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Special Issue "Smart Mobility for Future Cities"

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

Deadline for manuscript submissions: closed (1 May 2019).

Special Issue Editor

Guest Editor
Assoc. Prof. Hussein Dia

Chair, Department of Civil and Construction Engineering, Deputy Director and Program Leader (Future Urban Mobility), Smart Cities Research Institute, Swinburne University of Technology, Melbourne, Australia
Website | E-Mail
Interests: smart mobility; intelligent transport systems; smart cities; smart infrastructure; transport modelling; infrastructure monitoring; transportation systems engineering

Special Issue Information

Dear Colleagues,

The 2020s are predicted to be a decade of transformation for urban mobility. There are at least six forces that are expected to disrupt the urban mobility landscape in the coming years. From self-driving vehicles and the sharing economy, through to vehicle electrification, mobile computing, the Internet of Things and Blockchain technologies, each of these trends is quite significant on its own. However, the convergence and coming together of their disruptive forces is what will create real value and provide smart urban mobility innovations. Once converged, they will enhance the travel experience for millions of people and businesses every day. Many questions remain though: How will the future of transport look with autonomous on-demand shared electric vehicles? Will personal ownership of cars decline, giving way to fleet-operated shared vehicles? Will mobility be offered as a subscription service? Are we edging closer to a vision of “zero accidents, zero emissions, and zero car ownership”? And how do we prepare for the coming transport revolution?

This Special Issue will comprise a selection of papers presenting original and innovative contributions to the advancement of smart urban mobility research in areas related to intelligent transport systems, advanced transport modelling and simulation, Artificial Intelligence and machine learning applications, autonomous urban mobility, and development of disruptive interventions and technologies that will enable step-changes in emerging and future modes of urban mobility. Papers selected for this Special Issue will be subject to a rigorous peer-review process with the aim of rapid and wide dissemination of research results, developments, and applications.

Associate Professor Hussein Dia
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 papers will be 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 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

  • smart urban mobility
  • intelligent transport systems
  • autonomous urban mobility
  • transport modelling and simulation
  • digital innovations in urban mobility
  • future urban mobility systems

Published Papers (13 papers)

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Research

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Open AccessArticle
A System of Shared Autonomous Vehicles Combined with Park-And-Ride in Residential Areas
Sustainability 2019, 11(11), 3113; https://doi.org/10.3390/su11113113
Received: 28 April 2019 / Revised: 15 May 2019 / Accepted: 28 May 2019 / Published: 2 June 2019
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Abstract
As suburbanization and unprecedented population aging are converging, enhanced personal mobility for suburban residents is required. In this study, a collaborative scheme involving park-and-ride services associated with public transport and a shared autonomous vehicle system are proposed. Two residential areas in the Nagoya [...] Read more.
As suburbanization and unprecedented population aging are converging, enhanced personal mobility for suburban residents is required. In this study, a collaborative scheme involving park-and-ride services associated with public transport and a shared autonomous vehicle system are proposed. Two residential areas in the Nagoya metropolitan region, Japan, are considered: a residential area at the outer edge of a subway line and a commuter town with a nearby railway station. Three user groups are assumed: park-and-ride commuters who park shared autonomous vehicles at the station and take the train to their workplaces; inbound commuters who disembark from trains at the station and use the vehicles to reach their workplaces within the target area; and elderly and disabled residents, who use shared autonomous vehicles for trips within the target area. The system performance is investigated through agent-based simulation. The results suggest that, in the edge case, approximately 400 shared autonomous vehicles can facilitate more than 10,000 trips at an appropriate level of service. For the commuter town, fewer than 400 vehicles can provide rapid responses with a wait time of approximately 5 min for more than 5000 trips per day. Thus, the proposed system can feasibly provide a quick response service. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs
Sustainability 2019, 11(10), 2736; https://doi.org/10.3390/su11102736
Received: 9 April 2019 / Revised: 4 May 2019 / Accepted: 4 May 2019 / Published: 14 May 2019
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Abstract
Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and [...] Read more.
Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
Sustainability 2019, 11(9), 2624; https://doi.org/10.3390/su11092624
Received: 27 March 2019 / Revised: 25 April 2019 / Accepted: 27 April 2019 / Published: 7 May 2019
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Abstract
The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular [...] Read more.
The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Evaluation of the Use of a City Center through the Use of Bluetooth Sensors Network
Sustainability 2019, 11(4), 1002; https://doi.org/10.3390/su11041002
Received: 4 January 2019 / Revised: 11 February 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
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Abstract
In order to achieve the objectives of Smart Cities, public administrations need to take measures to regulate mobility, which undoubtedly requires a high level of information and sensorization. Until the implementation of the connected vehicle takes place, it is still necessary to install [...] Read more.
In order to achieve the objectives of Smart Cities, public administrations need to take measures to regulate mobility, which undoubtedly requires a high level of information and sensorization. Until the implementation of the connected vehicle takes place, it is still necessary to install sensors to obtain information about mobility. Bluetooth sensors are becoming a useful tool due to the low cost of equipment and installation. The use of Bluetooth sensors in cities, with short distances between sensors, makes it necessary to propose new classification algorithms that allow the trips of pedestrians and vehicles to be differentiated. This article presents the study carried out in the city of Valencia to determine the use of motor vehicles in the historic center and propose a new classification algorithm to distinguish between an onboard Bluetooth device and the same device carried by a pedestrian when it is not possible to use the travel time for the classification due to the short distance between sensors. This causes very similar or even indistinguishable travel times for vehicles and for pedestrians. We also propose an algorithm that allows vehicles to be classified according to what type of trip is made always through the historical center of Valencia, whether it is to make a shorter itinerary through the city or to access the center for any type of business. This algorithm would enable the Origin-Destination matrix of an urban network with short distances between sensors if they are available in all entries and exits. Likewise, the results obtained have allowed to positively evaluate the algorithm defined to distinguish between trips made by a pedestrian or a vehicle in a city, using the MAC address of their mobile devices with very short distances among sensors. The results of this study show that it is possible to use Bluetooth technology, with low cost installations, to evaluate the use of the city by motor vehicles. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
The Consumer Acceptance of Smart Product-Service Systems in Sharing Economy: The Effects of Perceived Interactivity and Particularity
Sustainability 2019, 11(3), 928; https://doi.org/10.3390/su11030928
Received: 22 January 2019 / Revised: 9 February 2019 / Accepted: 10 February 2019 / Published: 12 February 2019
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Abstract
With the rapid growth of the sharing economy, access-based services have emerged as an alternative and/or complementary to traditional ownership-based services. The access-based services are enabled by means of Smart Product-Service Systems (SPSSs) that integrate smart products and e-services into a single solution. [...] Read more.
