Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Li, Pengfei; Mirchandani, Pitu B.; Zhou, Xuesong

doi:10.1016/j.trb.2015.08.011

Cited by 66 publications

(28 citation statements)

References 80 publications

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“…(), Beard and Ziliaskopoulos (), Li et al. (), and Hajbabaie and Benekohal () proposed central models for simultaneous signal timing optimization and traffic assignment. Karoonsoontawong and Waller () incorporated network capacity expansion in central traffic signal optimization and dynamic traffic assignment.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Mehrabipour

Hajbabaie

2017

Computer aided Civil Eng

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This article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.

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Section: Literature Reviewmentioning

confidence: 99%

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Mehrabipour

Hajbabaie

2017

Computer aided Civil Eng

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“…4(a) depicts a simple physical network with three nodes, two links, one O-D pair (1, 3), and time-dependent travel demand input. By using dynamic network modeling processed described in Tong et al (2015) and Li et al (2015), the corresponding expanded space-time network is displayed in Fig. 4(b).…”

Section: About Here>mentioning

confidence: 99%

Eco-system optimal time-dependent flow assignment in a congested network

Liu

et al. 2016

Transportation Research Part B: Methodological

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This research addresses the eco-system optimal dynamic traffic assignment (ESODTA) problem which aims to find system optimal eco-routing or green routing flows that minimize total vehicular emission in a congested network. We propose a generic agent-based ESODTA model and a simplified queueing model (SQM) that is able to clearly distinguish vehicles' speed in free-flow and congested conditions for multi-scale emission analysis, and facilitates analyzing the relationship between link emission and delay. Based on the SQM, an expanded space-time network is constructed to formulate the ESODTA with constant bottleneck discharge capacities. The resulting integer linear model of the ESODTA is solved by a Lagrangian relaxation-based algorithm. For the simulation-based ESODTA, we present the column-generation-based heuristic, which requires link and path marginal emissions in the embedded timedependent least-cost path algorithm and the gradient-projection-based descent direction method. We derive a formula of marginal emission which encompasses the marginal travel time as a special case, and develop an algorithm for evaluating path marginal emissions in a congested network. Numerical experiments are conducted to demonstrate that the proposed algorithm is able to effectively obtain coordinated route flows that minimize the system-wide vehicular emission for large-scale networks. Travel time-based and eco-cost-based traffic assignment modelsFollowing the pioneering work of Merchant and Nemhauser (1978), various approaches have been proposed in the past decades to formulate and solve the travel time-based system optimal dynamic traffic assignment problem in ideal or general networks, such as mathematical programming (e.Shen and Zhang, 2009) and variational inequality (Shen et al., 2007a).As extensions of the user equilibrium and system optimum principles, eco-cost-based, or emission-based, assignment principles have been adopted in a number of STA models. For instance, Benedek and Rilett (1998) presented the emission optimal principle which describes that travellers choose paths so as to minimize the total network emission, rather than total travel time. They also discussed an extension of the user equilibrium principle, the environmental equity principle, in which travellers are assigned in such a way that the amounts of emission on all selected routes are the same. Another line of research was to employ the multi-objective or multi-criterion approaches in traffic assignment models. For example, a multi-criterion system optimum model was proposed by Tzeng and Chen (1993), where the system optimum objective is the sum of total travel time for road users and air pollution for nonusers. Nagurney et al. (1998Nagurney et al. ( , 2002 presented a multi-class user equilibrium traffic assignment model in which each class of users was assumed to select a route with the least weighted sum of travel time, travel cost and emissions. Zhang et al. (2010) developed a system optimal STA model with the objective being the weighted sum of tr...

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“…Optimization‐based systems implement sophisticated optimization techniques to produce optimal traffic management schemes that cope with the observed traffic network conditions (Hashemi and Abdelghany, ; Lin et al., ). Early efforts focused on determining the optimal settings of a single traffic management strategy (e.g., signal timing—Arel et al., ; Prashanth and Bhatnagar, ; Mehrabipour and Hajbabaie, ; road guidance—Angelelli et al., ; Li et al., ; ramp metering—Hegyi et al., ; Papamichail et al., ; Agarwal et al., ; and congestion pricing—Hassan et al., ; Chen and Yang, ; Zheng et al., ). An example of recent work that develops comprehensive traffic management schemes that incorporate multiple management strategies can be found in Hashemi and Abdelghany (), which is used in this study to benchmark the developed learning‐based traffic management system.…”

Section: Introductionmentioning

confidence: 99%

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

Hashemi

Abdelghany

2018

Computer aided Civil Eng

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This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.

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Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

Cited by 66 publications

References 80 publications

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Eco-system optimal time-dependent flow assignment in a congested network

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

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