Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control

Traffic-responsive signal control is a cost-effective and easy-to-implement network management strategy with high potential in improving performance in congested networks with dynamic characteristics. Max Pressure (MP) distributed controller gained significant popularity due to its theoretically proven ability of queue stabilization and throughput maximization under specific assumptions. However, its effectiveness under saturated conditions is questionable, while network-wide application is limited due to high instrumentation cost. Perimeter control (PC) based on the concept of the Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated strategy that regulates exchange flows between regions, in order to maintain maximum regional travel production and prevent over-saturation. Yet, homogeneity assumption is hardly realistic in congested states, thus compromising PC efficiency. In this paper, the effectiveness of network-wide, parallel application of PC and MP embedded in a two-layer control framework is assessed with mesoscopic simulation. Aiming at reducing implementation cost of MP without significant performance loss, we propose a method to identify critical nodes for partial MP deployment. A modified version of Store-and-forward paradigm incorporating finite queue and spill-back consideration is used to test different configurations of the proposed framework, for a real large-scale network, in moderately and highly congested scenarios. Results show that: (i) combined control of MP and PC outperforms separate MP and PC applications in both demand scenarios; (ii) MP control in reduced critical node sets leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under demand fluctuations of up to 20% of mean.Comment: Submitted to Transportation Research Part C: Emerging Technologie

An optimization framework for exclusive bus lane allocation in large networks with dynamic congestion

Road space distribution among multiple modes of transport in urban networks has attracted the interest of several researchers and policy planners. Traditionally, these problems are solved by ignoring operational and dynamic characteristics of multi-modal congestion due to the computational burden involved. Revealing the relation between road space share and global performance of the network can lead to more efficient transport system design. The introduction of dedicated bus lanes in some parts of the network has been proposed as a measure to allow high occupancy vehicles to travel through regions with high traffic load without long delays. In this way, the bus transit system operates more efficiently and is competitive to the private car option in terms of travel time. This effect can stimulate a significant mode shift from car to bus and help alleviate congestion by reducing the number of low occupancy vehicles. In this work we address the problem of optimal allocation of exclusive bus lanes in a multi-modal urban network of fixed total road infrastructure and passenger demand, with the aim of minimizing the total passenger hours travelled (PHT) for all travel modes, within a framework which is consistent with the dynamics of congestion. A queueing theory based traffic flow model with proper treatment of spillbacks and traffic signal settings, known as the Store-and-Forward (SaF) model, is extended to simulate the dynamics of congestion inside the network by keeping track of the queues inside all links over time. The SaF simulation model can be used to evaluate the performance of different allocation policies

Efficient max-pressure traffic signal control for large-scale congested urban networks

Traffic responsive signal control systems bear high potential in reducing delays in congested networks due to their ability of dynamically adjusting right-of-way assignment among conflicting movements, based on real-time traffic measurements. In this work, we focus on distributed traffic signal control for large-scale networks based on the existing Max-Pressure controller, which has been shown to stabilize queues and maximize throughput in congested conditions. Max-Pressure constitutes a feedback control law that tries to balance queues around an intersection by updating green times between signal stages as a function of current queue measurements. Nevertheless, its increased infrastructure requirements impose high implementation costs. Our objective is to investigate how network performance changes when controller is installed only in subsets, (instead of all) of network nodes, while exploring strategies of identifying the most critical nodes. A modified version of Store-and-Forward traffic model is used to emulate spatio-temporal traffic evolution in a large-scale network and evaluate system performance for different controller layouts. Firstly, we observe significant improvement in terms of total delay and network MFD production when Max-Pressure control is applied. More than 85% of the improvement observed when controlling all network nodes can be achieved by controlling only 25% of properly selected nodes, thus reducing implementation costs to one fourth. Further research is needed in order to optimize node selection for the Max-Pressure layout, through evaluation of node impact to network performance. Moreover, investigating the potential of further gains via combining Max-Pressure with centralized control strategies, e.g. perimeter control, is a promising research direction

Combinatorial optimization of Dedicated Bus Lane layout in urban networks with dynamic congestion

Transit priority based in exclusive right-of-way is a low-cost way of improving transit service by minimizing delays caused by interaction with other vehicles. This effect can increase the share of public transit against private cars in the mode preferences of commuters and consequently alleviate heavy congestion resulting from the dominance of single-occupancy cars. In this work we propose a modeling and optimization framework for the problem of Dedicated Bus Lanes (DBL) location selection in bi-modal urban networks with time-varying traffic congestion. Traffic flow is replicated by dynamic macroscopic traffic model with queueing characteristics instead of steady state models, to capture potential spill-back effects caused by poor DBL planning. A combinatorial optimization problem is formulated aiming at minimizing total passenger delay. Optimization is performed by Local Search (LS) and Neighborhood Search (NS) algorithms, while a network decomposition technique is proposed for improved computational cost. The results show the proposed algorithms effective in significantly improving an initial DBL plan with reasonable computational cost

Modeling and optimization of dedicated bus lanes space allocation in large networks with dynamic congestion

Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban network while balancing the trade-off between bus priority and regular traffic disturbance is a challenging task. This paper studies the problem of optimal dedicated bus lane allocation and proposes a modeling framework based on a link-level dynamic traffic modeling paradigm, which is compatible with the dynamic characteristics of congestion propagation that can be correlated with bus lane relative positions. The problem is formulated as a non-linear combinatorial optimization problem with binary variables. An algorithmic scheme based on a problem-specific heuristic and Large Neighborhood Search metaheuristic, potentially combined with a network decomposition technique and a performance-based learning process for increased efficiency, is proposed for deriving good quality solutions for large-scale network instances. Numerical application results for a real city center demonstrate the efficiency of the proposed framework in finding effective bus lane network configurations; when compared to the initial network state they exhibit the potential of bus lanes to improve travel time for car and bus users

Optimizing dedicated bus lane allocation in bi-modal networks with dynamic congestion

Increasing urbanization and car ownership rates in developed countries result in hyper-congested cities with long delays in everyday commutes. Since scarcity of space forbids road space expansion, increasing the effectiveness and attractiveness of mass transit seems as a viable way to increase the systems supply. Dedicated Bus Lanes (DBL) provide exclusive space for public transit vehicles and therefore reduce mean travel time as also the congestion-related travel time variance; however, they decrease road capacity for regular traffic that may lead to severe congestion propagation due to the spillback effect. In this work, Variable Neighborhood Search is applied to seek for the optimal DBL plan in terms of total passenger hours travelled. A queueing theory based traffic flow model with proper treatment of spill-backs and traffic signal settings called "Store-and-Forward" is extended to simulate the evolution of traffic congestion in the network. The evaluation of the resulting optimal solution in comparison to state-of-practice solutions through microsimulation can reveal the degree of improvement in terms of decreasing total passenger delay

Modeling and Optimization of Dedicated Bus Lane network design under dynamic traffic congestion

Collective transport has been seen for long as a proper solution to fight congestion. While collectivity has been successful with respect to subways, it is not the norm in road traffic. Dedicated Bus Lanes (DBL) have been proposed as a measure to reduce the impact of traffic congestion in the travel time and accuracy of buses, by providing them with exclusive road space. However, DBL presence decreases road capacity, which may induce local congestion. Balancing this inherent trade-off of a DBL network, by carefully selecting the location of DBLs while considering the dynamics of congestion propagation is challenging. This work aims at finding a reliable modeling and optimization methodology to address this problem. An adjusted version of Store-and-Forward queuing model and microscopic simulation are used to simulate the traffic dynamics in presence of DBLs and assess the global network performance, while a local search algorithm is used to improve some state-of-practice solutions

Modeling and optimization of dedicated bus lanes space allocation in large networks with dynamic congestion

Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban network while balancing the trade-off between bus priority and regular traffic disturbance is a challenging task. This paper studies the problem of optimal dedicated bus lane allocation and proposes a modeling framework based on a link-level dynamic traffic modeling paradigm, which is compatible with the dynamic characteristics of congestion propagation that can be correlated with bus lane relative positions. The problem is formulated as a non-linear combinatorial optimization problem with binary variables. An algorithmic scheme based on a problem-specific heuristic and Large Neighborhood Search metaheuristic, potentially combined with a network decomposition technique and a performance-based learning process for increased efficiency, is proposed for deriving good quality solutions for large-scale network instances. Numerical application results for a real city center demonstrate the efficiency of the proposed framework in finding effective bus lane network configurations; when compared to the initial network state they exhibit the potential of bus lanes to improve travel time for car and bus users.ISSN:0968-090

Critical node selection method for efficient max-pressure traffic signal control in large-scale congested networks

Decentralized signal control of congested traffic networks based on the Max-Pressure (MP) controller is theoretically proven to maximize throughput, stabilize the system and balance queues for single intersections under specific conditions. Increased implementation cost related to queue monitoring reduces MP applicability. We propose a strategy for identifying the most critical network intersections for MP control, with the aim of reaching high efficiency without a full-network implementation. The strategy is based on node congestion and queue variance data. A modified version of Store-and- Forward model is used to emulate spatio-temporal traffic evolution in a large-scale network with more than 500 intersections and evaluate system performance for different MP node layouts. Results show that more than 90% of the maximum observed improvement can be achieved by controlling only 20% of nodes, selected via the proposed strategy, thus significantly reducing implementation cost. The impact of MP in network traffic characteristics is demonstrated

An optimization framework for exclusive bus lane allocation in large networks with dynamic congestion

Road space distribution among multiple modes of transport in urban networks has attracted the interest of several researchers and policy planners. Traditionally, these problems are solved by ignoring operational and dynamic characteristics of multi-modal congestion due to the computational burden involved. Revealing the relation between road space share and global performance of the network can lead to more efficient transport system design. The introduction of dedicated bus lanes in some parts of the network has been proposed as a measure to allow high occupancy vehicles to travel through regions with high traffic load without long delays. In this way, the bus transit system operates more efficiently and is competitive to the private car option in terms of travel time. This effect can stimulate a significant mode shift from car to bus and help alleviate congestion by reducing the number of low occupancy vehicles. In this work we address the problem of optimal allocation of exclusive bus lanes in a multi-modal urban network of fixed total road infrastructure and passenger demand, with the aim of minimizing the total passenger hours travelled (PHT) for all travel modes, within a framework which is consistent with the dynamics of congestion. A queueing theory based traffic flow model with proper treatment of spillbacks and traffic signal settings, known as the Store-and-Forward (SaF) model, is extended to simulate the dynamics of congestion inside the network by keeping track of the queues inside all links over time. The SaF simulation model can be used to evaluate the performance of different allocation policies