scholarly journals Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 498
Author(s):  
Xinhua Mao ◽  
Xiandong Jiang ◽  
Changwei Yuan ◽  
Jibiao Zhou
Keyword(s):  
Programming Model ◽  
User Equilibrium ◽  
Maintenance Scheduling ◽  
Traffic Demand ◽  
Scheduling Strategy ◽  
Bridge Networks ◽  
Traffic Delays ◽  
Optimal Maintenance ◽  
Level Model ◽  
Upper Level

An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.

scholarly journals The Shrinking of Beijing and the Rising of Xiong’an: Optimize Population Migration in terms of Transport Service

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hongzhi Lin
Keyword(s):  
Bilevel Programming ◽  
Central Government ◽  
Programming Model ◽  
Service Level ◽  
User Equilibrium ◽  
Population Migration ◽  
Transport Service ◽  
Level Model ◽  
Upper Level ◽  
Bilevel Programming Model

The population of Beijing has already come to its loading capacity. The China central government plans to build an ideal city named Xiong’an nearby Beijing. The city is expected to work as a carrying hub for noncapital functions of Beijing. The central government does not rush to build before a deliberated urban planning is accomplished. For sustainable development, a difficulty faced by urban planners is that the maximum number of people can be migrated from Beijing to Xiong’an with constraint on level of transport service. This paper developed a specialized bilevel programming model where the upper level is to ensure a predetermined transport service level regarding to population migration, while the lower level is feedback equilibrium between trip generation and traffic assignment. To be more specific, trip is generated by the gravity model, and traffic is assigned by the user equilibrium model. It is well known that the bilevel programming problem is tough and challenging. A try-and-error algorithm is designed for the upper-level model, and a method of successive average (MSA) is developed for the lower-level model. The effectiveness of the model and algorithm is validated by an experimental study using the current transport network between Beijing and Xiong’an. It shows that the methods can be very useful to identify the maximum population migration subject to level of transport service.

scholarly journals Evaluation of Transportation Network Reliability under Emergency Based on Reserve Capacity

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xiongfei Zhang ◽  
Qi Zhong ◽  
Qin Luo
Keyword(s):  
Evaluation Method ◽  
Programming Model ◽  
Transportation Network ◽  
Emergency Evacuation ◽  
Reserve Capacity ◽  
Capacity Limits ◽  
Traffic Demand ◽  
Solution Algorithms ◽  
Level Model ◽  
Upper Level

There are differences between the requirements for traffic network for traffic demand in daily and emergency situations. In order to evaluate how the network designed for daily needs can meet the surging demand for emergency evacuation, the concept of emergency reliability and corresponding evaluation method is proposed. This paper constructs a bilevel programming model to describe the proposed problem. The upper level problem takes the maximum reserve capacity multiplier as the optimization objective and considers the influence of reversible lane measures taken under emergency conditions. The lower level model adopts the combined traffic distribution/assignment model with capacity limits, to describe evacuees’ path and shelter choice behavior under emergency conditions and take into account the traits of crowded traffic. An iterative optimization method is proposed to solve the upper level model, and the lower level model is transformed into a UE assignment problem with capacity limits over a network of multiple origins and single destination, by adding a dummy node and several dummy links in the network. Then a dynamic penalty function algorithm is used to solve the problem. In the end, numerical studies and results are provided to demonstrate the rationality of the proposed model and feasibility of the proposed solution algorithms.

scholarly journals Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhibo Gao ◽  
Zhizhou Wu ◽  
Wei Hao ◽  
Kejun Long
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Programming Model ◽  
Travel Cost ◽  
Heterogeneous Traffic ◽  
Traffic Network ◽  
Traffic Demand ◽  
Automated Vehicle ◽  
Traffic Efficiency ◽  
Upper Level

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.

scholarly journals A Multistage Multiobjective Model for Emergency Evacuation Considering ATIS

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Hua Zeng ◽  
Ke-Jun Long ◽  
Zi-Wen Ling ◽  
Xi-Yan Huang
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Performance ◽  
Programming Model ◽  
Choice Model ◽  
Compliance Rate ◽  
Emergency Evacuation ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Traffic Demand

The impacts of advanced traveler information system’s (ATIS’s) penetration and compliance rates on network performances during hybrid traffic emergency evacuation are investigated in a degraded road network. Before traffic incident a Path-Size Logit (PSL) route choice model is integrated with constraints on the level of service (LOS) of traffic to formulate a bilevel programming model. It aims at minimizing traffic demand in road network which may locally deteriorate the LOS. The lower level is a PSL-stochastic user equilibrium model for multiple classes of users. During the ongoing incident, a multiobjective multiuser-class stochastic optimization model is established with the objectives of maximizing evacuation reliability and minimizing expected network travel time. Furthermore, computations and analyses are completed for five designated scenarios including a method proposed in previous literature. The results show that the evacuation reliability and different kinds of total expected travel time costs regularly increase with emergency traffic’s ATIS compliance rate and decrease with general traffic’s ATIS penetration rate. The research will help improve transport network performance when considering ATIS’s effect on hybrid traffic.

scholarly journals Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Hua Wang ◽  
Ling Xiao ◽  
Zhang Chen
Keyword(s):  
Network Design ◽  
Transportation Network ◽  
Bilevel Optimization ◽  
User Equilibrium ◽  
Emergency Vehicle ◽  
Stochastic Demands ◽  
Road Users ◽  
Level Model ◽  
Access Rights ◽  
Upper Level

We study transportation network design with stochastic demands and emergency vehicle (EV) lanes. Different from previous studies, this paper considers two groups of users, auto and EV travelers, whose road access rights are differentiated in the network, and addresses the value of incorporating inverse-direction lanes in network design. We formulate the problem as a bilevel optimization model, where the upper-level model aims to determine the optimal design of EV lanes and the lower-level model uses the user equilibrium principle to forecast the route choice of road users. A simulation-based genetic algorithm is proposed to solve the model. With numerical experiments, we demonstrate the value of deploying inverse-direction EV lanes and the computational efficiency of the proposed algorithm. We reach an intriguing finding that both regular and EV lane users can benefit from building EV lanes.

Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops

2022 ◽  
pp. 1-18
Author(s):  
Nan-Yun Jiang ◽  
Hong-Sen Yan
Keyword(s):  
Genetic Algorithm ◽  
Production Planning ◽  
Programming Model ◽  
Expected Value ◽  
Fixed Position ◽  
Planning And Scheduling ◽  
Integrated Optimization ◽  
Production Planning And Scheduling ◽  
Level Model ◽  
Upper Level

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.

Optimal Tolling Strategies and Concession Period Required for Build-Operate-Transfer Road Projects in Network with Demand Uncertainty by Vehicle Types

2012 ◽  
Vol 209-211 ◽  
pp. 977-986 ◽  
Author(s):  
Liang Xu
Keyword(s):  
Programming Problem ◽  
Demand Uncertainty ◽  
Consumer Surplus ◽  
User Equilibrium ◽  
Solution Algorithm ◽  
Demand Elasticity ◽  
Stochastic User Equilibrium ◽  
Traffic Demand ◽  
Concession Period ◽  
Upper Level

In view of uncertainty of traffic demand by vehicle types during the concession period, the toll charges of these BOT roads should be time-dependent and varied for different vehicle types by time of the concession period so as to maximize the system performance.It can be formulated as a bi-level programming problem. At the upper level, the objective is to maximize the social surplus combining of consumer surplus and the investor’s net profit. With taking account the demand elasticity in respect to the toll variability and incorporating the demand uncertainty into the revenue-cost constraint, the lower level is a multi-class reliability-based stochastic user equilibrium model. A genetic solution algorithm is adopted for solving the bi-level programming problem. A numerical example is presented to illustrate the applications of the proposed model and solution algorithm and some conclusions are drawn.

scholarly journals Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

2021 ◽  
Vol 13 (23) ◽  
pp. 13057
Author(s):  
Hui Chen ◽  
Zhaoming Chu ◽  
Chao Sun
Keyword(s):  
Maximum Likelihood ◽  
Input Parameter ◽  
Weighted Average ◽  
Demand Estimation ◽  
User Equilibrium ◽  
Sensor Deployment ◽  
Stochastic User Equilibrium ◽  
Estimation Model ◽  
Traffic Demand ◽  
Level Model

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

Collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles

2018 ◽  
Vol 32 (22) ◽  
pp. 1850253
Author(s):  
Zhi-Yuan Sun ◽  
Yue Li ◽  
Wen-Cong Qu ◽  
Yan-Yan Chen
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Optimization Model ◽  
Programming Model ◽  
Urban Traffic ◽  
Middle Level ◽  
Internet Of Vehicles ◽  
Model Based ◽  
Level Model ◽  
Upper Level

In order to improve the comprehensive effect of Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS), this paper puts forward a collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles (IOV). With consideration of dynamic constraints of UTCS and UTFGS, UTCS is taken as the fast variable, and UTFGS is taken as the slow variable in the collaboration optimization modeling. The conception of Variable Cycle Management (VCM) is presented to solve the mathematical modeling problem under the background of the two variables. A unified framework for VCM is proposed based on IOV. The delay and travel time are calculated based on lane-group-based cell transmission model (LGCTM). The collaboration optimization problem is abstracted into a tri-level programming model. The upper level model is a cycle length optimization model based on multi-objective programming. The middle level model is a dynamic signal control decision model based on fairness analysis. The lower level model is a user equilibrium model based on average travel time. A Heuristic Iterative Optimization Algorithm (HIOA) is set up to solve the tri-level programming model. The upper level model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA II), the middle level model and the lower level model are solved by Method of Successive Averages (MSA). A case study shows the efficiency and applicability of the proposed model and algorithm.

scholarly journals Continuous Network Design Based on the Paired Combinatorial Logit Stochastic User Equilibrium Model

2014 ◽  
Vol 8 (1) ◽  
pp. 316-322
Author(s):  
Xuefei Li ◽  
Maoxiang Lang
Keyword(s):  
Network Design ◽  
Objective Function ◽  
Equilibrium Model ◽  
Design Problem ◽  
Programming Model ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Stochastic User Equilibrium ◽  
Level Model ◽  
The Impact

In order to design the traffic network more accurately, the bi-level programming model for the continuous network design problem based on the paired combinatorial Logit stochastic user equilibrium model is proposed in this study. In the model, the paired combinatorial Logit stochastic user equilibrium model which is used to characterize the route choice behaviors of the users is adopted in the lower level model, and the minimum summation of the system total costs and investment amounts is used in the upper objective function. The route-based self-regulated averaging (SRA) algorithm is designed to solve the stochastic user equilibrium model and the genetic algorithm (GA) is designed to get the optimal solution of the upper objective function. The effectiveness of the proposed combining algorithm which contains GA and SRA is verified by using a simple numerical example. The solutions of the bi-level models which use the paired combinatorial Logit stochastic user equilibrium model in the lower level model with different demand levels are compared. Finally, the impact of the dispersion coefficient parameter which influences the decision results of the network design problem is analyzed.

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