Urban Traffic Optimization and Parking Demand Analysis Utilizing Big Data

Abstract

With the popularization of motor vehicles, transportation convenience has brought significant improvements to people's lives and travel, but the ensuing urban road traffic congestion affects residents' daily lives. Therefore, this paper utilizes big data statistics provided by monitoring devices, including vehicle passage times, travel directions, intersections, etc. Using Excel, we identified data trends for analysis, established corresponding mathematical models, optimized constraint conditions, and employed Python software to obtain the total daily traffic volume. We also analyzed the frequency of vehicle license plate appearances and grasped the traffic volume at road intersections during different time periods and in different directions. Based on this, we optimized the traffic signal settings at intersections and estimated the number of parking spaces needed for the scenic areas during the May Day holiday. This series of measures effectively achieved the rational allocation of road resources, significantly improved residents' travel efficiency, and contributed to promoting the high-quality development of urban transportation construction.