Multimodal Mobility Framework: Towards Seamless Mobility Experience

Mobility is no longer just a necessity for travelers, but choices among several possible routes and transportation modes. Urban passenger rail transport plays an essential role because it is affordable, convenient, safe, and fast. On the other hand, rail lines are limited to high passenger density corridors. Inevitably, rail has to be placed together with different transport modes, forming a multimodal network. However, to enable this integration with other modes of transport, numerous practical problems remain, such as making a smooth transition from the existing siloed, mode specific operational structure towards an interconnected system of transportation modes and business models for a seamless connected journey. The current isolated operational structure lacks a single truth and accurate visibility, which further discourages participation from augmenting transportation modes and leads to the extended reaction time for new technology integration. This research article introduces a Multimodal Mobility (MMM) solution framework that provides a functional interface to integrate and synchronize the railroad operations with other public transit networks (including train-bus-rapid transits) and micro-mobility services. The known approach to addressing the users’ seamless mobility experience entails a centralized, prearranged, a priori knowledge and mechanism for operating intermodal transport systems. In contrast, the method defined in this paper focuses on a market-driven demand-responsive system that allows for dis-intermediation in a network of peer-level transportation modes operations. The framework facilitates blockchain-based decentralized and multi-organizational engagement. The focus here is the role of railroad in the multimodal ecosystem and its performance advancements in this integrated solutions framework. Leveraging a combination of graph analytics and machine learning algorithms, we provide methods to address challenges in encoding spatial and temporal dependencies of multimodal transit networks and handle complex optimization problems such as mixed time window and volume variation for resource allocation and transit operational analytics. This enables operation of different transit modes with varied resolution and flexibility for operational parameters like time, capacity, ridership, revenue management, etc. The analytics enable solutions for recommendations on synchronizing and integrating operations of transportation systems. Further, the network’s decentralization and modular handling enable market-driven co-optimization of operational resources across various transportation modes to ensure seamless transit experience for users.

Mobile Oriented Future Internet (MOFI): Architectural Designs and Experimentations

With the explosive growth of smart phones and Internet-of-Things (IoT) services, the effective support of seamless mobility for a variety of mobile devices and users is becoming one of the key challenging issues [...]

Bagging Ensembled Perceptron Classifier for Seamless Mobility System in Heterogeneous Network

Wireless mobile devices require a handover decision system to get a seamless connection in a heterogeneous wireless networking environment. The handover process is one of the most significant processes in a cellular network. Few research works have been developed for providing seamless connectivity using different handover techniques. But, controlling data traffic during the process of seamless mobile data connectivity was not solved. So, there is a necessity to introduce a new model to control the traffic and improving the seamless mobility management in heterogeneous network. A new model called Bagging Ensembled Perceptron Classification based Seamless Mobility (BEPC-SM) introduced to achieve higher data delivery rate with minimum packet loss rate and data transmission delay by means of classifying the mobile nodes in heterogeneous network. In BEPC-SM model, randomly considers a number of mobile nodes in the heterogeneous network as input. Then, BEPC-SM model determines signal strength for each mobile node in a heterogeneous network. Bagging Ensembled Perceptron Classification algorithm is used in BEPC-SM model with the aim of accurately classifying all mobile nodes as strong or weak strength node with a lower amount of time consumption. After that, the distance between the weak strength node and the access point in the network is measured. Lastly, BEPC-SM Model selects the nearby access point with maximum bandwidth availability for each weak strength node in the network to perform the handover process. Thus, the performance of seamless data communication in a heterogeneous network is improved in BEPC-SM model. The BEPC-SM model is used in traffic-aware seamless data communication in a heterogeneous network. Simulation evaluation of the BEPC-SM Model is carried out on factors such as data delivery rate, packet loss rate, data transmission delay with respect to a number of data packets. The simulation result depicts that the BEPC-SM Model is able to increases the data delivery rate and also reduces delay when compared to state-of-the-art works.