Development Of Truck Route Choice Data Using Truck Gps
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Development Of Truck Route Choice Data Using Truck Gps
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Author : Mohammadreza Kamali
language : en
Publisher:
Release Date : 2015
Development Of Truck Route Choice Data Using Truck Gps written by Mohammadreza Kamali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Transportation categories.
In the second part of the thesis, the methodologies developed in the first part are implemented in an FDOT sponsored project entitled "GPS Data for Truck-Route Choice Analysis of Port Everglades Petroleum Commodity Flows". This project aims to use truck-GPS data from ATRI to derive petroleum tanker trucks' travel path (or route) information, describing the routes that the tanker trucks take to travel from Port Everglades to their final delivery points.
Truck Route Choice Modeling Using Large Streams Of Gps Data
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Author : Divyakant Tahlyan
language : en
Publisher:
Release Date : 2017
Truck Route Choice Modeling Using Large Streams Of Gps Data written by Divyakant Tahlyan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Truck Gps Data In Freight Planning
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Author : Zun Wang
language : en
Publisher:
Release Date : 2014
Truck Gps Data In Freight Planning written by Zun Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Efficient and reliable goods movement via our nation's highway system is critical to the nation's economy and quality of life. Truck mobility is one of the key performance measures for evaluating the conditions of goods movement and supporting freight planning. Truck GPS data can be useful in developing truck mobility measures and providing insights into freight planning. This dissertation employs truck GPS data and proposes a set of methodologies for measuring and forecasting truck mobility performance, with particular emphases on truck travel time and travel time reliability. It also examines how GPS data can be used to support freight planning, using the analysis of impacts of a tolling project on truck mobility and routing as a case study. The first part of this dissertation investigates how to measure truck travel time reliability given the characteristics of GPS data. An improved spot-speed distribution based travel time reliability measure is proposed. The proposed approach is compared with a number of commonly applied reliability measures. The correlations among these measures reveal that the reliability measures are not highly correlated, demonstrating that different measures provide different conclusions for the same underlying data and traffic conditions. The author presents recommendations of the appropriate measures for different applications. Quantitative freight project prioritization processes require both pre- and post-investment truck mobility performance. Therefore, the second part of this dissertation develops quantitative methods for forecasting truck specific travel time and travel time reliability. For travel time prediction, a speed-density based approach is proposed to predict truck travel time associated with segment density changes. Traffic regimes are segmented using a cluster analysis approach. The travel time estimates are compared with two widely applied traditional methodologies. The results demonstrate that the proposed method is able to estimate more accurate travel times. For reliability prediction, we analyze the changes of GPS spot speed distribution in response to different traffic conditions. A relationship between truck spot speed distribution coefficient of variation and segment density is proposed to forecast reliability. The approach is transferable and sheds a light on forecasting travel time reliability. The third part of this dissertation focuses on examining how GPS data can be used to assist freight planning. The SR-520 toll bridge in the City of Seattle, Washington is selected as the case study. We quantify the toll project impacts on truck mobility and route choice. Truck GPS data is used to evaluate route choice and travel speed along SR-520 and the alternate toll-free route I-90. A logit model is developed to determine the influential factors in truck routing. The results indicate that travel time, travel time reliability and toll rate are all influential factors during both peak and off-peak periods. The values of truck travel time during different time periods are estimated, and the values vary with the definition of peak and off-peak periods. This dissertation provides decision makers with useful guidance and information on using GPS data for truck mobility measurement and forecasting. It also demonstrates the capability of GPS data in supporting freight planning.
Modelling Long Haul Truck Route Choice In Ontario
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Author : Syed Ubaid Ullah Ali
language : en
Publisher:
Release Date : 2020
Modelling Long Haul Truck Route Choice In Ontario written by Syed Ubaid Ullah Ali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
A route choice model is developed in this thesis to explain and predict long-haul truck vehicle movements in Ontario. An algorithm is devised to process approximately 58,000 observed trips from GPS data in ArcGIS to establish variable choice sets based on an optimal commonality factor that measures route overlap. Novel implications of the commonality factor add to the existing literature. Route characteristics are next used to estimate a C-logit model. Results indicate that truck drivers are more likely to select routes exhibiting lower minimum travel times, more freeway and Highway 401 usage, more diesel stations, and fewer intersections. The travel time is the most dominant variable based on measurements of elasticity. Two scenarios are tested using the final model to determine routing changes due to increased travel time on Highway 401 and other freeways. Further detailed scenarios can be used to predict long-haul trucking patterns for future transportation planning purposes.
