Suh-Wen Chiou | National Dong Hwa University (original) (raw)

Papers by Suh-Wen Chiou

Research paper thumbnail of An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation

Springer eBooks, 2019

A data-driven multi-objective bi-level signal design for urban network with hazmat transportation... more A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.

Research paper thumbnail of Maximizing reserve capacity for a signalized road network design problem

Abstract For an area traffic control road network with expansions of link capacity, the maximum p... more Abstract For an area traffic control road network with expansions of link capacity, the maximum possible increase in travel demands is considered while total delays for travelers are minimized with respect to the common cycle time, the starts and durations of green times and the ...

Research paper thumbnail of A Multi-Objective Sustainable Traffic Signal Control for Smart Cities Under Uncertainty

International series in management science/operations research, 2023

Research paper thumbnail of A New Solution Scheme for the Link Toll Optimisation Problem

The problem of determining link tolls in order to reduce traffic congestion is referred to as a t... more The problem of determining link tolls in order to reduce traffic congestion is referred to as a toll optimization problem (TOP). This paper addresses a new solution scheme for a toll optimization problem. In this paper, optimal tolls are determined for road networks where the rerouting traffic is properly taken into account. This problem can be formulated as a mathematical program with equilibrium constraints (MPEC) where the user equilibrium can be expressed as a variational inequality problem. A first order sensitivity analysis can be carried out by solving an affine variational inequality. The generalized gradients and associated directional derivatives of equilibrium flow with respect to the decision variables thus become obtainable. Due to the non-smoothness of the problem, an efficient solution scheme is established. A projected subgradient method (PSM) is presented for which the accumulation points to the link toll optimization problem can effectively obtained. Global convergence for the proposed PSM approach is established. Numerical calculations are conduced on a 9-node network. Computational comparisons are also made with earlier methods where the promising results have shown the capability of the proposed approach PSM in solving the link toll optimization problem with and without variable demands.

Research paper thumbnail of A Cooperative Agent-Based Traffic Signal Control for Vehicular Networks Under Stochastic Flow

IEEE Transactions on Vehicular Technology

Research paper thumbnail of Optimization of Traffic Signal Timings for User Equilibrium

A bi-level programming approach has been used to tackle traffic signal timings optimization probl... more A bi-level programming approach has been used to tackle traffic signal timings optimization problem subject to user equilibrium flow, in which the optimization for signal settings with respect to the common cycle time, and the states and durations of green was dealt with as the upper level problem whilst a user equilibrium traffic assignment was dealt with as the lower level problem. A sensitivity analysis method was used and the derivatives for the performance index have been derived and used. The mixed search procedure was proposed to solve the bi-level formulation. The gradient projection method was used in deciding the optimal step length. Aalsop and Charlesworth's aging results showed the robustness and effectiveness of the method as values of the performance index were improved further in comparison with other conventional methods.

Research paper thumbnail of An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation

Data Science and Digital Business, 2019

A data-driven multi-objective bi-level signal design for urban network with hazmat transportation... more A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.

Research paper thumbnail of Co-Evolutionary Traffic Signal Control Using Reinforcement Learning for Road Networks Under Stochastic Capacity

Research paper thumbnail of Optimisation Algorithms for Equilibrium Network Design Problem

… AND TRAFFIC THEORY: PAPERS PRESENTED AT …, 1999

P. 247-264: ILL.; INCLUDES BIBLIOGRAPHICAL REFERENCES (P. 261-263) TRANSPORTATION AND TRAFFIC THE... more P. 247-264: ILL.; INCLUDES BIBLIOGRAPHICAL REFERENCES (P. 261-263) TRANSPORTATION AND TRAFFIC THEORY: PAPERS PRESENTED AT THE ABBREVIATED PRESENTATION SESSIONS. SL: SN, 1999. ... INTERNATIONAL SYMPOSIUM ON TRANSPORTATION ...

