DOCSLIB.ORG

Winter Road Surface Condition Recognition Using a Pre-Trained Deep Convolutional Neural Network

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Winter Road Surface Condition Recognition Using a Pre-Trained Deep Convolutional Neural Network Winter Road Surface Condition Recognition Using a Pre-trained Deep Convolutional Neural Network Guangyuan Pan Postdoc Fellow, Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada Email: [email protected] Liping Fu* Professor, Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, ON, Canada, N2L 3G1; Intelligent Transportation Systems Research Center, Wuhan University of Technology, Mailbox 125, No. 1040 Heping Road, Wuhan, Hubei 430063 Email: [email protected] Ruifan Yu Master Student, David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada Email: [email protected] Matthew Muresan Ph. D. Student, Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada Email: [email protected] additional qualitative details, but suffering from the ABSTRACT drawbacks of being subjective, labor-intensive, and time-consuming. On the other hand, RWIS stations This paper investigates the application of the latest benefit from providing continuous information on a machine learning technique – deep neural networks wide range of road and weather conditions; for classifying road surface conditions (RSC) based however, they are costly and can only be installed a on images from smartphones. Traditional machine limited number of locations, limiting their spatial learning techniques such as support vector machine coverage. In recent years, new technologies have (SVM) and random forests (RF) have been been developed to automate the RSC monitoring attempted in literature; however, their classification process, such as CCTV cameras, in-vehicle video performance has been less than desirable due to recorders, smartphone-based system, and high-end challenges associated with image noises caused by imaging systems (3-7). However, these solutions sunlight glare and residual salts. A deep learning have been found to be still limited in terms of model based on convolutional neural network working conditions and classification accuracy. (CNN) is proposed and evaluated for its potential to address these challenges for improved This research focuses on exploring the potential of classification accuracy. In the proposed approach applying one of the most successful machine we introduce the idea of applying an existing CNN learning models - deep learning for classifying model that has been pre-trained using millions of winter road surface conditions based on image images with proven high recognition accuracy. The data. In Section 1, this paper first describes the idea model is extended with two additional behind the proposed RSC monitoring system and fully-connected layers of neurons for learning the subsequently evaluates its performance as intended specific features of the RSC images. The whole in the field. Section 2 reviews the previous studies model is then trained with a low learning rate for that are related to this research, including image fine-tuning by using a small set of RSC images. recognition and deep learning. Section 3 introduces Results show that the proposed model has the the model that we propose. Section 4 describes the highest classification performance in comparison to data information, designs the experiment, discusses the traditional machine learning techniques. The model setting, and analyzes the results. The paper testing accuracy with different training dataset then concludes in Section 5. sizes is also analyzed, showing the potential of achieving much higher accuracy with a larger LITERATURE REVIEW training dataset. The image recognition problem has been studied INTRODUCTION extensively in the literature. Many machine learning models have been proposed in the past Winter road surface condition (RSC) monitoring is decades. In our previous research, three machine of critical importance for winter road maintenance learning techniques, artificial neural networks contractors and the traveling public. Real-time (ANN), random trees (RT) and random forests reliable RSC data can enable winter maintenance (RF) were evaluated using the images of bare, personnel to deploy the right type of maintenance partly snow-covered or fully snow-covered winter treatments with the right amount of deicing roads collected during the winter season of 2014. materials at the right time, leading to significant ANN, which is a black-box model, is commonly savings in costs and reduction in salts. The used to recognize patterns and model complex traveling public could make more informative relationships among variables (8-9). Random decisions on whether or not to travel, what mode to Forest (RF) is an ensemble of classification trees use and which route to drive for safer mobility. that produces a result based on the majority output RSC monitoring is traditionally done by manual from the individual trees, in which each tree is patrolling by highway agencies and maintenance constructed using a bootstrapped sample of the contractors or using road weather information total data set (random sampling with replacement) system (RWIS) stations (1-2). Manual patrolling (10). With a sufficient number of trees the provides very high spatial resolution with predictions tend to converge, resulting in a reliable algorithm that is relatively robust to outliers and Pre-trained deep CNN Model Structure noise. Instead of training a completely new CNN model, Deep learning (DL), or deep neural network which often requires a significant amount of data (DNN), is a novel machine learning technique that and computational time, an alternative approach has been widely explored and successfully applied