Investigating Route Choices And Driving Behavior Using Gps Collected Data
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Author : Jianhe Du
language : en
Publisher:
Release Date : 2005
Investigating Route Choices And Driving Behavior Using Gps Collected Data written by Jianhe Du and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Electronic dissertations categories.
The overall objective of this research is to collect real world travel route data using Global Positioning System (GPS) receivers and to develop the models needed to use these data in route choice and other travel behavior research. To achieve the goal three specific analyses are conducted. First, a GIS model was developed to divide the data stream recorded by the in-vehicle GPS receivers into individual trips with the start and end point of the trip being specifically identified. Second, a spatial model was developed to change the typology of the routes (or trips) from representation as a series of points into a series of continuous network links. Automating this data processing will allow analysis of larger datasets for more generalizable results. Third, travel time on each road link in the entire network was estimated using the sparse sample of GPS travel data (256 vehicles each for 10 days spreading over the 18 month study period) as travel time probes. This model is necessary so that the link travel times on each alternative routes faced by the drivers for each trip are known by researchers. This knowledge of the full network travel times, which has not been available in any previous research, will allow for the generation of alternative routes and comparison with the chosen routes to determine the relative influences of different factors on route choice. One specific unique aspect of this work is that data for calibration and evaluation of models were available. The evaluations of the models indicated which combination of parameters was best. The trip dividing model correctly identified 94% of the trips. The accuracy level of the point-to-link data conversion model was 95%. The average difference between the GPS recorded travel time and the estimated travel time for a trip is 70.8 seconds for the 12, 767 trips (average trip length 5,226 meters). Overall, this research provides the first highly reliable and fully evaluated series of GIS models to automatically process GPS collected travel route data. The results will increase the confidence and reliability of GPS usage for route choice research and other transportation planning exercises.
Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data
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Author : Taslima Akter
language : en
Publisher:
Release Date : 2019
Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data written by Taslima Akter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Freight and freightage categories.
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.
Developing Freight Performance Measures To Address Map 21 Objectives Using Truck Gps Data
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Author : Maria Flaskou
language : en
Publisher:
Release Date : 2016
Developing Freight Performance Measures To Address Map 21 Objectives Using Truck Gps Data written by Maria Flaskou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Goods are moved across the US by trucks on a daily basis. These trucks affect traffic conditions on the roadway network and contribute significantly in congestion and air pollution. Thus, it is imperative to estimate truck trip data and identify segments of highly congested corridors as to propose future improvements. The past few years private and public transportation agencies utilize Global Positioning System (GPS) devices installed in trucks to gather information about their travel patterns. The main objective of this study is to develop a methodology for processing raw GPS data and to develop freight performance measures (FPMs). The algorithms proposed are used to estimate bi-directional link speeds, to analyze truck trips and develop Origin-Destination (OD) matrices between Traffic Analysis Zones (TAZs). A case study for the state of Tennessee (TN) is presented in the last part on how the output of these algorithms can be used to calibrate the & beta; factors between zones in Trip Distribution's Gravity Model.
