Impact of Operational Speed Characteristics of Heavy Vehicles on High-Speed Highways

This thesis explores the safety impact of differential speed limit (DSL) strategy by considering gross vehicle weight (GVW) combined with average speed enforcement (ASE) for heavy vehicles. The study used one-year of Weigh-in-Motion (WIM) data (2014) and one-month of Global Positioning System (GPS) data (Mar 2016) collected from along the Trans-Canada Highway 1 in British Columbia. The research consisted of a data-driven analysis and a two-part simulation analysis. As the DSL investigated was based on GVW, a Modified-Federal Highway Administration (M-FHWA) classification that explicitly considered GVW was tested alongside the FHWA classification regarding average speed and GVW. The simulation analysis assessed the DSL strategy associated with M-FHWA classification and ASE strategys impact on the safety of heavy vehicles. In general, the analyses showed that DSL adopted with M-FHWA classes combined with ASE would be effective in reducing heavy vehicle speed and improving highway safety

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations. To address this methodological need, we use advanced machine-learning techniques and spatial analyses to classify trucks by industry based on activity patterns derived from large streams of truck GPS data. The major components are: (1) derivation of truck activity patterns from anonymous GPS traces, (2) development of a classification model to distinguish trucks by industry, and (3) estimation of a spatio-temporal regression model to capture rerouting behavior of trucks. First, we developed a K-means unsupervised clustering algorithm to find unique and representative daily activity patterns from GPS data. For a statewide GPS data sample, we are able to reduce over 300,000 daily patterns to a representative six patterns, thus enabling easier calibration and validation of the travel forecasting models that rely on detailed activity patterns. Next, we developed a Random Forest supervised machine learning model to classify truck daily activity patterns by industry served. The model predicts five distinct industry classes, i.e., farm products, manufacturing, chemicals, mining, and miscellaneous mixed, with 90% accuracy, filling a critical gap in our ability to tie truck movements to industry served. This ultimately allows us to build travel demand forecasting models with behavioral sensitivity. Finally, we developed a spatio-temporal model to capture truck rerouting behaviors due to weather events. The ability to model re-routing behaviors allows transportation agencies to identify operational and planning solutions that mitigate the impacts of weather on truck traffic. For freight industries, the prediction of weather impacts on truck driver’s route choices can inform a more accurate estimation of billable miles

Integrating Public and Private Data Sources for Freight Transportation Planning

The Moving Ahead for Progress in the 21st Century Act (MAP-21) stipulates that state transportation agencies expand their interest in freight initiatives and modeling to support planning efforts, particularly the evaluation of current and future freight transportation capacity necessary to ensure freight mobility. However, the understanding of freight demand and the evaluation of current and future freight transportation capacity are not only determined by robust models, but are critically contingent on the availability of accurate data. Effective partnerships are clearly needed between the public and private sectors to ensure adequate freight planning and funding of transportation infrastructure at the state and local levels. However, establishing partnerships with firms who are both busy and suspicious of data-sharing, remains a challenge. This study was commissioned by the Texas Department of Transportation (TxDOT) to explore the feasibility of TxDOT entering into a data-sharing partnership with representatives of the private sector to obtain sample data for use in formulating a strategy for integrating public and private sector data sources. This report summarizes the findings, lessons learned, and recommendations formed from the outreach effort, and provides a prototype freight data architecture that will facilitate the storage, exchange, and integration of freight data through a data-sharing partnership.Texas Department of Transportation Research and Technology Implementation Office P.O. Box 5080 Austin, TX 78763-5080Civil, Architectural, and Environmental Engineerin

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

Speed estimation using single loop detector outputs

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way

Aplicaci\'on de tecnolog\'ias IoT en el control y seguimiento de trasporte de carga terrestre

Freight transport of goods and raw materials is a central part of the supply chain in the commercial exchange in Latin America. Control and monitoring of this activity are vital for an efficient economic flow and, more importantly, without losing money. Most of the problems that generate financial losses occur in cargo freight by land. Losses due to changes in the weight of the payload to be transported or fuel/time losses due to capricious changes by the driver on the scheduled route. This work aims to demonstrate use of Internet of Thing (IoT) techniques to propose a prototype of a telemetry system to monitor in real-time the payload weight and location of a cargo truck and become a technological tool that supports the tasks of monitoring and control of the use of cargo trucks, and together with other logistics measures, leads to minimizing economic losses. The development of this project was based on the IoT architecture reference model: an ATmega32u4 microcontroller was used together with a SIM808 GSM and GPS module as the main component of the IoT Node. In addition, Amazon Web Services (AWS) tools were used as an IoT web platform and cloud data storage. The main result was a prototype of a telemetry system to track a cargo truck via the web; the weight and position data are accessible from any device with internet access through a website. Preliminary field tests have shown the proposed system to be an efficient and low-cost option.Comment: in Spanish languag

