Prediction of Traffic Volume of Motor Vehicles Based on Mobile Phone Signaling Technology

Urban traffic volume detection is an essential part of trafficplanning in terms of urban planning in China. To improve the statisticsefficiency of road traffic volume, this thesis proposes a method for predictingmotor vehicle traffic volume on urban roads in small and medium-sizedcities during the traffic peak hour by using mobile signal technology. Themethod is verified through simulation experiments, and the limitations andthe improvement methods are discussed. This research can be divided intothree parts: Firstly, the traffic patterns of small and medium-sized cities areobtained through a questionnaire survey. A total of 19745 residents weresurveyed in Luohe, a medium-sized city in China and five travel modes oflocal people were obtained. Secondly, after the characteristics of residents’rest and working time are investigated, a method is proposed in this studyfor the distribution of urban residential and working places based on mobilephone signaling technology. Finally, methods for predicting traffic volume ofthese travel modes are proposed after the characteristics of these travel modesand methods for the distribution of urban residential and working placesare analyzed. Based on the actual traffic volume data observed at offlineintersections, the project team takes Luohe city as the research object and itverifies the accuracy of the prediction method by comparing the predictiondata. The prediction simulation results of traffic volume show that the averageerror rate of traffic volume is unstable. The error rate ranges from 10% to 30%.In this thesis, simulation experiments and field investigations are adopted toanalyze why these errors occur.

Spatial patterns of residents’ daily activity space and its influencing factors based on the CatBoost model: A case study of Nanjing, China

The complexity and fragmentation of people’s activity space are challenging to planners.However,the relevant studies are mostly concerned on the relationship between the social attributes and the activity space of residents in a single or several communities,or the spatiotemporal laws of activity space on a macro scale.The research on the spatial characteristics of residents’activity space still needs to be strengthened.The present study analyses the spatial patterns of residents’activity space based on mobile phone signaling data to fill the gap of previous studies that assessed residents’activity space across small geographic areas.First,according to the spatial scope and direction of an activity space and residents’activity coverage rate,spatial patterns can be divided into three types:compact,extended,and directional extension patterns.The CatBoost method is then used to statistically analyze the influencing variables of spatial patterns,and the order of importance of the following influencing factors is determined:the built environment is more influential than social and economic situations.This study aims to strengthen the understanding of residents’activity space at the spatial level and provide a basis for the optimization of communities with different spatial patterns.

Development of Suburban New Towns in Shanghai: Jobs-Housing Spatial Relationship Analysis

This paper identifies the employment and housing locations of residents in Shanghai based on mobile phone signaling data, so as to obtain the employment density and commuting data and analyze the development of nine suburban new towns from the perspective of jobs-housing spatial relationship. Firstly, the paper defines employment-intensive areas and gets the average employment density of each new town according to the employment density data. Then it marks out the scope of the employment influence through analyzing the sources of workers in each new town in accordance with the commuting data. Finally, it analyzes the jobs-housing balance of each new town using independence index, finding that suburban new towns in Shanghai have become main clusters of economic activities, while the scope of employment influence in each new town is still concentrated in its administrative area, with less attraction to residents in other areas. The independence index demonstrates a law that the suburban new town which is farther from the central city sees a higher degree of jobs-housing balance. Among them, new towns located in the outer suburbs with a low independence index indicate their special development situation, the reason of which is worth further study.