Real-Time Precision LiDAR Transportation Density Mapping in the UCLA-Westwood Vicinity

Research Lead: Chee Wei Wong

UC Campus(es): UCLA

Problem Statement: Metropolitan areas across the world use travel demand models and expensive data collected from various labor-intensive sources to forecast road traffic in order to assist decision making on regional transportation system operation, infrastructure investment, environmental protection and land use planning. The ability of the model to produce base year volume estimates within acceptable ranges of tolerance compared to actual ground counts is essential to validate the entire travel demand model. However, metropolitan transportation demand models rely primarily on expensive and infrequent household door-to-door surveys and limited observed data from sensors. Other GPS-based data such as mobile phones and Google maps only provide coarse and/or fragmented data due to decentralized collection and data ownership, often extremely high data processing cost, and inadequate precision on how a subset of travelers and vehicles move around in the city. Moreover, the currently available data is rather inadequate in providing observations on the vehicle type, non-vehicle traffic, and vehicular occupancy. Such deficiencies are critical barriers to further improvement of the transportation demand model as multimodal mixed traffic and high-occupancy vehicles (HOV) are increasingly more important in planning.

Project Description: Recently UCLA has been developing state-of-the-art chip-scale LiDAR sensors for autonomous self-driving vehicles and improved driving safety. The chip-scale LiDAR sensors, with our high-performance precision laser characteristics enable next-generation transportation density mapping across metropolitan Los Angeles. We will use the LiDAR sensor to map out real-time multimodal traffic and network characteristics in multiple road links and intersections in the UCLA-Westwood area. To evaluate this project, we will compare the LiDAR datasets obtained versus prior data used by SCAG, estimate the potential benefits of the new generation of scalable LiDAR precision mapping to SCAG’s regional transportation model, and evaluate the potential of low-cost LiDAR inputs into the transportation planning process. By understanding the Westwood traffic characteristics in finer precision and in real-time along the high-density corridors, we provide previously unavailable inputs to the SCAG transportation planning model and to the intelligent traffic optimization of the Westwood area. The proposed research explores providing previously unavailable data at a low cost. If scalable, this would greatly assist SCAG in acquiring big data for future transportation demand models.

Status: In Progress

Budget: $34,000