Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short gaps (less than 5 seconds) can be recovered with simple linear regression, and longer gaps are recovered with the proposed method that makes use of car following models calibrated by assigning weights to known points based on proximity to the gaps. Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps' calibrated model yielded the best result

Estimating Freeway Traffic Volume Using Shockwaves and Probe Vehicle Trajectory Data

Probe vehicle data are increasingly becoming the primary source of traffic data. In current practice, traffic volumes and speeds are collected from inductive loop or similar devices. As probe vehicle data become more widespread, it is imperative that methods are developed so that traffic state estimators like speed, density and flow can be derived from probe vehicle data as well. In this paper, a methodology to estimate traffic flow on a freeway based on probe vehicle trajectory data combined with traffic shockwave theory is proposed. In essence, probe vehicle trajectory can indicate the free-flowing and congested regimes. By using LWR kinematic wave model, a shockwave can be identified that separates both regimes. From the formation of the shockwave, flows for each regime are estimated. To identify the shockwave, k-means clustering is applied to the data. When applied to simulated data, the error of the estimated flow during free-flow ranges from -9% to 1% with an average of -5%. The estimated flow during congestion has an error of 0%. Based on the results, this paper shows that the proposed method can predict traffic flow with a reasonable accuracy under congested and free-flow conditions. © 2017 The Authors

Guidelines for Using StreetLight Data for Planning Tasks

The Virginia Department of Transportation (VDOT) has purchased a subscription to the StreetLight (SL) Data products that mainly offer origin-destination (OD) related metrics through crowdsourcing data. Users can manipulate a data source like this to quickly estimate origin-destination trip tables. Nonetheless, the SL metrics heavily rely on the data points sampled from smartphone applications and global positioning services (GPS) devices, which may be subject to potential bias and coverage issues. In particular, the quality of the SL metrics in relation to meeting the needs of various VDOT work tasks is not clear. Guidelines on the use of the SL metrics are of interest to VDOT. This study aimed to help VDOT understand the performance of the SL metrics in different application contexts. Specifically, existing studies that examined the potential of SL metrics have been reviewed and summarized. In addition, the experiences, comments, and concerns of existing users and potential users have been collected through online surveys. The developed surveys were primarily distributed to VDOT engineers and planners as well as other professionals in planning organizations and consultants in Virginia. Their typical applications of the SL metrics have been identified and feedback has been used to guide and inform the design of the guidelines. To support the development of a set of guidelines, the quality of the SL metrics has been independently evaluated with six testing scenarios covering annual average daily traffic (AADT), origin-destination trips, traffic flow on road links, turning movements at intersections, and truck traffic. The research team has sought ground-truth data from different sources such as continuous count stations, toll transaction data, VDOT’s internal traffic estimations, etc. Several methods were used to perform the comparison between the benchmark data and the corresponding SL metrics. The evaluation results were mixed. The latest SL AADT estimates showed relatively small absolute percentage errors, whereas using the SL metrics to estimate OD trips, traffic counts on roadway segments and at intersections, and truck traffic did not show a relatively low and stable error rate. Large percentage errors were often found to be associated with lower volume levels estimated based on the SL metrics. In addition, using the SL metrics from individual periods as the input for estimating these traffic measures resulted in larger errors. Instead, the aggregation of data from multi-periods helped reduce the errors, especially for low volume conditions. Depending on project purposes, the aggregation can be based on metrics of multiple days, weeks, or months. The results from the literature review, surveys, and independent evaluations were synthesized to help develop the guidelines for using SL data products. The guidelines focused on five main aspects: (1) a summary for using SL data for typical planning work tasks; (2) general guidance for data extraction and preparation; (3) using the SL metrics in typical application scenarios; (4) quality issues and calibration of the SL metrics; and (5) techniques and tools for working with the SL metrics. The developed guidelines were accompanied with illustrative examples to allow users to go through the given use cases. Based on the results, the study recommends that VDOT’s Transportation and Mobility Planning Division (TMPD) should encourage and support the use of the guidelines in projects involving SL data, and that TMPD should adopt a checklist (table) for reporting performance, calibration efforts, and benchmark data involved in projects that use the SL metrics

