Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.1
Journals
Information
Special Issues
Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation...
share announcement

Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 17404

Special Issue Editor

Prof. Dr. Bartłomiej Płaczek

E-Mail Website
Guest Editor
Institute of Computer Science, University of Silesia, 41-200 Sosnowiec, Poland
Interests: sensor networks; machine learning; intelligent transportation systems; sensor fusion; smart sensors; cellular automata
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent transportation systems are expected to improve utilization of the existing infrastructure, alleviate the congestion problem, increase safety, and enhance quality of life. Modern logistics systems are being introduced to improve the performance of supply chains and reduce the costs of logistics operations. The implementation of these systems requires advanced sensing and communication technologies for real-time monitoring of the transportation processes. Moreover, new methods have to be elaborated to effectively utilize the collected transportation data for decision making and control tasks.

Therefore, the purpose of this Special Issue is to present the latest developments in sensing, data communication, and intelligent systems for transportation and logistics. Investigators in the field are invited to contribute with their original, unpublished works. Both research and review papers are welcome.

Topics of interest include but are not limited to:

Communication technologies for transportation and logistics:

- Sensor and vehicular networks;
- Smart sensing;
- In-network data processing;
- Internet of Things and Internet of Vehicles;
- Mobile edge computing;
- Fifth-generation mobile communication (5G).

Applications of artificial intelligence in transportation and logistics:

- Decision support systems;
- Machine learning;
- Deep learning;
- Evolutionary computations;
- Self-organizing systems;
- Swarm intelligence and bio-inspired computing;
- Multiagent systems;
- Modeling and simulation of transportation systems;
- Cellular automata;
- Pattern recognition and image processing.

Dr. Bartłomiej Płaczek
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 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. 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 1600 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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 2490 KiB  
Article
Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco
by Mouna Jiber, Abdelilah Mbarek, Ali Yahyaouy, My Abdelouahed Sabri and Jaouad Boumhidi
Information 2020, 11(12), 542; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120542 - 24 Nov 2020
Cited by 9 | Viewed by 3119
Abstract
An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In [...] Read more.
An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance. Full article
(This article belongs to the Special Issue Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems)
►▼ Show Figures

Figure 1

17 pages, 1618 KiB  
Article
A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks
by Bartłomiej Płaczek, Marcin Bernas and Marcin Cholewa
Information 2020, 11(11), 496; https://doi.org/10.3390/info11110496 - 23 Oct 2020
Cited by 1 | Viewed by 1532
Abstract
This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the [...] Read more.
This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control. Full article
(This article belongs to the Special Issue Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems)
►▼ Show Figures

Figure 1

22 pages, 8239 KiB  
Article
An Improved Traffic Congestion Monitoring System Based on Federated Learning
by Chenming Xu and Yunlong Mao
Information 2020, 11(7), 365; https://0-doi-org.brum.beds.ac.uk/10.3390/info11070365 - 16 Jul 2020
Cited by 9 | Viewed by 4743
Abstract
This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in [...] Read more.
This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion. Full article
(This article belongs to the Special Issue Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems)
►▼ Show Figures

Figure 1

19 pages, 3984 KiB  
Article
T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities
by Nyothiri Aung, Weidong Zhang, Sahraoui Dhelim and Yibo Ai
Information 2020, 11(3), 149; https://0-doi-org.brum.beds.ac.uk/10.3390/info11030149 - 09 Mar 2020
Cited by 23 | Viewed by 4225
Abstract
Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting [...] Read more.
Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system. Full article
(This article belongs to the Special Issue Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems)
►▼ Show Figures

Graphical abstract

16 pages, 2490 KiB  
Article
An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car
by Bo Sun, Ming Wei and Wei Wu
Information 2019, 10(12), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/info10120370 - 26 Nov 2019
Cited by 12 | Viewed by 3199
Abstract
Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning [...] Read more.
Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared. Full article
(This article belongs to the Special Issue Smart Sensing, Data Communication, and Decision Making in Intelligent Transportation and Logistics Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop