Smart Mobility Management Research Articles

How smart is mobility in smart cities? An analysis of citizens' value perceptions through ICT applications

Urban mobility plays a key role in any modern society. The advent of digital platforms and dedicated mobile apps, as well as new business models based on the concept of access over ownership and sharing economy (sharing mobility and Mobility as a Service), promises to reshape this sector. It will facilitate and speed up the diffusion of more sustainable and efficient means of transportation based on an intermodal mobility management perspective. The main goal of this paper is to examine the current state of smart mobility together with those factors that may influence the extent of modern sharing mobility proliferation and penetration through ICT applications and social media. The authors applied the content analysis using a sample of more than 1000 citizens' and visitors' reviews published on the official digital platforms of the smart city of Milan. The paper includes a comparison with data drawn from the public urban mobility services users. Based on the nodes resulting from the content analysis, the STEEP method was adopted in order to apply a more complex perspective concerning social, technological, environmental, economic and political issues. This strategic analytical tool was combined with the SWOT analysis dimensions to provide a comprehensive practical contribution to modern municipal smart mobility management systems. The results highlight the need for both smart mobility solutions suppliers and institutions to properly understand and communicate how digital services can be implemented across different users' touchpoints and communication channels to increase the value-added provided to citizens and visitors alike. • Urban mobility services assessment in a smart city context • Analyses of digital platforms and mobile apps for sharing mobility services • Content analysis and Network analysis of the reviews of mobility services' users • Multilevel approach based on the integration of STEEP and SWOT analyses • Theoretical contribution and strategic insights for policy-makers

Smart mobility management supported by modern information and communication technologies

Modern information and communication technologies such as artificial intelligence, big data, or the Internet of Things bring modern solutions not only for businesses and households but also for the state and municipalities. Today, many countries are trying to build a network of so-called smart cities. Their core areas are smart economy, environment, governance, housing, mobility, and people. This article looks at the field of smart mobility at a global level. On the one hand, the article aims to compare and summarize the latest rankings of smart cities. On the other hand, the authors of the article try to identify those applications of modern information and communication technologies that ensure innovative city development and thus their smart mobility. These applications are clearly described in the article to use in addressing the smart city issue in future research activities.

Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management

Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel rewards and carbon credits in transportation) have been discussed.

5G Smart Mobility Management Based Fuzzy Logic Controller Unit

In the paper, we propose a fuzzy logic controller system to be implemented for smart mobility management in the 5G wireless communication network. Mobility management is considered as a main issue for all-IP mobile networks future generation. As a network-based mobility management protocol, Internet Engineering Task Force developed the Proxy Mobile IPv6 (PMIPv6) in order to support the mobility of IP devices, and many other results were presented to reduce latency handover and the amount of PMIPv6 signaling, but it is not enough for the application needs in real-time. The present paper describes an approach based on the IEEE 802.21 Media Independent Handover (MIH) standard and PMIPv6, so we present a new vertical handover algorithm for anticipating handover process efficiently. Our object is to propose a smart mobility management that contribute in 5G wireless communication system network operating functions. Two proposed dynamic thresholds were successfully made to guaranty process triggering, and a new primitive MIH is proposed for signaling a needed handover to be done. Simulation results demonstrate a significant reduction of the handover delay, packet loss, handover blocking probability and signaling overhead. Simulation results and tests are accomplished.

A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Future mobile networks are envisioned to have critical limitations in terms of latency, energy usage, capacity and network resources since these networks are expected to become extremely dense and complex. The rapid enormous advances in recent technologies such as Internet of Things (IoT) highlights the urgent need for network performance enhancement as well. To this end, self-organizing networks are a promising solution to push the network performance to the next level. These scalable networks can dynamically adapt to possible changes in the network. Smart mobility management, in particular mobility prediction, is a subsection of self-organizing functions which are mainly based on the machine learning techniques. In this paper, we propose to estimate user’s future trajectory using machine learning approaches for a better network management. We propose a novel bidirectional trajectory prediction model called BTPM to model the user mobility behavior. The proposed method exploits the potential benefits of bidirectional gated recurrent unit (GRU) for having an accurate prediction. Moreover, we introduce a data preprocessing phase to obtain better results with significantly lower execution time. The proposed approach takes full advantage of data analysis in both directions (backward and forward) in order to provide a long-term prediction and model user’s mobility even with complex patterns. Experimental results show that the proposed bidirectional approach significantly improves the performance of the mobility predictor in terms of model accuracy, robustness and execution time. It achieves a model error of 0.014 and decreases the execution time up to 97%.

Deriving Daily Activity Schedules from Dynamic, Purpose-Dependent Origin–Destination Matrices

Emerging transport modeling approaches such as agent/activity-based modeling as well as the shift toward data-driven paradigm have stressed the need for high-quality travel behavior data at a disaggregate level. Despite the advances in personal mobility tracking and capturing, aggregate methods such as origin–destination (OD) matrices are still the most widespread means to organize and represent travel demand information. Nonetheless, traditional ODs cannot directly capture substantial elements which can considerably affect travel behavior (e.g., trip interdependency, trip chaining, etc.) therefore they are not the most suitable mean to facilitate relevant analysis. The currently presented framework alleviates this limitation by combining the individual trips present in multi-period, purpose-dependent OD matrices into sequences of trips originating and ending at users’ home location (i.e., tours). This is achieved by integrating graph-theoretical with combinatorial optimization concepts. A graph theory-based methodology is applied to first examine the spatiotemporal information in ODs and then identify all the plausible tours. Then the sequence of activities (i.e., daily activity schedule [DAS]) taking place during each diurnal tour is inferred based on the trip-purpose information contained in the original ODs. Finally, a combinatorial optimization routine identifies the optimum combination of DAS whose aggregated trips represent the total travel demand as observed in the OD matrices. The resulting DAS by the proposed estimation framework has proven to be of significant accuracy and provide valuable information in relation to the characteristics of the population’s travel behavior within the urban space environment. This information is particularly useful to mitigate against the upcoming challenges in the field of smart mobility management.

