In recent years, there has been a growing interest in real estate investments that utilize blockchain technology. Traditional real estate investments usually involve third-party intermediaries for verifying and recording real estate informal transactions. This paper proposes a blockchain-based real estate investment model and presents a detailed description of the real estate register authentication aspect of the model. The model uses blockchain technology to create tamper-evident records of real estate transactions and provide secure authentication and verification of real estate informal transactions. Meanwhile, each real estate transaction is recorded in a block, and all transaction records are kept on the blockchain. This means that inventors can access these transaction records and verify their authenticity and validity. The system can also use smart contracts to automate the process of real estate transactions, which further improves transaction efficiency and reduces costs. Further, the model's timestamp and authentication mechanism can eliminate third-party intermediaries and ensure the authenticity and validity of real estate transactions through distributed ledgers and verification mechanisms. Overall, blockchain-based real estate systems offer advantages of security, transparency, efficiency, and cost reduction. With ongoing blockchain advancements, these systems are expected to play a crucial role in future real estate investment transactions.

This paper concerns predictions of freeway traffic flow under non-recurrent events using multivariate machine learning models, including the multilayer perceptron network and the one-dimensional CNN long short-term memory network. The machine learning architectures and loss functions for training neural networks are presented. The study region is a portion of the Kwinana Freeway northbound in Perth, Western Australia. The study dataset, obtained by matching the timestamp of all available data, has various features, including traffic volume (flow rate), speed, density and road incident. Using the root mean squared error and mean absolute error, results from the two learning models are compared to the baseline model to determine the suitable model for traffic prediction under non-recurrent events.

This paper investigates the effectiveness of multi-variate deep learning models for traffic flow prediction with road incidents. Multiple features of the data are considered in the analysis including traffic features, road incident features and cyclical features. Three multivariate deep learning models based on the stacked LSTM network, the CNN LSTM network, and the Autoencoders-LSTM network are developed for long-short term traffic forecasting under road incidents. The results obtained from the analysis are then compared to determine the best suitable approach.

This paper focuses on the microsimulation of traffic flow on freeways with on-ramps, off-ramps and bottlenecks. The road network model with many vehicle routes is constructed based on the detector flow data recorded every minute on each freeway lane. A comparative study evaluates the effectiveness of the proposed traffic flow model. It is noted that the model can successfully capture the essential features of traffic flow on the freeway. With the calibrated model, various numerical experiments are then conducted to investigate the impact of ramp metering (RM) and variable speed limit (VSL) on average waiting time, average travel time, average traffic speed, and the total number of running vehicles over certain period of time on the road network. The results indicate that the RM and VSL control can significantly improve traffic flow on the freeway.