scholarly journals A Multi-Objective Meta-Heuristic Approach to Improve the Bus Transit Network: A Case Study of Fargo-Moorhead Area

2021 ◽  
Vol 13 (19) ◽  
pp. 10885
Author(s):  
Mohsen Momenitabar ◽  
Jeremy Mattson
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Nsga Ii ◽  
Transit Network ◽  
Multi Objective ◽  
Total Travel Time ◽  
Initial Route

In this study, the Transit Network Design Problem (TNDP) is studied to determine the set of routes and frequency on each route for public transportation systems. To ensure the important concerns of planners like route length, route configuration, demand satisfaction, and attractiveness of the transit routes, the TNDP is solved to generate a set of routes by proposing an initial route set generation (IRSG) procedure embedded into the NSGA-II algorithm. The proposed IRSG algorithm aims to produce high-quality initial route set solutions to reach better optimization procedures. Moreover, the Multi-Objective Mixed-Integer Non-Linear Programming (MOMINLP) model is proposed to formulate the frequency setting problem on each route by minimizing the total travel time of passengers (user costs) and operator costs simultaneously, while maximizing the service coverage area near all the bus stops. The MOMINLP model is solved by applying the NSGA-II algorithm to produce a Pareto front between the first and the second objective functions. The model was applied to the Fargo-Moorhead Area (FMA), a small urban area. Results were compared with the existing transit network to measure the efficiency of the NSGA-II solution methodology. The proposed algorithm was found to considerably decrease the total travel time of passengers.

Multi-Objective Optimization for Skip-Stop Strategy Based on Smartcard Data Considering Total Travel Time and Equity

2021 ◽  
pp. 036119812110130
Author(s):  
Sang-Wook Han ◽  
Eun Hak Lee ◽  
Dong-Kyu Kim
Keyword(s):  
Travel Time ◽  
Programming Model ◽  
Mixed Integer ◽  
Average Deviation ◽  
Multi Objective ◽  
Nonlinear Programming Model ◽  
Total Travel Time ◽  
Transfer Stations ◽  
The City

With the rise of urban sprawl, urban railways extend out further to the city’s outer district, installing additional stations. Passengers who travel from the outer district to the center of the city therefore experience long travel times. Although skip-stop strategy helps save total travel time, deviation of travel time among all origin–destination pairs may be increased, leading to equity problems. This study aims to minimize the inequity and total travel time through train stop planning and train scheduling. A coefficient of variation is adopted as a measure of inequity. The problem is formulated as a multi-objective mixed integer nonlinear programming model. Origin–destination demand is extracted from smartcard data and a case study of four urban railway lines in Seoul is conducted. The results indicate that the number of transfer stations for equity-oriented skip-stop strategy is smaller than that for total-travel-time-oriented skip-stop strategy. We also discover that as the number of transfer stations rises, inequity increases and total travel time is reduced. For skip-stop strategy considering total travel time and equity simultaneously, average total travel time and the average deviation are reduced by up to 10.3% and 10.6%, respectively, compared with those of all-stop strategy. We analyze the gradient of Pareto optimal sets to find out which factors (equity or total travel time) are more significant. Skip-stop strategy on lines 5 and 9 can be designed based on equity, while line 4 can be planned based on total travel time.

scholarly journals Application of Modified NSGA-II to the Transit Network Design Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Jie Yang ◽  
Yangsheng Jiang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Pareto Front ◽  
Network Design Problem ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Transit Network ◽  
Set Size ◽  
Transit Network Design ◽  
Initial Route

The transit network design problem involves determining a certain number of routes to operate in an urban area to balance the costs of the passengers and the operator. In this paper, we simultaneously determine the route structure of each route and the number of routes in the final solution. A novel initial route set generation algorithm and a route set size alternating heuristic are embedded into a nondominated sorting genetic algorithm-II- (NSGA-II-) based solution framework to produce the approximate Pareto front. The initial route set generation algorithm aims to generate high-quality initial solutions for succeeding optimization procedures. To explore the solution space and to have solutions with a different number of routes, a route set size alternating heuristic is developed to change the number of routes in a solution by adding or deleting one route. Experiments were performed on Mandl’s network and four larger Mumford’s networks. Compared with a fixed route set size approach, the proposed NSGA-II-based solution method can produce an approximate Pareto front with much higher solution quality as well as improved computation efficiency.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

2021 ◽  
Vol 12 (3) ◽  
pp. 163-179
Author(s):  
Amruta Rout ◽  
Deepak BBVL ◽  
Bibhtui Bhusan Biswal ◽  
Golak B. Mahanta
Keyword(s):  
Travel Time ◽  
Optimal Trajectory ◽  
Pareto Front ◽  
Industrial Robot ◽  
Trajectory Generation ◽  
Fuzzy Membership Function ◽  
Positional Accuracy ◽  
Nsga Ii ◽  
Dynamic Variations ◽  
Total Travel Time

