Nikolas Geroliminis

Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach

Recent analysis of empirical data from cities showed that a macroscopic fundamental diagram (MFD) of urban traffic provides for homogenous network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. In this paper, the optimal perimeter control for two-region urban cities is formulated with the use of MFDs. The controllers operate on the border between the two regions and manipulate the percentages of flows that transfer between the two regions such that the number of trips that reach their destinations is maximized. The optimal perimeter control problem is solved by model predictive control, where the prediction model and the plant (reality) are formulated by MFDs. Examples are presented for different levels of congestion in the regions of the city and the robustness of the controller is tested for different sizes of error in the MFDs and different levels of noise in the traffic demand. Moreover, two methods for smoothing the control sequences are presented. Comparison results show that the performances of the model predictive control are significantly better than a “greedy” feedback control. The results in this paper can be extended to develop efficient hierarchical control strategies for heterogeneously congested cities.

An optimization framework for the development of efficient one-way car-sharing systems

Electric vehicle-sharing systems have been introduced to a number of cities as a means of increasing mobility, reducing congestion, and pollution. Electric vehicle-sharing systems can offer one or two-way services. One-way systems provide more flexibility to users since they can be dropped-off at any station. However, their modeling involves a number of complexities arising from the need to relocate vehicles accumulated at certain stations. The planning of one-way electric vehicle-sharing systems involves a host of strongly interacting decisions regarding the number, size and location of stations, as well as the fleet size. In this paper we develop and solve a multi-objective MILP model for planning one-way vehicle-sharing systems taking into account vehicle relocation and electric vehicle charging requirements. For real world problems the size of the problem becomes intractable due to the extremely large number of relocation variables. In order to cope with this problem we introduce an aggregate model using the concept of the virtual hub. This transformation allows the solution of the problem with a branch-and-bound approach. The proposed approach generates the efficient frontier and allows decision makers to examine the trade-off between operator’s and users’ benefits. The capabilities of the proposed approach are demonstrated on a large scale real world problem with available data from Nice, France. Extensive sensitivity analysis was performed by varying demand, station accessibility distance and subsidy levels. The results provide useful insights regarding the efficient planning of one-way electric vehicle-sharing systems and allow decision makers to quantify the trade-off between operator’s and users’ benefits.

Real-Time Monitoring and Control on Signalized Arterials

An analytical model for real-time estimation of travel times along signalized arterials was developed. The application of the model on two arterial sites and comparisons of the estimated travel times with simulated and field data show that the model accurately predicts travel times on the selected sites. In this article, we present several important extensions and refinements to the model including treatment of long queues and queue spillovers, and algorithms for signal priority to transit vehicles. We also describe the integration of the model into an archival data management system for continuous measurement and monitoring of traffic performance along arterials.

Cordon Pricing Consistent with the Physics of Overcrowding

This paper describes the modeling of recurring congestion in a network. It is shown that the standard economic models of marginal cost cannot describe precisely traffic congestion in networks during time-dependent conditions. Following a macroscopic traffic approach, we describe the equilibrium solution for a congested network in the no-toll case. A dynamic model of cordon-based congestion pricing (such as for the morning commute) for networks is developed consistent with the physics of traffic. The paper combines Vickrey’s theory with a macroscopic traffic model, which is readily observable with existing monitoring technologies. The paper also examines some policy implications of the cordon-based pricing to treat equity and reliability issues, i.e. in what mobility level a city should choose to operate. An application of the model in a downtown area shows that these schemes can improve mobility and relieve congestion in cities.

A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network

Emergency response services are critical for modern societies. This paper presents a model and a heuristic solution for the optimal deployment of many emergency response units in an urban transportation network and an application for transit mobile repair units (TMRU) in the city of Athens, Greece. The model considers the stochastic nature of such services, suggesting that a unit may be already engaged, when an incident occurs. The proposed model integrates a queuing model (the hypercube model), a location model and a metaheuristic optimization algorithm (genetic algorithm) for obtaining appropriate unit locations in a two-step approach. In the first step, the service area is partitioned into sub-areas (called superdistricts) while, in parallel, necessary number of units is determined for each superdistrict. An approximate solution to the symmetric hypercube model with spatially homogeneous demand is developed. A Genetic Algorithm is combined with the approximate hypercube model for obtaining best superdistricts and associated unit numbers. With both of the above requirements defined in step one, the second step proceeds in the optimal deployment of units within each superdistrict.

