The location of joint distribution center (JDC) is of great importance in urban freight system. This work aims to minimize the total cost, including the fixed cost, the transportation cost, and the penalty cost for the missed deliveries, considering the joint distribution willingness (JDW) of restaurant and the coverage radius of JDC. The integer programming model is formulated to select the optimal location of JDC and opens no more than k-JDCs. To solve the problem, an improved K-means (I-K-means) algorithm combining local search with penalty is proposed. The delivery problems obtained by freight survey from 114 restaurants in Beijing, China are taken as a case study. The formulation in this paper can reduce the cost of deliveries in restaurant industry, decrease the number of freight vehicles on road, and promote the joint distribution mode in urban freight system.

With the accelerated operation of subway networks, the increasing number of subway transfer stations results in inefficient passenger travel. The target of this paper is to solve the research question of how to reduce transfer waiting time (TWTT) for heterogeneous passengers. The key problem is to determine the optimal concerted train timetable considering the transfer walking time (TWKT) of the passengers. On the basis of field survey data, the regression method was used to establish a TWKT prediction model for general passengers (G) and vulnerable passengers (V), including the elderly, passengers traveling with children, and those carrying large luggage. Afterward, a two-objective integer programming model was formulated to minimize the subway operating costs and TWTT for each group, in which V is given the priority weight to ensure social equity. The headway, loading capacity, and TWKT of heterogeneous passengers were set as optimization model constraints. A genetic algorithm (GA) was designed to find the optimal solution. A case study in which the Beijing Jianguomen Station was selected as the key transfer station was conducted to verify the performance of the proposed model. Key results show that the total TWTT for V and G can be reduced by 18.6% and 27.2%, respectively, with one train saved. Results of the parameter sensitivity analysis reveal the interconnection between the operating cost, heterogeneous passenger proportion, and transfer time. The proposed model can be used for improving transfer efficiency for passengers while considering the enterprise operating costs.

Establishing a passenger flow prediction mechanism is necessary for quickly evacuating many passengers in an emergency, which can improve the service quality of urban rail transit (URT). To effectively forecast origin-destination (OD) passenger flows in URT under emergency conditions, 35-day automatic fare collection (AFC) data are used for a statistical analysis of the time, location and passenger flow aspects. The influence range of the OD passenger flow during an emergency is determined by analyzing the degree of passenger flow fluctuation. Considering the time period of an emergency occurrence and its continuous influence, this paper also studies the influence of an emergency occurring at a station, a section between two stations or a section across several stations. A spatial-temporal correlation prediction model of OD passenger flow based on nonlinear regression is constructed by introducing the concept of passenger flow spatial-temporal influencing parameters. According to the characteristics of URT lines, a passenger flow prediction algorithm is proposed to predict the OD passenger flow for different line categories for an emergency. A real typical emergency involving the Beijing urban rail transit (BURT) system in 2017 is analyzed to verify the effectiveness of the proposed model. The results show that this model can effectively predict OD passenger flow in a URT system during an emergency, which provides basic support for the evacuation of passengers.