would be to make use of a CNN model which has for a variety of problems such as applications in already been trained with proven performance image and voice recognition and games (11-12). In (19-20). Such a model would have already learned particular, convolutional neural networks (CNN) features that are useful for most computer vision have recently shown great success in pattern problems; leveraging such features would allow us recognition problems, such as large-scale image to reach a better accuracy than any method that and video analysis. This achievement results from would only rely on the available data. In this both the large public image repositories (such as research, we use a pre-trained deep CNN model ImageNet) and high-performance computing called VGG16 as has been introduced before. systems like GPU and the recent tensor processing unit (TPU) manufactured by Google (13-14). Since Figure 1(b) shows the overall structure of the CNN is becoming very common in many machine VGG16 deep CNN. The network can be divided learning fields, and better performances have been into five convolutional blocks and a fully achieved by improving the original architecture connected block. The first two convolutional and algorithms. For example, researchers have blocks contain two convolutional layers with a been proposing models with larger layers and receptive field with dimensions of 3 × 3 and deeper structures; however, the deeper the network convolutional kernels 64 and 128 respectively. The is, the more difficult the training process is (15-16). receptive field dimensions in this case refer to how In Karen Simonyan’s work, the model, VGG16, big an area (pixels) the next layer can observe from secured the first and the second places in the the previous layer. The number of convolutional localization and classification tracks respectively in kernels (have carefully been studied in their paper) ImageNet Challenge 2014 (17). However, deep can decide how many features a convolutional learning is generally a big data technology, thus, in layer can learn from its previous layer. The last studies which contain insufficient samples; deep three convolutional blocks contain three learning will struggle to learn useful features from convolutional layers in each block; they also have a the input. In these cases, the raw images often need receptive field with dimensions of 3 × 3, and the to be preprocessed like other traditional machine convolutional kernels are 256, 512 and 512, learning approaches would do. In our research, we respectively. The original raw images are first will present a simple yet effective method that resized into a three channeled (Red, Green, Blue) builds a powerful image classifier, using raw data image with dimensions of 150 × 150. The RGB from only a small set of training examples. Our values are then normalized by subtracting the RGB model is based on the model of VGG16 (17), value of each pixel by the mean RGB value of the which is pre-trained with learned features that are image. While this process results in some minor useful for most image recognition problems. information loss, it significantly helps reduce computation times. DEEP LEARNING MODEL During training, the image is passed through a Convolutional neural network (CNN) is one of the stack of convolutional layers, where only a very deep learning models that have been especially small receptive field of 3 × 3 is used. The successful for image classification. An example convolution step is fixed to 1 pixel. Spatial pooling structure of CNN is shown in Figure 1(a), which is is applied to the output at the end of each of the also included as one of the model options in our five convolutional blocks. This is a technique subsequent analysis. commonly used in computer vision which applies a statistical measure across a group of pixels by scanning a window of
Load more

Recommended publications
  • Eu Road Surfaces: Economic and Safety Impact of the Lack of Regular Road Maintenance
    DIRECTORATE GENERAL FOR INTERNAL POLICIES POLICY DEPARTMENT B: STRUCTURAL AND COHESION POLICIES TRANSPORT AND TOURISM EU ROAD SURFACES: ECONOMIC AND SAFETY IMPACT OF THE LACK OF REGULAR ROAD MAINTENANCE STUDY This document was requested by the European Parliament's Committee on Transport and Tourism. AUTHORS Steer Davies Gleave - Roberta Frisoni, Francesco Dionori, Lorenzo Casullo, Christoph Vollath, Louis Devenish, Federico Spano, Tomasz Sawicki, Soutra Carl, Rooney Lidia, João Neri, Radu Silaghi, Andrea Stanghellini RESPONSIBLE ADMINISTRATOR Piero Soave Policy Department Structural and Cohesion Policies European Parliament B-1047 Brussels E-mail: [email protected] EDITORIAL ASSISTANCE Adrienn Borka LINGUISTIC VERSIONS Original: EN. ABOUT THE EDITOR To contact the Policy Department or to subscribe to its monthly newsletter please write to: [email protected] Manuscript completed in July, 2014 © European Union, 2014. DISCLAIMER The opinions expressed in this document are the sole responsibility of the author and do not necessarily represent the official position of the European Parliament. Reproduction and translation for non-commercial purposes are authorized, provided the source is acknowledged and the publisher is given prior notice and sent a copy. DIRECTORATE GENERAL FOR INTERNAL POLICIES POLICY DEPARTMENT B: STRUCTURAL AND COHESION POLICIES TRANSPORT AND TOURISM EU ROAD SURFACES: ECONOMIC AND SAFETY IMPACT OF THE LACK OF REGULAR ROAD MAINTENANCE STUDY Abstract This study looks at the condition and the quality of road surfaces in the EU and at the trends registered in the national budgets on the road maintenance activities in recent years, with the aim of reviewing the economic and safety consequences of the lack of regular road maintenance.
