Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

2021 ◽  
pp. 036119812110556
Author(s):  
Hongzhi Lin ◽  
Yongping Zhang
Keyword(s):  
Traffic Flow ◽  
Waiting Time ◽  
Road Network ◽  
Travel Demand ◽  
Nonlinear Integer Programming ◽  
Network Equilibrium ◽  
Level Model ◽  
Optimal Deployment ◽  
Upper Level ◽  
Cordon Sanitaire

During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system’s total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources.

scholarly journals Optimal Design of Cordon Sanitaire for Regular Epidemic Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongzhi Lin
Keyword(s):  
Programming Model ◽  
Queueing Network ◽  
Optimal Solution ◽  
Epidemic Control ◽  
Level Model ◽  
Optimal Deployment ◽  
State And Local ◽  
Upper Level ◽  
Bilevel Programming Model ◽  
Cordon Sanitaire

The outbreak of COVID-19 has disrupted our regular life. Many state and local authorities have enforced a cordon sanitaire for the protection of sensitive areas. Travelers can only travel across the cordon after being qualified. This paper aims to propose a method to determine the optimal deployment of cordon sanitaire in terms of the number of parallel checkpoints at each entry link for regular epidemic control. A bilevel programming model is formulated where the lower-level is the transport system equilibrium with queueing to predict traffic inflow, and the upper-level is queueing network optimization, which is an integer nonlinear programming. The objective of this optimization is to minimize the total operation cost of checkpoints with a predetermined maximum waiting time. Note that stochastic queueing theory is used to represent the waiting phenomenon at each entry link. A heuristic algorithm is designed to solve the proposed bilevel model where the method of successive averages (MSA) is adopted for the lower-level model, and the genetic algorithm (GA) is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that the methods can find a good heuristic optimal solution. These methods are useful for policymakers to determine the optimal deployment of cordon sanitaire for hazard prevention and control.

scholarly journals Last train timetable optimization considering detour routing strategy in an urban rail transit network

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1461-1479
Author(s):  
Yu Yao ◽  
Xiaoning Zhu ◽  
Hua Shi ◽  
Pan Shang
Keyword(s):  
Waiting Time ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Train Timetable ◽  
Routing Strategy ◽  
Urban Rail ◽  
Level Model ◽  
Upper Level ◽  
Urban Rail Transit Network ◽  
Timetable Optimization

As an important means of transportation, urban rail transit provides effective mobility, sufficient punctuality, strong security, and environment-friendliness in large cities. However, this transportation mode cannot offer a 24-h service to passengers with the consideration of operation cost and the necessity of maintenance, that is, a final time should be set. Therefore, operators need to design a last train timetable in consideration of the number of successful travel passengers and the total passenger transfer waiting time. This paper proposes a bi-level last train timetable optimization model. Its upper level model aims to maximize the number of passengers who travel by the last train service successful and minimize their transfer waiting time, and its lower level model aims to determine passenger route choice considering the detour routing strategy based on the last train timetable. A genetic algorithm is proposed to solve the upper level model, and the lower level model is solved by a semi-assignment algorithm. The implementation of the proposed model in the Beijing urban rail transit network proves that the model can optimize not only the number of successful transfer directions and successful travel passengers but also the passenger transfer waiting time of successful transfer directions. The optimization results can provide operators detailed information about the stations inaccessible to passengers from all origin stations and uncommon path guides for passengers of all origin–destination pairs. These types of information facilitate the operation of real-world urban rail transit systems.

A Bi-Level Model to Estimate the US Air Travel Demand

2015 ◽  
Vol 32 (02) ◽  
pp. 1550009 ◽  
Author(s):  
Tao Li
Keyword(s):  
Data Collection ◽  
Travel Demand ◽  
Air Travel ◽  
Least Square ◽  
The Us ◽  
Additional Data Collection ◽  
Level Model ◽  
Air Travel Demand ◽  
Upper Level ◽  
Data Collection Effort

