport modeling
transport planning and analysis
big data analytics
sustainable transport analysis

Published Papers (4 papers)

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Research

17 pages, 5197 KiB

Open AccessArticle

Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach

by Yikang Rui, Yannan Gong, Yan Zhao, Kaijie Luo and Wenqi Lu

Sustainability 2024, 16(1), 190; https://doi.org/10.3390/su16010190 - 25 Dec 2023

Viewed by 800

Abstract

The long-term prediction of highway traffic parameters is frequently undermined by cumulative errors from various influencing factors and unforeseen events, resulting in diminished predictive accuracy and applicability. In the pursuit of sustainable highway development and eco-friendly transportation strategies, forecasting these traffic flow parameters has emerged as an urgent concern. To mitigate issues associated with cumulative error and unexpected events in long-term forecasts, this study leverages the empirical mode decomposition (EMD) method to deconstruct time series data. This aims to minimize disturbances from data fluctuations, thereby enhancing data quality. We also incorporate the BiLSTM model, ensuring bidirectional learning from extended time series data for a thorough extraction of relevant insights. In a pioneering effort, this research integrates the attention mechanism with the EMD–BiLSTM model. This synergy deeply excavates the spatiotemporal characteristics of traffic volume data, allocating appropriate weights to significant information, which markedly boosts predictive precision and speed. Through comparisons with ARIMA, LSTM, and BiLSTM models, we demonstrate the distinct advantage of our approach in predicting traffic volume and speed. In summary, our study introduces a groundbreaking technique for the meticulous forecasting of highway traffic volume. This serves as a robust decision-making instrument for both sustainable highway development and transportation management, paving the way for more sustainable, efficient, and environmentally conscious highway transit. Full article

(This article belongs to the Special Issue Big Data Analytics in Sustainable Transport Planning and Management)

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14 pages, 1458 KiB

Open AccessArticle

Evaluating Road Hazard Maintenance Efficiency Using Citizen Science Data to Improve Road Safety

by Jinguk Kim, Woohoon Jeon and Seoungbum Kim

Sustainability 2023, 15(20), 15074; https://doi.org/10.3390/su152015074 - 19 Oct 2023

Viewed by 805

Abstract

Accidents caused by road hazards can be prevented through regular inspections by road management agencies. To this end, traffic agencies allocate substantial budgets and workforces to maintain the performance of roads. Additionally, traffic agencies require comprehensive data such as the classifications and sizes of road hazards. However, collecting spatial–temporal data on various road hazards is challenging, and evaluating it comprehensively with respect to work efficiency and budget allocation is difficult due to stakeholder interests across agencies. This study proposes a process of evaluating operational efficiency in terms of maintaining roads and preventing hazards by analyzing citizen scientist-based data. First, we collected data from drivers through a mobile application and applied text mining techniques to classify each complaint into several types of road hazard maintenance. Second, we developed an indicator to measure operational efficiency using the processed data and evaluated each regional agency per each type of maintenance. The results of this study provide evidence that specific types of road hazards occur prominently under specific agencies. In addition, the time required to provide maintenance for identical road hazards can vary among agencies. These results suggest that the maintenance budget for the entire national highway may need to be distributed differently based upon regional characteristics. Full article

(This article belongs to the Special Issue Big Data Analytics in Sustainable Transport Planning and Management)

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17 pages, 3564 KiB

Open AccessArticle

Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data

by Jiusheng Du, Xingwang Liu and Chengyang Meng

Sustainability 2023, 15(19), 14299; https://doi.org/10.3390/su151914299 - 27 Sep 2023

Cited by 1 | Viewed by 929

Abstract

Global navigation satellite system (GNSS) vehicle trajectory data play an important role in obtaining timely urban road information. However, most models cannot effectively extract road information from low-frequency trajectory data. In this study, we aimed to accurately extract urban road network intersections and central locations from low-frequency GNSS trajectory data, and we developed a method for accurate road intersection identification based on filtered trajectory sequences and multiple clustering algorithms. Our approach was founded on the following principles. (1) We put in place a rigorous filtering rule to account for the offset characteristics of low-frequency trajectory data. (2) To overcome the low density and weak connection features of vehicle turning points, we adopted the CDC clustering algorithm. (3) By combining the projection features of orientation values in 2D coordinates, a mean solving method based on the DBSCAN algorithm was devised to obtain intersection center coordinates with greater accuracy. Our method could effectively identify urban road intersections and determine the center position and more effectively apply low-frequency trajectory data. Compared with remote sensing images, the intersection identification accuracy was 96.4%, the recall rate was 89.6%, and the F-value was 92.88% for our method; the intersection center position’s root mean square error (RMSE) was 10.39 m, which was 14.9% higher than that of the mean value method. Full article

(This article belongs to the Special Issue Big Data Analytics in Sustainable Transport Planning and Management)

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23 pages, 4732 KiB

Open AccessArticle

GPS Data Analytics for the Assessment of Public City Bus Transportation Service Quality in Bangkok

by Rathachai Chawuthai, Agachai Sumalee and Thanunchai Threepak

Sustainability 2023, 15(7), 5618; https://doi.org/10.3390/su15075618 - 23 Mar 2023

Cited by 1 | Viewed by 2003

Abstract

Evaluation of the quality of service (QoS) of public city buses is generally performed using surveys that assess attributes such as accessibility, availability, comfort, convenience, reliabilities, safety, security, etc. Each survey attribute is assessed from the subjective viewpoint of the service users. This is reliable and straightforward because the consumer is the one who accesses the bus service. However, in addition to summarizing personal feedback from humans, using data analytics has become another useful method for assessing the QoS of bus transportation. This work aims to use global positioning system (GPS) data to measure the reliability, accessibility, and availability of bus transportation services. There are three QoS scoring functions for tracking complete trips, on-path driving, and on-schedule operation. In the analytical process, GPS coordinates rounding is adopted and applied for detecting trips on each route path. After assessing the three QoS scores, it has been found that most bus routes have good operations with high scores, while some bus routes show room for improvement. Future work could use our data to create recommendations for policy makers in terms of how to improve a city’s smart mobility. Full article

(This article belongs to the Special Issue Big Data Analytics in Sustainable Transport Planning and Management)

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