    [Show full text]
  • PASER Manual Asphalt Roads
    Pavement Surface Evaluation and Rating PASER ManualAsphalt Roads RATING 10 RATING 7 RATING 4 RATING PASERAsphalt Roads 1 Contents Transportation Pavement Surface Evaluation and Rating (PASER) Manuals Asphalt PASER Manual, 2002, 28 pp. Introduction 2 Information Center Brick and Block PASER Manual, 2001, 8 pp. Asphalt pavement distress 3 Concrete PASER Manual, 2002, 28 pp. Publications Evaluation 4 Gravel PASER Manual, 2002, 20 pp. Surface defects 4 Sealcoat PASER Manual, 2000, 16 pp. Surface deformation 5 Unimproved Roads PASER Manual, 2001, 12 pp. Cracking 7 Drainage Manual Patches and potholes 12 Local Road Assessment and Improvement, 2000, 16 pp. Rating pavement surface condition 14 SAFER Manual Rating system 15 Safety Evaluation for Roadways, 1996, 40 pp. Rating 10 & 9 – Excellent 16 Flagger’s Handbook (pocket-sized guide), 1998, 22 pp. Rating 8 – Very Good 17 Work Zone Safety, Guidelines for Construction, Maintenance, Rating 7 – Good 18 and Utility Operations, (pocket-sized guide), 1999, 55 pp. Rating 6 – Good 19 Wisconsin Transportation Bulletins Rating 5 – Fair 20 #1 Understanding and Using Asphalt Rating 4 – Fair 21 #2 How Vehicle Loads Affect Pavement Performance Rating 3 – Poor 22 #3 LCC—Life Cycle Cost Analysis Rating 2 – Very Poor 23 #4 Road Drainage Rating 1 – Failed 25 #5 Gravel Roads Practical advice on rating roads 26 #6 Using Salt and Sand for Winter Road Maintenance #7 Signing for Local Roads #8 Using Weight Limits to Protect Local Roads #9 Pavement Markings #10 Seal Coating and Other Asphalt Surface Treatments #11 Compaction Improves Pavement Performance #12 Roadway Safety and Guardrail #13 Dust Control on Unpaved Roads #14 Mailbox Safety #15 Culverts-Proper Use and Installation This manual is intended to assist local officials in understanding and #16 Geotextiles in Road Construction/Maintenance and Erosion Control rating the surface condition of asphalt pavement.
