Rapid exploitation of city underground space has led to the development of increasingly more underground slender-shape infrastructure like pedestrian tunnels, concourses, subway walkways, underground shopping streets, etc. Pedestrian evacuation in those public places in case of emergency can be disastrous if not properly guided. Therefore, it is important to understand how to enhance the evacuation efficiency through proper active guidance. In this study, we propose a digital twin based guiding system for pedestrian emergency evacuation inside a slender-shape infrastructure, aiming at enhancing the overall evacuation efficiency. Composition and calibration process of the guiding system are described, and a cellular automata based model is established to serve as the digital twin model. Two guidance strategies, namely traditional fixed guidance and smart guidance, are adopted by the digital twin to generate guidance instructions. A smart guidance strategy using a semi-empirical approach is proposed based on the understanding of the free movement and congested movement of pedestrian flow. Systems under different guiding strategies are compared and discussed over their effectiveness to promote excavation efficiency in different pedestrian population distribution settings. The simulation results show that a system under smart guidance tends to have shorter evacuation time (up to 23.8% time saving) and performs with more stability for pedestrian evacuations over the traditional fixed guided systems. The study provides insight for potential real applications of a similar kind.

Flexible transit services, which bring together the characteristics of fixed-route transit and demand-responsive transit, have been proven to be cost-efficient in low-density residential areas. In this paper, a methodology is proposed to assist planners in making better decisions when choosing between route deviation policy and point deviation policy, which are two promising types of flexible transit services. A user cost function is developed to measure the service quality of the transit systems, and analytical models are constructed to compare the system performance under both expected and unexpected demand levels. Based on the experiments for various scenarios over a real-life transit example, the critical demands, which represent the switching point between the two competing service policies, have been derived. Our findings show that point deviation policy is more efficient at low-demand levels, while route deviation policy is a better choice at low-to-moderate demand levels. At unexpectedly high demand levels, route deviation policy is better able to accommodate rejected passengers than point deviation policy.

Urban road traffic flow prediction is the key basis for the development of intelligent transportation systems. Lane-level heterogeneous traffic flow prediction will become a new and important challenge in the future development of intelligent transportation. In this study, a spatiotemporal attention-based encoder-decoder model is proposed to solve the lane-level heterogeneous traffic flow prediction problem. Specifically, the traffic flows of different locations and different types of vehicles are all regarded as traffic flows running in independent spaces. A spatial attention layer is added to the encoder to capture the spatial characteristics of traffic flows, and a temporal attention layer is added to the decoder to analyze the temporal correlation of each time interval. Finally, we validate the model by using real traffic monitoring data in the Shunyi District, Beijing, China, and prove the superior performance of the model. Meanwhile, the output of attention weights can deepen the further interpretation of the spatiotemporal correlation of heterogeneous traffic flows.

The purpose of this paper is to examine the influence factors on willingness to use a public bicycle system and to analyze the impact of each. Ordered Logit and Probit models were used to analyze the change in user willingness after improvements were made. Data were collected from a random survey conducted in September and October 2014 in Nanjing, China. The findings of this research indicate seven factors that most influence willingness to use a public bicycle system, including socioeconomic factors and journey restrictions. Socioeconomic factors (gender, employment, and car ownership) have more impact on user willingness than journey restrictions. In addition, socioeconomic and station facility considerations have the greatest impact on increasing the probability of users being “very willing” to use a public bicyucle system.

In order to provide a more cost-efficient and environmentally-friendly logistics service in economic activity of cities, an innovative urban logistics transport system based on the existing intensive public transit network is proposed. In comparison with current dedicated freight operating mode in the logistics industry, this new freight system could give goods a ride on the basis of transporting passenger flow and remarkably reduce the freight fleet of logistics enterprises. In this paper the structure of the public transit-based logistics transport system is preliminarily introduced and the service procedure of executing transport tasks is displayed. An urban transit-based freight system is designed for a chosen mid-size city of China, with regard to the characteristic of local logistics service, and the results indicate a considerable system benefit compared with the previous logistics system.