Capacity index of Urban Rail Transit (URT) Network plays an improtant role in rational utilization of system capacity and operation management. A definition and calculating method of the capacity of URT Network was first proposed according to the features of URT network and route choice behavior of rail passengers in this paper. Several aspects of influencing factors of URT capacity were analyzed. A bi-level programming model was presented to optimize the URT capacity besides the system utility. Upper level of the model aims at maximizing the total OD flow through the URT network, and the lower level model is one kind of Fisk Equilibrium model. A new kind of impendence function relevant to the lower level model was put forward in consideration of practical traveler behavior. Genetic algorithm technique was applied to solve the bi-level programming model on the premise that the bi-level programming problem be converted into a single-level programming which was achieved by reformulating the lower-level problem model to its equivalent Karush-Kuhn-Tucker conditions. Effective crossover and mutation operators were proposed to enhance the convergence of the Genetic algorithm. A simplified network of Beijing URT was designed and numerical examples were conducted to prove that the proposed model and algorithm are feasible and valid in calculating the capacity of such network.

Since the regenerative braking technique can recover considerable electricity from braking trains, it is maturely applied in subway systems. Generally speaking, except a small part of the recovery energy is used by the on-board auxiliary services, most of them is fed back into the overhead contact line. If the feedback energy cannot be absorbed by adjacent accelerating trains timely, it will be consumed by resistances. For maximizing the utilization of recovery energy, this paper proposes a timetable optimization model to coordinate the accelerating and braking processes of up trains and down trains. Firstly, we analyze the coordinating rules. Secondly, we propose an integer programming model to maximize the utilization of recovery energy with headway time and dwell time control. Furthermore, we design a genetic algorithm to solve the optimal timetable. Finally, we conduct numerical examples based on the operation data from Beijing Yizhuang subway line of China. The results illustrate that the proposed model can significantly save energy by 21.58% compared with the current timetable.

Two-track passenger rail lines typically operate with all trains serving every station. Without additional infrastructure, transit planners have limited options to improve travel times. Service could be improved by operating a skip-stop service where trains only serve a subset of all the station stops. A skip-stop pattern must find an optimal balance between faster passenger travel times and lower service frequencies at each station. A mixed integer formulation is proposed to analyze this tradeoff; however, the mixed integer formulation could not scale efficiently to analyze a large scale commuter line. A genetic algorithm is presented to search the solution space incorporating a larger problem scope and complexity. In a case study of a Midwest commuter line, overall passenger travel time could be decreased by 9.5%. Both analyses can give insights to transit operators on how to improve their service to their customers and increase ridership.

Track irregularity is the main excitation source of wheel-track interaction. Due to the difference of speed, axle load and suspension parameters between track inspection train and the operating trains, the data acquired from the inspection car cannot completely reflect the real status of track irregularity when the operating trains go through the rail. In this paper, an estimation method of track irregularity is proposed using genetic algorithm and Unscented Kalman Filtering. Firstly, a vehicle-track vertical coupling model is established, in which the high-speed vehicle is assumed as a rigid body with two layers of spring and damping system and the track is viewed as an elastic system with three layers. Then, the static track irregularity is estimated by genetic algorithm using the vibration data of vehicle and dynamic track irregularity which are acquired from the inspection car. And the dynamic responses of vehicle and track can be solved if the static track irregularity is known. So combining with vehicle track coupling model of different operating train, the potential dynamic track irregularity is solved by simulation, which the operating train could goes through. To get a better estimation result, Unscented Kalman Filtering (UKF) algorithm is employed to optimize the dynamic responses of rail using measurement data of vehicle vibration. The simulation results show that the estimated static track irregularity and the vibration responses of vehicle track system can go well with the true value. It can be realized to estimate the real rail status when different trains go through the rail by this method.

Passenger share research of different transport models will help the transport sector to develop a more rational operating strategy and provide an important basis for the future transport planning and construction. The development of the Agent theory provides a new direction for the simulation of the passenger share. In this paper, we use the Agent theory combining with the Genetic Algorithms to simulate the passenger share of the four travel modes (the High-speed rail, the common rail, the highways and the civil aviation) in the ShangHai-NanJing transport line. First, we consider each passenger as an Agent according to the Agent theory, and then design the passenger’s travel choice and transform mechanism according to the passenger’s travel choice behavior. Second, this paper has used the genetic algorithm to design the passengers’ interaction mechanism under the interaction in the passengers’ travel choice, including the elimination, crossover and mutation mechanism. Third, we choose the five factors, such as Security, Convenience, Comfort, Ticket Price and Train speed to be the indexes to evaluate the passenger’s satisfaction and conducted a questionnaire survey in the ShangHai-NanJing transport Line. Based on this, this paper uses the entropy theory to get the weight of different types of travelers to different factors and establishes evaluation criteria for the passenger’s satisfaction. At last we use the dates that surveyed in the ShangHai-NanJing transport line to simulate the passenger share of the four transport models (the High-speed rail, the common rail, the highways and the civil aviation) and the characteristic of each type of passenger flow structure. Based on the result of the passenger share simulation, we have also analyzed the sensitivity of the five factors (Security, Convenience, Comfort, Ticket Price and Speed) that affect the passenger’s travel choice and researched how these travel factors affect the passengers’ travel choice and the passenger share of different travel modes.

Recently, Harrod has revealed that the occupancy constraints are not sufficient to timetable rail traffic. This insufficiency occurs when opposite movements of trains share the same route. In this paper, we propose a quick algorithm that considers overtaking and opposite train’s movements. This algorithm builds a train timetable from a proposed sequence of trains. It is based on an elementary model of an invariant resource sharing system. The algorithm is proposed in order to extend the genetic algorithm that calculates the sequence of trains for maximizing the frequency of passenger’s train.

This paper presents an approach to optimize the running speed code of each signaling block between two neighboring stations of mass rapid transit (MRT) systems with fixed-block signaling (FBS) system. Its objective is to minimize train energy consumption under maximum train capacity with considerations of gradients of alignment, minimum headway of automatic train operation (ATO) system and train average speed and to reduce train peak power under proper selection of running speed code of each signaling block. The speed codes and the shortest block length are searched by using genetic algorithms (GA) with different aspects. Dynamic programming (DP) is used to determine the optimal combination of running speed code of each signaling block.

This paper presents a coupled electromechanical optimization of the cost of high speed railway overheads. The proposed electromechanical optimization solves the coupled mechanical and electrical problems by obtaining the railway overhead with minimum cost. A simple model cost of the railway overhead is proposed. This model cost defines the global cost per kilometer, which is mainly composed by the costs of material used in the construction of the overhead supports and the electric lines respectively. Using a standard genetic algorithm the optimized railway overhead is obtained. The parameters which describe the railway overhead are defined by: (i) sizing and (ii) configuration of the overhead supports; (iii) geometric location and (iv) type of electric conductors. The constraints considered are: (i) maximum allowable stress, and (ii) structural static stability; (iii) structure gauge to limit the position of physical conductors, (iv) minimum distance between conductors or between conductor and earth and (v) maximum allowable current of each conductor. In addition, the fitness function also considers the minimization of the equivalent electrical system impedance as a secondary optimization criterion. This optimization method has been successfully applied to the design of the high speed railway overhead C-350, used in the new line Madrid-Barcelona-French Border. The optimized railway overhead shows an overall improvement at two levels. Firstly the performance is enhanced and secondly the global cost is reduced. The obtained results are compared with the non-optimized configuration in order to demonstrate the obtained improvements.