It is a new attempt to use an adaptive neuro-fuzzy inference system (ANFIS) as an adaptive traffic signal control method for an isolated intersection under mixed traffic conditions in Hanoi City, the capital of Vietnam. The proposed method using ANFIS can work more effectively as it gives full play to both an artificial neural network and a fuzzy logic system, hence can intelligently control the green time for each phase of the traffic signal lights according to the fluctuating traffic volume under mixed traffic conditions to improve the vehicular throughput and reduce delays. Taking a typical signalized intersection in Hanoi City as a case study is to evaluate the performance of the proposed method through a microscopic traffic simulator with an interface between VISSIM traffic simulation model and MATLAB. Simulation results of the proposed method using ANFIS indicate better performances and adaptability compared with the fixed-time control method and the fuzzy logic method.

This study explores the significant factors affecting risky riding behaviors of Vietnamese motorcyclists using a contextual mediated model (CMM) in Hanoi, the capital of Vietnam, where motorcycle crashes are prevalent. The affecting factors include personality traits, riding self-confidence, riding attitude, and risk perception. Personality traits and riding self-confidence are distal factors of CMM that affect risky riding behaviors. On the other hand, riding attitude and risk perception are proximal factors in CMM. A survey was conducted to collect information on motorcyclists’ risky riding behaviors related to the four factors mentioned through a self-reported questionnaire. Statistical Package Social Science (SPSS) and structural equation modeling (SEM) with analysis of moment structures (AMOS) are used to determine the effects of the factors on risky riding behaviors. The results discovered that riding attitude and risk perception were the intermediate variables of personality trait and riding self-confidence affecting the risky riding behaviors, and personality trait and riding self-confidence also affected the risky riding behaviors directly. Findings in the model also show that riding attitude was perceived to play a significant role in increasing risky driving behavior. The recommendation is to increase the safety education programs that reduce risky driving behavior.

Safety evaluation of traffic conflict is a very important and challenging issue in evaluating intersection safety under incomplete traffic accident data conditions and is also one of the main safety surrogate measures of analyzing accident data recently. It helps to analyze and solve intersection problems comprehensively and deeply. From there, it helps to improve traffic safety as well as reduce the risk of traffic accidents at intersections. Various evaluation methods based on traffic conflict have been proposed to make conflict safety levels at intersections more consistent and objective. However, a major concern is that many existing measurements are still subjective and are not easy to obtain uniformly. This study aimed to develop a model for safety evaluation at intersections in a comprehensive way that may be expected to directly link to the severity of the accident from different evaluation indicators. First, the three factors, including time to collision (TTC), conflicting speed (CS), and deceleration rate (DR) to avoid a crash, are introduced into safety evaluation of conflicts as the indicators. And then, as regards the fuzziness and randomness of the evaluation indicators, the qualitative concept has to be converted into a quantitative one utilizing cloud model, which implements the natural transformation between the qualitative concept of the safety level of traffic conflict and the membership degree of the evaluation indicators corresponding to the different safety levels. Finally, an indicator weight model is built based on the information entropy and the AHP method to determine the safety level. We illustrate the practical implementation of the proposed method using actual data of a typical signalized intersection from Hanoi City of Vietnam. The results indicate that traffic conflict analyzed by the proposed method was appropriate with actual state of the intersection, and the proposed method is simple, effective, and feasible, so it has a certain application value.

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

This paper presents a method to estimate vehicle speed automatically, including cars and motorcycles under mixed traffic conditions from video sequences acquired with stationary cameras in Hanoi City of Vietnam. The motion of the vehicle is detected and tracked along the frames of the video sequences using YOLOv4 and SORT algorithms with a custom dataset. In the method, the distance traveled by the vehicle is the length of virtual point-detectors, and the travel time of the vehicle is calculated using the movement of the centroid over the entrance and exit of virtual point-detectors (i.e., region of interest), and then the speed is also estimated based on the traveled distance and the travel time. The results of two experimental studies showed that the proposed method had small values of MAPE (within 3%), proving that the proposed method is reliable and accurate for application in real-world mixed traffic environments like Hanoi, Vietnam.

Along with the development of the economy and society and the speedy increasing number of personal vehicles, especially motorcycles, many cities in developing countries like Hanoi (Vietnam) are facing many transportation problems, notably traffic accidents involving motorcyclists. Motorcycle accidents are caused by main human-error factors, and could be considered as the typical grey system with the features of complexity and imperfect information. In this study, a grey relational analysis (GRA) model of the human-error factors is presented to quickly explore the main human-error factors contributing to traffic accidents caused by motorcycles. In this model, the grey relational degree and the grey relational order of each human-error factor also are determined to rank the main contributing human-error factors. Taking the data of road traffic accidents caused by motorcycles in Hanoi, the capital of Vietnam between 2015 and 2017 as experimental data, the grey relational degrees of human-error factors have been analyzed quantitatively through the GRA model. The experimental results show the first three human-error factors include wrong lane shifting, poor road observation, and speeding, respectively. The results also help to conduct effective countermeasures for controlling human errors and reducing road traffic accidents.