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Advances in Public Transport Platform for the Development of Sustainability...
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Advances in Public Transport Platform for the Development of Sustainability Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 49044

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Juan M. Corchado

E-Mail Website
Guest Editor
1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Josep L. Larriba-Pey

E-Mail Website
Guest Editor
Data Maagement Group, Polytechnic University of Catalonia, Catalonia, Spain
Interests: computer science; decision sciences; social sciences; chemical engineering
Dr. Pablo Chamoso

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, Calle Espejo sn, 24.2, 37007 Salamanca, Spain
Interests: smart cities; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando De la Prieta Pintado

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern societies demand a high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. 

Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change of trend due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. 

This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency, which has become one of the neuralgic centers of sustainability. It is about producing, consuming, and moving people and goods better, with fewer resources and less environmental impact. 

The topics of interest for this issue include but are not limited to: 

•    Public transport;

•    Traffic management;

•    Smart cities;

•    Location-based systems;

•    Expert systems;

•    Routing algorithms;

•    Recommender systems;

•    Path planning and path finding;

•    Users’ profile analysis;

•    Distributed systems and platforms;

•    Smart city modeling and simulation;

•    Smart mobility and transportation;

•    Intelligent vehicles;

•    Smart traffic system operations;

•    Smart integrated grids;

•    Intelligent infrastructure;

•    Sensors and actuators;

•    Data visualization;

•    Cybersecurity;

•    Blockchain.

Prof. Dr. Juan Manuel Corchado Rodríguez
Dr. Josep L. Larriba-Pey
Dr. Pablo Chamoso Santos
Dr. Fernando De la Prieta Pintado
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • Traffic management
  • Smart cities
  • Recommender systems
  • Intelligent infrastructure

Published Papers (17 papers)

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Editorial

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3 pages, 176 KiB  
Editorial
Advances in Public Transport Platform for the Development of Sustainability Cities
by Juan M. Corchado, Josep L. Larriba-Pey, Pablo Chamoso-Santos and Fernando De la Prieta Pintado
Electronics 2021, 10(22), 2771; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10222771 - 12 Nov 2021
Cited by 1 | Viewed by 1394
Abstract
There is high and varied mobility in modern societies which requires a complex transport system that adapts to social needs and guarantees the movement of people and goods in an economically efficient and safe way [...] Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)

