Working(Paper(Series(
|
|
- Laurence Robbins
- 8 years ago
- Views:
Transcription
1 GeoCHOROS Geospa8alAnalysis&GISResearchGroup Working(Paper(Series( GeoCHOROSJGeospa8alAnalysisandGISResearchGroupNa8onalTechnicalUniversityofAthens Mathema2cal(characteriza2on(of( conges2on(based(on(speed(distribu2on:(a( case(study(of(greater(toronto(and(hamilton( area,(ontario,(canada( NataliaKyriakopoulou,PavlosKanaroglouandYorgosN.Pho8s ( ( GCWP ( Paperpresentedatthe13thInterna8onalConferenceonEnvironmentalScienceandTechnology (CEST2013),Athens,Greece,5J7September2013 July2013
2 MATHEMATICAL CHARACTERIZATION OF CONGESTION BASED ON SPEED DISTRIBUTION: A CASE STUDY OF GREATER TORONTO AND HAMILTON AREA, ONTARIO, CANADA KYRIAKOPOULOU NATALIA 1 PAVLOS KANAROGLOU 2 YORGOS PHOTIS 3 1 Kyriakopoulou A. Natalia, School of Rural and Survey Engineering, National Technical University of Athens, 9 Iroon Polutechneiou Street, Zografou, Attiki, Greece, address: natalia.kiriakopoulou@gmail.com 2 Pavlos S. Kanaroglou, Professor, Center for Spatial Analysis (CSpA), School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S-4K1. address: pavlos@mcmaster.ca 3 Yorgos N. Photis, Professor, School of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, Magnesia, Greece, address: yphotis@gmail.com EXTENDED ABSTRACT This study formulates a comprehensive methodology for quantifying and identifying congestion characteristics based on speed distribution. For the purposes of our analyses, we utilize speed data collected by INRIX inc. from vehicles traveling through the Greater Toronto and Hamilton area in A mathematical approach is applied in order to characterize the roadway segments in terms of travel reliability as well as congestion severity and duration. We argue that the Gaussian mixture model and the combination of its parameters constitute a useful tool in order to obtain quantitative congestion measures and to rank the roadway performance. A plethora of measures have been developed to valuate traffic congestion levels of urban roadways. Two aspects that have been investigated are the duration of congestion in a roadway segment (Stathopoulos and Karlaftis, 2002) and the bimodalitity in the speed distribution curve under mixed traffic conditions (Partha and Satish, 2006). In a similar context, our methodology is based on assumptions regarding mixed components and speed distribution. Processing starts with the Gaussian mixture model parameters calculations. Then the investigation of the bimodality of the distributions categorizes every roadway link as unreliable, reliable slow or reliable fast. At the last stage a ranking process prioritizes all the segments from worst to best using the Analytical Hierarchy process. Finally, GIS mapping capabilities provide spatiotemporal information of congestion characteristics by identifying hot spots according to congestion level, severity and duration. We conclude that the Gaussian mixture model can be a useful tool in congestion quantification. Moreover our methodological framework can be applied to large databases. Results indicate that speed patterns differ both between counties and days of the week in the study area. Keywords: Traffic congestion, Gaussian mixture model, speed distribution, EM algorithm, bimodal distribution.