Real Time Vehicle Reidentification and Performance Measures on Signalized Arterials

An analytical model was developed and tested to estimate the travel times on signalized arterials in real-time based on data commonly provided by system loop detectors (flow and occupancy) and the signal settings (cycle length, green times and offsets). The model was extended to account for the effects of detector placement and queue spillovers. The results from model applications show that it accurately estimates travel times for a wide range of operating conditions. A vehicle re-identification methodology also was developed based on data from conventional loop detectors. Sample application indicates that it can be used to produce performance measures such as travel time variability and quality of signal progression

Traffic signal perimeter control with multiple boundaries for large urban networks

A new gating strategy based on the notion of the macroscopic or network fundamental diagram (MFD or NFD) and the feedback-based gating concept is introduced and tested successfully. Different regions of large-scale urban networks may experience congestion at different times during the peak period. In this paper, the zone including the initial core of congestion is considered as the first region which has to be protected from congestion via gating; eventually, as the congestion continues to expand, the border of an extended network part becomes the second perimeter for gating control. Extensions while distributing the ordered controller flow to the gated traffic signals in case of low demand or occurrence of spillback are also introduced. A greater part of the San Francisco urban network is used as test-bed within a microscopic simulation environment. Significant improvements in terms of average travel time and average delay are obtained compared to the single perimeter gating and non-gating cases.

On the allocation of city space to multiple transport modes

Abstract This paper analyzes urban multimodal transportation systems in an aggregated way. To describe the aggregate behavior of traffic in cities, use is made of an idea that is now receiving some attention: the macroscopic fundamental diagram (MFD). We demonstrate through simulation how the MFD can be used to monitor and control a real network, in this case a portion of San Francisco, using readily available input data. We then show how different modes interact on the same network and discuss how these interactions might be incorporated into an MFD for multimodal networks. The work unveils two main results: first, it confirms recent results showing that restricting access to a citys congested areas can improve mobility for all travelers, including those who endure the restrictions; and second, that dedicating street space to collective transport modes can improve accessibility for all modes, even those from which space is taken away.

Economic Model Predictive Control of Large-Scale Urban Road Networks via Perimeter Control and Regional Route Guidance

Local traffic control schemes fall short of achieving coordination with other parts of the urban road network, whereas a centralized controller based on the detailed traffic models would suffer from excessive computational burden. State estimation for detailed traffic models with limited observations and unpredictability of individual driver behavior create additional complications in the applicability of these models for large-scale traffic control. This point toward the need for designing network-level controllers building on aggregated traffic models, which have recently attracted attention through the macroscopic fundamental diagram (MFD) of urban traffic. Under some conditions, the MFD provides a unimodal, low-scatter, and demand-insensitive relationship between vehicle accumulation and travel production inside an urban region. In this paper, we propose MFD-based economic model predictive control (MPC) schemes to improve mobility in heterogeneously congested large-scale urban road networks. For more realistic simulations of urban networks with route guidance actuation-based control, a new model with cyclic behavior prohibition is developed. This paper extends upon earlier works on perimeter control-based MPC schemes with MFD modeling by integrating route guidance type actuation, which distributes flows exiting a region over its neighboring regions. Performance of the proposed schemes is evaluated via simulations of congested scenarios with noise in demand estimation and measurement errors. Results show the possibility of substantial improvements in urban network performance, in terms of network delays and traveled distance, even for low levels of driver compliance to route guidance.

Exploring spatial characteristics of urban transportation networks

It has been shown recently that a Macroscopic Fundamental Diagram (MFD) exists in urban transportation networks under certain conditions. However, MFD is not universally expected. Previous research demonstrates the existence of MFDs in homogeneous networks with similar link densities. More recent work focuses on the partitioning of a heterogeneous transportation network based on different congestion levels. A desired partitioning produces homogeneous regions with similar link densities to guarantee a well-defined MFD and spatially compact shapes to ease the implementation of control measurements [1]. Based on recently proposed partitioning mechanism, this paper further explores the spatial characteristics of sub-networks (sub-regions or clusters) in urban transportation networks. In this paper, a metric is defined to evaluate the spatial compactness of each cluster in the network. In order to obtain the metric, a fast graph traversal algorithm is proposed, which can produce a clockwise sequence for the spatially coordinated boundary nodes along a network. The algorithm takes O(n) and the effectiveness is proved and validated. By applying the boundary smoothness metric to our previous clustering results, we show that the spatial compactness is appropriately guaranteed for each region and the future control policies can therefore be easily implemented based on the partitioning and MFDs. The proposed algorithms can have more general applications in fields of network and graph theory.