Analytical Modeling for Mobility Signalling in Ultradense HetNets

Multiband and multitier network densification is being considered the most promising solution to overcome the capacity-crunch problem of cellular networks. In this direction, small cells are being deployed within the macro cell (MC) coverage, to off-load some of the users associated with the MCs. This deployment scenario raises several problems. Among others, signalling overhead and mobility management will become critical considerations. Frequent handovers (HOs) in ultradense small cell (SC) deployments could lead to a dramatic increase in signalling overhead. This suggests a paradigm shift toward a signalling conscious cellular architecture with smart mobility management. To this end, the control/data separation architecture (CDSA) with dual connectivity is being considered for the future radio access. Considering the CDSA as the radio access network architecture, we quantify the reduction in HO signalling w.r.t. the conventional approach. We develop analytical models which compare the signalling generated during various HO scenarios in the CDSA and conventionally deployed networks. New parameters are introduced, which can with optimum value significantly reduce the HO signalling load. The derived model includes HO success and HO failure scenarios along with specific derivations for continuous and noncontinuous mobility users. Numerical results show promising CDSA gains in terms of saving in HO signalling overhead.

Smart mobility management in 5G heterogeneous networks

Mobility management has always been a challenging and important issue for the next-generation all-IP mobile networks. Seamless mobility is one of the requirements to provide real-time services in such networks. Proxy Mobile IPv6 (PMIPv6) has been developed by the Internet Engineering Task Force (IETF) as a network-based mobility management protocol to support the mobility of IP devices. Although several designs have been presented in the literature to reduce the amount of PMIPv6 signalling and latency handover, they do not satisfy the real-time application requirements. In this study, the authors first propose a novel vertical handover algorithm that helps to anticipate the handover process in an efficient manner. The suggested approach is based on PMIPv6 and IEEE 802.21 Media Independent Handover (MIH) standard. The authors also added a new MIH primitive in order to indicate that a handover is needed to be done very soon. The triggering of such process is done thanks to two proposed dynamic thresholds. According to the analytical evaluation and simulation results, the proposed method guarantees a significant reduction of the handover delay and the signalling overhead, handover blocking probability and packet loss.

Smart city as a tool for sustainable mobility and transport decarbonisation

The sustainable governance of transport systems remains a significant challenge for policy makers worldwide, particularly in cities. Urban areas are developing rapidly from a technological viewpoint, and innovative technologies create new possibilities for smart mobility management. Therefore, this study investigates the relationship between the implementation of the smart city concept and the idea of sustainable transport, particularly with regard to the reduction of transport generated CO2 emissions. The study estimates CO2 emissions for different potential scenarios of development for the Warsaw transport system until 2050 using the United Nations' ForFITS (For Future Inland Transport Systems) model. The study also analyses the additional impact on CO2 emissions of smart city elements as determinants of mobility. The results show that meeting the reduction targets set by the European Union 2011 White Paper on Transport will be challenging, requiring an in-depth transformation of the transport and energy sectors. This study also confirms that smart city solutions can play a crucial role in mitigating transport emissions and meeting reduction goals. The conclusions provide important insights for the design of smart mobility governance and enhance the relationship between transport policy and research.

Policy 2.0 Platform for Mobile Sensing and Incentivized Targeted Shifts in Mobility Behavior.

Sustainable mobility and smart mobility management play important roles in achieving smart cities’ goals. In this context we investigate the role of smartphones as mobility behavior sensors and evaluate the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones. The empirical results are based on mobile sensed data collected from more than 3400 people’s real life over a period of six months. The findings show which user profiles are most likely to accept such incentives and how likely they are to result in more sustainable mode choices. In addition we provide insights into tendencies towards accepting more sustainable route options for different trip purposes and illustrate smart city platform potential (for collection of mobility behavior data and delivery of incentives) as a tool for development of personalized mobility management campaigns and policies.

Challenges for Coexistence of Machine to Machine and Human to Human Applications in Mobile Network: Concept of Smart Mobility Management

A key factor for the evolution of the mobile networks towards 4G is to bring to fruition high bandwidth per mobile node. Eventually, due to the advent of a new class of applications, namely, Machine-to-Machine, we foresee new challenges where bandwidth per user is no more the primal driver. As an immediate impact of the high penetration of M2M devices, we envisage a surge in the signaling messages for mobility and location management. The cell size will shrivel due to high tele-density resulting in even more signaling messages related to handoff and location updates. The mobile network should be evolved to address various nuances of the mobile devices used by man and machines. The bigger question is as follows. Is the state-of-the-art mobile network designed optimally to cater both the Human-to-Human and Machine-to-Machine applications? This paper presents the primary challenges for the coexistence of M2M and H2H devices in a mobile network and draws emphasis for revisiting the mobility management aspects and congestion control in the-state-of-the-art network. Further, we set out a mobile network architecture with smart mobility management which aims to reduce the signaling interaction between the device and the network to optimise the power and bandwidth.