Evaluation of the Feasibility of an Intelligent Transport System for an Urban Area

The Intelligent Transportation System is one of the burgeoning inventions that uses new technology to solve a variety of issues. Its compatibility with real-world issues in developing nations like India, such as traffic congestion, infrastructure demand, high traffic loads, and non-lane traffic systems. It is critical to assess a technology's potential in order to determine its viability. The goal of this article is to determine the utility cost ratio of implementation so that it may be evaluated without changing the existing infrastructure design. The end result is a utility cost analysis approach that takes social, economic, and environmental issues into account. As a result, the analysis is quickly examined so that the technology may be applied according to its appropriateness. Keywords: Investments, Congestion, Intelligent Transportation System (ITS), Benefits, Traffic.

Classroom as Complex Adaptive System and the Emergence of Learning

Complex adaptive systems (CAS) theory is offering new perspectives on the nature of learning in school classrooms. In CAS such as social networks, city traffic systems and insect colonies, innovation, and change are occasioned through non-linear, bottom-up emergence rather than linear, top-down control. There is a growing body of evidence and discourse suggesting that learning in school classrooms, particularly in the early years and primary phases, has non-linear, emergent qualities and that teachers, school leaders, and educational researchers can gain valuable insights about the nature of interactive group learning by analyzing classrooms through a CAS lens. This chapter discusses the usefulness of a CAS framing for conceptualizing learning in primary school classrooms. It will explore key arguments, discuss relevant objections and draw on my own research to make the case for a measured application of CAS theory to primary classroom teaching and learning, explaining how it can support the development of innovative pedagogies.

An efficient routing strategy for coupled spatial networks

The study of traffic dynamics on couple networks is important for the design and management of many real systems. In this paper, an efficient routing strategy on coupled spatial networks is proposed, considering both traffic characteristics and network topology information. With the routing strategy, the traffic capacity can be greatly improved in both scenarios of identical and heterogeneous node capacity allocation. Heterogeneous allocation strategy of node delivery capacity performs better than identical capacity allocation strategy. The study can help to improve the performance of real-world multi-modal traffic systems.

An Assessment of the Impact of Industrialization on Physical Environment and Socio-economic Conditions around the Alipur Industrial Area, Bangladesh

Massive industrialization promotes economic growth and causes environmental pollution and degradation. The purpose of this research is to determine the impact of industrialization on the physical environment and socio-economic condition at Alipur industrial areas, Habiganj, Bangladesh, by measuring water, soil, air, sound quality parameters and a random sampling questionnaire survey on socio-economic conditions. Most of the measured physicochemical parameters exceeded the acceptable limit of inland surface water. The pH of the effluent water ranged from 4.83 to 8.58, which was found lower than the standard level for two points. The DO level was within the range of 1.98 to 3.32 mg/L indicating that aquatic life is in danger because of the lower level of DO. BOD, COD, TSS, and TDS ranged from 133 to 255.8 mg/L, 330 to 566 mg/L, 1960 to 2170 mg/L and 4110 to 5500 mg/L, respectively. The concentration of Nitrates (14.63 mg/l), Phosphate (10.33 mg/l), and Copper (5.49 mg/l) in the water samples found more than the inland surface, public sewer STP, and Irrigated land standard. The concentrations of CO (10.71), NO2 (90.56), and SO2 (104.34) in the air are near the acceptable level, indicating that the air was moderately polluted. The Durbin-Watson statistic is 0.495 from the model summary indicating the research model has a positive auto-correction, and the coefficient of significance is at 0.00, and Test F at 150.345 suggests that the model is suitable. Furthermore, the coefficient of the land area lost due to industrial park construction is found at 0.00, indicating household income increased when people lose land and non-agricultural sectors like building houses, investing in services, traffic systems. On the other hand, it is undeniable that few members whose land is acquired turned unemployed during industrial parks, resulting in the high number of unemployed workers being high and income declines.

Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies.

Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques

According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes desirable for computer vision researchers. Nowadays, Deep Learning (DL)-based approaches have shown high performance in computer vision tasks that involve a complex features relationship. Therefore, this work develops an automated DL-based method capable of detecting traffic accidents on video. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure.

Research of Surrogate Measure for Freeway Crashes Based on Tire Skid Marks

Traditional approaches to evaluating and predicting safety issues in traffic systems are via crash records. However, considering the characteristics of scarcity, inconsistency, inaccuracy, and incompleteness of crash records, conclusions and recommendations drawn purely based on crashes have limitations. Tire skid marks are considered an indication of some safety hazards, and it could have good potential to be used as surrogates for crashes. By collecting and analyzing the data based on selected arterial and freeway segments in the Reno-Sparks area in northern Nevada, a methodology was developed to categorize different tire skid marks. Sliding window and linear regression techniques were applied to determine any correlation between tire skid marks and crashes. The analyses indicated that there was a relatively strong linear correlation between skid marks and crashes on freeway segments.