3 1. INTRODUCTION Traffic congestion is known to exacerbate emissions from mobile sources in urban areas, thus contributing to air quality deterioration with significant health, environmental and economic impacts (Smit et al., 2008). A comprehensive selection of mitigation policies should include a sound understanding of congestion characteristics, which are known to vary significantly over the time of day and day of the week. Congestion also is not uniform over space, varying significantly between roadway sections of the transport network. This paper proposes a mathematical approach using vehicle speed data for the identification and quantification of congestion characteristics using a Gaussian mixture model. The effectiveness of the method is demonstrated using speed data at the transportation link level for the Greater Toronto and Hamilton Area (GTHA), Canada. 2. BACKGROUND Literature review reveals that there are many different definitions and analytical expressions for congestion. A widely used definition is: congestion is the time or the delay in excess of that normally incurred under light or free flow traffic condition (Turner et al., 1996). The selection of the congestion measures is not an easy task and each study depending on its purpose focuses on a suitable methodological framework. Although the aforementioned definition points out that the travel delay or the amount of extra time is the basic measure (Shrank and Lomax, 2011), there are many studies that deal with the problem using other methods based on fuzzy logic (Hamad and Kikuchi, 2002) or mathematical models. A growing body of research has focused on evaluating traffic patterns on congested highway systems using mathematical distributions (Junkwood, 2009). With the respect to the duration of congestion, Stathopoulos and Karlaftis (2002) argue that is best described by the Loglogistic functional form while Vlachogianni et al. (2011) apply a multiregime nonlinear autoregressive conditional model. Introducing the issue of bimodality Ko and Guesnier (2004) identify congested and uncongested components in order to quantify the characteristics of congestion while Partha and Satish, (2006) examine it under mixed traffic conditions. Finally, Junkwood, (2009) focuses on the variability on speed patterns due to holiday traffic using a Gaussian mixture distribution estimated by the Expectation-Maximization (EM) algorithm. A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of M Gaussian component densities, as follows: = (, Σ ), where x is a D-dimensional continuous-valued data vector, wi, i= 1,, M, are the mixture weights, and φ(x, Σ ), i = 1,, M, are the component Gaussian densities. This paper builds on this body of research and aims to provide a mathematical characterization of congestion in the GTHA road network, using average speed data, obtained from INRIX inc. (
4 3. METHODOLOGICAL FRAMEWORK Our methodology is based on three assumptions. First, that speed distribution has a mixed form with congested and uncongested periods; second that the speed distribution reveals the traffic characteristics without a need to account for roadway capacity and traffic volume; and third that the speed distribution over a given time period is normal. The methodology consists of four stages. First, the Gaussian Mixture parameters are estimated with the EM algorithm, which for robustness is initialized with multiple runs of the k-means algorithm (Marakakis et al., 2006). A mixture of two normal densities must be either unimodal or bimodal. To test for modality we employ a methodology used by Schillilng et al. (2002). If + and, 75, then the distribution is bimodal. Here, are the means of the two distributions, σ is the standard deviation and V is the free flow speed computed as the average of night-time speed data. The modality of the density functions is related to the travel conditions, especially to the reliability of the roadway performance. The result of this step is the characterization of every roadway link as unreliable, reliable slow or reliable fast. The last step entails a ranking process that prioritizes all the segments from the worst to best and identifies the hot spots segments based on their level of congestion severity and duration. This ranking process requires the development of an indicator which is composed of a set of weighted variables that characterize congestion. One of these variables is the travel time index, as used by Schrank et al (2012), which represents congestion level comparing travel time in peak period with travel time at free flow conditions. We propose the use of the Analytical Hierarchy process as described by Saaty (1990), which is a structure technique for computing the relative weights of the selected variables in order to develop the indicator. 4. CASE STUDY 4.1 Study Area GTHA is located in Our Southern Ontario and has a population of 6,574,140 (Canadian Census Analyzer, 2011). It is Canada s largest and fastest growing urban region. It comprises two single-tier municipalities (Hamilton and Toronto) and four regional municipalities (Durham, Halton, Peel and York). Congestion in the Greater Toronto and Hamilton Area is presently a serious problem and is expected to become worst as the region grows. 4.2 Data The data utilized in this work were collected by INRIX inc. from vehicles traveling on roads throughout the Greater Toronto and Hamilton Area per day in The data set includes average speed on 7,879 roadway segments with length range [3.15m 13.45m] every 15 minutes per week day, segments ID and the associated roadway attribute.
