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

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.

Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map

“…This study aims at keeping the users away from virus transmission spots [10]. Environmental factors such as air cleanliness [11], land use mix, infrastructure, aesthetics and accessibility [12], transport carbon footprint [13], and postrain accumulated water [14] and traffic factors [12] such as traffic volume and speed, quality of path surface, and safety level of road crossings have also been made primary factors for route recommendation algorithm design. The Land Use Regression (LUR) model was used to model the concentration of black carbon to be calculated in the route candidates for home-to-school commuting [11].…”

Contextual Route Recommendation System in Heterogeneous Traffic Flow

“…Yi et al claimed Deep Neural Network could estimate traffic congestion [27]. By using three hidden layers (40,50, and 40 neurons), the tanh activation function, and AdaGrad optimization algorithm, the system achieved 99% accuracy in predicting congestion. On the other hand, Lv et al, stated that Deep Learning can understand the traffic feature without prior knowledge.…”

“…Intersections and road segments in road network will be nodes and edges in a graph. Meanwhile, the traffic conditions [40], travel distance [41], travel time [42], pricing (ridesharing) [43,44], etc. could be described as the weight of the graph.…”

Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data

“…Multi-agent systems (MAS) are used in smart city application in order to automate and monitor different processes. A guidance system for route recommendations for travelers in Nottingham and Sofia is described in [ 14 ]. The authors propose a multi-agent system composed of the following: a managing agent, transport agents, a traffic data fetcher, a commuter agent, a route recommender agent and a visualization agent.…”