Java Computer Animation for Effective Learning of the Cholesky Algorithm with Transportation Engineering Applications

In this paper, the well-known Cholesky Algorithm (for solving simultaneous linear equations, or SLE) is re-visited, with the ultimate goal of developing a simple, user-friendly, attractive, and useful Java Visualization and Animation Graphical User Inter-face (GUI) software as an additional teaching tool for students to learn the Cholesky factorization in a step-by-step fashion with computer voice and animation. A demo video of the Cholesky Decomposition (or factorization) animation and result can be viewed online from the website: http://www.lions.odu.edu/~imako001/cholesky/demo/index.html. The software tool developed from this work can be used for both students and their instructors not only to master this technical subject, but also to provide a dynamic/valuable tool for obtaining the solutions for homework assignments, class examinations, self-assessment studies, and other coursework related activities. Various transportation engineering applications of SLE are cited. Engineering educators who have adopted “flipped classroom instruction” can also utilize this Java Visualization and Animation software for students to “self-learning” these algorithms at their own time (and at their preferable locations), and use valuable class-meeting time for more challenging (real-life) problems’ discussions. Statistical data for comparisons of students’ performance with and without using the developed Java computer animation are also included

Path Clearance for Emergency Vehicles Through the Use of Vehicle-to-Vehicle Communication

The study described in this paper evaluated and tested a new strategy to enable emergency response vehicles (EVs) to navigate through congestion at signalized intersections more efficiently. The proposed strategy involves the use of vehicle-to-vehicle communication to send messages to alert vehicles to the approach of the EV and to provide specific instructions on maneuvering to allow the EV to proceed through congested signalized intersections as quickly as possible. This movement is achieved by creation of a split in the vehicle queue in one lane at a critical location to allow the EV to proceed at its desired speed but minimize the disruption to the rest of the traffic. The proposed method uses kinematic wave theory (i.e., shock wave theory) to determine the critical point in the vehicle queue. The proposed method is simulated in a microscopic traffic simulator for evaluation. The results show that this strategy can significantly shorten the travel time for EVs through congested signalized intersections

Supporting Transportation System Management and Operations Using Internet of Things Technology

Low power wide-area network (LPWAN) technology aims to provide long range and low power wireless communication. It can serve as an alternative technology for data transmissions in many application scenarios (e.g., parking monitoring and remote flood sensing). In order to explore its feasibility in transportation systems, this project conducted a review of relevant literature to understand the current status of LPWAN applications. An online survey that targeted professionals concerned with transportation was also developed to elicit input about their experiences in using LPWAN technology for their projects. The literature review and survey results showed that LPWAN’s application in the U.S. is still in an early stage. Many agencies were not familiar with LPWAN technology, and only a few off-the-shelf LPWAN products are currently available that may be directly used for transportation systems. To conceptually explore data transmission, a set of lab tests, using a primary LPWAN technology, namely LoRa, were performed on a university campus area as well as in a rural area. The lab tests showed that several key factors, such as the mounting heights of devices, distance between the gateway and sensor nodes, and brands of devices affected the LPWAN’s performance. Building upon these efforts, the research team proposed a high-level field test plan for facilitating a potential Phase 2 study that will address primary technical issues concerning the feasibility of transmitting data of different sizes, data transmission frequency, and transmission rate, deployment requirements, etc

Backward Dijkstra Algorithms for Finding the Departure Time Based on the Specified Arrival Time for Real-Life Time-Dependent Networks

A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “Dynamic ” Networks is considered in this study. Although shortest path (SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network’s connectivity, a concept of Time Delay Factor (TDF) combining with a “general piece- wise linear function” to describe the link cost as a function of time for Non-FIFO links’ costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale (academic) networks as well as large- scale (real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks’ connectivity