Research

Jump to: Editorial

16 pages, 1105 KiB  
Article
Extending a Trust model for Energy Trading with Cyber-Attack Detection
by Rui Andrade, Sinan Wannous, Tiago Pinto and Isabel Praça
Electronics 2021, 10(16), 1975; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10161975 - 17 Aug 2021
Cited by 2 | Viewed by 1908
Abstract
This paper explores the concept of the local energy markets and, in particular, the need for trust and security in the negotiations necessary for this type of market. A multi-agent system is implemented to simulate the local energy market, and a trust model [...] Read more.
This paper explores the concept of the local energy markets and, in particular, the need for trust and security in the negotiations necessary for this type of market. A multi-agent system is implemented to simulate the local energy market, and a trust model is proposed to evaluate the proposals sent by the participants, based on forecasting mechanisms that try to predict their expected behavior. A cyber-attack detection model is also implemented using several supervised classification techniques. Two case studies were carried out, one to evaluate the performance of the various classification methods using the IoT-23 cyber-attack dataset; and another one to evaluate the performance of the developed trust mode. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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15 pages, 4234 KiB  
Article
Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting
by Regina Sousa, Tiago Lima, António Abelha and José Machado
Electronics 2021, 10(14), 1630; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10141630 - 08 Jul 2021
Cited by 7 | Viewed by 2581
Abstract
Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time [...] Read more.
Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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19 pages, 3861 KiB  
Article
My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles
by Alberto Rivas, Alfonso González-Briones, Juan J. Cea-Morán, Arnau Prat-Pérez and Juan M. Corchado
Electronics 2021, 10(11), 1263; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111263 - 25 May 2021
Cited by 4 | Viewed by 1717
Abstract
New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during [...] Read more.
New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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19 pages, 406 KiB  
Article
Using a Hybrid Recommending System for Learning Videos in Flipped Classrooms and MOOCs
by Jaume Jordán, Soledad Valero, Carlos Turró and Vicent Botti
Electronics 2021, 10(11), 1226; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111226 - 21 May 2021
Cited by 6 | Viewed by 2040
Abstract
New challenges in education require new ways of education. Higher education has adapted to these new challenges by means of offering new types of training like massive online open courses and by updating their teaching methodology using novel approaches as flipped classrooms. These [...] Read more.
New challenges in education require new ways of education. Higher education has adapted to these new challenges by means of offering new types of training like massive online open courses and by updating their teaching methodology using novel approaches as flipped classrooms. These types of training have enabled universities to better adapt to the challenges posed by the pandemic. In addition, high quality learning objects are necessary for these new forms of education to be successful, with learning videos being the most common learning objects to provide theoretical concepts. This paper describes a new approach of a previously presented hybrid learning recommender system based on content-based techniques, which was capable of recommend useful videos to learners and lecturers from a learning video repository. In this new approach, the content-based techniques are also combined with a collaborative filtering module, which increases the probability of recommending relevant videos. This hybrid technique has been successfully applied to a real scenario in the central video repository of the Universitat Politècnica de València. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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16 pages, 685 KiB  
Article
UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities
by Subrata Saha, Alex Elkjær Vasegaard, Izabela Nielsen, Aneta Hapka and Henryk Budzisz
Electronics 2021, 10(10), 1193; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10101193 - 17 May 2021
Cited by 14 | Viewed by 2101
Abstract
Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework [...] Read more.
Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework to design trajectories that enable a set of UAVs to execute surveillance tasks. The first objective maximizes the cumulative probability of target detection to aim for mission planning success. The second objective ensures minimization of cumulative path length to provide a higher resource utilization goal. A two-step variable neighborhood search (VNS) algorithm is offered, which addresses the combinatorial optimization issue for determining the near-optimal sequence for cell visiting to reach the target. Numerical experiments and simulation results are evaluated in numerous benchmark instances. Results demonstrate that the proposed approach can favorably support practical deployability purposes. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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22 pages, 535 KiB  
Article
Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities
by Pedro Oliveira, Bruno Fernandes, Cesar Analide and Paulo Novais
Electronics 2021, 10(10), 1149; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10101149 - 12 May 2021
Cited by 17 | Viewed by 2412
Abstract
A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of [...] Read more.
A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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18 pages, 2096 KiB  
Article
Reputation System for Increased Engagement in Public Transport Oriented-Applications
by David García-Retuerta, Alberto Rivas, Joan Guisado-Gámez, Eleni Antoniou and Pablo Chamoso
Electronics 2021, 10(9), 1070; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10091070 - 30 Apr 2021
Cited by 2 | Viewed by 1564
Abstract
Increasing user engagement is one of the biggest challenges when a new application is developed. An engaged user is one who finds a product valuable; highly engaged users generate profit. This study focuses on increasing user engagement in a transport application, via a [...] Read more.
Increasing user engagement is one of the biggest challenges when a new application is developed. An engaged user is one who finds a product valuable; highly engaged users generate profit. This study focuses on increasing user engagement in a transport application, via a user reputation score feature. The score is to reward application users and activity organisers, as well as to motivate beginners by offering a high reputation score in the first days of use. The algorithms are based on exponential and logarithmic functions, and were first tested on synthetic data. Real-world tests have shown that the algorithms behave as expected, but the COVID-19 pandemic created a disturbance which prevented any user from achieving the maximum score and many users from registering altogether. Data show positive results, although the real number of users is not sufficient to certify a correct behaviour. Further tests will be carried out when transport activities return to normal. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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21 pages, 2913 KiB  