Research paper thumbnail of AMM-2011

Research paper thumbnail of Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty

Handbook of Research on Big Data Clustering and Machine Learning, 2020

A data-driven stochastic program for bi-level network design with hazardous material (hazmat) tra... more A data-driven stochastic program for bi-level network design with hazardous material (hazmat) transportation is proposed in this chapter. In order to regulate the risk associated with hazmat transportation and minimize total travel cost on interested area under stochasticity, a multi-objective stochastic optimization model is presented to determine generalized travel cost for hazmat carriers. Since the bi-level program is generally non-convex, a data-driven bundle method is presented to stabilize solutions of the proposed model and reduce relative gaps between iterations. Numerical comparisons are made with existing risk-averse models. The results indicate that the proposed data-driven stochastic model becomes more resilient than others in minimizing total travel cost and mitigating risk exposure. Moreover, the trade-offs among maximum risk exposure, generalized travel costs, and maximum equitable risk spreading over links are empirically investigated in this chapter.

Research paper thumbnail of Integrating the Inventory Management and Vehicle Routing Problems for Congested Urban Logistics Network

Abstract: Consider a congested urban logistics network with one depot and many geographically dis... more Abstract: Consider a congested urban logistics network with one depot and many geographically dispersed retailers facing demands at constant and deterministic rate over a period of planning horizon, but the lead time is variable due to traffic congestion. All stock enters the logistics network through the depot and from where it is distributed to the retailers by a fleet of vehicles. In this paper, we propose a new class of strategies for giving the optimal inventory replenishments for each retailer while the efficient delivery design is taken into account such that the minimization of total inventory cost and transportation cost is achieved. A mathematical program is formulated for this combined problem and a new class of iterative solution strategies is developed. Numerical computations are conducted and the proposed strategies obtain better results in comparison with other alternative with reasonable computational efforts.

Research paper thumbnail of A risk-averse user equilibrium model for area traffic control road networks

Research paper thumbnail of Optimization of Stock Management for a Supply Chain Network with Information Sharing

For a supply chain network, it includes the suppliers, warehouses and customers. The value of inf... more For a supply chain network, it includes the suppliers, warehouses and customers. The value of information sharing in a supply chain network has been discussed over a variety of aspects. In this paper, we present an optimized stock allocation and distribution policy of the stock levels at warehouses and the product flows for customers in a supply chain network with information sharing. The objective for this supply chain network is to pursue total cost minimization. An optimization of mathematical programme for the stock distribution in the supply chain network is proposed. The development of algorithms based on the minimum cost flows is established. Empirical studies for the comparison of the values of information sharing and non-information sharing in a two-level supply chain have been done in terms of the bullwhip effects and order up to inventory by real sales data over consecutive four years, which has been supported by project 89-EC-2-A-14-0314. Experiments for a three-echelon ...

Research paper thumbnail of A Data-Driven Traffic-Responsive Signal Control for a Smart City Road Network Under Uncertainty

Introduction to Internet of Things in Management Science and Operations Research

For a smart city with stochastic demand, a data-driven signal control is considered for traffic r... more For a smart city with stochastic demand, a data-driven signal control is considered for traffic responsive and time-varying road traffic networks. For period-dependent stochastic demand, a flow-based scenario design for traffic assignment can be formulated as a complementarity. A new solution using stochastic gradients is proposed. To demonstrate feasibility of proposed approach, numerical experiments using real-world example road networks are performed. To investigate computational tractability of proposed approach, numerical comparisons are made with the state-of-the-art existing traffic signal control strategies. These results reported indicate that the proposed data-driven traffic-responsive signal control can achieve reliably better results for a smart city road network than did those recently proposed against a high consequence of stochastic demands.