5 4.3 Gaussian Mixture Analysis- Results Using the speed data from 5am to 10pm and following the methodology described in the previous section, two distributions are estimated for every roadway segment based on twocomponents Gaussian Mixture Model. We adopt the Expectation-Maximization algorithm to elicit these parameters. Night-time data are excluded from the traffic analysis due to consistently high vehicle speeds associated with low standard deviation. The results of the Gaussian Mixture Model analysis include a set of 3 parameters mean standard deviation and mixture weight for two distributions per roadway segment per day of the week. Wednesday (Table 1) had the greatest difference in average mean speeds between the congested and uncongested distributions, which indicates congestion severity. Also, Saturday and Sunday followed different trends relative to the other week days. Table&1&Results&of&the&Gaussian&Mixture&distribution&by&day&of&the&week.& Mixture&Component&1&& Mixture&Component&2& Days& (congested)& (uncongested)& Speed& Mean% Standard% Mixing% Mean% Standard% Mixing% difference& Speed% Deviation% Proportion% Speed% Deviation% Proportion% Monday& 34.24% % 0.44% 38.65% % 0.56% 4.40% Tuesday& 33.74% % 0.45% 38.47% % 0.55% 4.73% Wednesday& 32.94% % 0.46% 38.07% % 0.54% 5.13% Thursday& 33.36% % 0.45% 38.27% % 0.55% 4.91% Friday& 33.97% % 0.46% 38.54% % 0.54% 4.57% Saturday& 36.19% % 0.43% 39.06% % 0.57% 2.87% Sunday& 37.11% % 0.38% 39.33% % 0.62% 2.22% A mathematical description of the speed distribution curve should be at the roadway segment level in order to understand where the hotspots are, to compare and analyze the speed profiles spatially and temporally. Special caution is required to interpret the distributional characteristics in terms of congestion of the two speed components estimated by the EM algorithm. After estimating the two-component mixtures, the investigation of the bimodality of the distributions is necessary. Using the estimated parameters, a set of rules is proposed to understand whether a distribution is bimodal as we assumed or unimodal. A bimodal distribution (Figure 1) shows that the roadway segment experiences congested and uncongested conditions while a unimodal distribution (Figure 2) indicates either congested condition or free flow condition. Figure 2 depicts a reliable slow roadway segment because the weighted average speed of the two components is below the congestion threshold which is the 0.75*free flow speed (Schranket al., 2012) and the segment is experiencing serious traffic congestion.
6 Figure&1&Bimodal&estimated&Gaussian&mixtures& of&travel&speed.&roadway&segment s&id:&2659& Figure&2&Unimodal&estimated&Gaussian&mixtures& of&travel&speed.&roadway&segment s&id:&6969& The performance of each roadway segment per day of the week is evaluated by using the algorithms presented. Table 2 summarizes the number of the unimodal and bimodal roadway segments per day of the week. Wednesday had the most bimodal segments which experienced serious congestion, while Sunday had the fewest with 1,194 and 88 respectively. Table&2&Counts&of&Unimodal&and&Bimodal&roadway&segments&by&day&of&the&week.&& Unimodal& Days Reliable&fast& Reliable&slow& Bimodal& Monday& 6,970% 27% 882% Tuesday& 6,860% 2% 1,017% Wednesday& 6,683% 2% 1,194% Thursday& 6,744% 2% 1,133% Friday& 6,971% 4% 904% Saturday& 7,673% 2% 204% Sunday& 7,790% 1% 88% The map 1 shows the classification of the roadway segments into three categories -unreliable, reliably slow and reliably fast- for Wednesday. We observe that the unreliable segments are mainly concentrated in the centre of Toronto. The average speed difference for the unreliable segments is 13 mph and the average Travel Time Index is Unreliable travel conditions over these links indicate that the travel time on the section is unpredictable and it could cause commuters to spend an average of extra 72 minutes of travel time during the Wednesdays in The locations of the bimodal links identify the regions with the serious traffic problem and further analysis is needed. & & & Map&1&Classification&of&the&roadway&segments
