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.)

Optimizing fare structure and service frequency for an intercity transit system

This study presents an approach to jointly optimize service headway and differentiated fare for an intercity transit system with an objective of total profit maximization and with consideration given to the economic and social sustainability of the system. Service capacity and fleet size constraints are considered. The optimization problem is structured into four scenarios which are comprised of the combinations of whether the Ranges of Travel Distance (RTD) is fixed or variable and if the time period is for a single period or for multiple periods. A successive substitution method (specifically, a modified Gauss Southwell method) is applied to solve for the optimal solutions when the RTD is considered fixed, while a heuristic solution algorithm (specifically, a Genetic Algorithm) is developed to find the optimal solutions when the RTD is considered to be optimized. The methodology discussed in this dissertation contributes to the field of transportation network modeling because it establishes how to solve the fare and headway design problem for an intercity transit system. Intercity transit agencies are faced with the challenge of determining fares for a very complicated setting in which demand elasticity, realistic geographic conditions, and facility locations of the transit system all must be taken into account. A real world case study - Taiwan High Speed Rail is used to demonstrate the applicability of the developed methodology. Numerical results of optimal solutions and sensitivity analyses are presented for each scenario. The sensitivity analyses enable transit planners to quantify the impact of fare policies and address social equity issues, which can be a major hurdle of implementing optimal fare policy to achieve maximum profit operation. According to the sensitivity analysis, the total profit surfaces for various headways, fares, and RTD are relatively flat near the optimum. This indicates that the transit operator has flexibility in shifting the solution marginally away from the optimum without significantly reducing the maximum profit. By varying the elasticity parameters of fare and demand one can observe how these variables affect the optimized RTD. The results indicate that as the elasticity parameters of fare increase or demand decreases, the optimal number of RTD increase while the boundaries of RTD are concentrated in the range of shorter travel distances

Some Computational Insights on the Optimal Bus Transit Route Network Design Problem

The objective of this paper is to present some computational insights based on previous extensive research experiences on the optimal bus transit route network design problem (BTRNDP) with zonal demand aggregation and variable transit demand. A multi-objective, nonlinear mixed integer model is developed. A general meta-heuristics-based solution methodology is proposed. Genetic algorithms (GA), simulated annealing (SA), and a combination of the GA and SA are implemented and compared to solve the BTRNDP. Computational results show that zonal demand aggregation is necessary and combining metaheuristic algorithms to solve the large scale BTRNDP is very promising

Urban Transit Network Design Problems: A Review of Population-based Metaheuristics

The urban transit network design problem (UTNDP) involves the development of a transit route set and associated schedules for an urban public transit system. The design of efficient public transit systems is widely considered as a viable option for the economic, social, and physical structure of an urban setting. This paper reviews four well-known population-based metaheuristics that have been employed and deemed potentially viable for tackling the UTNDP. The aim is to give a thorough review of the algorithms and identify the gaps for future research directions

Optimizing integrated service for a transit route with heterogeneous demand

The methodology developed in this dissertation attempts to optimize integrated service that minimizes the total cost, including user and supplier costs, of a transit route with heterogeneous demand. While minimizing total cost, a set of practical constraints, such as capacity, operable fleet size and frequency conservation, are considered. The research problem is presented in three scenarios, consisting of various service patterns (e.g., all-stop, short-turn and express) under heterogeneous demand. A logit-based model was used to estimate passenger transfer demand. An exhaustive search method was developed to find the optimal solutions for a simplified transit route with six stops, and a Genetic Algorithm (GA) was developed to find the optimal solution for a real-world, large scale transit route. The optimized variables include the combination of service patterns, the associated service frequencies, and stops skipped by the express service. A six-stop transit route was designed and analyzed via a proof-of-concept demonstration to ensure that the developed models are capable of finding the optimal solutions. A sensitivity analysis was conducted, which enables transit planners to quantify the impact of various model parameters (e.g., user value of time, vehicle capacity, operating cost, etc.) to the decision variables and the objective function. Finally, the developed models and solution algorithm were applied to optimize integrated service for a real world bus route in New Jersey

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

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