Research paper thumbnail of A data-driven bi-level program for knowledge-based signal control system under uncertainty

Knowledge-Based Systems

Abstract A data-driven knowledge based system (DDKS) is considered for urban signal control with ... more Abstract A data-driven knowledge based system (DDKS) is considered for urban signal control with hazardous material (hazmat) transportation. A data-driven bi-level program (DDBP) is presented to determine generalized travel cost for hazmat carriers and regular traffic flows. A risk-averse (RA) signal control is developed for DDKS with uncertain risk in the presence of hazmat transportation. Since DDBP is generally non-convex, a stochastic program using two-stage approach is proposed to find local optimal solutions. Numerical computations using a real-data city network are made and good results are obtained. As compared with conventional signal controls such as delay-minimizing (DM) and risk-neutral (RN) signal control, the proposed RA exhibits considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven alternatives in all cases.

Research paper thumbnail of A resilience-based signal control for a time-dependent road network with hazmat transportation

Reliability Engineering & System Safety

Abstract For a time-dependent urban road network with hazardous materials (hazmat) transportation... more Abstract For a time-dependent urban road network with hazardous materials (hazmat) transportation, a resilience-based signal control is proposed to manage maximum risk over links. In order to promote equity of risk in a spatial distribution, a stochastic program to reduce maximum time-varying risk over links is proposed together with a time-dependent scenario-driven decomposition (TSD). To demonstrate feasibility of proposed TSD, numerical experiments using a realistic road network are performed and comparisons are made with recently proposed ones. The results showed that the proposed TSD can successfully reduce maximum risk ratio in a time-dependent road network whilst being incurred much less computational effort in comparison with others.

Research paper thumbnail of A robust signal control system for equilibrium flow under uncertain travel demand and traffic delay

Automatica

Abstract A robust signal control system (AROSS) for equilibrium flow is presented for urban road ... more Abstract A robust signal control system (AROSS) for equilibrium flow is presented for urban road networks under uncertain travel demand and traffic delay. The optimal solutions for AROSS can be formulated as a smoothing mathematical program with equilibrium constraints (SMPEC) and efficiently solved by a projected bundle set method with global convergence. While conventional robust optimization is considered a good alternative against high-consequence realization of uncertainty, a traffic responsive signal control using a hybrid strategy of scenarios is considered. Sensitivity analysis of SMPEC with respect to signal settings against randomized budget uncertainty in travel demand and link delay is performed. Numerical comparisons are also made with other heuristics using real-data road networks. Results indicate that AROSS enjoyed a greater gain of achieving road network robustness while effectively attenuating sub-optimality of robust optimum as compared to recent alternatives.

Research paper thumbnail of A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods

Reliability Engineering & System Safety

To enhance resilience of urban road networks, a flexible signal control is proposed to mitigate p... more To enhance resilience of urban road networks, a flexible signal control is proposed to mitigate period-dependent travel delay and random risk associated with hazardous materials (hazmat) transportation. A mathematical optimization model is presented to find period-dependent traffic responsive signal control subject to equilibrium traffic assignments. In the presence of hazmat transportation, a set of scenarios for uncertain exposure risk on links is investigated. A two-stage new solution scheme is proposed to solve a traffic responsive signal control in multiple periods. In order to demonstrate robustness of period-dependent signal control for hazmat transportation, numerical computations using realistic road network are made with recently proposed ones. These results reported obviously indicate that proposed period-dependent signal control can be more resilient than existing ones against a high-consequence of exposure risk in the presence of hazmat transportation.

Research paper thumbnail of A two-stage model for period-dependent traffic signal control in a road networked system with stochastic travel demand

Information Sciences

Abstract For a road networked system with stochastic travel demand, a two-stage model is proposed... more Abstract For a road networked system with stochastic travel demand, a two-stage model is proposed for period-dependent area traffic signal control. In this paper, a period-dependent mathematical program with equilibrium constraints (PMPEC) is presented to minimize total travel delay over successive time periods. For stochastic travel demand over multiple time periods, a period-dependent user equilibrium traffic assignment can be formulated as a variational inequality. Due to non-linearity of equilibrium constraints, a two-stage model is presented in this paper. In order to understand feasibility of the proposed model, numerical experiments using a real-data road network are conducted. The results indicate that the proposed model attains a promising system performance and exhibit computational advantage over existing alternatives.

