Optimizing feeder bus network based on access mode shifts

The methodology introduced in this dissertation is to optimally find a feeder bus network in a suburban area for an existing rail system that connects the suburban area with the Central Business District (CBD). The objective is to minimize the total cost, including user and supplier costs. Three major access modes (walk, feeder bus, and auto) for the rail station are considered and the cost for all modes makes up the user cost. The supplier cost comes from the operating cost of the feeder bus network. The decision variables include the structure of the feeder bus network, service frequencies, and bus stop locations. The developed methodology consists of four components, including a Preparation Procedure (PP), Initial Solution Generation Procedure (ISGP), Network Features Determination Procedure (NFDP) and Solution Search Procedure (SSP). PP is used to perform a preliminary processing on the input data set. An initial solution that will be used in SSP is found in ISGP. The NFDP is a module to determine the network related features such as service frequency, mode split, stop selections and locations. A logit-based Multinomial Logit-Proportional Model (MNL-PM) model is proposed to estimate the mode shares of walk, bus and auto. A metaheuristic Tabu Search (TS) method is developed to find the optimal solution for the methodology. In the computational experiments, an Exhaustive Search (ES) method is designed and tested to validate the effectiveness of the proposed methodology. The results of networks of different sizes are presented and sensitivity analyses are performed to investigate the impacts of various model parameters (e.g., fleet size, parking fee, bus fare, etc.)

Differential evolution for urban transit routing problem

The urban transit routing problem (UTRP) involves the construction of route sets on existing road networks to cater for the transit demand efficiently. This is an NP-hard problem, where the generation of candidate route sets can lead to a number of potential routes being discarded on the grounds of infeasibility. This paper presents a new repair mechanism to complement the existing terminal repair and the make-small-change operators in dealing with the infeasibility of the candidate route set. When solving the UTRP, the general aim is to determine a set of transit route networks that achieves a minimum total cost for both the passenger and the operator. With this in mind, we propose a differential evolution (DE) algorithm for solving the UTRP with a specific objective of minimizing the average travel time of all served passengers. Computational experiments are performed on the basis of benchmark Mandl’s Swiss network. Computational results from the proposed repair mechanism are comparable with the existing repair mechanisms. Furthermore, the combined repair mechanisms of all three operators produced very promising results. In addition, the proposed DE algorithm outperformed most of the published results in the literature

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Optimization of headway, stops, and time points considering stochastic bus arrivals

With the capability to transport a large number of passengers, public transit acts as an important role in congestion reduction and energy conservation. However, the quality of transit service, in terms of accessibility and reliability, significantly affects model choices of transit users. Unreliable service will cause extra wait time to passengers because of headway irregularity at stops, as well as extra recovery time built into schedule and additional cost to operators because of ineffective utilization of allocated resources. This study aims to optimize service planning and improve reliability for a fixed bus route, yielding maximum operator’s profit. Three models are developed to deal with different systems. Model I focuses on a feeder transit route with many-to-one demand patterns, which serves to prove the concept that headway variance has a significant influence on the operator profit and optimal stop/headway configuration. It optimizes stop spacing and headway for maximum operator’s profit under the consideration of demand elasticity. With a discrete modelling approach, Model II optimizes actual stop locations and dispatching headway for a conventional transit route with many-to-many demand patterns. It is applied for maximizing operator profit and improving service reliability considering elasticity of demand with respect to travel time. In the second model, the headway variance is formulated to take into account the interrelationship of link travel time variation and demand fluctuation over space and time. Model III is developed to optimize the number and locations of time points with a headway-based vehicle controlling approach. It integrates a simulation model and an optimization model with two objectives - minimizing average user cost and minimizing average operator cost. With the optimal result generated by Model II, the final model further enhances system performance in terms of headway regularity. Three case studies are conducted to test the applicability of the developed models in a real world bus route, whose demand distribution is adjusted to fit the data needs for each model. It is found that ignoring the impact of headway variance in service planning optimization leads to poor decision making (i.e., not cost-effective). The results show that the optimized headway and stops effectively improve operator’s profit and elevate system level of service in terms of reduced headway coefficient of variation at stops. Moreover, the developed models are flexible for both planning of a new bus route and modifying an existing bus route for better performance

Time-dependent discrete network design frameworks considering land use

This article proposes optimization frameworks for discrete road network design considering the land-use transport interaction over time. Unlike existing models, the optimization frameworks can determine the optimal designs automatically without trial-and-error once the objective(s) is/are clearly defined. Moreover, these frameworks allow the evaluation of the impacts of the optimal designs on the related parties including landowners, toll road operators, transit operators, and road users, and help network planners and profit-makers with decision making by eliminating many alternative designs. A numerical study is set up to examine road network design's effects on these related parties under three road construction schemes: exact cost recovery, build-operate-transfer, and cross-subsidization. The results show that the changes in landowner profits are not the same after implementing any scheme. These unequal changes raise the issue of the landowner equity. This implies that the government has to consider trade-offs between parties' objectives carefully. © 2010 Computer-Aided Civil and Infrastructure Engineering.postprin

