Jeng S.-T.,CLR Analytics Inc. |
Chu L.,CLR Analytics Inc. |
Chu L.,University of California at Irvine
IEEE Transactions on Intelligent Transportation Systems | Year: 2015
Weigh-in-motion (WIM) has been employed as a major technology to collect heavy vehicles' data on the freeways. Because WIM is one of the most costly and sophisticated data collection systems, how to effectively utilize the valuable WIM data and monitor the performance of WIM stations are particularly important. In this paper, we proposed an innovative and yet practical approach for heavy vehicle tracking that combines the use of both WIM data and the inductive loop signature data. The proposed multilevel vehicle reidentification approach was able to generate promising tracking performance with both inductive loop signatures and WIM data applied. © 2000-2011 IEEE.
Jeng S.-T.,CLR Analytics Inc. |
Chu L.,CLR Analytics Inc.
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014 | Year: 2014
With continuing emphasis on transportation sustainability and fiscal stewardship, utilizing existing loop detector infrastructure to obtain more accurate, reliable, and comprehensive traffic system performance measures is desired by many transportation agencies. We found that the capability of the Inductive Loop Detector (ILD) signature technology to reidentify and classify vehicles along a section of roadway have the potential to provide better performance measures. Therefore, we proposed a high-definition traffic performance monitoring system (Traffic Monitor HD) based on the ILD signature technology and existing loop infrastructure for both freeway and arterial applications. Compared to the traditional performance measurement system, the advantages of the ILD signature technology allow Traffic Monitor HD to provide more comprehensive and accurate performance measurements, including point-based measures (i.e., vehicle counts, classification, and alerts on problematic detectors), section-based measures (i.e., travel time, speed, and estimates on emission), and O-D based measures (i.e., O-D matrix and trip travel time). © 2014 IEEE.
Dong H.,Beijing Jiaotong University |
Wu M.,Beijing Jiaotong University |
Ding X.,Beijing Jiaotong University |
Chu L.,University of California at Irvine |
And 4 more authors.
Transportation Research Part C: Emerging Technologies | Year: 2015
Call detail record (CDR) data from mobile communication carriers offer an emerging and promising source of information for analysis of traffic problems. To date, research on insights and information to be gleaned from CDR data for transportation analysis has been slow, and there has been little progress on development of specific applications. This paper proposes the traffic semantic concept to extract traffic commuters' origins and destinations information from the mobile phone CDR data and then use the extracted data for traffic zone division. A K-means clustering method was used to classify a cell-area (the area covered by a base stations) and tag a certain land use category or traffic semantic attribute (such as working, residential, or urban road) based on four feature data (including real-time user volume, inflow, outflow, and incremental flow) extracted from the CDR data. By combining the geographic information of mobile phone base stations, the roadway network within Beijing's Sixth Ring Road was divided into a total of 73 traffic zones using another K-means clustering algorithm. Additionally, we proposed a traffic zone attribute-index to measure tendency of traffic zones to be residential or working. The calculated attribute-index values of 73 traffic zones in Beijing were consistent with the actual traffic and land-use data. The case study demonstrates that effective traffic and travel data can be obtained from mobile phones as portable sensors and base stations as fixed sensors, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning. © 2015 Elsevier Ltd.
Jeng S.-T.,CLR Analytics Inc. |
Chu L.,CLR Analytics Inc. |
Hernandez S.,University of California at Irvine
Transportation Research Record | Year: 2013
In this study, a new vehicle classification algorithm was developed with inductive loop signature technology. There were two steps to the proposed algorithm. The first step was to use the Haar wavelet to transform and reconstruct inductive vehicle signatures, and the second step was to group vehicles into FHWA vehicle types through the use of the k nearest neighbor (KNN) approach with a Euclidean distance classifier. To determine the proper proportion of the wavelet to apply for reconstruction and feature extraction, transformed signatures were examined with percentages of large components of their corresponding wavelets. To implement the KNN approach, a library of vehicle signature templates for each FHWA vehicle class was composed. The proposed vehicle classification algorithm demonstrated promising classification results, with a 92.4% overall accuracy. The algorithm can be applied to the real world without the concerns about recalibration and transferability that arise with the use of signature data from single loops. Two additional vehicle classification schemes were applied for performance evaluation. For the inductive signature performance evaluation classification scheme, which aimed to facilitate emission analysis and easy interpretation, the overall accuracy was 94.1%. For the axle-based vehicle classification scheme proposed in this project, which aimed to group vehicles by use and the number of axles, the overall accuracy was 93.8%. Future research will focus on refinement of the signature template library for each FHWA vehicle type to further improve the performance of the proposed vehicle classification algorithm. The selection of the value of k for the KNN approach will be investigated also.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.99K | Year: 2013
