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

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.)

Applying metaheuristics to feeder bus network design problem

Master'sMASTER OF ENGINEERIN

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Transit Route Planning to Improve Accessibility: A Reinforcement Learning Approach

Public transport has a key role in social sustainability by reducing social isolation and improving access to essential opportunities. The conventional approaches to transit network and service planning do not consider the social role of public transport, and attempt to maximise the cost savings of users and the operational efficiency of operators. Cost-based network design tends to allocate more transit routes and/or services to high population density areas. This can create transit service gaps between high and low population density areas and leave some people poorly served with low accessibility. This study proposed a new algorithm for transit network design by incorporating a transit accessibility measure in the routing optimisation. Transit accessibility refers to the ease of reaching opportunities from a location by public transport. The proposed approach aims to improve the transit connectivity between residential zones and major activity centres, which contain a range of opportunities. This study also developed a transit need index and integrated it into route planning to account for the socio-demographic characteristics of potential transit users in the network design to benefit those who need the transit service to reach essential opportunities. This index is formulated by combining selected socio-economic and demographic variables and then integrating them with the transit accessibility measure as the optimisation objective. This study developed a reinforcement learning method for the purpose of the bus network and service planning. This study used reinforcement learning to address the complicated interactions of associated features in bus network and service planning. Reinforcement learning methods achieve a complex goal or optimise long-term performance over many experiences. As reinforcement learning can work in changing environments, reinforcement learning methods effectively solve sequential decision-making problems such as bus route and service planning. The reinforcement learning method was examined to validate the effectiveness of adapting the transit accessibility measure in transit route planning, with and without the transit need index, on the existing bus network in a case study area. The existing bus network in the case study area of Penrith local government area in western Sydney, Australia, has 56 bus routes and 1,940 stops, which provides a realistic and challenging test environment. The performance results indicate that the proposed algorithm, which adopts the measure of transit accessibility combined with the transit need index as an optimisation objective, can both improve overall transit accessibility and provide an appropriate level of service across areas

Designing multimodal public transport networks using metaheuristics

The public transport system in South Africa is in a precarious state, capturing no more than 50% of the passenger market. The three public transport modes that are currently utilized—train, bus, and minibus-taxi—are competing for market share instead of complementing one another. Furthermore, most public transport networks have not been properly redesigned over the past three decades. Improvements were initiated reactively in the past: transit stops and routes were added or removed from the network when demand fluctuated. This reactive process has diminished the confidence of commuters in the public transport networks, forcing commuters to use private transport. A proactive redesign method is needed—one that includes all the modes of public transport, and anticipates an increase in demand and rapid development in geographic areas, while ensuring good accessibility to the network. Current network design models do not include multiple modes of public transport, and are based on the geographical layout of developed cities and their particularities, which makes them unsuitable for the South African environment with its unique land use disparities. This dissertation proposes a multimodal network design model that is capable of designing real world and large scale networks for the South African metropolitan areas. The City of Tshwane Metropolitan Municipality (CTMM) transport network area was used to develop and test the model, which consists of four components. The Geographic Information System (GIS) component has a central role in storing, manipulating, and exchanging the geographic data within the model. For the GIS the appropriate input data is identified, and a design for the geo-database is proposed. The Population Generation Algorithm (PGA) component translates the demographic data into point data representing the transit demand in the study area. The Bus Stop Placement Algorithm (BSPA) component is a metaheuristic that searches for near-optimal solutions for the placement of bus stops in the study area. A novel solution approach proposed in this dissertation uses geographic data of commuters to evaluate the bus stop placement in the study area. The Multimodal Network Design Algorithm (MNDA) component also employs a metaheuristic, enabling the design of near-optimal multimodal networks. The addition of multiple modes to the Transit Network Design Problem (TNDP) is also a novel and significant contribution. The two metaheuristic components are first tested on a test network, and subjected to a comprehensive sensitivity analysis. After identifying suitable parameter values and algorithm settings, the components are applied to the entire CTMM.Dissertation (MSc)--University of Pretoria, 2009.Industrial and Systems Engineeringunrestricte

