Network Design of Park-and-Ride System to Promote Transit Patronage

2014 ◽  
Vol 1030-1032 ◽  
pp. 2050-2053 ◽  
Author(s):  
Xin Yuan Chen ◽  
Zhi Yuan Liu ◽  
Wei Deng
Keyword(s):  
Logit Model ◽  
Programming Model ◽  
Search Algorithm ◽  
Multinomial Logit Model ◽  
Tabu Search Algorithm ◽  
Park And Ride ◽  
Assignment Model ◽  
Mode Split ◽  
Level Model ◽  
Upper Level

This paper proposed a bi-level programming model to optimize the locations and capacity for rail-based park-and-ride sites to promote transit patronage. A multinomial logit model was incorporated in a mode split/traffic assignment model to assess any given park-and-ride scheme. This model was then taken as the lower level model, and the upper level programming model is established to optimize the location and capacity of park-and-ride with the goal of promoting transit patronage. A heuristic tabu search algorithm is then adopted to solve this model.

Park-and-Ride Network Design: A Goal Programming Approach

2014 ◽  
Vol 1030-1032 ◽  
pp. 2065-2068
Author(s):  
Xin Yuan Chen ◽  
Zhi Yuan Liu ◽  
Wei Deng
Keyword(s):  
Network Design ◽  
Goal Programming ◽  
Design Problem ◽  
Programming Model ◽  
Search Algorithm ◽  
Network Design Problem ◽  
Programming Approach ◽  
Multi Objective ◽  
Park And Ride ◽  
Upper Level

The paper addresses a park and ride network design problem in a bi-model transport network in a multi-objective decision making framework. A goal programming approach is adopted to solve the multi-objective park and ride network design problem. The goal programming approach considers the user-defined goals and priority structure, which are (i) traffic-efficient goal, (ii) total transit usage goal, (iii) spatial equity goal. This problem is formulated as a bi-level programming model. The upper level programming leads to minimize the deviation from stated goals in the context of a given priority ranking. While the lower level programming model is a modal split/traffic assignment model which is used to assess any given park and ride scheme. A heuristic tabu search algorithm is then adopted to solve this model.

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.

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.

Bi-Level Model for Design of Transit Short-Turning Service Considering Bus Crowding

2017 ◽  
Vol 2649 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Xiyu Yang ◽  
Yuxiong Ji ◽  
Yuchuan Du ◽  
H. Michael Zhang
Keyword(s):  
Optimal Design ◽  
Logit Model ◽  
Operational Cost ◽  
Time Cost ◽  
Total Cost ◽  
Proposed Model ◽  
Level Model ◽  
Upper Level ◽  
Vehicle Travel Time

A bi-level model was developed to design the short-turning strategy on a bus route. The upper-level model aimed at minimizing the total cost, including operational cost, passengers’ waiting time cost, and in-vehicle travel time cost. The lower-level model was a logit model to capture the service choices of passengers. The effects of bus crowding and seat availability were considered explicitly in the proposed model. An algorithm was developed to determine the frequencies of different services and the turnback points of the short-turning service. A case study demonstrates the superiority of the proposed model over alternative models. Sensitivity of the optimal design to seat capacity was also investigated.

scholarly journals The Analysis of the Factors Determining the Choice of Park and Ride Facility Using a Multinomial Logit Model

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 203
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Logit Model ◽  
Structural Parameters ◽  
Influence Factors ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Basic Function ◽  
Park And Ride ◽  
Potential Factors ◽  
Subway Train ◽  
The City

The basic function of the Park and Ride (P&R) facility is to allow users to leave their vehicle on the outskirts of the city and to continue their journey to the city center using means of public transport, e.g., bus, tram, trolleybus, subway, train, or bike. In the first part of the paper, an analysis of the selected factors related to the functioning of P&R facilities in Warsaw (Poland) was performed. The main purpose of this paper was to identify and quantify the influence factors determining the choice of P&R facility during a journey. This analysis was performed for three hypothetical journey scenarios. A list of potential factors determining the choice of P&R facility during travel was compiled after conducting previous research in this area and studying the worldwide scientific literature on the subject. The structural parameters of the multinomial logit model were estimated based on the data from the survey conducted in Warsaw. The results of the analyses indicate that the decision to choose a hypothetical journey scenario depends on many factors, but primarily on the level of education, the number of years of having a driving license, age, the number of kilometers traveled during the year, and the performed activity.