Research paper thumbnail of An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation

Springer eBooks, 2019

A data-driven multi-objective bi-level signal design for urban network with hazmat transportation... more A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.

Research paper thumbnail of Maximizing reserve capacity for a signalized road network design problem

Abstract For an area traffic control road network with expansions of link capacity, the maximum p... more Abstract For an area traffic control road network with expansions of link capacity, the maximum possible increase in travel demands is considered while total delays for travelers are minimized with respect to the common cycle time, the starts and durations of green times and the ...

Research paper thumbnail of A Multi-Objective Sustainable Traffic Signal Control for Smart Cities Under Uncertainty

International series in management science/operations research, 2023

Research paper thumbnail of A New Solution Scheme for the Link Toll Optimisation Problem

The problem of determining link tolls in order to reduce traffic congestion is referred to as a t... more The problem of determining link tolls in order to reduce traffic congestion is referred to as a toll optimization problem (TOP). This paper addresses a new solution scheme for a toll optimization problem. In this paper, optimal tolls are determined for road networks where the rerouting traffic is properly taken into account. This problem can be formulated as a mathematical program with equilibrium constraints (MPEC) where the user equilibrium can be expressed as a variational inequality problem. A first order sensitivity analysis can be carried out by solving an affine variational inequality. The generalized gradients and associated directional derivatives of equilibrium flow with respect to the decision variables thus become obtainable. Due to the non-smoothness of the problem, an efficient solution scheme is established. A projected subgradient method (PSM) is presented for which the accumulation points to the link toll optimization problem can effectively obtained. Global convergence for the proposed PSM approach is established. Numerical calculations are conduced on a 9-node network. Computational comparisons are also made with earlier methods where the promising results have shown the capability of the proposed approach PSM in solving the link toll optimization problem with and without variable demands.

Research paper thumbnail of A Cooperative Agent-Based Traffic Signal Control for Vehicular Networks Under Stochastic Flow

IEEE Transactions on Vehicular Technology

Research paper thumbnail of Optimization of Traffic Signal Timings for User Equilibrium

A bi-level programming approach has been used to tackle traffic signal timings optimization probl... more A bi-level programming approach has been used to tackle traffic signal timings optimization problem subject to user equilibrium flow, in which the optimization for signal settings with respect to the common cycle time, and the states and durations of green was dealt with as the upper level problem whilst a user equilibrium traffic assignment was dealt with as the lower level problem. A sensitivity analysis method was used and the derivatives for the performance index have been derived and used. The mixed search procedure was proposed to solve the bi-level formulation. The gradient projection method was used in deciding the optimal step length. Aalsop and Charlesworth's aging results showed the robustness and effectiveness of the method as values of the performance index were improved further in comparison with other conventional methods.

Research paper thumbnail of An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation

Data Science and Digital Business, 2019

A data-driven multi-objective bi-level signal design for urban network with hazmat transportation... more A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.

Research paper thumbnail of Co-Evolutionary Traffic Signal Control Using Reinforcement Learning for Road Networks Under Stochastic Capacity

Research paper thumbnail of Optimisation Algorithms for Equilibrium Network Design Problem

… AND TRAFFIC THEORY: PAPERS PRESENTED AT …, 1999

P. 247-264: ILL.; INCLUDES BIBLIOGRAPHICAL REFERENCES (P. 261-263) TRANSPORTATION AND TRAFFIC THE... more P. 247-264: ILL.; INCLUDES BIBLIOGRAPHICAL REFERENCES (P. 261-263) TRANSPORTATION AND TRAFFIC THEORY: PAPERS PRESENTED AT THE ABBREVIATED PRESENTATION SESSIONS. SL: SN, 1999. ... INTERNATIONAL SYMPOSIUM ON TRANSPORTATION ...

