Assessing the effectiveness of park-and-ride facilities on multimodal networks in smart cities

TL;DR

The paper addresses the problem of selecting park-and-ride facilities within multimodal, time-dependent urban networks. The authors propose an integer programming formulation that minimizes a generalized cost composed of travel time, parking fees, and an attractiveness term tied to occupancy risk, parameterized by weights $\alpha$, $\beta$, and $\gamma_0$. The model captures scenario-based information and two user profiles (with or without reservation) and applies a time-expanded network to derive feasible routes that visit a park facility. A Seville case study demonstrates the method's ability to reveal trade-offs between fastest and cheapest options under congestion and information conditions, highlighting practical implications for smart city parking guidance.

Abstract

This paper presents an optimization procedure to choose a parking facility according to different criteria: total travel time including transfers, parking fee and a factor depending on the risk of not having an available spot in the parking facility at the arrival time. An integer programming formulation is proposed to determine an optimal strategy of minimum cost considering the available information, different scenarios, and each user profile. To evaluate the performance, a computational experience has been carried out on Seville (Spain), where a historical city center restricts the traffic of private vehicles and encourages the use of parking facilities.

Figures (3)

• Figure 1: Signaling system of park facilities in the city of Seville
• Figure 2: Graph associated to the selection of best strategy
• Figure 3: Map of traffic intensities in the city of Seville