Mohsen Ramezani

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.

Queue Profile Estimation in Congested Urban Networks with Probe Data

Queues at signalized intersections are the main cause of traffic delays and travel time variability in urban networks. In this article, we propose a method to estimate queue profiles that are traffic shockwave polygons in the time-space plane describing the spatiotemporal formation and dissipation of queues. The method integrates the collective effect of dispersed probe vehicle data with traffic flow shockwave analysis and data mining techniques. The proposed queue profile estimation method requires position and velocity data of probe vehicles; however, any explicit information of signal settings and arrival distribution is indispensable. Moreover, the method captures interdependencies in queue evolutions of successive intersections. The significance of the proposed method is that it is applicable in oversaturated conditions and includes queue spillover identification. Numerical results of simulation experiments and tests on NGSIM field data, with various penetration rates and sampling intervals, reveal the promising and robust performance of the proposed method compared with a uniform arrival queue estimation procedure. The method provides a thorough understanding of urban traffic flow dynamics and has direct applications for delay analysis, queue length estimation, signal settings estimation, and vehicle trajectory reconstruction.

Exploiting probe data to estimate the queue profile in urban networks

Queues at signalized intersections are one of the main causes of traffic delays and urban traffic state variability. Hence, a method to estimate queue characteristics provides a better understanding of urban traffic dynamics and a performance measurement of signalized arterials. In order to capture the evolution of queues, we aim at leveraging the collective effect of spatially and temporally dispersed probe data to identify the formation and dissipation of queues in the time-space plane. The queue profile characterizes the evolution of both queue front and back, which consequently can be separated in a two-step estimation process resulting to the queue profile polygon. The evolution of queue front, in the time-space diagram, based on the kinematic traffic shockwave theory is modeled as a line with the known slope of queue-discharging shockwave and estimated with a constrained optimization and a technique known as support vector machine. The evolution of back of queue is more challenging and modeled as a piecewise linear function where slope of segments is between the queue-discharging shockwave and zero. In the proposed method, the input data consists of position and velocity of probe vehicles. The queue profile estimation method does not require any explicit information of signal settings and arrival distribution. The proposed method is tested with various penetration rates and sampling intervals of probe data, which reveals promising results once compared to a uniform arrival queue profile estimation procedure. The proposed method could be beneficial for spillback identification, vehicle trajectory construction, and fuel consumption and emission estimation.

A link partitioning approach for real-time control of queue spillbacks on congested arterials

ABSTRACT In oversaturated urban traffic conditions when traffic demand exceeds capacity at signalised intersections, queues fail to clear during the allocated green times. Once a queue reaches the upstream intersection in an arterial, a queue spillback occurs that reduces the upstream link capacity. To mitigate the negative impacts of spillbacks, this article introduces a real-time adaptive traffic signal control method for global management of spillbacks along signalised arterials. The key idea of the proposed method is to implement a real-time partitioning of the arterial to detect critical cluster(s) of consecutive links with oversaturated traffic conditions. The partitioning approach enables to develop locally smaller-sized decentralised signal control strategies operating on the most upstream and downstream intersections of each cluster. Micro-simulation investigations on a real-world arterial site demonstrate the benefits of the proposed approach compared to an existing pre-timed signal control strategy and a classical decentralised green extension strategy. Utilising an advanced queue length detection method and specific focus on queue spillbacks prevention, the control strategy leads to significant reduction of congestion, and arterial total delay.

Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers’ path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities’ utility and that BEV drivers incline to choose the route with charging stations and less charging time.

Macroscopic Traffic Control of a Mixed Urban and Freeway Network

Abstract In this paper, the macroscopic traffic control of a large-scale mixed transportation network consisting of freeway and urban network is tackled. The urban network is partitioned in two regions, each one with a well-defined macroscopic fundamental diagram (MFD), i.e. a unimodal and low-scatter relationship between network density and outflow. The freeway is regarded as one alternative commuting route which has one on-ramp and one off-ramp within each urban region. The urban and freeway flow dynamics are formulated with the tool of MFD and asymmetric cell transmission models, respectively. Four controllers are considered to control the flow distribution between urban regions and freeway: (i) two on the border of urban regions operating to manipulate the perimeter interflow rates between the two regions, and (ii) two other controllers on the on-ramps for ramp metering to control the flow rates from urban roads to the freeway. The optimal traffic control problem for the mixed network is solved by a receding horizon approach in order to maximize the number of trips that reach their destinations. The results of this paper can be extended to develop efficient control strategies for large-scale mixed traffic networks.

