Potential Application of iRap for Road Safety Assessment under Mixed Traffic Condition Case Study: Le Van Viet Street in Ho Chi Minh City

Road transport safety policies have emphasized road infrastructure safety design and engineering as a core function.However,in developing countries like Vietnam,this approach has been slower to adopt,resulting in substandard roads.In-depth studies of accident locations indicate that road environment factors contribute significantly to road accidents in Vietnam and road design features are associated with specific accident types and hazards.Proactive and reactive approaches,such as road safety audit,inspection,assessment,and treatment of hazardous locations,are necessary to ensure that the road and its environment are safe.This paper provides an overview of road safety in Vietnam in general,and Ho Chi Minh in particular,including its factors and characteristics,as well as road infrastructure safety improvements.The iRap tool for road safety inspection and assessment is highlighted as a potential method for systematically analyzing road infrastructure deficiencies and providing targeted countermeasures to improve road safety under mixed traffic conditions.

Traffic Flow Model Based on Power Flow

The article is research on the traffic situations of freeways. Different rules make different traffic situations. It is meaningful to research on traffic situations under different conditions. The author analyzes factors like traffic flow and safety, peflbrmance respectively, and proposes the theor3, basis for making more reasonable rules. The author first establishes evaluation system of traffic safety. Then, we compare the freeway network to power network to find a solution. and establish a traffic flow model based on power flow （TFPF）. It calculates power flow. So it applies the formula mode back to the traffic network, chalking up the perforumnce of the traffic condition. We utilize cellular automata （CA） method to simulate traffic circulation, and verify the accuracy of above model with the obtained data.

Traffic condition estimation with pre-selection space time model

A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

Shaping a New Level of Bus Service under a Novel Concept of Bus Interaction: A Meta-Review

Public transport services, particularly bus services, play an important role in a sustainable transportation system. However, despite various efforts, bus ridership has decreased. The appearance of shared and on-demand vehicle services is one of the main reasons for this issue. In addition, bus tourism services have been successfully developed to meet the exigent needs of tourists. Therefore, a new level of daily bus service is necessary to adapt to the changing demands of customers. Bus interaction (BI) plays an important role in bus services. Nevertheless, the conventional concept of BI mainly refers to users, physical interaction, and safety, but it does not address non-users, non-physical interactions, service quality, and other aspects. This study aims to elaborate on a new concept of bus services. Based on this, we developed a theoretical framework for BI. A meta-analysis was then conducted to identify the achievements and untouched aspects. The results of this study provide three main contributions. First, an unprecedented novel concept of BI is defined, including 13 types of interactions. Second, a comprehensive theoretical framework of BI is established based on the relationships between eight sustainable bus system sub-aspects and 13 BI types. Third, based on the theoretical framework and findings of the reviewed studies, a common finding comprehensive framework of BI is completed, which is accompanied by 1) key findings of the 13 BI types, 2) conclusions of traffic conditions affecting BI research, 3) BI research gaps, and 4) 16 main suggestions for future BI research.

Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data

Damage alarming and safety evaluation using long-term monitoring data is an area of significant research activity for long-span bridges. In order to extend the research in this field, the damage alarming technique for bridge expansion joints based on long-term monitoring data was developed. The effects of environmental factors on the expansion joint displacement were analyzed. Multiple linear regression models were obtained to describe the correlation between displacements and the dominant environmental factors. The damage alarming index was defined based on the multiple regression models. At last, the X-bar control chart was utilized to detect the abnormal change of the displacements. Analysis results reveal that temperature and traffic condition are the dominant environmental factors to influence the displacement. When the confidence level of X-bar control chart is set to be 0.003, the false-positive indications of damage can be avoided. The damage sensitivity analysis shows that the proper X-bar control chart can detect 0.1 cm damage-induced change of the expansion joint displacement. It is reasonably believed that the proposed technique is robust against false-positive indication of damage and suitable to alarm the possible future damage of the expansion joints.