To solve the increasingly serious traffic congestion and reduce traffic pressure, the bidirectional long and short-term memory (BiLSTM) algorithm is adopted to the traffic flow prediction. Firstly, a BiLSTM-based urban road short-term traffic state algorithm network is established based on the collected road traffic flow data, and then the internal memory unit structure of the network is optimized. After training and optimization, it becomes a high-quality prediction model. Then, the experimental simulation verification and prediction performance evaluation are performed. Finally, the data predicted by the BiLSTM algorithm model are compared with the actual data and the data predicted by the long short-term memory (LSTM) algorithm model. Simulation comparison shows that the prediction results of LSTM and BiLSTM are consistent with the actual traffic flow trend, but the data of LSTM deviate greatly from the real situation, and the error is more serious during peak periods. BiLSTM is in good agreement with the real situation during the stationary period and the low peak period, and it is slightly different from the real situation during the peak period, but it can still be used as a reference. In general, the prediction accuracy of the BiLSTM algorithm for traffic flow is relatively high. The comparison of evaluation indicators shows that the coefficient of determination value of BiLSTM is 0.795746 greater than that of LSTM (0.778742), indicating that BiLSTM shows a higher degree of fitting than the LSTM algorithm, that is, the prediction of BiLSTM is more accurate. The mean absolute percentage error (MAPE) value of BiLSTM is 9.718624%, which is less than 9.722147% of LSTM, indicating that the trend predicted by the BiLSTM is more consistent with the actual trend than that of LSTM. The mean absolute error (MAE) value of BiLSTM (105.087415) is smaller than that of LSTM (106.156847), indicating that its actual prediction error is smaller than LSTM. Generally speaking, BiLSTM shows advantages in traffic flow prediction over LSTM. Results of this study play a reliable reference role in the dynamic control, monitoring, and guidance of urban traffic, and congestion management.

The present research showcases the Indoor Energy-Saving Optimization Design of green buildings. This integrated approach synergizes a building’s Indoor Energy-Saving process based on the intelligent GANN-BIM model. The GANN-BIM model is driven by genetic algorithms (GAs), which include artificial neural networks (ANNs) along with building information models (BIMs). Building information modeling (BIM) is a technology that involves the modeling and management of digital representation of all forms of structural buildings. These intelligent models can be exchanged and extracted in the form of files and are mainly used for the designing and decision-making of a building. The BIM model is empowered as an intelligent technology by incorporating artificial neural networks (ANNs) and genetic algorithms (GAs). The main objective of the research is Indoor Energy-Saving by implementing an optimized design of green buildings. Green buildings can benefit from the GANN-BIM model’s ability to handle complex and conflicting design requirements while using less computational power during the evaluation of the proposed approach. There are a variety of new green building technologies being developed, but they all share a common goal: to reduce human health and environmental impact by maximizing energy, water, and other resource efficiencies; protecting occupant health; reducing waste; and decreasing pollution. The empirical results of GANN-BIM proved that the proposed model outperforms well in enhancing energy evaluation.

Purpose: Due to the different actual construction conditions in different cities, the requirements for community livability may also differ due to different geographical locations and urban construction priorities. The research system in this paper can be applied to study similar old communities in old urban areas. The indicator system would need to be adjusted in different places, based on specific construction situations and higher planning requirements. This process would provide valuable insights for effective construction projects that support the livability of the old communities. Design/methodology/approach: Based on sustainable and people-oriented development principles, this study considered the development of old urban communities during today’s rapid urban renewal and development. Using previous literature and related research experience, this study established an evaluation indicator system to assess the livability of old urban communities. Based on the local resident experience and satisfaction, the study investigated current weaknesses in the construction of livable old urban communities and developed corresponding recommendations for reform based on these. The goal was to provide guidance and recommendations for renewing old communities in during urban development and further promote the sustainable development of the city. Findings: Based on the people-oriented principle and focusing on old urban communities as the research object, this study constructed an evaluation indicator system to evaluate the livability of urban old communities. The goal was to identify the weaknesses in the construction of old urban communities, with a focus on livability. Using the Bei’anmen community in Nanjing as a case study, the AHP method and fuzzy comprehensive evaluation method were applied to evaluate the overall target level and specific indicators, with the goal of assessing the level of livability in the Bei’anmen community.[AQ2] The results show that the livability of the Bei’anmen community is “very poor,” with significant room for improvements in community livability. This study also proposed corresponding measures for improving problems related to livability in the old urban community. Establishing the indicator system may help evaluate the livability of similar old communities in Nanjing and the same types of old communities in other cities. Understanding the overall livability of communities under construction can help identify weaknesses in other own construction approaches and may inform appropriate steps to improve the sustainable construction of the community in the wave of continuous urban renewal. This may realize the further development of livability in the community. Originality/value: The community is an integral part of the city and strengthening the community’s civilization can support a harmonious and stable social environment. In constructing livable communities, improving the community civilization can promote social progress and civilization, promote social harmony and support the harmonious and sustainable development of communities. To strengthen the construction of a livable community, it is important to apply a residential perspective and provide a good platform for managing community participation and interaction. This may include organizing community-level cultural activities and strengthening communication between residents to increase the residents’ affection for the community. This would enhance the residents’ sense of belonging, forming a harmonious and stable atmosphere of community life, mutual help and mutual tolerance.