7 The literature review has observed that the estimated model parameters mean, standard deviation, and mixture weight, represent the traffic conditions in a quantitative manner. The mean of the lower speed distribution approximates the severity of the congestion; the mean of higher speed distribution defines the acceptable speed range; the variance represents the reliability of the roadway performance and the mixture weight is an index of the duration of congestion (Junkwood, 2009; Ko and Guesnier, 2004). Although each parameter can have its own implications, combining them may give comprehensive insights about the congestion. By combining the beneficial effects of using the parameters of the Gaussian mixture analysis with the travel time index and the free flow speed, an index can be formed. The Analytical Hierarchy process (Saaty, 1990) is applied using four main criteria: The ratio of the high speed distribution to the low speed distribution (variable 1). This ratio measures the severity of congestion. We propose this ratio because it is unitless and comparable among all the roadway links. The mixture weight of the low speed distribution (variable 2). The two weights of the Gaussian Mixture Model can be adopted as a congestion index on a scale of 0 to1 in terms of severity and duration. However, we propose the weight of the lower speed distribution because lower speeds lead to traffic congestion. The lower values of this weight are related to better traffic conditions. The ratio of the free flow speed to the speed of the distribution with the greatest weight (variable 3) It measures the severity of congestion. This criterion shows the relation between the main distribution and the free flow speed. The Travel Time Index (variable 4). The Travel Time Index compares peak period travel time to free-flow travel time. This ratio is includes the concept of the delay which is a basic performance measure. Also, this unitless feature allows the comparison of roadway segments with different characteristics such as length and number of lanes. In order to calculate the weights (table 4), we apply the analytical hierarchy process (AHP) as recommended by Saaty (1990). In the same study, the scale of numbers that indicates how many times more important is one element over another element is explained. Table 3 shows the relative importance between variables using pairwise comparison (PC). Variable 1 Variable 2 Variable 3 Variable 4 Variable Variable Variable Variable Table&3 Weights Variable Variable Variable Variable The spatial variation of the indicator across the study area constitutes the last step of the analysis and it is visualized in map 2. Table 5 presents the indicator statistics per county. We include results only for Wednesday since this is the most problematic day of the week. With a 28.9% coefficient of variation (CV) and 0.9 mean, Toronto area is the most congested. In a similar manner, the 10 worst-performing segments are presented in Table 6. For these, average weekly delay was about 500 minutes while vehicle speed was less than half of the free flow speed. Table&4
8 Table&5&Indicator s&statistics&for&the&counties&in&the>ha& County Min Max Mean St. Deviation CV % Durham Halton Hamilton Peel Toronto York Table&6&The&top&10&worstTperforming&segments&in&the>HA& % Code Route name County Length(miles) Mean 1 (mph) Mean 2 (mph) Indicator ON-401- Southbound Toronto ON-401- Collector Toronto ON-401- Collector Toronto , James Street- Eastbound Hamilton Eglinton Avenue- Northbound Toronto ON-401- Collector Westbound Toronto Park Lawn Road- Southbound Toronto , ON-401- Collector- Eastbound Toronto ON-401 Exp- Eastbound Toronto Don Valley Pky- Southbound Toronto Map&2&Congestion&hot&spots The map 2 locates hot spots around Toronto centre where high values of the indicator concentrate. And thus, commuters traveling to Toronto on an average Wednesday experience bad and unstable traffic conditions in terms of both trip duration and severity meaning that recorded vehicle speeds in these areas were less than half of free flow for approximately 100 minutes weekly. 5. CONCLUSIONS This paper proposes an approach to identify and quantify the congestion based on speed distribution. A case study is carried out using speed data for the Greater Toronto and Hamilton Area. The results from the proposed mathematical approach show that Gaussian Mixture Model analysis is a useful tool for describing the characteristics of congestion. It is beneficial to investigate and understand the historical trends of speed patterns and congestion characteristics because congestion is related to significant health, environmental and economic impacts. Also, the suggested methodological framework is relatively easy to apply in large database. The main advantages of this methodology is that the only speed data are used and that it combines a mathematical approach with fundamental measures of congestion in order to
