Tennessee Section of ITE-Meaningful Meetings, Meet-Ups, and More!

The Tennessee Section of ITE (TSITE) has strong local Section meetings, which were allowed and encouraged in 2022 as conditions improved from the COVID-19 pandemic. In-person meetings resumed in each of the larger cities: Memphis, Nashville, Chattanooga, Knoxville, and Johnson City. Technical sessions at meetings included applications of cutting-edge transportation elements and offered professional development hours (PDHs) for participants. TSITE was able to host all four of its quarterly Section meetings in person. The quarterly meetings rotate across the state and are typically full-day events. They begin in the morning with technical presentations and lunch is provided, followed by the business meeting, additional technical sessions, and/or a technical tour. In 2022, the Summer and Fall Section meetings were 3-day events. In 2022, the quarterly meetings were hosted and well-attended. The Winter Meeting in Cookeville TN at Tennessee Tech University in February had 71 participants. Meeting presentations included "MDOT Traffic Signal Asset Management & Preventative Maintenance," "Campus Like Emergence Evaluation Modeling," "Evaluating factors associated with Abandoned and Disabled Vehicle Incidents in Tennessee," and "MAQ award for deploying an NDOT Traffic Management Center and other emerging projects."

Monte Carlo Tree Search improved Genetic Algorithm for unmanned vehicle routing problem with path flexibility

With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm.

Intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities

One of the greatest challenges for a traffic control system is to synchronize the flow of vehicles to prevent traffic jams. This issue gets worse when there are priority vehicles, such as ambulances, trying to move through the traffic. Given the current situation, with the COVID-19 pandemic, and the trends of smart cities, in this work, we propose and simulate a traffic control system that prioritizes ambulances within large urban centers, using Fuzzy logic and IoT devices. The simulation of our proposed model was performed on the software Dojot, which is an open platform for IoT modeling. It addressed a real situation, in a path that is usually used by ambulances to get to a reference hospital in the city of Campinas, Brazil. The proposed traffic control system can also be used after the COVID-19 pandemic is over in order to improve traffic flow for other priority vehicles (e.g., firefighters and police) and increase people’s life quality within smart cities. © 2022, Springer Nature Switzerland AG.

Cycling Infrastructure for All EPACs Included?

A modal shift to electric pedal-assisted cycles (EPACs) can help with reaching the transport emission goals of the European Green Deal. With the rising sales of EPACs in Europe, a lack of appropriate (electric) cycling infrastructure remains a major barrier for many potential users. This paper discusses the results of a survey about the requirements of (potential) cyclists to design a better cycling infrastructure. The differences in requirements for non-cyclists vs. cyclists and electric cyclists vs. conventional cyclists are discussed using statistical analysis. The key findings are that cyclists and non-cyclists both require wide quality cycling infrastructure with safe crossing points, secure bicycle parking and smart traffic lights. Non-cyclists’ requirements significantly differ from cyclists’ on 12 items, of which rain cover while cycling and parking spots for the car are the most noteworthy. There is (but) one significant difference between the requirements of EPAC users and conventional cyclists: the need for charging points for EPACs along the cycle route.

Leading Through Changes

An interview with Jenn Conley, director of Vermont Transportation Systems, VHB South Burlington, VT, USA is presented. Conley said that my role as Director of Vermont Transportation Systems at VHB includes a lot of variety. In our office, Transportation Systems encompasses all aspects of Traffic Engineering. I am leading efforts in traffic operations analysis, traffic signal designs, traffic safety, and transportation planning efforts. Our group is small enough that I have a balance of the administrative duties involved in managing a group of professionals and I can still be actively involved with technical work. The pandemic definitely created challenges and forced us to find creative solutions to being able to reach our membership. The New England Section saw benefits from the move to virtual meetings. There was increased attendance at local state Chapter events.

