AVL Data for Arterial Performance Metrics and Incident Detection
A major concern for traffic operators is the early detection of incidents. With early detection, resources can be allocated and the incident can be addressed more quickly resulting in significant delay savings, which can improve safety by providing critical moments for emergency personnel. While many tools are currently available, such as traffic maps by companies like Google or reports from Waze, their data is designed for public consumption rather than for traffic operations. A speed map might be helpful for navigating efficiently on a commute but the data is not necessarily well-suited or even available for more detailed transportation analysis. To this end, the Regional Transportation Commission of Southern Nevada (RTC) has equipped many of its transit vehicles with automatic vehicle location (AVL) transponders in order to have access to real-time roadway conditions. The purpose of this project is to develop an incident detection system on top of the AVL data and develop a web-based dashboard for corridor performance grade and ranking.
Smart Intersection Sensor for Unsafe/Illegal Pedestrian Crossing
Pedestrian safety continues to be a major concern for Las Vegas. Over the past few years an alarming number of pedestrian fatalities have occurred. Many accidents have occurred in places such as Boulder Highway where pedestrians risk crossing many lanes of high-speed traffic with low-illumination to avoid walking the extra mile to a marked crosswalk. The State of Nevada has been examining technologies to help prevent such issues such as LiDAR sensors. While LiDAR is effective, the sensors are expensive, which limits wide-spread deployment. In contrast, camera-based systems are inexpensive and, with modern deep-learning techniques, can accurately detect pedestrians. REU students will develop a complete low-light camera system for detection of unsafe and illegal pedestrian crossings. The students will work with convolutional neural networks (CNNs) to detect and track pedestrians and count the number of illegal crossings. Deep trajectory forecasting techniques will be used to provide a prediction of the intent to cross illegally for advanced warning.
Traffic Signal Information Display
One tool for smoother, more energy efficient driving and improved traffic flow on arterials is the use of signal phase and timing (SPaT) information as generated from a traffic controller. While many intersections in Las Vegas are transmitting SPaT messages, few vehicles use the information as it is relegated to high-end (Audi Traffic Light Information) or self-driving vehicles. The purpose of this project is to develop a low-cost SPaT receiver and display to indicate the amount of time left for the signal to turn green, yellow, or red and to provide speed guidance to minimize wait time at an intersection. The REU students will explore V2X communication protocols to design and develop a radio receiver that can be paired with a Raspberry PI as a lightweight display device. The display will be tested at Las Vegas intersections to characterize the performance in terms of phase accuracy, delay, and distance.
Detection of Acoustic Events in Urban Environment
Detection of acoustic events in urban environments such as vehicle accidents, crowd behaviors, gun shots, etc. are of significant importance in developing safer and smarter cities. Traditionally, these activities are detected based on video/image processing methods. However, given the complex urban spatial constraints, such as congested and occluded roads and privacy issues, video/image based methods have not been pursued. An alternate form of such event detection is to use acoustic signals. However, acoustic methods pose their own challenges, such as background noise, and polyphonic signals make event detection difficult. This work will focus on real-time detection of acoustic events in real urban environments. The REU students will interface a digital microphone to an IoT enabled hardware system and use the python programming language to collect and store various urban acoustic data. This data will be stored in the cloud and then, using cloud tools, audio features will be extracted and machine learning deployed for classification and detection of various urban events – specifically unsafe and critical events.
Enrich Traffic Information with Real Time & Historical Vehicle Data
Autonomous vehicles and semi-autonomous vehicles capture continuous data from various sensors about the surrounding environment. This data is archived by various third-party data warehouse companies. These data are available for self-driving cars in real-time to select optimized routes to mitigate congestion and minimize travel time. In this project, we explore API to collect and visualize the above real-time data, dynamically update traffic patterns, and perform data analytics for optimal routing.
Requirement: Python, JS, etc.
Smart Parking Optimization
Parking lots and garages, especially in the areas where large numbers of vehicles arrive at the same time, have problems with traffic congestion. The goal of this project is to develop an IoT system to provide information about free parking spots and also to optimize the traffic in parking lots and garages, targeting one of the crucial needs of a Smart City. The key components of the system are the driver’s smartphone, the parking app (to be developed as part of this project), an embedded system installed in the car, and cloud service to collect and interchange the data (IoT architecture). The mobile app will monitor the driver’s behavior and detect when the driver is approaching the car. This information will be sent to the cloud and shared with drivers who are searching for a parking spot. The embedded system in the car will include a GPS receiver to post periodic vehicle location to the cloud and sense adjacent parking spots to provide additional information. The system will be able to provide the information about occupancy in each lot and provide live information about the closest parking lot with free spots. Students will work with smartphone programming, cloud technologies (including MQTT, SQL), and embedded systems (BLE, programming, interfacing with sensors).