A Multilayer Network Approach for the Bimodal Bus–Pedestrian Line Planning Problem

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseIn this paper, we formulate and solve the urban line planning problem considering a multilayer representation of a bimodal transportation network. Classical formulations are usually constructed over a planar network, which implies the need to introduce several strong non-linearities in terms of frequencies when modeling transfer times. In the proposed network representation, each candidate line is stored in a specific layer and the passengers’ movements for each origin– destination pair are modelled considering a strategy subgraph, contributing to a sparse model formulation that guarantees feasibility and simplifies the assignment process. The methodology is first tested using the Mandl network, obtaining results that are comparable in terms of quality with the best metaheuristic approaches proposed in the scientific literature. With the aim of testing its applicability to large scenarios, the proposed approach is then used to design the main urban transit network of Seville, a large scenario with 141 nodes and 454 links, considering artificial unfavorable demand data. The reasonable computation time required to exactly solve the problem to optimality confirms the possibility of using the multilayer approach to deal with multimodal network design strategic problems.Universidad de SevillaJunta de AndalucíaFondo Europeo de Desarrollo Regional (FEDER) US-138165

Joint Pricing, Operational Planning and Routing Design of a Fixed-Route Ride-sharing Service

Fixed-route ride-sharing services are becoming increasing popular among major metropolitan areas, e.g., Chariot, OurBus, Boxcar. Effective routing design and pricing and operational planning of these services are undeniably crucial in their profitability and survival. However, the effectiveness of existing approaches have been hindered by the accuracy in demand estimation. In this paper, we develop a demand model using the multinomial logit model. We also construct a nonlinear optimization model based on this demand model to jointly optimize price and operational decisions. Moreover, we develop a mixed integer linear optimization model to the routing design decision. And a genetic algorithm based approach is proposed to solve the optimization model. Two case studies based on a real world fixed-route ride-sharing service are presented to demonstrate how the proposed models are used to improve the profitability of the service respectively. We also show how this model can apply in settings where only limited public data are available to obtain effective estimation of demand and profit.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146788/1/49698122_Wanqing's Graduate Thesis (final).pdfDescription of 49698122_Wanqing's Graduate Thesis (final).pdf : Thesi

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

파레토최적해를 이용한 버스네트워크 설계 다목적 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 고승영.Public transportation is a service that provides access to various opportunities and can reduce the mobility gap through efficient network design. However, services are concentrated in a specific area considering economic efficiency, resulting in spatial imbalances in services and inefficiency to users. In this study, bus network design algorithms were presented, including operators, users, and public aspects. An efficiency of operators and users and the competitiveness of public transportation between modes and areas were considered. Toy network was organized according to the urban network topology and demand pattern, and the analysis was performed by applying the algorithm of this study. The applicability of the algorithm was confirmed through the actual network. An improved network could be derived from both operators and the public compared to previous research focused on operational efficiency. Suggested a method to select and apply Pareto optimal according to the planner's judgment. The bus network design algorithm in this study can be used as a means of decision criteria and it can be applied to cities that require a balanced network supply with limited resources.Chapter 1. Introduction ···········································1 1.1 Background ·····································································1 1.2 Research Scope ·····························································4 Chapter 2. Literature Review ·······························7 2.1 Transit Network Design ···········································7 2.2 Unmet Demand ······························································12 2.3 Equity ················································································17 2.4 Algorithm ·········································································26 2.4.1 Multi-objective Optimization ········································26 2.4.2 Local Search ·····································································31 2.5 Summary and Research Direction ·································34 Chapter 3. Methodology ··········································37 3.1 NSGA-II ···········································································37 3.2 Algorithm ·········································································40 3.2.1 Procedure and Network Encoding ······························40 3.2.2 Cross-over and Mutation ··············································43 3.2.3 Local search ······································································44 Chapter 4. Model Formulation ·····························47 4.1 Summary ··········································································47 4.2 Assumption and Variables ·······································48 4.3 Model Formulation ·······················································52 4.3.1 Objective Function ··························································52 4.3.2 Logit Model ······································································54 4.3.3 Traffic Assignment ·························································56 4.3.4 Transit Assignment ······················································58 Chapter 5. Numerical Example ····························61 5.1 Toy Network ································································61 5.1.1 Network Explanation ····················································61 5.1.2 Result Analysis ································································65 5.1.3 Marginal Effect ································································75 5.1.4 Comparison with Previous Research ·························80 5.2 Large Network ······························································83 5.2.1 Network Explanation ······················································83 5.2.2 Result Analysis ································································86 5.2.3 Marginal Effect ································································91 5.2.4 Comparison with Previous Study ·······························92 5.3 Discussion ·······································································94 Chapter 6. Result and Future Research ·········· 97 Reference ·······································································100 Appendix ·······································································110Docto