Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

TScan: Stationary LiDAR for Traffic and Safety Studies—Object Detection and Tracking

The ability to accurately measure and cost-effectively collect traffic data at road intersections is needed to improve their safety and operations. This study investigates the feasibility of using laser ranging technology (LiDAR) for this purpose. The proposed technology does not experience some of the problems of the current video-based technology but less expensive low-end sensors have limited density of points where measurements are collected that may bring new challenges. A novel LiDAR-based portable traffic scanner (TScan) is introduced in this report to detect and track various types of road users (e.g., trucks, cars, pedestrians, and bicycles). The scope of this study included the development of a signal processing algorithm and a user interface, their implementation on a TScan research unit, and evaluation of the unit performance to confirm its practicality for safety and traffic engineering applications. The TScan research unit was developed by integrating a Velodyne HDL-64E laser scanner within the existing Purdue University Mobile Traffic Laboratory which has a telescoping mast, video cameras, a computer, and an internal communications network. The low-end LiDAR sensor’s limited resolution of data points was further reduced by the distance, the light beam absorption on dark objects, and the reflection away from the sensor on oblique surfaces. The motion of the LiDAR sensor located at the top of the mast caused by wind and passing vehicles was accounted for with the readings from an inertial sensor atop the LiDAR. These challenges increased the need for an effective signal processing method to extract the maximum useful information. The developed TScan method identifies and extracts the background with a method applied in both the spherical and orthogonal coordinates. The moving objects are detected by clustering them; then the data points are tracked, first as clusters and then as rectangles fit to these clusters. After tracking, the individual moving objects are classified in categories, such as heavy and non-heavy vehicles, bicycles, and pedestrians. The resulting trajectories of the moving objects are stored for future processing with engineering applications. The developed signal-processing algorithm is supplemented with a convenient user interface for setting and running and inspecting the results during and after the data collection. In addition, one engineering application was developed in this study for counting moving objects at intersections. Another existing application, the Surrogate Safety Analysis Model (SSAM), was interfaced with the TScan method to allow extracting traffic conflicts and collisions from the TScan results. A user manual also was developed to explain the operation of the system and the application of the two engineering applications. Experimentation with the computational load and execution speed of the algorithm implemented on the MATLAB platform indicated that the use of a standard GPU for processing would permit real-time running of the algorithms during data collection. Thus, the post-processing phase of this method is less time consuming and more practical. Evaluation of the TScan performance was evaluated by comparing to the best available method: video frame-by-frame analysis with human observers. The results comparison included counting moving objects; estimating the positions of the objects, their speed, and direction of travel; and counting interactions between moving objects. The evaluation indicated that the benchmark method measured the vehicle positions and speeds at the accuracy comparable to the TScan performance. It was concluded that the TScan performance is sufficient for measuring traffic volumes, speeds, classifications, and traffic conflicts. The traffic interactions extracted by SSAM required automatic post-processing to eliminate vehicle interactions at too low speed and between pedestrians – events that could not be recognized by SSAM. It should be stressed that this post processing does not require human involvement. Nighttime conditions, light rain, and fog did not reduce the quality of the results. Several improvements of this new method are recommended and discussed in this report. The recommendations include implementing two TScan units at large intersections and adding the ability to collect traffic signal indications during data collection

A Robust Vehicle Detection model for LiDAR sensor using Simulation Data and Transfer Learning Methods