Aplicación de tecnologías IoT en el control y seguimiento de trasporte de carga terrestre

Freight transport of goods and raw materials is a main part of the supply chain in the commercial exchange in Latin America. Control and monitoring of this activity are vital for an efficient economic flow and more importantly without losing money. Most of the problems that generate economic losses occur in cargo freight by land. Losses due to changes in the weight of the payload to be transported or fuel/time losses due to capricious changes by the driver on the scheduled route. This work aims to demonstrate use of Internet of Thing (IoT) techniques to propose a prototype of a telemetry system to monitor in real-time the payload weight and location of a cargo truck and become a technological tool that supports the tasks of monitoring and control of the use of cargo trucks, and together with other logistics measures, leads to minimizing economic losses. The development of this project was based on the IoT architecture reference model: an ATmega32u4 microcontroller was used together with a SIM808 GSM and GPS module as the main component of the IoT Node. In addition, Amazon Web Services (AWS) tools were used as an IoT web platform and cloud data storage. The main result was a prototype of a telemetry system to track a cargo truck via the web, the weight and position data are accessible from any device with internet access through a website. Preliminary field tests have been successful and have shown the proposed system to be an efficient and low-cost optionEl transporte de carga vía terrestre es una parte importante en la cadena de suministro del comercio en Latinoamérica. El control y seguimiento de esta actividad es vital para un flujo eficiente y sin pérdidas económicas. La mayoría de los problemas son pérdidas por cambios en el peso de la carga útil a transportar o pérdidas de combustible/tiempo por cambios, caprichosos del conductor, en la ruta programada. Este trabajo tiene como objetivo demostrar el uso de técnicas de Internet de las cosas (IoT) para proponer un prototipo de telemetría para monitorear en tiempo real el peso y la ubicación de un camión de carga, y convertirse en una herramienta tecnológica que soporte tareas de logística de control que conlleven a minimizar las pérdidas económicas. El desarrollo de este proyecto se basó en el modelo de referencia de la arquitectura IoT. En el diseño electrónico la estación IoT se usó un microcontrolador Atmega32u4 junto con un módulo SIM808 GSM y GPS como componente principal. Además, como plataforma de almacenamiento y presentación IoT se utilizaron herramientas de Amazon Web Services (AWS). El principal resultado fue un prototipo de un sistema de telemetría para rastrear un vehículo de mediano tonelaje, los datos de peso y posición son accesibles desde cualquier dispositivo con acceso a internet. Las pruebas de campo preliminares han sido satisfactorias y han demostrado que el sistema propuesto es una opción eficiente y de bajo costo para el monitoreo de la posición global y del nivel de carga del camión. La primera etapa del proyecto, se enfocó en el diseño y construcción de un prototipo de IoT para la obtención remota de datos del camión en tiempo real, queda para próximas etapas del proyecto escalar a más nodos y expandir los tiempos de recolección de datos, que servirán para realizar estudios para verificar posibles efectos lógicos y económicos, además de realizar pronósticos con los datos recogidos y así realizar propuestas sobre mejoras en la logística, verificación de rutas, kilometraje y gasto de combustibl

Multi-scale Structural Health Monitoring using Wireless Smart Sensors

Tremendous progress has been made in recent years in the wireless smart sensor (WSS) technology to monitor civil infrastructures, shifting focus away from traditional wired methods. Successful implementations of such WSS networks for full-scale SHM have demonstrated the feasible use of the technology. Much of the previous research and application efforts have been directed toward single-metric applications. Multi-metric monitoring, in combination with physics-based models, has great potential to enhance SHM methods; however, the efficacy of the multi-metric SHM has not been illustrated using WSS networks to date, due primarily to limited hardware capabilities of currently available smart sensors and lack of effective algorithms. This research seeks to develop multi-scale WSSN strategies for advanced SHM in cost effective manner by considering: (1) the development of hybrid SHM method, which combine numerical modeling and multi-metric physical monitoring, (2) multi-metric and high-sensitivity hardware developments for use in WSSNs, (3) network software developments for robust WSSN, (4) algorithms development to better utilize the outcomes from SHM system, and (5) fullscale experimental validation of proposed research. The completion of this research will result in an advanced multi-scale WSS framework to provide innovate ways civil infrastructure is monitored.Financial support for this research was provided in part by the National Science Foundation under NSF Grants No. CMS-0600433 and CMMI-0928886.Ope

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al