Research paper thumbnail of AMM-2011

Research paper thumbnail of Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty

Handbook of Research on Big Data Clustering and Machine Learning, 2020

A data-driven stochastic program for bi-level network design with hazardous material (hazmat) tra... more A data-driven stochastic program for bi-level network design with hazardous material (hazmat) transportation is proposed in this chapter. In order to regulate the risk associated with hazmat transportation and minimize total travel cost on interested area under stochasticity, a multi-objective stochastic optimization model is presented to determine generalized travel cost for hazmat carriers. Since the bi-level program is generally non-convex, a data-driven bundle method is presented to stabilize solutions of the proposed model and reduce relative gaps between iterations. Numerical comparisons are made with existing risk-averse models. The results indicate that the proposed data-driven stochastic model becomes more resilient than others in minimizing total travel cost and mitigating risk exposure. Moreover, the trade-offs among maximum risk exposure, generalized travel costs, and maximum equitable risk spreading over links are empirically investigated in this chapter.

Research paper thumbnail of Integrating the Inventory Management and Vehicle Routing Problems for Congested Urban Logistics Network

Abstract: Consider a congested urban logistics network with one depot and many geographically dis... more Abstract: Consider a congested urban logistics network with one depot and many geographically dispersed retailers facing demands at constant and deterministic rate over a period of planning horizon, but the lead time is variable due to traffic congestion. All stock enters the logistics network through the depot and from where it is distributed to the retailers by a fleet of vehicles. In this paper, we propose a new class of strategies for giving the optimal inventory replenishments for each retailer while the efficient delivery design is taken into account such that the minimization of total inventory cost and transportation cost is achieved. A mathematical program is formulated for this combined problem and a new class of iterative solution strategies is developed. Numerical computations are conducted and the proposed strategies obtain better results in comparison with other alternative with reasonable computational efforts.

Research paper thumbnail of A risk-averse user equilibrium model for area traffic control road networks

Research paper thumbnail of Optimization of Stock Management for a Supply Chain Network with Information Sharing

For a supply chain network, it includes the suppliers, warehouses and customers. The value of inf... more For a supply chain network, it includes the suppliers, warehouses and customers. The value of information sharing in a supply chain network has been discussed over a variety of aspects. In this paper, we present an optimized stock allocation and distribution policy of the stock levels at warehouses and the product flows for customers in a supply chain network with information sharing. The objective for this supply chain network is to pursue total cost minimization. An optimization of mathematical programme for the stock distribution in the supply chain network is proposed. The development of algorithms based on the minimum cost flows is established. Empirical studies for the comparison of the values of information sharing and non-information sharing in a two-level supply chain have been done in terms of the bullwhip effects and order up to inventory by real sales data over consecutive four years, which has been supported by project 89-EC-2-A-14-0314. Experiments for a three-echelon ...

Research paper thumbnail of A Data-Driven Traffic-Responsive Signal Control for a Smart City Road Network Under Uncertainty

Introduction to Internet of Things in Management Science and Operations Research

For a smart city with stochastic demand, a data-driven signal control is considered for traffic r... more For a smart city with stochastic demand, a data-driven signal control is considered for traffic responsive and time-varying road traffic networks. For period-dependent stochastic demand, a flow-based scenario design for traffic assignment can be formulated as a complementarity. A new solution using stochastic gradients is proposed. To demonstrate feasibility of proposed approach, numerical experiments using real-world example road networks are performed. To investigate computational tractability of proposed approach, numerical comparisons are made with the state-of-the-art existing traffic signal control strategies. These results reported indicate that the proposed data-driven traffic-responsive signal control can achieve reliably better results for a smart city road network than did those recently proposed against a high consequence of stochastic demands.