With the rapid growth of the sharing economy, access-based services have emerged as an alternative and/or complementary to traditional ownership-based services. The access-based services are enabled by means of Smart Product-Service Systems (SPSSs) that integrate smart products and e-services into a single solution. However, there is a lack of studies that cover the acceptance factors for both smart products and e-services of SPSSs. Therefore, it is important to have a study to explore the factors that influence the acceptance of SPSSs. This study develops a conceptual framework which consists of the perceived interactivity of mobile apps and the particularity of the smart shared products as antecedents apart from perceived usefulness and perceived ease of use, as suggested by the Technology Acceptance Model. To test the research framework empirically, a self-reported online survey was conducted among bike sharing program users in China. A total of 520 valid responses were collected, and the partial least-square structural equation modeling (PLS-SEM) technique was used to examine the research model. The empirical results suggest that the perceived interactivity of mobile apps and the particularity of smart shared products are two significant sets of antecedents that influence consumers’ perceived ease of use and perceived usefulness, and the perceived ease of use and perceived usefulness are preconditions for the acceptance of SPSSs. The findings generate practical suggestions for SPSSs providers to increase the network size of users, improve the interactivity of mobile apps, and manage the distributions of service points. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan
Sustainability 2019, 11(3), 808; https://doi.org/10.3390/su11030808
Received: 7 January 2019 / Revised: 17 January 2019 / Accepted: 21 January 2019 / Published: 4 February 2019
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Abstract
In recent years, car sharing has emerged as a novel alternative to private car ownership in urban areas worldwide. Potential benefits of this system include improved mobility and reduced congestion, vehicle ownership, parking issues, and greenhouse gas (GHG) emissions. This study aimed to [...] Read more.
In recent years, car sharing has emerged as a novel alternative to private car ownership in urban areas worldwide. Potential benefits of this system include improved mobility and reduced congestion, vehicle ownership, parking issues, and greenhouse gas (GHG) emissions. This study aimed to investigate travelers’ acceptance of car sharing systems through a stated preference survey in the city of Peshawar, Pakistan. The questionnaires were distributed online via a Google form. Questions were designed from numerous aspects of car sharing systems, such as awareness of car sharing systems, attributes related to travel modes in the choice set, and demographic characteristics. A total of 453 valid responses were received. The Multinomial and Nested Logit models were employed for evaluation and analysis of survey responses. Demographic characteristics including gender, job, and income were found to be significant. Service attributes including travel time, travel cost, registration fees, and capital cost, were also significant. The multinomial logit model based on both car-owners and non-car-owners fit a little better than the nested logit model. Our findings in the present study could be beneficial for transport planners and policy makers to timely implement car sharing systems in cities in order to mitigate increased car ownership and traffic congestion. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Towards Autonomous Transportation. Passengers’ Experiences, Perceptions and Feelings in a Driverless Shuttle Bus in Finland
Sustainability 2019, 11(3), 588; https://doi.org/10.3390/su11030588
Received: 19 December 2018 / Revised: 8 January 2019 / Accepted: 19 January 2019 / Published: 23 January 2019
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Abstract
Autonomous vehicles, electrification, and ride-sharing appear to be the next big change in the field of mobility. It can lead to safer roads, less congestion, and reduced parking. In this research, we focus on real-life user experiences of a driverless shuttle bus. We [...] Read more.
Autonomous vehicles, electrification, and ride-sharing appear to be the next big change in the field of mobility. It can lead to safer roads, less congestion, and reduced parking. In this research, we focus on real-life user experiences of a driverless shuttle bus. We are interested to know what kind of perceptions and feelings people have when they travel in an autonomous shuttle bus. Therefore, we apply Harry Triandis´ Theory of Interpersonal Behaviour (TIB), which recognizes that human behavior is not always rational. Human behaviour, and its change, is linked to the intention, the habitual responses, and the situational constraints and conditions. The qualitative data (n = 44) were collected in 2017 by semi-structured interviews in Espoo, Finland. The interviewees were passengers who travelled a predefined route in a driverless shuttle bus. We applied inductive content analysis. The findings were compared in the theoretical framework of TIB. According to the results, a lack of human driver was not a problem for the passengers. They were surprised how safe and secure they felt in the autonomous vehicle. More specifically, passengers´ perceptions were similar to when travelling by a metro or a tram, where a passenger rarely interacts with the driver, or even witnesses the existence of the driver. However, the results suggest that people are much more intolerant of accidents caused by autonomous vehicles than by humans. On a general level, positive attitudes towards autonomous vehicles can be supported by giving people possibilities to try autonomous vehicles in a safe, real-life environment. The decision whether to use a driverless shuttle bus or not correlates highly with the contextual factors. Route and flexibility are the most important reasons for behavioral changes. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
Implementation of Connected and Autonomous Vehicles in Cities Could Have Neutral Effects on the Total Travel Time Costs: Modeling and Analysis for a Circular City
Sustainability 2019, 11(2), 482; https://doi.org/10.3390/su11020482
Received: 12 December 2018 / Revised: 10 January 2019 / Accepted: 11 January 2019 / Published: 17 January 2019
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Abstract
Autonomous vehicles promise to revolutionize the automobile market, although their implementation could take several decades in which both types of cars will coexist on the streets. We formulate a model for a circular city based on continuous approximations, considering demand surfaces over the [...] Read more.
Autonomous vehicles promise to revolutionize the automobile market, although their implementation could take several decades in which both types of cars will coexist on the streets. We formulate a model for a circular city based on continuous approximations, considering demand surfaces over the city. Numerical results from our model predict direct and indirect effects of connected and autonomous vehicles. Direct effects will be positive for our cities: (a) less street supply is needed to accommodate the traffic; (b) congestion levels decrease: travel costs may decrease by 30%. Some indirect effects will counterbalance these positive effects: (c) a decrease of 20% in the value of travel time can reduce the total cost by a third; (d) induced demand could be as high as 50%, bringing equivalent total costs in the future scenario; (e) the vehicle-kilometers traveled could also affect the future scenario; and (f) increases in city size and urban sprawl. As a conclusion, the implementation of autonomous vehicles could be neutral for the cities regarding travel time costs. City planning agencies still have to promote complementary modes such as active mobility (walking and bicycle), transit (public transportation), and shared mobility (shared autonomous vehicles and mobility as a service). Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