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor

A Novel Collision Avoidance System for a Bicycle

This project focuses on development of a sensing and estimation system for a bicycle to accurately detect and track vehicles for two types of car-bicycle collisions. The two types of collisions considered are collisions from rear vehicles and collisions from right-turning vehicles at a traffic intersection. The collision detection system on a bicycle is required to be inexpensive, small and lightweight. Sensors that meet these constraints are utilized.To monitor side vehicles and detect danger from a right-turning car, a custom sonar sensor is developed. It consists of one ultrasonic transmitter and two receivers from which both the lateral distance and the orientation of the car can be obtained. A Kalman Filter-based vehicle tracking system that utilizes this custom sonar sensor is developed and implemented. Experimental results show that it can reliably differentiate between straight driving and turning cars. A warning can be provided in time to prevent a collision. For tracking rear vehicles, an inexpensive single-beam laser sensor is mounted on a rotationally controlled platform. The rotational orientation of the laser sensor needs to be actively controlled in real-time in order to continue to focus on a rear vehicle, as the vehicle’s lateral and longitudinal distances change. This tracking problem requires controlling the real-time angular position of the laser sensor without knowing the future trajectory of the vehicle. The challenge is addressed using a novel receding horizon framework for active control and an interacting multiple model framework for estimation. The features and benefits of this active sensing system are illustrated first using simulation results. Then, extensive experimental results are presented using an instrumented bicycle to show the performance of the system in detecting and tracking rear vehicles during both straight and turning maneuvers

Cyclist Stress and Biometric Sensing in Naturalistic Cycling

Cycling is gaining traction in the United States as a mode of transportation due to its plethora of benefits. However, cycling still makes up a very low percentage of modal share. One major hurdle to increased cycling modal share is that people feel cycling is unsafe and stressful. Many studies have considered cyclists’ stress, but these studies have not allowed participants to self-define their stressors during a cycling experience. This dissertation fills this gap by combining in-ride, open-ended surveys/interviews with naturalistic cycling methods. Cyclists wore eye tracking glasses and rode instrumented bicycles equipped with GPS and LiDAR to allow researchers to gain a deeper knowledge of their surroundings and reaction to them. This dissertation uses different combinations of sensors and survey techniques to explore cyclists’ stress and demonstrate the value of these methods. The first study uses in-ride surveys and instrumented bicycle data to explore the top causes of cyclists’ stress in an emerging and an established cycling city. The second study uses eye tracking glasses and survey techniques to better understand cyclists’ gaze behavior with varying stress, complexity, and stated skill. The last study uses eye tracking and survey techniques as well but uses them to give practical guidance for cyclist-focused pavement asset management. Various data analysis methods are used to assess these data individually and in combination including thematic analysis, GPS analysis, exploratory eye tracking measures, frame-by-frame video analysis, descriptive, and inferential statistics. These studies demonstrate that cyclists prefer separated infrastructure with smooth pavements. Although there were some differences by location or rider characteristics, the preferences for separated, smooth facilities are largely universal among cyclists. Although what caused cyclists stress was mostly consistent, gaze behavior did change with stated skill in unexpected ways demonstrating that researchers cannot assume cyclists’ gaze behavior will match what is known about drivers’ gaze behavior. These findings can contribute to bike infrastructure design and maintenance and the methods have opened the door to plenty of opportunity for future research into cyclist and other road user behavior.Ph.D