Article
Two Advanced Models of the Function of MRT Public Transportation in Taipei
by You-Shyang Chen, Chien-Ku Lin, Su-Fen Chen and Shang-Hung Chen
Electronics 2021, 10(9), 1048; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10091048 - 29 Apr 2021
Viewed by 1809
Abstract
Tour traffic prediction is very important in determining the capacity of public transportation and planning new transportation devices, allowing them to be built in accordance with people’s basic needs. From a review of a limited number of studies, the common methods for forecasting [...] Read more.
Tour traffic prediction is very important in determining the capacity of public transportation and planning new transportation devices, allowing them to be built in accordance with people’s basic needs. From a review of a limited number of studies, the common methods for forecasting tour traffic demand appear to be regression analysis, econometric modeling, time-series modeling, artificial neural networks, and gray theory. In this study, a two-step procedure is used to build a predictive model for public transport. In the first step of this study, regression analysis is used to find the correlations between two or more variables and their associated directions and strength, and the regression function is used to predict future changes. In the second step, the regression analysis and artificial neural network methods are assessed and the results are compared. The artificial neural network is more accurate in prediction than regression analysis. The study results can provide useful references for transportation organizations in the development of business operation strategies for managing sustainable smart cities. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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17 pages, 2093 KiB  
Article
A Data Mining and Analysis Platform for Investment Recommendations
by Elena Hernández-Nieves, Javier Parra-Domínguez, Pablo Chamoso, Sara Rodríguez-González and Juan M. Corchado
Electronics 2021, 10(7), 859; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10070859 - 04 Apr 2021
Cited by 6 | Viewed by 3775
Abstract
This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model [...] Read more.
This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model and the improvement of functionalities. The modules are: analysis and data mining, the forecasting system, the technical analysis module, the recommender system, and the visualization platform. The specification of each module is presented, as well as the dependencies and communication between them. Moreover, the proposal includes a visualization platform for high-level interaction between the user and the recommender system. This platform presents the conclusions that were abstracted from the resulting values. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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24 pages, 2941 KiB  
Article
A Mathematical Study of Barcelona Metro Network
by Irene Mariñas-Collado, Elisa Frutos Bernal, Maria Teresa Santos Martin, Angel Martín del Rey, Roberto Casado Vara and Ana Belen Gil-González
Electronics 2021, 10(5), 557; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10050557 - 27 Feb 2021
Cited by 8 | Viewed by 2900
Abstract
The knowledge of the topological structure and the automatic fare collection systems in urban public transport produce many data that need to be adequately analyzed, processed and presented. These data provide a powerful tool to improve the quality of transport services and plan [...] Read more.
The knowledge of the topological structure and the automatic fare collection systems in urban public transport produce many data that need to be adequately analyzed, processed and presented. These data provide a powerful tool to improve the quality of transport services and plan ahead. This paper aims at studying, from a mathematical and statistical point of view, the Barcelona metro network; specifically: (1) the structural and robustness characteristics of the transportation network are computed and analyzed considering the complex network analysis; and (2) the common characteristics of the different subway stations of Barcelona, based on the passenger hourly entries, are identified through hierarchical clustering analysis. These results will be of great help in planning and restructuring transport to cope with the new social conditions, after the pandemic. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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17 pages, 3088 KiB  
Article
The Influence of Public Transport Delays on Mobility on Demand Services
by Layla Martin, Michael Wittmann and Xinyu Li
Electronics 2021, 10(4), 379; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10040379 - 04 Feb 2021
Cited by 8 | Viewed by 3439
Abstract
Demand for different modes of transportation clearly interacts. If public transit is delayed or out of service, customers might use mobility on demand (MoD), including taxi and carsharing for their trip, or discard the trip altogether, including a first and last mile that [...] Read more.
Demand for different modes of transportation clearly interacts. If public transit is delayed or out of service, customers might use mobility on demand (MoD), including taxi and carsharing for their trip, or discard the trip altogether, including a first and last mile that might otherwise be covered by MoD. For operators of taxi and carsharing services, as well as dispatching agencies, understanding increasing demand, and changing demand patterns due to outages and delays is important, as a more precise demand prediction allows for them to more profitably operate. For public authorities, it is paramount to understand this interaction when regulating transportation services. We investigate the interaction between public transit delays and demand for carsharing and taxi, as measured by the fraction of demand variance that can be explained by delays and the changing OD-patterns. A descriptive analysis of the public transit data set yields that delays and MoD demand both highly depend on the weekday and time of day, as well as the location within the city, and that delays in the city and in consecutive time intervals are correlated. Thus, demand variations must by corrected for these external influences. We find that demand for taxi and carsharing increases if the delay of public transit increases and this effect is stronger for taxi. Delays can explain at least 4.1% (carsharing) and 18.8% (taxi) of the demand variance, which is a good result when considering that other influencing factors, such as time of day or weather exert stronger influences. Further, planned public transit outages significantly change OD-patterns of taxi and carsharing. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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26 pages, 11215 KiB  
