A Dynamic Data Driven Application System for Vehicle Tracking

AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described

Regional Emission Analysis Using Travel Demand Models and MOVES-Matrix

DTRT13-G-UTC29Citation: Xiaodan Xu, Haobing Liu, Yanzhi \u201cAnn\u201d Xu, Michael O. Rodgers, and Randall L. Guensler (2018) Regional Emission Analysis using Travel Demand Models and MOVES-Matrix, 97th Annual Meeting of the Transportation Research Board, Washington, D.C., January 7-11, 2018.Travel demand models (TDM) are developed by Metropolitan Planning Organizations (MPOs) for analyzing regional travel patterns but are often used to prepare activity inputs for use with the U.S. Environmental Protection Agency\u2019s (EPA) MOtor Vehicle Emission Simulator (MOVES) emissions model for air quality conformity analysis. This latter application requires modelers to either prepare multiple MOVES runs for various scenarios, or to develop their own pre- and post-processors for emission modeling. Both approaches involve a cumbersome and time consuming process

Combined Effect of Changes in Transit Service and Changes in Occupancy on Per-Passenger Energy Consumption

69A3551747114Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in MARTA service configurations by reviewing the pre-pandemic vs. during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates. MARTA automated passenger count (APC) data were appended to the routes, and the energy use per passenger mile was compared across routes for the three periods. The results showed that the coupled effect of shift in transit frequency and decrease in ridership from 2019 to 2020 increased route-level energy use for more than 87% of the routes and per-passenger mile energy use for more than 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about the pre-pandemic level, but per-passenger energy use remained higher than pre-pandemic for more than 91% of the routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, per-passenger energy use depends on both trip service and ridership. The results also indicated a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service

Understanding the Emission Impacts of High-Occupancy Vehicle (HOV) to High-Occupancy Toll (Hot) Lane Conversions: Experience From Atlanta, Georgia

DTRT13-G-UTC29Open Access Select Journal: Yanzhi (Ann) Xu, Haobing Liu, Michael O. Rodgers, Angshuman Guin,Michael Hunter, Adnan Sheikh & Randall Guensler (2017) Understanding the emission impactsof high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experiencefrom Atlanta, Georgia, Journal of the Air & Waste Management Association, 67:8, 910-922, DOI: https://doi.org/10.1080/10962247.2017.1302518Converting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011

An Incident Detection Algorithm Based On a Discrete State Propagation Model of Traffic Flow

Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance. This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.Ph.D.Committee Co-Chair: Billy Williams; Committee Co-Chair: Karen Dixon; Committee Member: Adjo Amekudzi; Committee Member: John D. Leonard II; Committee Member: Yichang (James) Tsa

Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region

Community-based carpooling has the potential to alleviate traffic congestion and reduce the transportation carbon footprint. Once technology, communication, demographic, and economic barriers are overcome, community-based carpooling can be fully exploited. One of the major barriers to implementation is the difficulty of optimizing carpool formation in large systems. This study utilizes two different methods to solve the carpooling optimization problem: 1) bipartite algorithm and 2) integer linear programming. The bipartite method determines the maximum number of carpooling pairs given acceptable reroute costs and travel delays. The linear programming method defines the most optimal performance that minimizes the most vehicular travel mileage. These two methods are carefully compared to evaluate the carpooling potentials among single-occupancy vehicles based on the output of activity-based model’s (ARC ABM) home-to-work single-occupancy vehicle (SOV) trips that can be paired together towards designated regional employment centers. The experiment showed that under strict assumptions, an upper bound of around 13.6% of such trips could carpool together. The results are promising in terms of higher-than-anticipated carpool match rates and the predicted decrease in total vehicle mileage. Moreover, the framework is flexible enough with the potential to act as a simulation testbed, to optimize vehicular operations, and to match potential carpool partners in real-time.View the NCST Project Webpag

Assessment of the Current Status of Incident Detection Algorithms: Results of a Nationwide Survey

Use of automatic incident detection algorithms (AIDA) in advanced freeway management systems has been sporadic and scarce. The current study investigates the causes of such limited implementation. A survey was carried out among the system managers, operators and end users, as well as the decision makers who set the operational policies and the priorities for future system enhancements. The survey responses point to a general consensus that the unacceptably high rates of false alarms generated by available incident detection algorithms is the major deterrent. This study not only provides an understanding of the causes of the limited implementation of incident detection algorithms, but also allows a direct comparison between the conventional incident detection technologies and automatic incident detection technology on the basis of their performance. Despite the lackluster performance of AIDA to date, 90% of the survey respondents feel that the current methods of incident detection are insufficient either at present (70%) or will be so in the future (20%). This finding alone motivates a need to redouble research efforts aimed at developing robust and accurate automatic detection methods. In this regard, the paper presents promising directions to overcome the past AIDA deficiencies

Using Machine Learning to Identify High Impact Incidents in Automatic Incident Detection (AID) System Generated Alarms

Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), January 27-28, 2020, Georgia Tech Global Learning Center, Atlanta, GA.The Career, Research, and Innovation Development Conference (CRIDC) is designed to equip on-campus and online graduate students with tools and knowledge to thrive in an ever-changing job market.Video-based Automatic Incident Detection (AID) technologies have been used in the past by Traffic Management Agencies to aid Incident Management. While significant improvements in video quality and computing resources have substantially improved the potential accuracy and efficiency of video-based AID, AID technologies typically still struggle to separate recurrent congestion-related stoppage of vehicles from incident related stoppages. Consequently, the number of false alarms (or non-critical alarms) remains unmanageably high. This study develops a machine learning framework for developing consolidation strategies to minimize false and non-critical alarms and associates confidence values with the alarms, thereby allowing operators to focus on higher confidence alarms during busy periods. The study first investigates the clustering and evolution patterns of the appearance of alarms over time and space. Then it uses this information in the development of a cluster identification algorithm with both spatial and temporal datasets. Finally, the study develops a method for selection of optimal parameters of the machine learning algorithm to separate the alerts for potential high-impact incidents from the alerts related to congestion and other non-critical stops or slowdowns. The results indicate a massive reduction in non-critical alerts without a significant reduction in detection rate or time-to-detect the incidents. While the framework has been used with a video-based AID system, the framework has been developed with interoperability in mind and has the potential to be applicable across all types of AID systems.GDOT Research Project 17-20 (Georgia. Department of Transportation

Energy and Air Quality Impacts of Truck-Only Lanes: A Case Study of Interstate 75 Between Macon and McDonough, Georgia

Since heavy-duty truck operations can significantly affect traffic congestion, especially on road grade, the creation of exclusive lanes for trucks has been viewed as a potential alternative to reduce congestion delay, fuel consumption, and emissions. However, few studies have rigorously evaluated the effectiveness of truck-only lanes in achieving these benefits. This study demonstrates a model framework that combines a microscopic traffic simulation with emissions and microscale dispersion models to quantify the potential impacts of truck-only lanes on fuel consumption, emissions, and near-road pollutant concentrations. As a case study, the framework was used to evaluate a proposed $2 billion project to construct 40-miles of truck-only lanes on Interstate 75 (I-75) between Atlanta and Macon, Georgia (USA).The findings of this study suggest that truck-only lanes could significantly improve the traffic flow, and reduce energy, emissions, and pollutant concentrations. The research team expects that the extensive simulation results of this study help to understand the performance of truck-only lanes on a large-scale network with a heavy mixture of truck and general purpose lane traffic. The methodology and framework developed in this study can be effectively and efficiently applied to a wide variety of scenarios to evaluate the environmental impacts of other transportation projects under various conditions.View the NCST Project Webpag