A Feedback Control Framework for Incentivised Suburban Parking Utilisation and Urban Core Traffic Relief

TL;DR

This paper introduces a feedback control system that incentivizes suburban parking to reduce urban congestion, using driver behavior models and ergodic control theory to ensure predictable long-term outcomes.

Contribution

It presents a novel incentivization framework combining driver choice modeling and ergodic control to optimize parking distribution and traffic flow in smart cities.

Findings

Effective redistribution of vehicles between city and suburban parking.

Guarantees predictable long-term system behavior.

Reduces urban congestion and environmental impact.

Abstract

Urban traffic congestion, exacerbated by inefficient parking management and cruising for parking, significantly hampers mobility and sustainability in smart cities. Drivers often face delays searching for parking spaces, influenced by factors such as accessibility, cost, distance, and available services such as charging facilities in the case of electric vehicles. These inefficiencies contribute to increased urban congestion, fuel consumption, and environmental impact. Addressing these challenges, this paper proposes a feedback control incentivisation-based system that aims to better distribute vehicles between city and suburban parking facilities offering park-and-charge/-ride services. Individual driver behaviours are captured via discrete choice models incorporating factors of importance to parking location choice among drivers, such as distance to work, public transport connectivity, charging infrastructure availability, and amount of incentive offered; and are regulated through principles of ergodic control theory. The proposed framework is applied to an electric vehicle park-and-charge/-ride problem, and demonstrates how predictable long-term behaviour of the system can be guaranteed.