9 identify the reliability of the roadway segments and to rank their level of congestion. This methodology provides an efficient tool for decision makers to select an appropriate congestion mitigation strategy. Future research will extend the framework to estimate the traffic emission, especially in the identified hot spots of congestion. Also, the results could be incorporated into the path selection process such as in GPS devices where you can avoid unreliable segments akin to avoiding highways with tolls. REFERENCES 1. Assimakopoulos V., Moussiopoulos N. and Apsimon H.M. (2000), Effects of Street Canyon Geometry on the Dispersion Characteristics in Urban Areas, 16th IMACS World Congress 2000, August Lausanne, Switzerland. 2. Hamad K., Kikuchi S. (2002), Developing a measure of traffic congestion: fuzzy inference approach, Transportation Research Record: Journal of the Transportation Research Board, No 1802, Transportation Research Board on the National Academies, Washington, D.C.,2002, pp Junkwood J. (2009), Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic, Transportation Research C, 18, pp Ko J., Guesnier R. (2004), Characterization of congestion based on speed distribution: A statistical approach using Gaussian mixture model, Transportation Research Board, Washington, D.C.. 5. Marakakis A., Galatsanos N., Likas A., Stafylopatis A. (2006) A Relevance feedback approach forcontent based image retrieval using Gaussian mixture models, Proc. International Conference Artificial Neural Networks, Athens, pp Partha P., Satish C. (2006), Speed distribution curves under mixed traffic conditions, Journal of Transportation Engineering 132, pp Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), Schilling M., Watkins A., Watkins W. (2002), Is human height bimodal?, Americal Statistical Association, Vol.56, pp Schrank D., Eisele B., Lomax T. (2012), TTI s 2012 Urban Mobility Report, Texas A&M Transportation Institute. 10. Shrank D., Lomax T. (2011). The 2011 urban mobility report, Texas Transportation Institute. 11. Smit R., Brown Al., Chan Y.C. (2008), Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow? Environmental Modellingand Software 23, pp Stathopoulos A., Karlaftis M. (2002), Modeling duration of urban traffic congestion, Journal of Transportation Engineering128, pp Turner S.M., Lomax T.J. and Levinson H.S. (1996), Measuring and estimating congestion using travel time-based procedures, Transportation Research Record, 1564, Vlachogianni E., Karlaftis M., Kepatsoglou K. (2011), Nonlinear autoregressive conditional duration models for traffic congestion estimation, Journal of Probability and Statistics.
FINAL REPORT DEVELOPMENT OF CONGESTION PERFORMANCE MEASURES USING ITS INFORMATION. Sarah B. Medley Graduate Research Assistant
FINAL REPORT DEVELOPMENT OF CONGESTION PERFORMANCE MEASURES USING ITS INFORMATION Sarah B. Medley Graduate Research Assistant Michael J. Demetsky, Ph.D., P.E. Faculty Research Scientist and Professor of
More informationTIME-VARIANT TRAVEL TIME DISTRIBUTIONS AND RELIABILITY METRICS AND THEIR UTILITY IN RELIABILITY ASSESSMENTS
TIME-VARIANT TRAVEL TIME DISTRIBUTIONS AND RELIABILITY METRICS AND THEIR UTILITY IN RELIABILITY ASSESSMENTS Patricio Alvarez, Universidad del Bío Bío, palvarez@ubiobio.cl Mohammed Hadi, Florida International
More informationUSING GPS DATA TO MEASURE THE PERFORMANCE AND RELIABILITY OF TRANSPORTATION SYSTEMS
COST TU0801. Workshop: "3D issues for Transport System" Proceedings of the 11 th International Conference Reliability and Statistics in Transportation and Communication (RelStat 11), 19 22 October 2011,
More informationTransit Pass-Through Lanes at Freeway Interchanges: A Life-Cycle Evaluation Methodology
Transit Pass-Through Lanes at Freeway Interchanges: A Life-Cycle Evaluation Methodology Michael Mandelzys and Bruce Hellinga University of Waterloo Abstract Transit pass-through lanes provide transit vehicle
More informationBig Data and Analytics: Getting Started with ArcGIS. Mike Park Erik Hoel
Big Data and Analytics: Getting Started with ArcGIS Mike Park Erik Hoel Agenda Overview of big data Distributed computation User experience Data management Big data What is it? Big Data is a loosely defined