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.

Bi-Level Programming Model and Solution Algorithm for the Location of E-Business Logistics Distribution

2014 ◽  
Vol 1030-1032 ◽  
pp. 2532-2537
Author(s):  
Zhi Ping Du ◽  
Feng Zhi Qi
Keyword(s):  
Programming Model ◽  
Current Status ◽  
Fixed Costs ◽  
Transport Costs ◽  
Customer Demand ◽  
Customer Choice ◽  
Level Model ◽  
Upper Level ◽  
End Node ◽  
The Impact

For the current status of e-commerce logistics and distribution terminal node layout,this article considers planners and customers both benefit, while also considering the impact of the end node distribution route for cost.A bi-level programming model of end nodes location was constructed. The upper level model is the smallest in the conditions of transport costs and fixed costs as much as possible to attract customer demand, lower programming model considers the distance, the price of services and quality of service that influence customer choice behavior, and customer demand for the costs of terminal nodes distribution is studied.Finally, a numerical example verifies the feasibility of the model and algorithm.

scholarly journals Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 598 ◽  
Author(s):  
Zhiping Zuo ◽  
Yanhui Li ◽  
Jing Fu ◽  
Jianlin Wu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Search Algorithm ◽  
Time Windows ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Service Personnel ◽  
The Stability

In situations where an organization has limited human resources and a lack of multi-skilled employees, organizations pay more and more attention to cost control and personnel arrangements. Based on the consideration of the service personnel scheduling as well as the routing arrangement, service personnel of different skills were divided into different types according to their multiple skills. A mathematical programming model was developed to reduce the actual cost of organization. Then, a hybrid meta heuristic that combines a tabu search algorithm with a simulated annealing was designed to solve the problem. This meta heuristic employs several neighborhood search operators and integrates the advantages of both the tabu search algorithm and the simulated annealing algorithm. Finally, the stability and validity of the algorithm were validated by the tests of several kinds of examples.

Simulation-based optimization for urban transportation demand management strategy

SIMULATION ◽  
2018 ◽  
Vol 94 (7) ◽  
pp. 637-647 ◽  
Author(s):  
Baozhen Yao ◽  
Qianqian Yan ◽  
Qian Chen ◽  
Zhihui Tian ◽  
Xuefeng Zhu
Keyword(s):  
Traffic Congestion ◽  
Demand Management ◽  
Research Area ◽  
Urban Traffic ◽  
Transportation Demand Management ◽  
Transportation Demand ◽  
Assignment Model ◽  
Level Model ◽  
Upper Level ◽  
Scientific Decision

Transportation demand management (TDM) is one of the important methods for solving the problem of increasingly severe urban traffic congestion. This paper proposes a bi-level model to optimize urban TDM strategies based on simulation. The upper level is the TDM strategy optimization model, searching for the optimal TDM strategy. The lower level is a traffic assignment model based on the simulation, assigning traffic flow to multimodal transport networks according to the candidate TDM strategies. A heuristic algorithm is also defined and implemented to optimize TDM strategies. Based on VISSIM simulation, the optimization for TDM strategies proposed in this paper is validated with the research area of Harbor Square–San Ba Square–Er Qi Square in Dalian in China. The results show that the optimization of urban TDM strategies can effectively alleviate urban traffic congestion. It provides a scientific decision basis for urban TDM policy.

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.

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