A single-level optimization model (i.e., a Route Flow Estimator (RFE)) has been proposed to estimate the historical air travel demand. However, the RFE may require a significant amount of additional data collection effort when applied to estimate travel demand in small or medium-sized networks. We propose a novel bi-level model as an alternative to the RFE to handle demand estimation for small or medium-sized networks. The upper-level model is designed as a constrained least square (LS) model. The lower-level model is designed based on the RFE. The bi-level model estimates travel demand by considering travelers' choice behaviors and some observed data. It requires less data collection effort yet it produces estimation results consistent with those from the RFE. A Gauss–Seidel type (GST) algorithm is proposed to solve the bi-level model. To solve the upper-level model, we propose a heuristic algorithm, which is designed to solve the dual of the upper-level model. The estimation results from the two models are compared using two numerical examples: a small-sized example with one OD pair and a medium-sized example with 400 OD pairs.

scholarly journals Non-linear optimization for continuous travel demand estimation

2021 ◽  
Vol 17 (1) ◽  
pp. 40-46
Author(s):  
Anastasiya P. Raevskaya ◽  
◽  
Alexander Y. Krylatov ◽  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Optimization Problem ◽  
Travel Demand ◽  
Linear Optimization ◽  
Demand Estimation ◽  
Theory And Practice ◽  
Traffic Distribution ◽  
Non Linear ◽  
Fresh Perspective

Models and methods of traffic distribution are being developed by researchers all over the world. The development of this scientific field contributes to both theory and practice. In this article, the non-linear optimization of traffic flow re-assignment is examined in order to solve continuously the travel demand estimation problem. An approach has been developed in the form of computational methodology to cope with the network optimization problem. A uniqueness theorem is proved for a certain type of road network. Explicit relations between travel demand and traffic flow are obtained for a single-commodity network of non-intersecting routes with special polynomial travel time functions. The obtained findings contribute to the theory and provide a fresh perspective on the problem for transportation engineers.

scholarly journals Optimizing capacity of signalized road network with reversible lanes

Transport ◽  
2015 ◽  
Vol 33 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Jian Wang ◽  
Wei Deng
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Flow Direction ◽  
Network Capacity ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Mixed Network ◽  
Level Problem ◽  
Upper Level

This paper studies the network capacity problem on signalized road network with reversible lanes. A Mixed Network Design Problem (MDNP) is formulated to describe the problem where the upper-level problem is a mixed integer non-linear program designed to maximize the network capacity by optimizing the input parameters (e.g. the signal splits, circles, reassigned number of lanes and O–D demands), while the lower-level problem is the common Deterministic User Equilibrium (DUE) assignment problem formulated to model the drivers’ route choices. According to whether one way strategy is permitted in practice, two strategies for implementing reversible roadway are considered. In the first strategy, not all lanes are reversible and the reversible roadways always hold its ability to accommodate the two-way traffic flow. In the second strategy, one-way road is allowed, which means that all the lanes are reversible and could be assigned to one flow direction if the traffic flow in both directions is severally unsymmetrical. Genetic Algorithm (GA) is detailedly presented to solve the bi-level network capacity problem. The application of the proposed method on a numerical example denotes that Strategy 2 can make more use of the physical capacity of key links (signal controlled links), thus, the corresponding network capacity outperforms it is of Strategy 1 considerably.

scholarly journals Estimating Demand for a New Travel Mode in Boise, Idaho

2021 ◽  
Vol 13 (3) ◽  
pp. 1209
Author(s):  
Mandar Khanal
Keyword(s):  
Road Network ◽  
Travel Demand ◽  
Stated Preference ◽  
State University ◽  
University Campus ◽  
City Center ◽  
Road Congestion ◽  
The Us ◽  
Potential Mode ◽  
The City

The 20,000-student Boise State University campus is located about 3 km from the center of the city of Boise. There is a significant amount of travel between the campus and the city center as students and staff travel to the city to visit restaurants, shops, and entertainment centers. Currently, people make this trip by car, shuttle bus, bike, or walking modes. Cars and shuttle buses, which share the same road network, constitute about 76% of the total trips. As road congestion is expected to grow in the future, it is prudent to look for other modes that can fulfill the travel demand. One potential mode is an aerial tramway. However, an aerial tramway is not a common mode of urban travel in the US. This research describes how the stated preference method was used to estimate demand for a mode that does not currently exist. An online stated preference survey was sent out to 8681 students, faculty, and staff and 1821 valid responses were received. Only about 35% of the respondents expressed their willingness to choose an aerial tramway for various combinations of cost and convenience of the new mode. Respondents were also found to favor convenience over cost for the new mode.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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