    [Show full text]
  • Guidelines for Safe Ice Construction
    GUIDELINES FOR SAFE ICE CONSTRUCTION 2015 GUIDELINES FOR SAFE ICE CONSTRUCTION Department of Transportation February 2015 This document is produced by the Department of Transportation of the Government of the Northwest Territories. It is published in booklet form to provide a comprehensive and easy to carry reference for field staff involved in the construction and maintenance of winter roads, ice roads, and ice bridges. The bearing capacity guidance contained within is not appropriate to be used for stationary loads on ice covers (e.g. drill pads, semi-permanent structures). The Department of Transportation would like to acknowledge NOR-EX Ice Engineering Inc. for their assistance in preparing this guide. Table of Contents 1.0 INTRODUCTION .................................................5 2.0 DEFINITIONS ....................................................8 3.0 ICE BEHAVIOR UNDER LOADING ................................13 4.0 HAZARDS AND HAZARD CONTROLS ............................17 5.0 DETERMINING SAFE ICE BEARING CAPACITY .................... 28 6.0 ICE COVER MANAGEMENT ..................................... 35 7.0 END OF SEASON GUIDELINES. 41 Appendices Appendix A Gold’s Formula A=4 Load Charts Appendix B Gold’s Formula A=5 Load Charts Appendix C Gold’s Formula A=6 Load Charts The following Appendices can be found online at www.dot.gov.nt.ca Appendix D Safety Act Excerpt Appendix E Guidelines for Working in a Cold Environment Appendix F Worker Safety Guidelines Appendix G Training Guidelines Appendix H Safe Work Procedure – Initial Ice Measurements Appendix I Safe Work Procedure – Initial Snow Clearing Appendix J Ice Cover Inspection Form Appendix K Accident Reporting Appendix L Winter Road Closing Protocol (March 2014) Appendix M GPR Information Tables 1. Modification of Ice Loading and Remedial Action for various types of cracks .........................................................17 2.
    [Show full text]
  • Town of Warren Road Department Winter Road
    TOWN OF WARREN ROAD DEPARTMENT WINTER ROAD MAINTENANCE POLICY The Warren Road Department's Winter Maintenance Policy is based on the goal of obtaining safe highway travel surfaces during winter months. It is our goal to achieve this at the earliest practica! time and in the most cost efficient manner during and after a storm event. Providing bare dry travel surfaces during a winter storm event is not practical and therefore not expected. There are many variables affecting winter maintenance operations such as type of precipitation, air and pavement temperature/ traffic volume/ wind, time of day/ and even the day of the week. Type and volume of traffic and road gradient are the primary factors in determining the order of winter maintenance service. Therefore/ during periods of time when school is in session, top priority is given to clearing roads utilized by the school buses. Emergency service buildings shall receive necessary maintenance to provide for emergency personnel to arrive and for vehicles to depart and return safely. As necessary/ snow and ice control equipment shall be redirected by the Road Foreman from assigned routes to assist emergency response vehicles in reaching the destination. Roads heavily used by commuters and hills are next in priority. Each winter storm event is unique. It is impractical to develop specific rules on winter maintenance operations. Therefore, the Judgment of the Road Foreman often governs the quantities and type of applications used to control snow and ice. Public safety is always our top priority. The following are general guidelines for the winter maintenance by the Warren Road Department: Highway Department Call Outs: Road Department's regular working hours are 6 a.m.
    [Show full text]
  • Lempster Winter Road Maintenance Policy Approved February 16, 2000
    Lempster Winter Road Maintenance Policy Approved February 16, 2000 Scheduled Review September 2002 Reviewed January 2005 Objective: Given that Lempster has unique road and winter weather conditions, and that no amount of plowing, sand, salt, or public expenditure is a substitute for responsible motorists’ good winter driving sense and equipment, it is the intent and responsibility of the Town of Lempster to provide timely and cost-effective winter road maintenance for the safety and benefit of the Town's residents and the general motoring public. Procedure: The objective will be achieved by implementing the Lempster Winter Road Maintenance Procedures, below. Due to the many variables that are inherent to New England and indeed Lempster, each storm and/or weather event may require a different emphasis or strategy. Level of Service:. While the Town endeavors to provide safe, practical access to homes, businesses and municipal facilities during winter storms, it is not possible to maintain roads completely snow and ice-free during a storm. The Department usually begins snow removal operations upon snow accumulations of two to four inches. The Road Agent may, at his discretion based upon weather reports, initiate removal at a greater or lesser accumulation. Pre-treatment and ice control may occur prior, during, and following the storm. Road salt has a declining effect on melting snow and ice as road surface temperatures drop below 25 degrees, therefore it might not be applied until warmer temperatures are expected. Command: Direction of all winter maintenance activities for the Town of Lempster is vested with the Road Agent or his designee.
