Multimodal Mobility Framework: Towards Seamless Mobility Experience

2021 ◽  
Author(s):  
Ravigopal Vennelakanti ◽  
Malarvizhi Sankaranarayanasamy ◽  
Ramyar Saeedi ◽  
Rahul Vishwakarma ◽  
Prasun Singh ◽  
...  
Keyword(s):  
Business Models ◽  
Optimization Problems ◽  
Machine Learning Algorithms ◽  
Smooth Transition ◽  
Transport Systems ◽  
Transit Networks ◽  
Seamless Mobility ◽  
Transportation Modes ◽  
Market Driven ◽  
Operational Structure

Abstract 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.

scholarly journals The Business Case for a Journey Planning and Ticketing App—Comparison between a Simulation Analysis and Real-World Data

2020 ◽  
Vol 12 (10) ◽  
pp. 4005 ◽  
Author(s):  
Gillian Harrison ◽  
Astrid Gühnemann ◽  
Simon Shepherd
Keyword(s):  
Market Failure ◽  
Business Models ◽  
Simulation Analysis ◽  
Business Case ◽  
Transport Systems ◽  
Real World Data ◽  
Realistic Potential ◽  
Potential Adopter ◽  
Targeted Marketing ◽  
Uptake Data

Successful development of “Mobility-as-a-Service” (MaaS) schemes could be transformative to our transport systems and critical for achieving sustainable cities. There are high hopes for mobile phone applications that offer both journey planning and ticketing across all the available transport modes, but these are in their infancy, with little understanding of the correct approach to business models and governance. In this study, we develop a system dynamics diffusion model that represents the uptake of such an app, based on one developed and released in West Yorkshire, UK. We perform sensitivity and uncertainty tests on user uptake and app operating profitability, and analyse these in three key areas of marketing, competition, and costs. Comparison to early uptake data is included to demonstrate accuracy of model behaviour and would suggest market failure by month 12 without stronger marketing, even if additional tickets and functions are offered. In response to this, we offer further insights on the need for direct targeted marketing to ensure mass market adoption, the importance of understanding a realistic potential adopter pool, the awareness of competing apps, and the high uncertainty that exists in this market.

scholarly journals A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Conner Sharpe ◽  
Tyler Wiest ◽  
Pingfeng Wang ◽  
Carolyn Conner Seepersad
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Design Space ◽  
Optimization Problems ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Design Exploration ◽  
Machine Learning Classification

Abstract Supervised machine learning techniques have proven to be effective tools for engineering design exploration and optimization applications, in which they are especially useful for mapping promising or feasible regions of the design space. The design space mappings can be used to inform early-stage design exploration, provide reliability assessments, and aid convergence in multiobjective or multilevel problems that require collaborative design teams. However, the accuracy of the mappings can vary based on problem factors such as the number of design variables, presence of discrete variables, multimodality of the underlying response function, and amount of training data available. Additionally, there are several useful machine learning algorithms available, and each has its own set of algorithmic hyperparameters that significantly affect accuracy and computational expense. This work elucidates the use of machine learning for engineering design exploration and optimization problems by investigating the performance of popular classification algorithms on a variety of example engineering optimization problems. The results are synthesized into a set of observations to provide engineers with intuition for applying these techniques to their own problems in the future, as well as recommendations based on problem type to aid engineers in algorithm selection and utilization.

Robust control for railway transport networks based on stochastic P-timed Petri net models

2019 ◽  
Vol 233 (7) ◽  
pp. 830-846
Author(s):  
Mouhaned Gaied ◽  
Anis M’halla ◽  
Dimitri Lefebvre ◽  
Kamel Ben Othmen
Keyword(s):  
Robust Control ◽  
Petri Nets ◽  
Discrete Event Systems ◽  
Optimization Problems ◽  
Discrete Event ◽  
Transport Systems ◽  
Time Constraints ◽  
Railway Transport ◽  
Transport Networks ◽  
Event Systems

This article is devoted to the modeling, performance evaluation and robust control of the railway transport network in Sahel Tunisia. The regular increase in the number of passengers makes the management of transportation systems more and more complex. Railway transport requires specific needs. Indeed, many decision and optimization problems occur from the planning phase to the implementation phase. Railway transport networks can be considered as discrete event systems with time constraints. The time factor is a critical parameter, since it includes schedules to be respected in order to avoid overlaps, delays and collisions between trains. The uncertainties affect the service and the availability of transportation resources and, consequently, the transport scheduling plan. Petri nets have been recognized as powerful modeling and analysis tools for discrete event systems with time constraints. Consequently, they are suitable for railway transport systems. In this article, stochastic P-time Petri nets are used for the railway transport networks in Sahel Tunisia. A global model is first detailed. Then, this model is used to analyze the network traffic and evaluate the performance of the system. Robustness again disturbances is introduced and a control strategy is developed to reduce the consequences of the disturbances in order to maintain the expected schedule.

scholarly journals When Is an Innovative Urban Mobility Business Model Sustainable? A Literature Review and Analysis

2019 ◽  
Vol 11 (6) ◽  
pp. 1761 ◽  
Author(s):  
João Valsecchi Ribeiro de Souza ◽  
Adriana Marotti de Mello ◽  
Roberto Marx
Keyword(s):  
Business Model ◽  
Business Models ◽  
Research Question ◽  
Scale Up ◽  
Clean Energy ◽  
Transport Systems ◽  
Urban Mobility ◽  
Wide Range ◽  
Research Gap ◽  
Sustainable Urban Mobility