COVID-19 Face Mask Detection Using Deep Learning

Corona Virus Disease (COVID-19) pandemic is causing a health crisis all over the world. One of the key weapons against Corona virus is wearing a face mask. In this paper, the automatic face mask detection system is presented using deep learning approach. It is used by authorities to check and monitor face mask wearing by civilians in various places like traffic signals, parks, cinema theatres and in other embedded systems. This system assists them to evaluate, track, mitigate and prevent COVID-19 spread. The face mask detection model is developed with a pre-trained Convolutional Neural network (CNN) model. Initially model is trained for detecting human face and in the later stage it will detect the mask. Exhaustive experimentation is carried out with the collection of datasets from Bing search API, Kaggle and RMFD and achieved a training accuracy of 98.6% and validation accuracy of 95.7%. Thus, the proposed system has successfully detected face mask and otherwise too. © 2021 IEEE.

Social Distance Monitoring And Face Mask Detection System For Covid-19 Pandemic

In this time when COVID-19 is spreading rapidly, it is essential to maintain social distance and wearing a mask is compulsory and to avoid large public gatherings at one place to break the chain of corona infection. But maintaining this is not easy. Many people, knowingly or unknowingly, gather and roam on the streets without wearing a mask. Keeping an eye fixed on of these activities isn't a simple job. The authorities need reliable technology to keep track of these activities. This Project can help in monitoring the social distance and also it detects face mask. This whole system is placed in Public gatherings, traffic signals, on roads and also at the entrance of schools and college gates. This system uses a Raspberry Pi with an RPi camera for capturing live video. The video is then processed frame-by-frame. By using image processing with the help of TensorFlow and OpenCV people, vehicles in the video and are identified. First, it detects the presence of the crowd and then with the help of object tracking algorithm and distance algorithm the centroid distances from one person to another person is measured if the distance is violated it gives an alert and by using Keras, OpenCV and Mobilenet people without a mask are detected when this happens it gives alert by speaking.

Traffic Analysis on Intersection Using PTV Vissim

As India is in its developing stage and the traffic on the other side in India is very heterogeneous or mixed in its nature and the average growth rate of vehicles in India is about 8%. With the increase rate of urbanization in India it will lead to the considerable traffic and travel growth on the roads which will result in vehicular delays, long queues and traffic congestion. So, in this paper with the help of traffic simulation software, i.e. VISSIM, three simulation of an unsignalized intersection {Dadour and Una-Jahu, Nerchowk Rd. (NH-21),H.P} will be analyzed and will compare them on the basis of vehicular delays and long queues. These three simulation will be analyzed on the basis of real world traffic data which is less from the expectations due to the pandemic covid-19, theoretical traffic data (increase in real data by 30%) and theoretical traffic data {with traffic signals as theoretical data follows warrant 1 (Min. Vehicular Volume) shown in IRC:93:1985}. Result showed that with increase in vehicular data there was not so much variation in vehicular delays, whereas there was an increase in long queues or queue stops and whilst third simulation (with traffic lights) is done it shows that it overcomes the queue stops of the intersection. © 2022, Springer Nature Singapore Pte Ltd.

NOx and CO Fluctuations in a Busy Street Canyon

Busy street canyons can have a large flow of vehicles and reduced air exchange and wind speeds at street level, exposing pedestrians to high pollutant concentrations. The airflow tended to move with vehicles along the canyon and the 1-s concentrations of NO, NO2 and CO were highly skewed close to the road and more normally distributed at sensors some metres above the road. The pollutants were more autocorrelated at these elevated sensors, suggesting a less variable concentration away from traffic in the areas of low turbulence. The kerbside concentrations also showed cyclic changes approximating nearby traffic signal timing. The cross-correlation between the concentration measurements suggested that the variation moved at vehicle speed along the canyon, but slower vertically. The concentrations of NOx and CO were slightly higher at wind speeds of under a metre per second. The local ozone concentrations had little effect on the proportion of NOx present as NO2. Pedestrians on the roadside would be unlikely to exceed the USEPA hourly guideline value for NO2 of 100 ppb. Across the campaign period, 100 individual minutes exceeded the guidelines, though the effect of short-term, high-concentration exposures is not well understood. Tram stops at the carriageway divider are places where longer exposures to higher levels of traffic-associated pollutants are possible.