A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

Dotoli, Mariagrazia; Zgaya, Hayfa; Russo, Carmine; Hammadi, Slim

doi:10.1109/tits.2016.2645278

Cited by 33 publications

(12 citation statements)

References 35 publications

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“…Recently, an optimal trip system was claimed by Dotoli et al [35]. At this point, we highlight the discussion about what is actually an optimal trip in the context of a multicultural SC?…”

Section: Smart Routing Problems: Multi-objective Optimizationmentioning

confidence: 86%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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“…Recently, an optimal trip system was claimed by Dotoli et al [35]. At this point, we highlight the discussion about what is actually an optimal trip in the context of a multicultural SC?…”

Section: Smart Routing Problems: Multi-objective Optimizationmentioning

confidence: 86%

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…Recent traffic modeling systems addressed traffic management problems by integrating multiple modes of transport and considering eco-friendliness [44][45][46][47]. For example, a multi-agent decision support framework used the genetic algorithm to optimize transport graphs and recommend the best combination of transport modes to commuters [45].…”

Section: State-of-the-art Intelligent Transport Management Systemsmentioning

confidence: 99%

“…Platform Used for Implementation/Simulation: This criterion reports on the technical frameworks and libraries used to realize the ITS architecture. Indeed, the existing multi-agent-based approaches suffered from several shortcomings [46,51,54]. They did not propose a tool for the authorities, there is no application for route guidance, and they do not use the real-time traffic data in their experiments.…”

mentioning

confidence: 99%

“…They did not propose a tool for the authorities, there is no application for route guidance, and they do not use the real-time traffic data in their experiments. Moreover, in some studies, the primary beneficiaries are commuters and not the authorities [46,51]. While the work proposed in [54] might be beneficial to the authorities, however, they focused on the tramway attributes and did not consider other factors like congestion cost, route guidance, and carbon emissions.…”

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confidence: 99%

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An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

et al. 2021

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The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

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“…The clustering of mobility flows is a research field that creates algorithms to identify trip patterns. There are two main types of clustering in urban scenarios: (i) trajectorybased clustering: the whole trajectory of the moving entity is known, and used [9,10]; and, (ii) Points-of-Presence based clustering: only a few points that are related to the moving entity are known [11][12][13]. Their main difference is the sampling rate of observed points.…”

Section: Clusteringmentioning

confidence: 99%

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

et al. 2021

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Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.

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A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

Cited by 33 publications

References 35 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

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