    [Show full text]
  • Alaskan Transportation for Pays for the Concept Equipment Beyond What the Articles About Alaska’S Activity)
    “Improving Alaska’s quality of transportation through technology application, training, and information exchange.” Local Technical Assistance Program Fall 2001 July–September Volume 26, Number 3 Alaska’s Plan for Adopting the 2000 MUTCD In this issue . • Adopting the 2000 MUTCD—NOT YET! Anyone planning to put the • Avalanche Forecasting 2000 MUTCD into practice in Alaska needs to be aware that Announcements Alaska Department of Transpor- tation and Public Facilities DOT&PF Research (DOT&PF) has not yet adopted it. • Vetch The implementation schedule is • Air Cooled Embankment contained later in this article. Design In Alaska, the Alaska Traffic • Eliminating Longitudinal Manual (ATM), not the Manual on Cracking Traffic Control Devices (MUTCD), addresses traffic control and work zone activities Planning, Design, and in construction, design, and maintenance. The ATM Field Notes includes the MUTCD. Besides the ATM, Alaska • Quick Slope Stability Analysis, Part I continued on page 2 • Winter Concept Vehicles • Winter Equipment Ideas Avalanche Forecasting • Nine New Snow Removal Gadgets Summer’s grasp on the high lanches. The era of modern ava- • Helping Make RWIS country is fading, the crispness of lanche forecasting had begun and Happen fall is in the air, and soon a blanket with it, the creation of a whole new of snow will descend on the moun- profession: the avalanche fore- Around Alaska tains, transforming the landscape caster. into the awesome beauty of winter. • Asphalt Surface Treatment DOT&PF Identified a Need for Guide With the first snows of winter • Roundabouts: The Next comes the threat of avalanche. In Forecasters Intersection the not too distant past, avalanches Managers at DOT&PF recog- were one of the biggest natural haz- nized the need to hire avalanche forecasters who could devote time Training and Meetings ards facing the traveling public and to avalanche safety and education, Calendar the transportation workers who live in snow country.
    [Show full text]
  • A "Roundabout" Way to the Future by Nicholas R
    ON THE MOVE Vol. 25, No. 4 Fall (October) 2012 Sharing Knowledge. Improving Communities. A "Roundabout" Way to the Future by Nicholas R. Jones, P.E., Director with excerpts from FHWA-SA-08-006 Roundabouts: An Informational Guide http://www.fhwa.dot.gov/publications/research/safety/00068/ The first roundabouts in Utah The following material is ex- were designed and constructed in cerpt from the Federal Highway 1996 in the Provo/Orem area. One Administration (FHWA) document at the Seven Peaks Water Park and FHWA-SA-08-006. It outlines another at the entrance to Utah Val- some of the benefits of roundabouts. ley University were opened at about If your city is considering a round- the same time. Over the years they about in its future, this information have become more popular and have about this safe and innovative ap- been constructed in many locations proach to intersection control may mostly in urban areas of Utah. be helpful. New concepts are often in- What is a Roundabout? timidating to some and take a little getting used to. However, once in A roundabout is a type of circu- place and used for a while, they be- lar intersection with yield control of come popular, easy to use, and offer entering traffic, islands on the ap- many advantages. proaches, and appropriate roadway curvature to reduce vehicle speeds. A Look Inside Modern roundabouts are differ- ent from rotaries and other traffic An Overview of MAP-21 .................................................. 3 circles. For example, roundabouts Psychological Infrastructure Development: Road Work on are typically smaller than the large, the Mind ......................................................................