Although researchers have increasingly examined how business models promote sustainable urban mobility through innovation, the literature has focused less attention on what constitutes a sustainable and innovative business model in the context of urban mobility. To fill this research gap, this article aims to answer the following research question: what elements characterize sustainable and innovative business models in the field of urban mobility? To identify whether and to what extent the existing intersection between business models and sustainable urban mobility literature contributes to the development of this concept, a systematic review and analysis of the literature was conducted. The results indicate that the following aspects contribute to the sustainability of an urban mobility business model: favoring the use of clean energy; maximizing the use of transport resources and capabilities; encouraging substitution using sustainable modes; offering service orientation and functionality; articulating initiatives that address the needs of a wide range of stakeholders in transport systems; reducing travel demands; extending benefits to society and the environment in a systemic perspective; and developing scale-up mobility solutions.

scholarly journals Towards a fully automated algorithm driven platform for biosystems design

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohammad HamediRad ◽  
Ran Chao ◽  
Scott Weisberg ◽  
Jiazhang Lian ◽  
Saurabh Sinha ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Machine Learning Algorithms ◽  
Successful Implementation ◽  
Large Scale Data ◽  
Automated Algorithm ◽  
Design Build ◽  
Cost Variability ◽  
Scale Data ◽  
Random Screening

Abstract Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Carsharing: Business models, and role of the decision maker

2020 ◽  
pp. 63-94
Author(s):  
Lucia Rotaris ◽  
Marko Bumbulovic
Keyword(s):  
Business Models ◽  
Smooth Transition ◽  
Special Focus ◽  
Market Opening ◽  
Latent Demand ◽  
Viable Solution ◽  
Technical Innovations ◽  
Italian Context ◽  
The Cost

Individuals' mobility needs are constantly increasing both in urban and in less-densely popu-lated areas. Private transport activities are intensifying, creating unsustainable environmental pressure and absorbing a too large amount of resources, forcing to social exclusion the popu-lation segments which cannot bear the cost of private transport. Car sharing has proven to be a viable solution to alleviate at least partially these problems. Many different business models are used to provide the service. Organizational and technical innovations have changed the market, opening the supply to new providers and serving segments of the latent demand which were not reached by the traditional operators. The role played by the decision marker to sup-port the development of this market in its various forms has been essential and will still be crit-ical in order to guide a smooth transition from the private use of traditional vehicles to the shared use of autonomous ones. The purpose of this paper is to shed light on these changing characteristics of the carsharing market with a special focus on the Italian context.

scholarly journals Estimating Bus Cross-Sectional Flow Based on Machine Learning Algorithm Combined with Wi-Fi Probe Technology

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 844
Author(s):  
Ting-Zhao Chen ◽  
Yan-Yan Chen ◽  
Jian-Hui Lai
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Transport Systems ◽  
Support Vector ◽  
Surveillance Video ◽  
K Nearest Neighbor ◽  
Cross Sectional ◽  
Passenger Flow

With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future.

scholarly journals Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

2021 ◽  
Vol 11 (14) ◽  
pp. 6449
Author(s):  
Fernando Peres ◽  
Mauro Castelli
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Definition Of ◽  
Problem Solvers ◽  
New Algorithms

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

scholarly journals Business Innovations in the New Mobility Market during the COVID-19 with the Possibility of Open Business Model Innovation

2021 ◽  
Vol 7 (3) ◽  
pp. 195
Author(s):  
Katarzyna Turoń ◽  
Andrzej Kubik
Keyword(s):  
Business Models ◽  
Point Of View ◽  
Urban Transport ◽  
Transport Systems ◽  
Business Practices ◽  
Difficult Situation ◽  
The World ◽  
Bike Sharing ◽  
The One ◽  
Desk Research

The current difficult situation in the world caused by the spread of the COVID-19 virus has led to the development of problems in many branches of the economy. However, it has significantly affected transport, which on the one hand, is the bloodstream of the economy and, on the other hand, creates a threat for virus infection. Thus, in various countries, different mobility-related restrictions during pandemic policies around the world have been introduced. What is more, plans for initiatives after lockdown have also started to appear. Moreover, not have only cities introduced appropriate management policies, but companies have also started providing logistics services, especially those offering new mobility solutions. We found a literature and research gap indicating the recording or combination of the different types of business practices and innovations used worldwide in new mobility companies in the case of a pandemic situation. Therefore, this article is dedicated to the business innovations that appear in the new mobility industry during the COVID-19 pandemic in connection to post-pandemic transportation plans in Asia, Europe, and America. In this work, we conducted two-level research based on the desk research and expert research methodologies. From the business point of view, the results show that car-sharing systems (most organizational practices) and ride-sharing services (most safety practices) have most adapted their business models to pandemic changes. In turn, bike-sharing services have implemented the fewest business practices and innovations. From the urban transport systems point of view, the results show that European authorities have proposed the most plans and practice projects for new mobility after the pandemic compared to Asia and America. The obtained results indicate, however, that business practices do not coincide with the authorities’ plans for transport after the pandemic. Moreover, the results show a lack of complementarity between the developed practices and a reluctance to create open innovations in the new mobility industry. The article supports the management of new mobility systems in times of pandemic and in post-COVID reality.

scholarly journals Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 274
Author(s):  
Álvaro Gómez-Rubio ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Adrián Jaramillo ◽  
David Mancilla ◽  
...  
Keyword(s):  
Big Data ◽  
Optimization Problems ◽  
Search Space ◽  
Efficient Solutions ◽  
Machine Learning Algorithms ◽  
Large Set ◽  
Clustering Methods ◽  
Distributed Models ◽  
Np Hard Problem ◽  
Complex Optimization

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

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