scholarly journals Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Ming Wei ◽  
Tao Liu ◽  
Bo Sun ◽  
Binbin Jing
Keyword(s):  
Walking Distance ◽  
Load Factor ◽  
Chongqing City ◽  
Proposed Model ◽  
Route Design ◽  
Feeder Transit ◽  
Total Travel Time ◽  
Test Instance ◽  
Np Hard Problem ◽  
Lower Limits

This study proposed a mathematical model for designing a feeder transit service for improving the service quality and accessibility of transportation hubs (such as airport and rail station). The proposed model featured an integrated framework, which simultaneously guided passengers to reach their nearest stops to get on and off the bus, designed routes to transport passengers from these selected pick-up stops to the transportation hubs, and calculated their departure frequencies. In particular, the maximum walking distance, the upper and lower limits of route frequencies, and the load factor rate of each route were fully accounted for in this study. The main objective of the proposed model was to simultaneously minimize the total walking, riding time, and waiting time of all passengers. As this study explored an NP-hard problem, a two-stage genetic algorithm combining the Dijkstra search method was further developed to yield metaoptimal solutions to the model within an acceptable time. Finally, a test instance in Chongqing City, China, demonstrated that the proposed model was an effective tool to generate a pedestrian, route, and operation plan; it reduced the total travel time, compared with the traditional model.

scholarly journals An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 370 ◽  
Author(s):  
Bo Sun ◽  
Ming Wei ◽  
Wei Wu
Keyword(s):  
Time Windows ◽  
Model Performance ◽  
Optimal Solution ◽  
Walking Distance ◽  
Ride Sharing ◽  
Chongqing City ◽  
Feeder Transit ◽  
Transit Services ◽  
Integrated Operation ◽  
The Difference

Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared.

Flexible feeder transit route design to enhance service accessibility in urban area

2015 ◽  
Vol 50 (4) ◽  
pp. 507-521 ◽  
Author(s):  
Xiaolin Lu ◽  
Jie Yu ◽  
Xianfeng Yang ◽  
Shuliang Pan ◽  
Nan Zou
Keyword(s):  
Urban Area ◽  
Service Accessibility ◽  
Route Design ◽  
Feeder Transit ◽  
Transit Route

scholarly journals Optimizing Centralized Dispatching of Flexible Feeder Transit considering Transfer Coordination with Regular Public Transit

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yu-Qiong Wang ◽  
Shun-Ping Jia ◽  
Run-Bin Wei ◽  
Min Wang
Keyword(s):  
Delay Time ◽  
Public Transit ◽  
Optimal Solution ◽  
Optimal Operation ◽  
Operating Costs ◽  
Response Ratio ◽  
Proposed Model ◽  
Feeder Transit ◽  
The One ◽  
Transfer Services

Optimizing centralized dispatching of flexible feeder transit to provide transport and transfer services is important and theoretically challenging for real-world applications. Considering transfer coordination with regular public transit, a multiobjective optimization model that can output an operation plan containing vehicle routes and a timetable for a bus fleet is proposed. By establishing constraints for parameters such as maximum acceptable advance or delay time of transfer, rated passenger capacity, and maximum travel time of a single trip, the proposed model attempts to maximize the successful response ratio, minimize the passengers’ average time costs, and minimize the operating costs of a single passenger. A genetic algorithm was designed to solve the optimal solution, and computational experiments were conducted in a residential area in Beijing. Results reveal that the proposed model and algorithm can be applied in the operation of flexible feeder transit. Moreover, compared with the distributed dispatching method, the value of the optimal objective function in the proposed model was improved by 26%. Although the successful response ratio showed a 29.3% increase and the average passenger time cost showed a small drop, the operating costs per passenger were reduced by 30.7%. The different weight coefficients of the subobjective function and maximum acceptable advance or delay time of transfer could result in different optimal operation plans. Essentially, the optimization procedures for the successful response ratio and the operating costs are in the same direction, whereas the one for the passenger’ cost is in the opposite direction. However, operators should select appropriate values to optimize operation plans.

scholarly journals Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 217 ◽  
Author(s):  
Wen-Yen Lin ◽  
Vijay Kumar Verma ◽  
Ming-Yih Lee ◽  
Horng-Chyuan Lin ◽  
Chao-Sung Lai
Keyword(s):  
Activity Index ◽  
Airflow Obstruction ◽  
Chronic Obstructive Lung Disease ◽  
Walking Distance ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Bode Index ◽  
Copd Patients ◽  
Proposed Model ◽  
Readmission Risk

Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk.

A Computational Grid Scheduling Model to Maximize Reliability Using Modified GA

2013 ◽  
pp. 16-35
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Grid Scheduling ◽  
Scheduling Model ◽  
Reliability Estimates ◽  
Proposed Model ◽  
Np Hard Problem ◽  
Simulation Results ◽  
Computational Resources ◽  
Elitist Selection ◽  
Scheduling Models ◽  
Maximum Reliability

This paper presents a grid scheduling model to schedule a job on the grid with the objective of ensuring maximum reliability to the job under the current grid state. The model schedules a modular job to those resources that suit the job requirements in terms of resources while offering the most reliable environment. The reliability estimates depict true grid picture and considers the contribution of the computational resources, network links and the application awaiting allocation. The scheduling executes the interactive jobs while considering the looping structure. As scheduling on the grid is an NP hard problem, soft computing tools are often applied. This paper applies Modified Genetic Algorithm (MGA), which is an elitist selection method based on the two threshold values, to improve the solution. The MGA works on the basis of partitioning the current population in three categories: the fittest chromosomes, average fit chromosomes and the ones with worst fitness. The worst are dropped, while the fittest chromosomes of the current generation are mated with the average fit chromosomes of the previous generation to produce off-spring. The simulation results are compared with other similar grid scheduling models to study the performance of the proposed model under various grid conditions.

An Angle Partitioning Based Algorithm for the ECFRD Problem in Wireless Sensor Network

2013 ◽  
Vol 427-429 ◽  
pp. 2408-2411
Author(s):  
Yong Wang ◽  
Qiang Dou ◽  
Wei Peng ◽  
Zheng Hu Gong
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Nearest Neighbor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Constraint ◽  
Route Design ◽  
Route Length ◽  
Np Hard Problem ◽  
Message Ferry

Energy-Constrained Ferry Route Design (ECFRD) Problem is an NP-hard problem to minimize the total route length of a message ferry to access all the sensor nodes in a sparse wireless sensor network, while the route length of a tour under a given value due to the energy constraint. In this paper, we propose an angle partitioning based algorithm (APBA) to solve the ECFRD problem. In APBA, the nodes are partitioned into groups according to the tangent angles of their coordinates, and the route length of each group will not exceed the energy constraint. The experimental results show that APBA can greatly reduce the total route length of the ferry. In the best case, 35% of the total route length can be saved, comparing previous nearest neighbor based split and route algorithms.

scholarly journals Optimal Design of Transportation Networks with Automated Vehicle Links and Congestion Pricing

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yipeng Ye ◽  
Hua Wang
Keyword(s):  
Network Design ◽  
Traffic Congestion ◽  
Solution Method ◽  
Congestion Pricing ◽  
Market Penetration ◽  
Travel Costs ◽  
Automated Vehicle ◽  
Proposed Model ◽  
Total Travel Time ◽  
Upper Level

We propose a bi-level network design model comprising automated vehicle (AV) links and congestion pricing to improve traffic congestion. As upper-level road planners strive to minimize total travel-time costs by optimizing both the network design and the congestion pricing, lower-level travelers make choices about their routes to minimize their individual travel costs. Our proposed model integrates a network design and congestion pricing to improve traffic congestion and we use a relaxation-based method to solve the model. We conducted a series of numerical tests to analyze the proposed model and solution method. Our results indicate that network design is more effective than congestion pricing when the AV market penetration is high and the opposite is true when AV penetration is low. More importantly, we find that a network design of automated vehicle links with congestion pricing is superior to a single network design or congestion pricing, especially when both AVs and conventional vehicles have a relatively large market penetration.

scholarly journals Urban Rail Transit Scheduling under Time-Varying Passenger Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Xing Zhao ◽  
Zhongyan Hou ◽  
Jihuai Chen ◽  
Yin Zhang ◽  
Junying Sun
Keyword(s):  
Urban Rail Transit ◽  
Load Factor ◽  
Rail Transit ◽  
Time Interval ◽  
Train Timetable ◽  
Passenger Demand ◽  
Scheduling Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Transit Scheduling

In view of the conflict between the time-variation of urban rail transit passenger demand and the homogeneity of the train timetable, this paper takes into account the interests of both passengers and operators to build an urban rail transit scheduling model to acquire an optimized time-dependent train timetable. Based on the dynamic passenger volumes of origin-destination pairs from the automatic fare collection system, the model focuses on minimizing the total passenger waiting time with constraints on time interval between two consecutive trains, number and capacity of trains available, and load factor of trains. A hybrid algorithm which consists of the main algorithm based on genetic algorithm and the nested algorithm based on train traction calculation and safety distance requirement is designed to solve the model. To justify the effectiveness and the practical value of the proposed model and algorithm, a case of Nanjing Metro Line S1 is illustrated in this paper. The result shows that the optimized train timetable has advantage compared to the original one.

Multi-objective optimization model for airport gate assignment problem

2021 ◽  
Vol 93 (2) ◽  
pp. 311-318
Author(s):  
Ramazan Kursat Cecen
Keyword(s):  
Assignment Problem ◽  
Air Traffic ◽  
Walking Distance ◽  
Content Type ◽  
Multi Objective ◽  
Air Traffic Controllers ◽  
Proposed Model ◽  
Milp Model ◽  
Gate Assignment ◽  
Short Time

Purpose The purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers. Design/methodology/approach The mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments. Findings The proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration. Practical implications The proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place. Originality/value The proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.

A Novel bi-key Computational Algorithm for Secure Data Transmission in Smart Meter Environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhirendra Bagri ◽  
Shashikant Rathore
Keyword(s):  
Data Transmission ◽  
Computational Algorithm ◽  
Original Data ◽  
Smart Meter ◽  
Cryptographic Algorithm ◽  
Proposed Model ◽  
Enormous Amount ◽  
Secure Data Transmission ◽  
Np Hard Problem ◽  
Security Algorithm

Objective: Smart grid is an entity which dwells on a network of Energy Transmission with Integrated data transmission it works for flow of energy and Transmission of data that is generated by the stakeholders and in return it provides them the required electricity without any hurdles or disturbance. The versatility and integrity of entire network completely rely on the truthful data which gets floated from variety of distributed users in the network. There is a huge chance to get attacked through hijacking of smart communication network. Another concern remains raised about unable to use the existing security algorithm due to the enormous amount of data that gets generated in the distributed environment. So, these issues facilitate to work on designing the security algorithm for providing better security and better efficiency in smart meter communication. Methods: This paper is presented with proposed model with enhanced security that not just only encrypt the enormous amount of data but also it does the work of encryption in very less time. The proposed algorithm is based on bi-key computational algorithm where two different entities are used as keys, with the use of this algorithm the task of finding the original data gets transformed into NP hard problem. Results: The Proposed algorithm is analyzed with existing cryptographic algorithm and found more secure and provide better performance. Conclusion: This paper presents simulated results obtained in MATLAB which Justify the adaptability and acceptability of proposed algorithms for enormous generated data.

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