Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing

Abstract

a b s t r a c tCruising-for-parking constraints mobility in urban networks. Car-users may have to cruise for on-street parking before reaching their destinations. The accessibility and the cost of parking significantly influence people's travel behavior (such as mode choice, or parking facility choice between on-street and garage). The cruising flow causes delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore important to understand the impact of parking limitation on mobility, and to identify efficient parking policies for travel cost reduction. Most existing studies on parking fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general, lack the treatment of dynamics of the cruising-for-parking phenomenon, or require detailed input data that are typically costly and difficult to collect. In this paper, we propose an aggregated and dynamic approach for modeling multimodal traffic with the treatment on parking, and utilize the approach to design dynamic parking pricing strategies. The proposed approach is based on the Macroscopic Fundamental Diagram (MFD), which can capture congestion dynamics at network-level for single-mode and bi-modal (car and bus) systems. A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion. Pricing strategies are developed with the objective of reducing congestion, as well as lowering the total travel cost of all users. A case study is carried out for a bi-modal city network with a congested downtown region. An elegant feedback dynamic parking pricing strategy can effectively reduce travel delay of cruising and the generic congestion. Remarkably, such strategy, which is applicable in real-time management with limited available data, is fairly as efficient as a dynamic pricing scheme obtained from system optimum conditions and a global optimization with full information about the future states of the system. Stackelberg equilibrium is also investigated in a competitive behavior between different parking facility operators. Policy indications on on-street storage capacity management and pricing are provided.

Is Parking in Europe Ready for Dynamic Pricing? A Reality Check for the Private Sector

“…Zheng and Geroliminis [19] develop a macroscopic fundamental diagram multimodal traffic modeling approach that applies different pricing strategies for parking. Their model, that incorporates price competition between parking operators, suggests that a dynamic parking pricing system is effective in order to reduce cruising and congestion.…”

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

“…For other kinds of constraints that cannot be converted into the three types examined here, the dynamic generation properties of solving the GA-based network path may be helpful in solving such problems according to the generation-refinement paradigm (e.g., multiconstraint transportation network design [42,43] and transit network analysis [12,44]). With the network represented in the GA framework, the route in the network can be directly generated and refined according to the properties of the algebra system.…”

How Many and Where to Locate Parking Lots? A Space–time Accessibility-Maximization Modeling Framework for Special Event Traffic Management

“…More technical details are provided in the references listed in Table 1. The research on travelers' parking searching behavior aims to find the user-equilibrium solutions under different parking facilities' constraints [8][9][10][11][12][13][14][15][16]. Based on the route choice behavior under parking facilities, researchers have extended the traditional network design problems [17][18][19][20][21][22] to park-and-ride facilities' network design and location problems [23][24][25][26].…”