Urban Informatics
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Author : Wenzhong Shi
language : en
Publisher: Springer Nature
Release Date : 2021-04-06
Urban Informatics written by Wenzhong Shi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-06 with Social Science categories.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Analysis Of The Drayage Truck Travel Patterns On I 710 Highway By Processing Gps Data For The Clean Truck Program
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Author : Genís Busquets Arpa
language : en
Publisher:
Release Date : 2020
Analysis Of The Drayage Truck Travel Patterns On I 710 Highway By Processing Gps Data For The Clean Truck Program written by Genís Busquets Arpa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This project arises due to the need to reduce environmental impact caused by drayage trucks from the ports of Los Angeles and Long Beach operating with freight transportation to warehouses in California. For this purpose, the study is focused on the analysis on I-710, the main freeway that links the port area with Los Angeles, which has become the major source of air pollution, caused mainly by diesel trucks. To reduce these levels of contamination, the I-710 Corridor Project includes a Clean Truck Incentive Program to encourage fleet turnover from diesel to Zero Emission / Nearly Zero Emission trucks. To demonstrate that these funded trucks are going to provide the necessary emission reduction benefits, the vehicle miles traveled (VMT) on I-710 must be determined. The main objective of this project is to develop a study method that allows analysis of drayage truck flows on I-710 from a raw GPS trace data set. The project is divided in two parts. The first part consists of converting GPS traces into a set of potential loading and unloading stops made by trucks. Determining these origins and destinations allows for the calculation of routes taken on the California road network, and consequently the analysis of their routes on I-710. The second part involves developing a custom Iterative Proportional Fitting (IPF) method, which allows scaling number of trips in the GPS data sample to total trips, according to a simulation model based on travel demand activity. The results obtained through utilizing trip length distributions have shown that the methodology developed for calculating routes and the customized method of the IPF are valid for route analysis. Although the methodologies employed have been shown to be feasible, it has been found that the available GPS dataset is not representative enough to perform the VMT analysis on I-710. Therefore, the results obtained as shown in this project are only illustrative due to these limitations of the sample. This is detected when, after scaling trips, the scaled GPS data sample only represents 10% of total miles traveled on I-710, so it does not allow reliable results to be obtained. The conclusions obtained are based on the fact that the study methods developed are valid to obtain analysis of the routes on the road network and also on I-710. But it is detected that reliable results cannot be obtained because the GPS data have oversampled the short trips and therefore are not representative of total population. With a larger data sample with a greater number of trips on I-710 (that ITS-Irvine researchers are currently in negotiations to obtain) it is believed that the developed methodologies could be applied to obtain useful results with high degree of confidence.
Using Truck Gps Data For Freight Performance Analysis In The Twin Cities Metro Area
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Author : Chen-Fu Liao
language : en
Publisher:
Release Date : 2014
Using Truck Gps Data For Freight Performance Analysis In The Twin Cities Metro Area written by Chen-Fu Liao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Freight and freightage categories.
Building on our previous efforts to study freight mobility and reliability, a GPS-based data analysis methodology was developed to study the freight performance of heavy commercial trucks along 38 key freight corridors in the Twin Cities metropolitan area (TCMA). One year of truck GPS data collected in 2012 was obtained from American Transportation Research Institute (ATRI) to study freight mobility and reliability. Several performance measures, such as truck mobility, delay, and reliability index, were computed and analyzed by route, roadway segment, and time of day. For data quality and reliability verification, average truck speed and hourly volume percentage computed from the truck GPS data were validated with weigh-in-motion (WIM) and automatic traffic recorders (ATR) data at selected locations. The GPS based freight analysis methodology offers potential opportunities for freight planners and managers to generate reliable measures in a timely manner. The resulting performance measures indicate that these measures derived from truck GPS data can be used to support the USDOT performance measure initiative and support regional surface freight planner in identifying freight bottlenecks, infrastructure improvement needs, and operational strategies to promote efficient freight movement. FHWA recently announced the National Performance Measurement Research Data Set (NPMRDS) to support its Freight Performance Measurement (FPM). The NPMRDS includes probe vehicle based travel time data in every 5-minute interval. This report also explored the feasibility of using one month of NPMRDS data in Minnesota to compute freight mobility and speed variations along the National Highway System (NHS) during AM and PM peak periods.According to statistics from the Federal Highway Administration (FHWA), each year approximately 17% of all work zone fatalities are pedestrians. People who are visually impaired often encounter physical and information barriers that limit their accessibility and mobility. A survey was conducted among 10 visually impaired participants as a starting point to understand their challenges and what types of information are helpful in providing bypass or routing instructions to them around work zones. The survey results were incorporated into development of guiding documents in determining information elements that are essential and useful for providing routing instructions to the visually impaired around work zones. Building on our previous efforts to provide geometry and signal timing to the visually impaired at signalized intersections, a smartphone-based navigation system was developed and integrated with navigational audible information to alert pedestrians at decision points prior to their arrival at a work zone. The recommended message elements from survey results were implemented in a smartphone app that uses GPS and Bluetooth technologies to determine a user's location. When a work zone is detected, the smartphone will vibrate to alert users and the app will then announce a corresponding audible message to users. The visually impaired users can perform a single tap on the smartphone to repeat the messages, if needed. Functionality testing and system validation of the smartphone app were performed by attaching four Bluetooth beacons to light posts near a construction site in St. Paul, MN. Additional research is needed to conduct experiments with visually impaired users and evaluate system reliability and usefulness.