Weigh-In-Motion (WIM) has been employed as a major technology to collect heavy vehicles’ data on the freeways. Because WIM is one of the most costly and sophisticated data collection system, how to effectively utilize the valuable WIM data, monitor WIM stations' performance, and identify out of calibration stations are especially important. In this project, we propose an innovative and yet practical approach to develop an Inductive Loop Signature-WIM based real-time heavy vehicle tracking system that combines the use of both WIM data and the inductive loop signature data. The integration of inductive loop signature technology offers a low-cost solution to monitor the performance of the WIM stations, identify out-of-calibration stations, and provide groundtruth truck movement data to be used for calibration. Therefore the development of the Inductive Loop Signature-WIM based Detection System will not only reduce the cost of WIM calibration, but also provide the ability to monitor system performance continuously. In this Phase I project, we will demonstrate the proof of concept of the proposed approach in the real world and investigate its potential applications, such as the estimation truck activities and loading distributions and the calibration of WIM stations.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.97K | Year: 2013
With continuing emphasis on transportation sustainability and fiscal stewardship, utilizing existing loop detector infrastructure to obtain more accurate, reliable and comprehensive traffic system performance measures is desired by many transportation agencies. In Phase I study, we found that the capability of the Inductive Loop Detector (ILD) signature technology to re-identify and classify vehicles along a section of roadway creates numerous business opportunities for commercial development. Therefore, in this Phase II project we will design and develop a cost-effective loop signature card for data collection, enhance the vehicle re-identification and classification algorithms investigated in Phase I, and develop a high-definition traffic performance monitoring system (Traffic Monitor HD) for both freeway and arterial applications. Traffic Monitor HD has a web-based interface for real-time traffic monitoring, historical performance analysis, and report. Compared to the traditional performance measurement system, Traffic Monitor HD provides more accurate performance measurements, such as travel time, vehicle class, estimates on emission, and alerts on problematic detectors. To commercialize Traffic Monitor HD, we will develop a commercialization strategy plan to rapidly move the proposed system to widespread commercial use. We will also demonstrate the capability and the applicability of the system in real-world implementations.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 99.63K | Year: 2010
Inductive loop detection systems are currently the most invested technology for obtaining traffic data in the United States, and are widely deployed on most major freeway networks. With the push towards sustainability in transportation, there is an increasing need to obtain further insight into traffic system performance by obtaining more accurate and comprehensive traffic system performance measures. Inductive loop signature-based system has shown the potential to address the needs with the advantage of its direct compatibility with existing traffic controllers and traffic management center operations. This project proposes a real-time inductive loop signature based vehicle re-identification and vehicle classification system. It will demonstrate a real-time classification of vehicles at each station to FHWA classification scheme and a real-time vehicle tracking system between two locations using inductive loop signature technology. A ground-truth system and evaluation procedure will be set up for system assessment, and the rigorous statistic analysis of tracked against ground-truthed vehicles will be performed. Finally, a point-based performance measures system will be developed using the outputs form the proposed vehicle tracking system.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 99.97K | Year: 2011
We propose a Smartphone based system that can integrate information commonly available on a mobile phone and real time traffic signal phasing data to provide assistance to pedestrians and cyclists for safer intersection crossing. GPS accelerometer and digital compass information from a smart phone together with digital maps will be integrated to determine the location and orientation of a user. Real time traffic signal phasing data can be obtained through wireless communication with either a local signal controller or a central traffic management system. The information together can then inform pedestrians and cyclists particularly those with vision or hearing or walking impairment as to when to cross through audible or haptic feedback.An automated ‘pedestrian call’ request can also be sent to the traffic controller wirelessly from the smart phone of registered users after confirming the direction and orientation that the pedestrian is intending to cross. The proposed system will eliminate the need of physically locating and pressing a pushbutton at the intersection crossing and provide supportive information to the pedestrians and cyclists while traveling along the crosswalk.
Agency: Department of Transportation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 149.97K | Year: 2015
The rapid development of automated vehicles has attracted a lot of attentions from the public in recent years. Current studies on automated vehicles mainly focus on microscopic simulations with simple network topologies and driver behaviors, and few has considered to incorporate automated vehicles into macroscopic travel demand models for the analysis in a regional network. In this project, we propose a multiple-resolution approach that allows us to model the impacts of automated vehicles for both transportation and traffic operation analysis. The approach hinges on the development of a capacity adjustment factor (CAF) for automated vehicles, similar to the heavy vehicles adjustment factor used in highway capacity analysis. CAF will be linked to input variables such as roadway facility types, traffic demand levels, and market penetration rates of automated vehicles. CAF will be derived from a microsimulation study, which involves the development of an integrated car-following model for both human drivers and automated vehicles, calibration of the model using NGSIM data, implementation of the model as a plugin in microsimulation. The proposed modeling approach can then be used to analyze the impact of automated vehicles in a regional network through additional traffic assignment runs using the adjusted capacity based on CAF.