## 5 Conclusions  
The current urban road traffic operational status assessment criteria focus primarily on the road sections between upstream and downstream detectors, using the vehicle travel time as the main evaluation index obtained within a certain time interval. Although this approach is simple and straightforward, it neglects the uneven distribution of vehicles arriving at adjacent road sections due to the periodic conversion of traffic rules at intersections. In this paper, we defined the traffic spatiotemporal parameters considering travel time, speed, and queuing situation by selecting traffic parameters from the time and space dimensions, and the phase space of multivariate traffic parameters was reconstructed using the single variable phase space. The chaotic characteristics were also analyzed. Next, we applied an LSTM model considering multivariate traffic parameters to predict traffic variables, and traffic parameters were predicted and traffic spatiotemporal parameters with were analyzed using RFID data in practical examples. By a results comparison, we found that the parameter prediction precision was better than the basic LSTM model. The main contributions of this article are as follows. First, we selected lane-level detection parameters, interpreted the meaning of the road traffic spatiotemporal state from the two dimensions of time and space, and devised a traffic spatiotemporal state calculation method that considers multiple traffic parameters. Second, we constructed a phase space of multivariate traffic parameters based on the nonlinear characteristics of the time series of multiple traffic parameters, integrated the LSTM prediction model, and constructed a phase space reconstruction LSTM prediction model that achieved a significantly improved accuracy. Third, we used real urban road data to predict the changing characteristics of multivariate traffic parameters and calculated the road traffic spatiotemporal state based on the predicted results, which can provide a theoretical basis and practical reference for road traffic state evaluations. In this paper, we analyzed the spatiotemporal evolution of traffic state parameters using RFID data and obtained prediction results. These results were derived from objective conditions. However, for urban traffic, using a large amount of sensory data to design a more reasonable traffic control strategy is an effective method to change the traffic operational status, which is also our future research direction. [PERSON], [PERSON] and H.K. were responsible for research conception and design. [PERSON], [PERSON], [PERSON] and [PERSON] were responsible for data processing and analysis. [PERSON], [PERSON] and H.K. were responsible for the analysis and discussion of results. [PERSON] and [PERSON] were responsible for draft manuscript writing and language. All authors have read and agreed to the published version of the manuscript. Figure 10: (**a**) The results for travel-time-related state parameters. (**b**) The results for spatial state parameters. (**c**) The results for traffic spatiotemporal state parameters. **Funding:** The work was supported by the Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (Changsha University of Science and Technology) (Grant No. kfj220702) and Opening Foundation of Engineering Research Center of Intelligent Transport of Zhejiang (Grant No. 2021 ERCITZ)-KF02). The authors also acknowledge financial support provided by the Hunan Natural Science Foundation (Grant No. 2021 JJ40577), Hunan Education Department Scientific Research Project (Grant No. 20B009), and Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway (Changsha University of Science & Technology) (Grant No. kfj220802). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study can be requested from the authors.