Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams

Yildirimoḡlu, Mehmet; Geroliminis, Nikolas

doi:10.1016/j.trb.2014.09.002

Cited by 167 publications

(106 citation statements)

References 36 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…The MFD can be utilized to monitor the performance of road network, requiring data that can be obtained from the existing information technologies from limited measurement points of the network (Ortigosa et al, 2014). An interested reader could refer to Yildirimoglu and Geroliminis (2014) and Leclercq et al (2014) for a comprehensive review of the recent development on MFD-related theories. Latest works extend the MFD from single-mode application to bi-modal, where cars and buses share the same infrastructure and look at passenger flow dynamics in addition to vehicular dynamics (Zheng and Geroliminis (2013), Geroliminis et al (2014), Chiabaut et al (2014) and Chiabaut (2015)).…”

Section: Multimodal Macroscopic Traffic Modelmentioning

confidence: 99%

“…This modeling framework should determine the sequence of the passing regions while moving from i to k. Recently such a modeling framework has been developed for car-only multi-region networks (Yildirimoglu and Geroliminis, 2014;Yildirimoglu et al, 2015). While the authors believe that integration of route choice in the current framework is feasible, this is avoided.…”

Section: System Dynamics With Mfd Representation and Parkingmentioning

confidence: 99%

“…Thus, the sequence of regions for specific ODs is considered known priori. Yildirimoglu and Geroliminis (2014) and Mahmassani et al (2013) showed that route choice adaptation is more important under congested conditions. While the pricing strategies that are presented here, are able to avoid such conditions, not integrating an assignment framework is not expected to significantly alter the result, with an important computational benefit in parallel.…”

Section: System Dynamics With Mfd Representation and Parkingmentioning

confidence: 99%

“…It becomes more complicated, however, if routing strategies are implemented. This is because routing may influence the trip lengths of users which have direct impact on the MFD (Yildirimoglu and Geroliminis, 2014). In addition, we consider that demand with destination outside of the center region where there is no parking limitation is not affected by pricing as they will not be subject to paying a price, thus can be considered as disturbance to the controlled system (the top part of the block diagram).…”

Section: Strategy P2: a Congestion-and Cruising-responsive Feedback Pmentioning

confidence: 99%

See 3 more Smart Citations

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

Zheng

Geroliminis

2016

Transportation Research Part B: Methodological

Self Cite

162

View full text Add to dashboard Cite

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.

show abstract

Section: Multimodal Macroscopic Traffic Modelmentioning

confidence: 99%

Section: System Dynamics With Mfd Representation and Parkingmentioning

confidence: 99%

Section: System Dynamics With Mfd Representation and Parkingmentioning

confidence: 99%

Section: Strategy P2: a Congestion-and Cruising-responsive Feedback Pmentioning

confidence: 99%

See 2 more Smart Citations

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

Zheng

Geroliminis

2016

Transportation Research Part B: Methodological

Self Cite

162

View full text Add to dashboard Cite

show abstract

“…In Yildirimoglu et al (2015), route choice models under dynamical constraint have been included to perimeter ow control decisions. Note that, as reported in Mahmassani et al (2013); Yildirimoglu and Geroliminis (2014); Kouvelas et al (2017), route choice e ects can in uence the trip length distribution of vehicles in the network and thereby approximation of out ow might experience some errors. In Haddad (2017b) optimal control for maximum queue length inclusion on aggregated inter-regional boundary queues is considered.…”

Section: Introductionmentioning

confidence: 96%

Network traffic flow optimization under performance constraints

Csikós

Charalambous

Farhadi

et al. 2017

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit ow characteristics. Within this framework of constrained optimal boundary ow gating, two main performance metrics are considered: (a) rst, connected to the NFD, the concept of average network travel time and delay as a performance metric is de ned;(b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled in ow demands. External queue capacities in terms of nite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network's aggregated behaviour.By controlling the number of vehicles joining the internal queue from the external ones, herewith a network tra c ow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically e ciently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be 1 interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satis ed. Comparative numerical simulation studies on a microscopic tra c simulator are carried out to show the bene ts of the proposed method.

show abstract

Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation

Zang

Chen

et al. 2021

Transportation

View full text Add to dashboard Cite

Network capacity, defined as the largest sum of origin–destination (O–D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches: single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α-max capacity model to estimate the maximum network capacity under trip or O–D LOS requirement α. The proposed model improves the existing models on three aspects: (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O–D travel time or planners’ requirement of O–D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.

show abstract

Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams

Cited by 167 publications

References 36 publications

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

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

Network traffic flow optimization under performance constraints

Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation

Contact Info

Product

Resources

About