Article
SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
by George Hatzivasilis, Konstantinos Fysarakis, Sotiris Ioannidis, Ilias Hatzakis, George Vardakis, Nikos Papadakis and George Spanoudakis
Electronics 2021, 10(1), 92; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10010092 - 05 Jan 2021
Cited by 8 | Viewed by 3964
Abstract
The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This [...] Read more.
The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This work presents SPD-Safe—an administration framework for railway CPS, leveraging artificial intelligence for monitoring and managing the system in real-time. The network layer protections integrated provide the core security properties of confidentiality, integrity, and authentication, along with energy-aware secure routing and authorization. The effectiveness in mitigating attacks and the efficiency under normal operation are assessed through simulations with the average delay in real equipment being 0.2–0.6 s. SPD metrics are incorporated together with safety semantics for the application environment. Considering an intelligent transportation scenario, SPD-Safe is deployed on railway critical infrastructure, safeguarding one outdoor setting on the railway’s tracks and one in-carriage setting on a freight train that contains dangerous cargo. As demonstrated, SPD-Safe provides higher security and scalability, while enhancing safety response procedures. Nonetheless, emergence response operations require a seamless interoperation of the railway system with emergency authorities’ equipment (e.g., drones). Therefore, a secure integration with external systems is considered as future work. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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20 pages, 3512 KiB  
Article
Bus Dynamic Travel Time Prediction: Using a Deep Feature Extraction Framework Based on RNN and DNN
by Yuan Yuan, Chunfu Shao, Zhichao Cao, Zhaocheng He, Changsheng Zhu, Yimin Wang and Vlon Jang
Electronics 2020, 9(11), 1876; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111876 - 08 Nov 2020
Cited by 21 | Viewed by 3311
Abstract
Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious [...] Read more.
Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious or even sudden, the changing mechanism of that is a hidden mode. Therefore, bus travel time prediction is a challenging problem in intelligent transportation system (ITS). Allowing for a large amount of traffic data can be collected at present but lack of precisely-conducting, it is still worth exploring how to extract feature sets that can accurately predict bus travel time from these data. Hence, a feature extraction framework based on the deep learning models were developed to reflect the state of bus travel time. First, the study introduced different historical stages of bus signaling time, taxi speed, the stop identity (ID) of spatial characteristics, and real-time possible arrival time, signified by fourteen spatiotemporal characteristic values. Then, an embedding network is proposed to leverage a wide and deep structure to mate the spatial and temporal data. In order to meet the temporal dependence requirements, an attention mechanism for a Recurrent Neural Network (RNN) was designed in this research in order to capture the temporal information. Finally, a Deep Neural Networks (DNN) was implemented in this research in order to achieve the dynamic bus travel time prediction. Two case studies of Guangzhou and Shenzhen were tested. The results showed that the performance of the algorithm was more efficient than that of the traditional machine-learning model and promoted by 4.82% compared to the deep neural network applied to the initial feature space. Moreover, the study visualized the weighted cost of attention on the bus’s travel time features during a certain running state. Therefore, the study demonstrated the proposed model enabled to understand the characteristic data of transit travel time with visualization. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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29 pages, 7943 KiB  
Article
Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit
by Guillermo L. Taboada and Liangxiu Han
Electronics 2020, 9(8), 1270; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9081270 - 07 Aug 2020
Cited by 15 | Viewed by 4986
Abstract
This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), [...] Read more.
This paper deals with the efficiency and sustainability of urban rail transit (URT) using exploratory data analytics (EDA) and data envelopment analysis (DEA). The first stage of the proposed methodology is EDA with already available indicators (e.g., the number of stations and passengers), and suggested indicators (e.g., weekly frequencies, link occupancy rates, and CO2 footprint per journey) to directly characterize the efficiency and sustainability of this transport mode. The second stage is to assess the efficiency of URT with two original models, based on a thorough selection of input and output variables, which is one of the key contributions of EDA to this methodology. The first model compares URT against other urban transport modes, applicable to route personalization, and the second scores the efficiency of URT lines. The main outcome of this paper is the proposed methodology, which has been experimentally validated using open data from the Transport for London (TfL) URT network and additional sources. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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24 pages, 1965 KiB  
Article
A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services
by Xiangpeng Wan, Hakim Ghazzai and Yehia Massoud
Electronics 2020, 9(4), 648; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9040648 - 15 Apr 2020
Cited by 19 | Viewed by 3821
Abstract
Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time [...] Read more.
Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers’ quality of experience and drivers’ benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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20 pages, 487 KiB  
Article
Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios
by Ivana Hartmann Tolić, Emmanuel Karlo Nyarko and Avishai (Avi) Ceder
Electronics 2020, 9(2), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9020360 - 20 Feb 2020
Cited by 10 | Viewed by 3415
Abstract
Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the [...] Read more.
Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the PT timetabling problem which takes into consideration the passenger waiting time (PWT) at a station and the vehicle occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. Due to the large search space of the problem, we use a multiobjective particle swarm optimization (MOPSO) algorithm to arrive at the solution of the problem. The results of the proposed method are compared with similar results from the existing literature. Full article
(This article belongs to the Special Issue Advances in Public Transport Platform for the Development of Sustainability Cities)
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