    [Show full text]
  • E. Transportation and Public Transit West Anchorage District Plan
    E. Transportation and Public Transit West Anchorage District Plan TRANSPORTATION The West Anchorage transportation system is comprised of surface road, railroad, aviation, public transit, and nonmotorized (pedestrian, bicycle, and trail) facilities. Other components of Anchorage’s transportation system include freight distribution, regional connections, and congestion management (MOA, 2005). Inter-Bowl travel is dominated by personal vehicles on the surface road network, but this chapter will discuss the current state of all elements of West Anchorage’s transportation system and its associated facilities. Relationship to Other Transportation Plans Anchorage Metropolitan Area Transportation Solutions (AMATS) is the federally designated metropolitan planning organization responsible for transportation planning in the entire Municipality. The Anchorage Bowl 2025 Long-Range Transportation Plan (LRTP) with 2027 Revisions (MOA, 2005) was developed through the AMATS planning process and is used to identify current and future system deficiencies that need improvement to meet MOA future traffic needs. It is subject to annual review and possible revision. The LRTP meets the federal long-range transportation planning requirements the MOA needs to apply for federal transportation funding. The Official Streets and Highways Plan (OSHP) identifies (by ordinance) the locations, classifications, and minimum right-of-way requirements of the street and highway system needed to meet LRTP goals over a 25 year planning period. LRTP recommended system improvements are funded through the Statewide Transportation Improvement Program (Federal), Alaska Transportation Fund (Alaska Department of Transportation and Public Facilities [ADOT&PF]), and Capital Improvements Program (MOA). This chapter will describe each mode of transportation as it relates to West Anchorage and how that would have an impact on land use planning.
    [Show full text]
  • Pub 23-Chapter 4
    PUB 23 - MAINTENANCE MANUAL Chapter 4: Winter Services CHAPTER 4 winTER sERviCEs TABLE OF CONTENTS 4.1 introduction . 4-1 4.2 winter-Related Activities During April and May . 4-4 County Spring Season Review. 4-5 District Spring Season Review. 4-5 Winter Equipment Inspection and Storage Preparation . 4-6 Winter Materials. 4-6 Spring Clean Up of Maintenance Stocking Areas. 4-7 Transfer of Remaining Salt to Covered Salt Storage Buildings. 4-7 Cleanup of Anti-Skid Materials from Highways . 4-7 Stockpile Location Plan Guidelines. 4-8 Municipal Agreements . 4-11 4.3 winter-Related Activities During June, July and August. 4-12 Guidelines for the Allocation of Department, Municipal, and Rental Equipment . 4-12 Rented Equipment Program. 4-13 Emergency Snow Removal Equipment not Under Rented Equipment Program. 4-13 Department Graders, Snow Blowers and Anti-Icing Trucks. 4-14 County Snow Map Preparation . 4-14 Equipment Operator Certification . 4-15 Winter Leadership Preparation Presentations. 4-16 4.4 winter Related Activities During september, October and November. 4-16 4-i Rev. (4-19) Chapter 4: Winter Services PUB 23 - MAINTENANCE MANUAL Situational Awareness . 4-19 Contingency Planning. 4-20 Fall Season District Maintenance Meeting . 4-22 Fall Season County Maintenance Meeting. 4-22 PennDOT Area Command - Road and Weather Condition Reporting . 4-23 Contacting External Agencies . 4-25 Weather Forecasting Service . 4-26 Emergency Procedures . 4-27 Proactive Call Procedures for Adverse Weather . 4-28 Radio Operation. 4-28 Standby Time . 4-29 Shift Plans . 4-29 Dry Run . 4-30 Verification of Winter Equipment - Department and Rental.