What is Smart for the Future City? Mobilities and Automation
Sustainability 2019, 11(1), 221; https://doi.org/10.3390/su11010221
Received: 26 November 2018 / Revised: 11 December 2018 / Accepted: 14 December 2018 / Published: 4 January 2019
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Abstract
Cities have changed their pulse, their pace, and reach, and the urban scale is an interconnected element of the global “network society” with new forms of social, cultural and economic life emerging. The increase in the amount and speed of mobilities has strong [...] Read more.
Cities have changed their pulse, their pace, and reach, and the urban scale is an interconnected element of the global “network society” with new forms of social, cultural and economic life emerging. The increase in the amount and speed of mobilities has strong impacts on ecological conditions, and, so far, no comprehensive sustainable solutions are in sight. This paper focuses on the discussion around smart cities, with a specific focus on automation and sustainability. Discourses on automated mobility in urban spaces are in a process of creation and different stakeholders contribute in shaping the urban space and its infrastructures for automated driving in the near or distant future. In many ways, it seems that the current storylines, to a high degree, reinforce and (re)produce the “system of automobility”. Automobility is still treated as the iconic and taken-for-granted form of modern mobility. It seems that most actors from industry, planning, and politics consider it as being sustained through smart and green mobility innovations and modifications. The paper discusses the implication of these techno-policy discourses and storylines for urban planning. It presents preliminary results from ongoing research on policy promotion strategies of automated driving in the region of Munich, Germany. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
Open AccessArticle
Public Transport-Based Crowdshipping for Sustainable City Logistics: Assessing Economic and Environmental Impacts
Sustainability 2019, 11(1), 145; https://doi.org/10.3390/su11010145
Received: 4 December 2018 / Revised: 20 December 2018 / Accepted: 21 December 2018 / Published: 28 December 2018
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Abstract
This paper aims at understanding and evaluating the environmental and economic impacts of a crowdshipping platform in urban areas. The investigation refers to the city of Rome and considers an environmental-friendly crowdshipping based on the use of the mass transit network of the [...] Read more.
This paper aims at understanding and evaluating the environmental and economic impacts of a crowdshipping platform in urban areas. The investigation refers to the city of Rome and considers an environmental-friendly crowdshipping based on the use of the mass transit network of the city, where customers/crowdshippers pick-up/drop-off goods in automated parcel lockers located either inside the transit stations or in their surroundings. Crowdshippers are passengers that would use the transit network anyhow for other activities (e.g., home-to-work), thus avoiding additional trips. The study requires firstly, estimating the willingness to buy a crowdshipping service like the one proposed here, in order to quantify the potential demand. The estimation is realized adopting an extensive stated preference survey and discrete choice modeling. Then, several scenarios with different features of the service are proposed and evaluated up to 2025 in terms of both externalities (local and global pollutant emissions, noise emissions and accidents reductions) and revenues. The results are useful to understand and quantify the potential of this strategy for last mile B2C deliveries. Moreover, it provides local policy-makers and freight companies with a good knowledge base for the future development of a platform for public transport-based crowdshipping and for estimating the likely impact the system could have both from an economic and environmental point of view. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessArticle
The Influence of Noise, Vibration, Cycle Paths, and Period of Day on Stress Experienced by Cyclists
Sustainability 2018, 10(7), 2379; https://doi.org/10.3390/su10072379
Received: 12 June 2018 / Revised: 30 June 2018 / Accepted: 5 July 2018 / Published: 9 July 2018
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Abstract
Urban and transport planners need to assess the stressful conditions experienced by cyclists, considering that highly stressful situations can discourage people from cycling as a transport mode. Therefore, this study has two objectives: (1) to present a method for monitoring stress and other [...] Read more.
Urban and transport planners need to assess the stressful conditions experienced by cyclists, considering that highly stressful situations can discourage people from cycling as a transport mode. Therefore, this study has two objectives: (1) to present a method for monitoring stress and other environmental factors along cycling routes using smart sensors; and (2) to analyze the influence of noise, vibration, presence of cycle paths, and the period of the day on stress experienced by cyclists. Data were collected in the city of São Carlos, Brazil, using stress and noise sensors, accelerometers, and Global Positioning System (GPS). Primarily, heat maps generated from the data made it possible to identify critical points of stress along the routes. In addition, the results of a logistic regression model were analyzed to identify the influence of the studied variables on stress. Although high levels of noise increased the odds of experiencing stress by 4%, very uncomfortable vibrations increased the odds by 14%, and the presence of cycle paths reduced the odds by 8%, an analysis of p-values and odds ratio confidence intervals shows, with a 95% confidence level, that only the period of the day influenced stress, as confirmed by the data. In this case, the odds of having stress increased by 24% in the afternoon rush hour compared to the morning rush hour. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Review