Transit Network Design using GIS and Metaheuristics in Sanandaj, IRAN

The public transit system in Sanandaj has been under review and modification for the last several years. The goal is to reduce the traffic congestion and the share of private car usage in the city and increase the very low share of the public transit. The bus routes in Sanandaj are not connected. There is no connected transit network with the ability to transfer between the routes in locations outside of the downtown terminal. The routes mostly connect the downtown core directly to the peripheries without providing travel options for passengers between peripheries. Although there has been some improvement in the transit system, but lack of service in many populated districts of Sanandaj and town nearby makes the transit system unpopular and unreliable. This research is an attempt to provide solutions for the transit network design (TND) problem in Sanandaj using the capabilities of GIS and artificial intelligence methods. GIS offers several tools that enables the decision-makers to investigate the spatial correlations between different features. One of the contributions of this research is developing a transit network design with utilizing a spectrum of GIS software modeling functionalities. The visual ability of GIS is used to generate TNDs. Many studies focus on artificial intelligence as the main method to generate the TNDs, but the focus of this research is to combine GIS and artificial intelligence capabilities in order to generate a multi-objective GIS-based procedure to construct different bus network designs and explore and evaluate them to find the suitable transit network alternative. The GIS-based procedure results will be assessed and compared with the results of metaheuristic approaches. Metaheuristic methods are partial search procedures that may provide sufficiently good solutions to an optimization problem characterized by incomplete information or limited computation capacity (Talbi, 2009). Yang, Cui, Xiao, Gandomi, and Karamanoglu (2013) classified metaheuristic methods into two groups: single-agent procedures (e.g., simulated annealing algorithm involves one agent navigating in the environment), and multiple agents (e.g., population-based genetic algorithm, and swarm intelligence methods). This study focuses on swarm intelligence methods, such as ant colony optimization and honeybee algorithm. These methods provide a multi-objective assessment of the TND scenarios generated by GIS applications. The outcome of this study will help us to find the optimal solutions for the TND in Sanandaj

GIS and genetic algorithm based integrated optimization for rail transit system planning

The planning of a rail transit system is a complex process involving the determination of station locations and the rail line alignments connecting the stations. There are many requirements and constraints to be considered in the planning process, with complex correlations and interactions, necessitating the application of optimization models in order to realize optimal (i.e. reliable and cost-effective) rail transit systems. Although various optimization models have been developed to address the rail transit system planning problem, they focus mainly on the planning of a single rail line and are therefore, not appropriate in the context of a multi-line rail network. In addition, these models largely neglect the complex interactions between station locations and associated rail lines by treating them in separate optimization processes. This thesis addresses these limitations in the current models by developing an optimal planning method for multiple lines, taking into account the relevant influencing factors, in a single integrated process using a geographic information system (GIS) and a genetic algorithm (GA). The new method considers local factors and the multiple planning requirements that arise from passengers, operators and the community, to simultaneously optimize the locations of stations and the associated line network linking them. The new method consists of three main levels of analysis and decision-making. Level I identifies the requirements that must be accounted for in rail transit system planning. This involves the consideration of the passenger level of service, operator productivity and potential benefits for the community. The analysis and decision making process at level II translates these requirements into effective criteria that can be used to evaluate and compare alternative solutions. Level III formulates mathematical functions for these criteria, and incorporates them into a single planning platform within the context of an integrated optimization model to achieve a rail transit system that best fits the desired requirements identified at level I. This is undertaken in two main stages. Firstly, the development of a GIS based algorithm to screen the study area for a set of feasible station locations. Secondly, the use of a heuristic optimization algorithm, based on GA to identify an optimum set of station locations from the pool of feasible stations, and, together with the GIS system, to generate the line network connecting these stations. The optimization algorithm resolves the essential trade-off between an effective rail system that provides high service quality and benefits for both the passenger and the whole community, and an economically efficient system with acceptable capital and operational costs. The proposed integrated optimization model is applied to a real world case study of the City of Leicester in the UK. The results show that it can generate optimal station locations and the related line network alignment that satisfy the various stakeholder requirements and constraints.Open Acces