The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

Planning, operation, and control of bus transport systems: A literature review

https://www.tandfonline.com/doi/pdf/10.1080/01441647.2020.1857886

Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions

Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research

In this paper we examine the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro. In this problem, investments in transit services have appreciable effects on modal split. Neglecting demand elasticity can lead to solutions that may not represent the actual objectives of the design. We propose four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs, and we define the constraints of the problem. Heuristic and meta-heuristic solution algorithms are also proposed. The models and algorithms are tested on a small network and on a real-scale network.

The transit network design problem with elastic demand and internalisation of external costs: An application to rail frequency optimisation

It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

In this paper, a bilevel programming model for the bus frequency design is presented, which determines the optimal bus frequencies aiming to minimize the total travel time of passengers subject to the constraint on the overall fleet size of each company by accounting for the route choice behaviors of the users. The objective of the lower level is to assign transit trips to bus route network based on optimal strategy. In the upper level, bus frequencies of routes are optimized as a result of passenger assignment. An iterative approach, which consists of a genetic algorithm and a label-marking method, is used to solve the bilevel model. Finally, the model and the algorithms are illustrated with two test examples. The results show that the optimization can improve the local service level of one company, and the proper integration of several companies probably improves the efficiency of resources and the service level of the whole transit system.

Zhongzhen Yang

Papers

Genetic Algorithm for Bus Frequency Optimization