Smart streetlights: a feasibility study

The world's cities are growing. The effects of population growth and urbanisation mean that more people are living in cities than ever before, a trend set to continue. This urbanisation poses problems for the future. With a growing population comes more strain on local resources, increased traffic and congestion, and environmental decline, including more pollution, loss of green spaces, and the formation of urban heat islands. Thankfully, many of these stressors can be alleviated with better management and procedures, particularly in the context of road infrastructure. For example, with better traffic data, signalling can be smoothed to reduce congestion, parking can be made easier, and streetlights can be dimmed in real time to match real-world road usage. However, obtaining this information on a citywide scale is prohibitively expensive due to the high costs of labour and materials associated with installing sensor hardware. This study investigated the viability of a streetlight-integrated sensor system to affordably obtain traffic and environmental information. This investigation was conducted in two stages: 1) the development of a hardware prototype, and 2) evaluation of an evolved prototype system. In Stage 1 of the study, the development of the prototype sensor system was conducted over three design iterations. These iterations involved, in iteration 1, the live deployment of the prototype system in an urban setting to select and evaluate sensors for environmental monitoring, and in iterations 2 and 3, deployments on roads with live and controlled traffic to develop and test sensors for remote traffic detection. In the final iteration, which involved controlled passes of over 600 vehicle, 600 pedestrian, and 400 cyclist passes, the developed system that comprised passive-infrared motion detectors, lidar, and thermal sensors, could detect and count traffic from a streetlight-integrated configuration with 99%, 84%, and 70% accuracy, respectively. With the finalised sensor system design, Stage 1 showed that traffic and environmental sensing from a streetlight-integrated configuration was feasible and effective using on-board processing with commercially available and inexpensive components. In Stage 2, financial and social assessments of the developed sensor system were conducted to evaluate its viability and value in a community. An evaluation tool for simulating streetlight installations was created to measure the effects of implementing the smart streetlight system. The evaluation showed that the on-demand traffic-adaptive dimming enabled by the smart streetlight system was able to reduce the electrical and maintenance costs of lighting installations. As a result, a 'smart' LED streetlight system was shown to outperform conventional always-on streetlight configurations in terms of financial value within a period of five to 12 years, depending on the installation's local traffic characteristics. A survey regarding the public acceptance of smart streetlight systems was also conducted and assessed the factors that influenced support of its applications. In particular, the Australia-wide survey investigated applications around road traffic improvement, streetlight dimming, and walkability, and quantified participants' support through willingness-to-pay assessments to enable each application. Community support of smart road applications was generally found to be positive and welcomed, especially in areas with a high dependence on personal road transport, and from participants adversely affected by spill light in their homes. Overall, the findings of this study indicate that our cities, and roads in particular, can and should be made smarter. The technology currently exists and is becoming more affordable to allow communities of all sizes to implement smart streetlight systems for the betterment of city services, resource management, and civilian health and wellbeing. The sooner that these technologies are embraced, the sooner they can be adapted to the specific needs of the community and environment for a more sustainable and innovative future

Bridges of the BeltLine

As currently realized, the Atlanta BeltLine weaves under, over, and through a multitude of overpasses, footbridges, and tunnels. As in any city, this significant feature is simultaneously an asset and a potential hazard. These types of structures are "vulnerable critical facilities" that should be included in emergency risk assessments and mitigation planning (FEMA, 2013). As such, the Bridges of the BeltLine project was proposed as a mixed-methods study to understand how people's movement along the BeltLine can inform emergency management mitigation, planning, and response. Understanding pedestrian flow in cities has been underfunded and understudied but is nonetheless critical to city infrastructure monitoring and improvement projects. This study focused on developing inexpensive, low-power consumption sensors capable of detecting human presence while preserving privacy, as well as a survey designed to collect data that the sensors cannot. The survey data were intended to describe BeltLine users, querying on demographics, reasons, frequency, duration of use, and mode of travel to and on the BeltLine. After conferring with the Atlanta BeltLine, Inc. (ABI) leadership, it became apparent that ABI's primary interest is in understanding which communities are being served by the BeltLine and whether it has changed commuting and travel behaviors or created new demand. As a result, the project's original focus on emergency management was expanded to explore which communities are being served and for what kind of use. As such, the project's revised objective was two-fold: to facilitate understanding of (a) whether the BeltLine is serving the adjacent communities and purpose of use and (b) to inform emergency mitigation, planning, and response.This research was made possible by a grant from Georgia Tech's Executive Vice President of Research, Small Bets Seed Grants program, with supplemental funding from the Center for the Development and Application of Internet of Things Technologies (CDAIT)

Surrogate safety measures and traffic conflict observations.

The chapter primarily focuses on observing traffic conflicts (also known as near-accidents) as a site-based road safety analysis technique. Traffic conflicts are a type of surrogate safety measure. The term surrogate indicates that non-accident-based indicators are used to assess VRU safety instead ofthe more traditional approach focusing on accidents (see chapter 2). The theory underpinning surrogate safety measures is briefly described, followed by a discussion on the characteristics of the traffic conflict technique. Next, guidelines for conducting traffic conflict observations using trained human observers or video cameras are presented. Chapter 4 concludes with examples of the use of the traffic conflict technique in road safety studies focusing on VRUs