Research paper thumbnail of A data-driven bi-level program for knowledge-based signal control system under uncertainty

Knowledge-Based Systems

Abstract A data-driven knowledge based system (DDKS) is considered for urban signal control with ... more Abstract A data-driven knowledge based system (DDKS) is considered for urban signal control with hazardous material (hazmat) transportation. A data-driven bi-level program (DDBP) is presented to determine generalized travel cost for hazmat carriers and regular traffic flows. A risk-averse (RA) signal control is developed for DDKS with uncertain risk in the presence of hazmat transportation. Since DDBP is generally non-convex, a stochastic program using two-stage approach is proposed to find local optimal solutions. Numerical computations using a real-data city network are made and good results are obtained. As compared with conventional signal controls such as delay-minimizing (DM) and risk-neutral (RN) signal control, the proposed RA exhibits considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven alternatives in all cases.

Research paper thumbnail of A resilience-based signal control for a time-dependent road network with hazmat transportation

Reliability Engineering & System Safety

Abstract For a time-dependent urban road network with hazardous materials (hazmat) transportation... more Abstract For a time-dependent urban road network with hazardous materials (hazmat) transportation, a resilience-based signal control is proposed to manage maximum risk over links. In order to promote equity of risk in a spatial distribution, a stochastic program to reduce maximum time-varying risk over links is proposed together with a time-dependent scenario-driven decomposition (TSD). To demonstrate feasibility of proposed TSD, numerical experiments using a realistic road network are performed and comparisons are made with recently proposed ones. The results showed that the proposed TSD can successfully reduce maximum risk ratio in a time-dependent road network whilst being incurred much less computational effort in comparison with others.

Research paper thumbnail of A robust signal control system for equilibrium flow under uncertain travel demand and traffic delay

Automatica

Abstract A robust signal control system (AROSS) for equilibrium flow is presented for urban road ... more Abstract A robust signal control system (AROSS) for equilibrium flow is presented for urban road networks under uncertain travel demand and traffic delay. The optimal solutions for AROSS can be formulated as a smoothing mathematical program with equilibrium constraints (SMPEC) and efficiently solved by a projected bundle set method with global convergence. While conventional robust optimization is considered a good alternative against high-consequence realization of uncertainty, a traffic responsive signal control using a hybrid strategy of scenarios is considered. Sensitivity analysis of SMPEC with respect to signal settings against randomized budget uncertainty in travel demand and link delay is performed. Numerical comparisons are also made with other heuristics using real-data road networks. Results indicate that AROSS enjoyed a greater gain of achieving road network robustness while effectively attenuating sub-optimality of robust optimum as compared to recent alternatives.

Research paper thumbnail of A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods

Reliability Engineering & System Safety

To enhance resilience of urban road networks, a flexible signal control is proposed to mitigate p... more To enhance resilience of urban road networks, a flexible signal control is proposed to mitigate period-dependent travel delay and random risk associated with hazardous materials (hazmat) transportation. A mathematical optimization model is presented to find period-dependent traffic responsive signal control subject to equilibrium traffic assignments. In the presence of hazmat transportation, a set of scenarios for uncertain exposure risk on links is investigated. A two-stage new solution scheme is proposed to solve a traffic responsive signal control in multiple periods. In order to demonstrate robustness of period-dependent signal control for hazmat transportation, numerical computations using realistic road network are made with recently proposed ones. These results reported obviously indicate that proposed period-dependent signal control can be more resilient than existing ones against a high-consequence of exposure risk in the presence of hazmat transportation.

Research paper thumbnail of A two-stage model for period-dependent traffic signal control in a road networked system with stochastic travel demand

Information Sciences

Abstract For a road networked system with stochastic travel demand, a two-stage model is proposed... more Abstract For a road networked system with stochastic travel demand, a two-stage model is proposed for period-dependent area traffic signal control. In this paper, a period-dependent mathematical program with equilibrium constraints (PMPEC) is presented to minimize total travel delay over successive time periods. For stochastic travel demand over multiple time periods, a period-dependent user equilibrium traffic assignment can be formulated as a variational inequality. Due to non-linearity of equilibrium constraints, a two-stage model is presented in this paper. In order to understand feasibility of the proposed model, numerical experiments using a real-data road network are conducted. The results indicate that the proposed model attains a promising system performance and exhibit computational advantage over existing alternatives.