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Intelligent Mobility: Technologies, Applications and Services
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Intelligent Mobility: Technologies, Applications and Services

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 16058

Special Issue Editors

Prof. Dr. Sadko Mandzuka

E-Mail Website
Guest Editor
ITS Department, Faculty of Transport and Traffic Sciences, University of Zagreb, Zagreb, Croatia
Interests: intelligent transport systems; intelligent mobility; cooperative systems; highway traffic management systems; traffic sustainability; expert systems in traffic
Special Issues, Collections and Topics in MDPI journals
Dr. Kresimir Vidovic

E-Mail
Guest Editor
Ericsson Nikola Tesla d.d., Zagreb, Croatia
Interests: intelligent transport systems; application of ICT technologies in traffic and transport; intelligent mobility; application of data science in traffic and logistics; transport planning; mobility indicators

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue is to present contemporary R & D achievements in urban mobility known as intelligent mobility. Modern urban traffic problems can no longer be solved solely by old approaches expanding various transport capacities. This approach only leads to more traffic induced by greater demands. The intelligent mobility approach addresses the problem of urban transport using information and communication technologies, via novel knowledge on how to plan and control such complex systems and processes and new business models in the transport of passengers and goods. The leading principles of intelligent mobility are: efficiency, safety, human friendliness, flexibility, sustainability, integration, and clean technologies. In that sense, intelligent mobility is one of the main avenues of the smart city concept.

Prof. Dr. Sadko Mandzuka
Dr. Kresimir Vidovic
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. Sustainability 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

  • Intelligent transport systems
  • Cooperative systems
  • Autonomous vehicles
  • Business models for new mobility platforms
  • Intelligent mobility and smart city
  • Sustainability
  • Technologies for intelligent transport planning
  • Sensor networks
  • Big data in urban mobility

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Published Papers (5 papers)

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Research

19 pages, 3394 KiB  
Article
A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles
by Youssef NaitMalek, Mehdi Najib, Anas Lahlou, Mohamed Bakhouya, Jaafar Gaber and Mohamed Essaaidi
Sustainability 2022, 14(16), 9993; https://doi.org/10.3390/su14169993 - 12 Aug 2022
Cited by 6 | Viewed by 2114
Abstract
Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to [...] Read more.
Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery’s capacity. Hence, knowing in advance the battery’s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Salé-Kénitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems. Full article
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
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24 pages, 5789 KiB  
Article
An MFD Construction Method Considering Multi-Source Data Reliability for Urban Road Networks
by Rongrong Hong, Huan Liu, Chengchuan An, Bing Wang, Zhenbo Lu and Jingxin Xia
Sustainability 2022, 14(10), 6188; https://doi.org/10.3390/su14106188 - 19 May 2022
Cited by 5 | Viewed by 2347
Abstract
Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core [...] Read more.
Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Although many studies have been conducted to construct MFDs, few studies are dedicated to improving the accuracy considering the reliability of different sources of data. To this end, we propose an MFD construction method using multi-source data based on Dempster–Shafer evidence (DS evidence) theory considering the reliability of different data sources. First, the MFD was constructed using VTD and CSD, separately. Then, the fused MFD was derived by quantifying the reliability of different sources of data for each MFD parameter based on DS evidence theory. The results under real data and simulated data show that the accuracy of the constructed MFDs was greatly improved considering the reliability of different data sources (the maximum MFD estimation error was reduced by 22.3%). The proposed method has the potential to support the evaluation of traffic operations and the optimization of signal control schemes for urban traffic networks. Full article
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
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20 pages, 8557 KiB  
Article
Transition State Matrices Approach for Trajectory Segmentation Based on Transport Mode Change Criteria
by Martina Erdelić, Tonči Carić, Tomislav Erdelić and Leo Tišljarić
Sustainability 2022, 14(5), 2756; https://doi.org/10.3390/su14052756 - 26 Feb 2022
Cited by 6 | Viewed by 2487
Abstract
Identifying distribution of users’ mobility is an essential part of transport planning and traffic demand estimation. With the increase in the usage of mobile devices, they have become a valuable source of traffic mobility data. Raw data contain only specific traffic information, such [...] Read more.
Identifying distribution of users’ mobility is an essential part of transport planning and traffic demand estimation. With the increase in the usage of mobile devices, they have become a valuable source of traffic mobility data. Raw data contain only specific traffic information, such as position. To extract additional information such as transport mode, collected data need to be further processed. Trajectory needs to be divided into several meaningful consecutive segments according to some criteria to determine transport mode change point. Existing algorithms for trajectory segmentation based on the transport mode change most often use predefined knowledge-based rules to create trajectory segments, i.e., rules based on defined maximum pedestrian speed or the detection of pedestrian segment between two consecutive transport modes. This paper aims to develop a method that segments trajectory based on the transport mode change in real time without preassumed rules. Instead of rules, transition patterns are detected during the transition from one transport mode to another. Transition State Matrices (TSM) were used to automatically detect the transport mode change point in the trajectory. The developed method is based on the sensor data collected from mobile devices. After testing and validating the method, an overall accuracy of 98% and 96%, respectively, was achieved. As higher accuracy of trajectory segmentation means better and more homogeneous data, applying this method during the data collection adds additional value to the data. Full article
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
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20 pages, 780 KiB  
Article
Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows
by Filip Vrbanić, Mladen Miletić, Leo Tišljarić and Edouard Ivanjko
Sustainability 2022, 14(2), 932; https://doi.org/10.3390/su14020932 - 14 Jan 2022
Cited by 16 | Viewed by 3049
Abstract
Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a [...] Read more.
Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway. Full article
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
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22 pages, 31544 KiB  
Article
Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement
by Marko Šoštarić, Krešimir Vidović, Marijan Jakovljević and Orsat Lale
Sustainability 2021, 13(13), 7162; https://doi.org/10.3390/su13137162 - 25 Jun 2021
Cited by 16 | Viewed by 4583
Abstract
The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, [...] Read more.
The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders. Full article
(This article belongs to the Special Issue Intelligent Mobility: Technologies, Applications and Services)
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