Congestion patterns of electric vehicles with limited battery capacity

The path choice behavior of battery electric vehicle (BEV) drivers is influenced by the lack of public charging stations, limited battery capacity, range anxiety and long battery charging time. This paper investigates the congestion/flow pattern captured by stochastic user equilibrium (SUE) traffic assignment problem in transportation networks with BEVs, where the BEV paths are restricted by their battery capacities. The BEV energy consumption is assumed to be a linear function of path length and path travel time, which addresses both path distance limit problem and road congestion effect. A mathematical programming model is proposed for the path-based SUE traffic assignment where the path cost is the sum of the corresponding link costs and a path specific out-of-energy penalty. We then apply the convergent Lagrangian dual method to transform the original problem into a concave maximization problem and develop a customized gradient projection algorithm to solve it. A column generation procedure is incorporated to generate the path set. Finally, two numerical examples are presented to demonstrate the applicability of the proposed model and the solution algorithm.

Capacity and delay analysis of arterials with mixed autonomous and human-driven vehicles

This paper investigates the traffic flow characteristics of mixed stream of autonomous and human-driven vehicles. The proposed model aims at understanding the fundamental properties of mixed flow of human-driven (N) and autonomous vehicles (AV) such as headway, capacity, and delay at signalized intersections. This is challenging because of intrinsic differences between longitudinal driving characteristics of these two types of vehicles and the convoluted dynamics of car following situation within various combinations of AV and human-driven vehicles. The expected headway of the mixed flow is determined based on the penetration rate of AV and the headways between two successive AV-AV, AV-N, N-AV, and N-N. Furthermore, the upper and lower bounds of mixed flow headway is presented. The theoretical headway is validated by microsimulation data. The estimated headways are then incorporated to derive the delay of a mixed flow at a signalized 2-lane link. Four combination of (i) mixed lanes, (ii) dedicated lanes for AV and human-driven vehicles, (iii) one mixed lane and one AV dedicated lane, and (iv) one mixed lane and one human-driven vehicle dedicated lane are considered. The results demonstrate the performance of the four lane configurations for various stages of AV deployment penetration rate.

Developing a large-scale taxi dispatching system for urban networks

Taxis are increasingly becoming a prominent mobility mode in many major cities due to their accessibility and convenience. The growing number of taxi trips is cause for concern when vacant taxis are not distributed optimally within the city and are unable to find waiting passengers effectively. A way of improving taxi operations is to deploy a taxi dispatch system that considers the interrelated effects of taxis on other traffic modes. This paper presents a taxi dispatch model that takes into account the impact of taxis on normal traffic flows while optimizing for an effective dispatch policy. The presented model builds on the concept of the macroscopic fundamental diagram (MFD) to represent the dynamic evolution of the traffic conditions. A model predictive control approach is devised to control the taxi dispatch system on a two-region city case study. The results show that the case of no network-scale taxi dispatching leads to severe accumulation of taxi passengers and vacant taxis in different regions whereas the dispatch system improves the taxi service performance and reduces traffic congestion by regulating the network towards the undersaturated condition.

Two-level hierarchical traffic control for heterogeneous urban networks

Field and simulation traffic data reveal that for some urban networks a well-defined Macroscopic Fundamental Diagram (MFD) exists, that provides a unimodal and low-scatter relationship between the network vehicle accumulation and outflow. Recent studies demonstrate that link density heterogeneity plays a significant role in the shape and scatter level of MFD and can cause hysteresis loops that deteriorate the network performance. This paper introduces a hierarchical perimeter flow control structure consisting of a high-level controller based on the model predictive control approach, where the prediction model is an aggregated parsimonious region-based MFD model and the plant is a detailed subregion-based MFD model. At the lower level, a feedback controller tries to maximize the outflow of critical regions by increasing their homogeneity. The hierarchical perimeter controller operates on the border between urban regions and manipulate the percentages of flows that transfer between the subregions such that the network delay is minimized and the distribution of congestion is more homogeneous. The proposed framework succeeds to increase network flows and decrease the hysteresis loop of the MFD compared to the existing perimeter controllers that are without heterogeneity controller.