Abstract
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.
Highlights
Traffic congestion remains a global challenge in the transport domain, causing significant greenhouse emissions [1]
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
Assisted by the Nottingham City Council (NCC) and Sofia Urban Mobility Center (SUMC), several users were recruited in both cities to take part in a pilot study
Summary
Traffic congestion remains a global challenge in the transport domain, causing significant greenhouse emissions [1]. The prospect of fuel efficient vehicles (e.g., hybrid and electric vehicles) has recently enjoyed wider acceptability and adoption [4], and stricter CO2 emission regulations have been introduced [5], road transport still produces approximately a fifth (21%) of the total carbon footprint in European countries [6,7] This increasingly worrisome phenomenon calls for further efforts to develop intelligent mobility frameworks that assist in (1) promoting sustainable commuting behavior and (2) balancing the flow of traffic across the entire transport network to reduce traffic congestions. The authors of [13] present Adapt-Traf, a hierarchical organizational multi-agent architecture, which models the real-time flow of traffic to overcome road traffic jams This approach endeavors to provide unimodal route guidance to car-only commuters.
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