The joint trajectory of the robot needs to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the trajectory path. This can be achieved by limiting the travel time, kinematic and dynamic variations of the robot joints like the jerks, and torque induced in the joints in the travel of the robot. As the objectives of total travel time and joint jerk and torque rate are contradictory functions, non-dominated sorting genetic algorithm-II (NSGA-II) approach has been used to obtain the pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the pareto front with best trade-off between objectives for further optimal trajectory generation. From the simulation results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of Kawasaki RS06L industrial manipulator with minimal jerk, torque rate, and total travel time for smooth travel of robot with higher positional accuracy.

scholarly journals Combinatorial Optimization of Service Order and Overtaking for Demand-Oriented Timetabling in a Single Railway Line

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Dewei Li ◽  
Shishun Ding ◽  
Yizhen Wang
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Cost Benefit ◽  
Theory And Practice ◽  
Mixed Integer ◽  
High Speed Rail ◽  
Railway Line ◽  
Integer Quadratic Programming ◽  
Total Travel Time ◽  
Mixed Integer Quadratic Programming

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixed-integer quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers’ waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers’ travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit.

scholarly journals An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Shushan Chai ◽  
Qinghuai Liang
Keyword(s):  
Network Design ◽  
Travel Time ◽  
Policy Formulation ◽  
Nsga Ii ◽  
Transit Network ◽  
Generation Mode ◽  
Np Hard Problem ◽  
Transit Network Design ◽  
Population Genetic Algorithm ◽  
Transit Routes

The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.

scholarly journals Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes

2021 ◽  
Vol 13 (6) ◽  
pp. 3454
Author(s):  
Yu Lin ◽  
Hongfei Jia ◽  
Bo Zou ◽  
Hongzhi Miao ◽  
Ruiyi Wu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Travel Time ◽  
Heterogeneous Traffic ◽  
System Level ◽  
Network Equilibrium ◽  
Nsga Ii ◽  
The Sustainable Development ◽  
Multi Objective ◽  
Upper Level

The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.

Stochastic Simulation of Bus Traveling Time

2011 ◽  
Vol 186 ◽  
pp. 556-559
Author(s):  
Lian Xue ◽  
Dan Jie Zhao ◽  
Gui Mei Liu
Keyword(s):  
Travel Time ◽  
Stochastic Simulation ◽  
Public Transportation ◽  
Simulation Method ◽  
Service Level ◽  
Transportation Systems ◽  
Travel Time Reliability ◽  
Public Transport System ◽  
Public Transportation Systems ◽  
Bus Travel Time

The development of the city's public transport system has an indispensable role to alleviate the pressure of urban roads. Bus travel time reliability is an important evaluation index of the bus operation service level. The simulation of bus travel time helps us understand the reliability of bus running time. In this paper, we use Monte Carlo stochastic simulation method to calculate the reliability of bus travel time. On this basis, we establish a model of the reliability of public transportation systems to research the reliability of bus travel time.

scholarly journals Travel mode choice preferences of urban commuters in Kuching City, Malaysia based on stated preference data

2018 ◽  
Vol 181 ◽  
pp. 02007
Author(s):  
Resdiansyah
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transportation ◽  
Stated Preference ◽  
Mode Choice ◽  
Travel Behaviour ◽  
Transportation Systems ◽  
Choice Modelling ◽  
Public Transport System ◽  
Bus Services

One aspect of Kuching City that has not progressed in tandem with the rest of the city is the public transport system, which is relatively old and almost non-existent. Transport and City planners seem to be at their wit’s end in coming up with satisfactory solutions to Kuching’s public transportation woes. In current situation, many proposals, but none have proven workable. As a result, representative buses remain a rare sight on Kuching city’s roads. To achieve a sustainable public transport industry, the old buses need to be regenerated and replaced with modern buses. The objectives of the intended study are to explore the consumer’s travel behaviour by employing mode choice modelling. Consequently, a study was conducted in Kuching City Area by using stated preference technique, analysed and compiled by using SPSS.17 multiple linear regressions analysis. In this context, discrete choice analysis was used to examine the relationship between independent variables (travel time, waiting time, fares and comfort) and dependent variables (choice of respondent whether to consume old bus or choose new bus services). A total of 2000 respondents were interviewed. The findings showed that for the trips purpose, fares and comfortability were the primary factors that reflected the decision or behaviour of the respondents asked. It was discovered that there is a significant relationship between the choice of the respondents and comfortability. It also appeared that longer travel time did not affect for the traveler’s choice at this stage. Hence, the study suggests that the local authority and the bus operators should establish a “quality partnership” and working together in order to come out with a much better and appropriate transport policy and schemes for the existing public transportation systems, especially bus services.

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