With the rapid economic development, parking problems have become increasingly prominent due to the city's development model and the emergence of a large number of private cars. Parking management departments around the world focus on intelligent parking system in order to solve parking problems, but most of them are limited to upgrading the parking infrastructure. There is no effective solution from a perspective of the fundamental cause to solve the parking problem. At present, it is generally believed that parking guidance systems can effectively alleviate parking problems and provide drivers and traffic managers with real-time and accurate parking information. As one of the prerequisites of the parking guidance system, the accuracy of the short-term parking demand prediction method determines whether the implementation of the parking guidance scheme can effectively solve the parking problem in a certain area. Based on this, we have studied a short-term parking demand prediction in this paper. First, focusing on the distribution of typical parking arrivals and departures regular pattern, a parking demand prediction model was constructed utilizing the Markov birth and death process, and model parameters were calibrated utilizing curve fitting method and undetermined coefficients method. The simulation environment was set up utilizing the Markov process to verify the accuracy of the availability of parameter estimation method. Secondly, a method for determining the prediction interval based on the parking trend was given for different situations with the parking rush hours and ordinary hours which parking arrival and departure parameters were different. In order to verify the effectiveness of the model in practical applications, parking arrival and departure data from June 17 to June 23, 2019 in Jilin University of Nanling Campus was used to verify the short-term parking demand prediction method proposed in this paper. The results show that the parking demand prediction model proposed in this paper can accurately calibrate model parameters, predict parking demand quickly and effectively and provide theoretical reference and technical support for parking planning and management.

Pedestrians are some of the worst victims, as one of the weaker groups in road traffic accidents, but, at the same time, their unsafe behaviors are also an important factor in traffic accidents. This paper builds a pedestrian crossing hazard automatic-balance model and waiting-time threshold model by analyzing the process by which pedestrians cross the street. Then, the reasons for pedestrians’ unsafe behavior when crossing the street are analyzed by using traffic psychology. Finally, this paper puts forward some measures, based on aspects of pedestrian psychology, to reduce or alleviate pedestrians’ unsafe behaviors.

In Mobility as a Service (MaaS), walking plays a crucial role in connecting various modes of transportation. In order to provide more accurate predictions of walking travel time, a comprehensive and in-depth study is required to examine the factors that influence walking speed. Many existing studies focus on exploring various factors affecting walking speed, but there is limited research on further investigating the magnitude of their impact and the reasons for differences among different pedestrians. This study examines the relationship between personal characteristics and the degree of influence of environmental factors on walking speed. We recruited 31 volunteers and investigated their traveler characteristics such as height, weight, and age, as well as environmental factors such as weather conditions, ground conditions, and sidewalk Level of Service (LOS). Descriptive statistics were performed on walking speed, revealing the influence of these factors. For example, the speed of females is 89% of that of males. When in a hurry, the speed increases by 17%, while on uneven roads, the speed decreases by 11%. We then proposed the influence coefficient $f$ to represent the degree of influence and analyzed its correlation with personal characteristics. We discovered some strong correlations. For instance, the greater the body weight, the more significant the reduction in walking speed due to precipitous weather or uneven roads. Similarly, the taller the person, the greater the increase in walking speed under the influence of a rushed situation. Finally, we constructed a series of regression models for “f” and a speed estimation model. Our findings provide support for predicting personalized speeds in various scenarios, based solely on the traveler’s personal characteristics and speeds in controlled group scenarios in the travel service system, and contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.

For better describing the network cascading failure caused by random attacks such as traffic accidents, this paper introduces the ORNL-PSerc-Alaska (OPA) model to analyze the urban regional traffic network vulnerability. Firstly, the double-layer network model is built for analyzing the complex characteristics of the regional traffic network. Secondly, the cascading failure model is built to better describe the process of the network cascading failure under random attacks. Finally, by simulating the network failure process, the vulnerability of the traffic network will be pointed out, which provides the theoretical basis for avoiding the network cascading failure under random attacks.