Jump to: Research

Open AccessReview
Applications of Artificial Intelligence in Transport: An Overview
Sustainability 2019, 11(1), 189; https://doi.org/10.3390/su11010189
Received: 4 November 2018 / Revised: 18 December 2018 / Accepted: 24 December 2018 / Published: 2 January 2019
Cited by 4 | PDF Full-text (1610 KB) | HTML Full-text | XML Full-text
Abstract
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain [...] Read more.
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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Open AccessReview
An Overview of Shared Mobility
Sustainability 2018, 10(12), 4342; https://doi.org/10.3390/su10124342
Received: 10 October 2018 / Revised: 12 November 2018 / Accepted: 16 November 2018 / Published: 22 November 2018
Cited by 3 | PDF Full-text (1524 KB) | HTML Full-text | XML Full-text
Abstract
In a wider understanding, shared mobility can be defined as trip alternatives that aim to maximize the utilization of the mobility resources that a society can pragmatically afford, disconnecting their usage from ownership. Then, shared mobility is the short-term access to shared vehicles [...] Read more.
In a wider understanding, shared mobility can be defined as trip alternatives that aim to maximize the utilization of the mobility resources that a society can pragmatically afford, disconnecting their usage from ownership. Then, shared mobility is the short-term access to shared vehicles according to the user’s needs and convenience. The contributions and added value of this paper are to provide an up-to-date and well-structured review on the area of shared mobility to researchers and practitioners of the transport sector. Hence, this paper presents a bibliographical review of shared mobility and its diverse modalities, as an alternative to individual transportation, especially in cases of individual automobiles or short trips restricted to an urban city. The present literature review on shared modes of transportation has discovered that the introduction of these modes alone will not solve transportation problems in large cities, with elevated and growing motorization rates. However, it can among the strategies employed to help alleviate the problems caused by traffic jams and pollution by reducing the number of vehicles in circulation, congestions, and the urban emission of polluting gases. Thus, the implementation of shared mobility schemes offers the potential to enhance the efficiency, competitiveness, social equity, and quality of life in cities. This paper covers the fundamental aspects of vehicle and/or ride sharing in urban centers, and provides an overview of current shared mobility systems. Full article
(This article belongs to the Special Issue Smart Mobility for Future Cities)
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