The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.

Transit-Oriented Development (TOD), which integrates the Urban Rail Transit (URT) and surrounding land-use planning, is a noteworthy way to achieve sustainable development of URT. Stations with different function orientations have different development goals. However, there are limited studies that focus on the TOD planning with distinct station types. This study aims to propose a method to fill this gap. Commercial and residential development types are two dominating Station-TOD types. In this study, a multi-objective TOD planning model which integrates urban rail transit station, commercial, residential and public land-uses is proposed. The objectives focus on land value, ridership of URT, the compactness and commercial accessibility of land cells. Meanwhile, a two-phase solution algorithm based on genetic algorithm is designed to search for a set of Pareto solutions of alternative land-use plans. Two URT stations in Beijing are taken as case studies. One is Sidaoqiao station as a commercial-dominating type case, the other one is Pingxifu station as a residential-dominating type case. The results show that the optimized plan is more reasonable than the current plan. Both of the cases demonstrate the effectiveness and superiority of the proposed model and algorithm.

In the process of urban rail transit network design, the urban road network, urban trips and land use are the key factors to be considered. At present, the subjective and qualitative methods are usually used in most practices. In this paper, a quantitative model is developed to ensure the matching between the factors and the urban rail transit network. In the model, a basic network, which is used to define the roads that candidate lines will pass through, is firstly constructed based on the locations of large traffic volume and main passenger flow corridors. Two matching indexes are proposed: one indicates the matching degree between the network and the trip demand, which is calculated by the deviation value between two gravity centers of the stations’ importance distribution in network and the traffic zones’ trip intensity; the other one describes the matching degree between the network and the land use, which is calculated by the deviation value between the fractal dimensions of stations’ importance distribution and the traffic zones’ land-use intensity. The model takes the maximum traffic turnover per unit length of network and the minimum average volume of transfer passengers between lines as objectives. To solve the NP-hard problem in which the variables increase exponentially with the increase of network size, a neighborhood search algorithm is developed based on simulated annealing method. A real case study is carried out to show that the model and algorithm are effective.

The passenger flow assignment in the rail transit network is the basis for determining the passenger spatiotemporal distribution and train operation organization plan. In previous studies, the passenger flow assignment problem mainly focused on lines within the same rail system. Few studies focus on lines with the integrated mode of cross-line and skip-stop operation between state and suburban railway due to fewer cases in practice. In this study, passenger congestion and fare policy are taken into account in the generalized travel cost function, and a passenger flow assignment model based on the path-sized logit (PSL) model considering train capacity is proposed. Meanwhile, a schedule-based spatiotemporal digraph is established to search for the shortest spatiotemporal travel path. Furthermore, an improved method of successive averages (MSA) algorithm is designed to solve the proposed model. The proposed model is verified by a numerical example. The sensitivities of three main parameters which influence the results are also analyzed. The results show that the assignment model based on PSL is practicable in integrated operation mode. The higher the passenger’s familiarity with network information, the more accurate the assignment results. State trains are more attractive when the fare is lower than 0.5 CNY/km or the hourly wage is higher than 50 CNY/h.

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified.