Johnson Adeiza Obari, Salawudeen Tijani A, Idakwo Monday A, Adebiyi Busayo H

An emission and weight of vehicles-based road traffic congestion pricing system and control with consideration of investment worthiness

Introduction

An emission and weight of vehicles-based road traffic congestion pricing system and control with consideration of investment worthiness. Fuzzy logic-based road traffic congestion pricing system. Considers vehicle emissions, weight, and traffic density to reduce pollution, optimize investment, and enhance control.

Abstract

This work presents a knowledge-based approach to traffic congestion pricing system and control. The road traffic congestion has attracted different intelligent contributions which have addressed many real-time traffic scenarios at a toll point unlike the flat toll system that renders parallel toll for every traffic condition. However, existing works on dynamic traffic congestion pricing systems focus entirely on the traffic parameters without taking cognizance of the impacts of the weight of vehicles on the road. More so, despite the numerous health hazards associated with air pollution from vehicle exhaust during traffic peak hour, effects of emission have not been conceived as pivotal input to be circumvented in road toll design. Therefore, a fuzzy logic-based approach to dynamic traffic congestion pricing problems in a 1*2 traffic scenario comprising of a fast lane and a slow lane, is presented. The inputs to the fuzzy inference system are the weights of vehicles, the rate of carbon dioxide emission, and the traffic density on the toll lane; while the output is the congestion price. Simulations results indicate the qualitative robustness of this approach in handling the inherent nonlinear nature of road pricing problems. Investors and traffic management systems can rely on the simplicity, reduced computation cost, reduced health hazards and the justified investment worthiness on road and toll facilities.

Review

This paper presents a knowledge-based, fuzzy logic approach to dynamic road traffic congestion pricing, distinguishing itself by incorporating vehicle weight and carbon dioxide emission rates alongside traditional traffic density as primary inputs. The authors identify a gap in existing dynamic pricing systems that often overlook these crucial environmental and infrastructural impact factors. By integrating these parameters into a fuzzy inference system, the work aims to create a more comprehensive and intelligent tolling mechanism that not only manages congestion but also addresses broader issues of road wear and air pollution, moving beyond simplistic flat or purely density-based systems. A key strength of this research is its innovative and timely consideration of vehicle weight and CO2 emissions. This addresses significant shortcomings in current models, offering a pricing structure that potentially incentivizes cleaner vehicles and reduces wear on road infrastructure, directly linking economic policy to environmental and maintenance objectives. The application of fuzzy logic is well-suited for the inherent non-linear complexities of road pricing problems, promising a robust and adaptable control system. The stated benefits—reduced computation cost, decreased health hazards, and justified investment worthiness for facilities—underscore the practical and societal value of the proposed system, particularly within the demonstrated 1*2 traffic scenario. While the proposed methodology offers compelling advantages, the abstract leaves certain aspects warranting further elucidation. The explicit mechanism by which "investment worthiness" is *considered* within the pricing *system and control* could be more clearly articulated; the abstract primarily frames it as a resulting benefit rather than an active input or constraint within the fuzzy logic framework. Furthermore, while the simulation results indicate "qualitative robustness," a more rigorous quantitative comparison against established dynamic pricing models and a detailed analysis of its economic and environmental impacts (e.g., in terms of specific emission reductions or cost savings) would substantially strengthen the paper's claims. Expanding on the generalizability of the 1*2 traffic scenario and the practical challenges of real-time data acquisition for vehicle weight and emissions would also enhance the work's practical applicability.

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