    [Show full text]
  • Feasibility of Using Traffic Data for Winter Road Maintenance Performance Measurement
    Ministry of Transportation University of Waterloo Feasibility of Using Traffic Data for Winter Road Maintenance Performance Measurement HIIFP-000 2014 Publication Feasibility of Using Traffic Data for Winter Road Title Maintenance Performance Measurement Luchao (Johnny) Cao and Liping Fu Author(s) iTSS Lab, Department of Civil and Environmental Engineering, University of Waterloo Originating Office Design and Contract Standards Office, MTO Report Number Publication Date 2014 Ministry Contact HIIFP/AURORA - Development of Output and Outcome Models for End-results Based Winter Road Maintenance Standards Abstract The research presented in this report is motivated by the need to develop an outcome based WRM performance measurement system with a specific focus on investigating the feasibility of inferring WRM performance from a traffic state. The research studied the impact of winter weather and road surface conditions (RSC) on the average traffic speed of rural highways with the intention of examining the feasibility of using traffic speeds from traffic sensors as an indicator of WRM performance. Detailed data on weather, RSC, and traffic over three winter seasons from 2008 to 2011 on rural highway sites in Iowa, US are used in this investigation. Three modelling techniques are applied and compared to model the relationship between traffic speed and various road weather and surface condition factors, including multivariate linear regression, artificial neural networks (ANN), and time series analysis. Multivariate linear regression models are compared by temporal aggregation (15 minutes vs. 60 minutes), types of highways (two-lane vs. four-lane), and model types (separated vs. combined). The research then examined the feasibility of estimating/classifying RSC based on traffic speed and winter weather factors using multi-layer logistic regression classification trees.
    [Show full text]
  • Maine Winter Roads: Salt, Safety, Environment and Cost
    Maine Winter Roads: Salt, Safety, Environment and Cost A Report by the Margaret Chase Smith Policy Center The University of Maine February 2010 Authors Jonathan Rubin, Professor, Margaret Chase Smith Policy Center & School of Economics Per E. Gårder, Professor of Civil Engineering Charles E. Morris, Senior Research Associate, Margaret Chase Smith Policy Center Kenneth L. Nichols, Professor of Public Administration John M. Peckenham, Assistant Director, Senator George J. Mitchell Center for Environmental and Watershed Research Peggy McKee, Research Associate, Margaret Chase Smith Policy Center Adam Stern, Research Assistant, School of Economics T. Olaf Johnson, Research Assistant, Civil Engineering Acknowledgements and Disclaimers This study would not have been possible without the great cooperation of the Maine Department of Transportation. We would like to specifically recognize the following individuals who participated on the MaineDOT Road Salt Risk Assessment Team: Dale Peabody, Bill Thompson, Peter Coughlan, Brian Burne, Joshua Katz, David Bernhardt, Chip Getchell, and Gary Williams. We also thank Joe Payeur, MaineDOT Region 2; Gregory J. Stone, Director of Highway Safety, Maine Turnpike Authority; Patrick Moody, Director, Public Affairs, AAA Northern New England; members of the project’s Advisory Committee; and countless others who graciously contributed to this effort. The views and opinions expressed in this report are solely those of the Margaret Chase Smith Policy Center and the individual authors. They do not represent those of Maine Department of Transportation or any other individual or organization that has provided information or assistance. i Road Salt Project Key Findings and Policy Recommendations Key Findings This section summarizes key findings from a yearlong study of the issues and practices in winter maintenance of Maine’s roads.
    [Show full text]
  • Driving Trucks in Siberia
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Aberdeen University Research Archive ROADS AND ROADlessness: DrivinG TruCKS in SIBeriA TAtiANA ArGOunOVA-LOW PhD, Lecturer Department of Anthropology University of Aberdeen Aberdeen AB24 3QY, UK e-mail: [email protected] ABstrACT This article relates to the studies of roads and engages with the experience of driv- ing in Sakha (Yakutia), Siberia. The article intends to contribute to the broad cor- pus of literature on mobility and argues that an alternative perspective on roads and road-users from a geographical area beyond the West might better inform and add another dimension to our understanding of roads and movement along them. The article examines the fluid nature of roads in Siberia and the social significance the roads carry by focusing on truck drivers and their perception of and engage- ment with the so-called winter roads through their sensory experiences. The article analyses narratives of the truckers who frame their experiences of the road with close reference to time and money and where notions of agency of the road become prominent. KeyWORDS: roads • roadlessness • truckers • winter roads • Siberia IntrODUCtiON At the 2011 World Routes workshop in Tartu the discussion of mobility in the North occupied a central place as the participants, among them many with fieldwork experi- ence in the northern regions of Russia, agreed that knowledge about roads and travel- ling in this part of the world makes an important and valuable contribution to the study of mobility and movement.
    [Show full text]