More informationHamilton Truck Route Study
Prepared for the City of Hamilton March 2012 Pavlos S. Kanaroglou, Ph.D. Vivek Korikanthimath, Ph.D. McMaster Institute of Transportation and Logistics McMaster University Hamilton, Ontario March 2012
More informationTRAVEL TIME BASED OKLAHOMA CONGESTION ANALYSIS: Pilot Study
Technical Memorandum TRAVEL TIME BASED OKLAHOMA CONGESTION ANALYSIS: Pilot Study Prepared for: Oklahoma Department of Transportation Prepared by: November 2014 Technical Memorandum The Technical Memos
More informationPRIORITY SCHEDULING OF URBAN REHABILITATION STREETS TO MINIMIZE CONGESTION, DELAYS, AND ACCIDENTS
PRIORITY SCHEDULING OF URBAN REHABILITATION STREETS TO MINIMIZE CONGESTION, DELAYS, AND ACCIDENTS ABSTRACT Wesley C. Zech 1, and Satish Mohan 2 This paper describes the framework of a priority scheduling
More informationThe Economic Cost of Traffic Congestion in Florida. Final Document Contract FDOT BDK75 977-19 (UF # 00072256)
August 2010 The Economic Cost of Traffic Congestion in Florida Final Document Contract FDOT BDK75 977-19 (UF # 00072256) Prepared for: Florida Department of Transportation Project Manager J. Darryll Dockstader
More informationEstimating Winter Weather Road Restoration Time using Outsourced Traffic Data: Three Case Studies in Maryland
Estimating Winter Weather Road Restoration Time using Outsourced Traffic Data: Three Case Studies in Maryland August 2014, NRITS Branson, MO Elham Sharifi Stanley E. Young Thomas H. Jacobs Steven M. Rochon
More informationPolicy Research CENTER
TRANSPORTATION Policy Research CENTER Truck Travel Cost Estimates on Tolled and Non-tolled Facilities A Central Texas Case Study Executive Summary Texas has actively pursued tolling as a means to provide
More informationApplication of GIS in Transportation Planning: The Case of Riyadh, the Kingdom of Saudi Arabia
Application of GIS in Transportation Planning: The Case of Riyadh, the Kingdom of Saudi Arabia Mezyad Alterkawi King Saud University, Kingdom of Saudi Arabia * Abstract This paper is intended to illustrate
More information2013 Student Competition
ITS Heartland Chapter 2013 Student Competition Shu Yang (syang32@slu.edu) Saber Abdoli (abdolis@slu.edu) Tiffany M. Rando (trando@slu.edu) Smart Transportation Lab Department of Civil Engineering Parks
More informationITS Devices Used to Collect Truck Data for Performance Benchmarks
ITS Devices Used to Collect Truck Data for Performance Benchmarks Edward McCormack and Mark E. Hallenbeck This paper documents the development of data collection methodologies that can be used to measure
More informationCloud ITS: Reducing Congestion & Saving Lives
Cloud ITS: Reducing Congestion & Saving Lives In 2008, for the first time in human history, the proportion of the worlds population based in urban areas was greater than 50 percent. *IBM Report: Transportation
More informationPerformance Measures for RIDOT s Traffic Management Center
Performance Measures for RIDOT s Traffic Management Center Catherine Burns, EIT Transportation Engineer Sudhir Murthy, PE, PTOE President 5/2011 Presentation Outline RIDOT Performance Measures Public Outreach
More informationA Case for Real-Time Monitoring of Vehicular Operations at Signalized Intersections
White Paper A Case for Real-Time Monitoring of Vehicular Operations at Signalized Intersections 10 1 0 1 0 TRAFINFO.COM TrafInfo Communications, Inc. 556 Lowell Street Lexington, MA 02420 www.trafinfo.com
More informationComparing data from mobile and static traffic sensors for travel time assessment
Comparing data from mobile and static traffic sensors for travel time assessment Nicolas Saunier and Catherine Morency Department of civil, geological and mining engineering, École Polytechnique de Montréal,
More informationUniversity of California Transportation Center UCTC-FR-2010-11. Real-World Carbon Dioxide Impacts of Traffic Congestion
University of California Transportation Center UCTC-FR-21-11 Real-World Carbon Dioxide Impacts of Traffic Congestion Matthew Barth and Kanok Boriboonsomsin University of California, Riverside May 21 Real-World
More informationCOMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES
COMBINING THE METHODS OF FORECASTING AND DECISION-MAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES JULIA IGOREVNA LARIONOVA 1 ANNA NIKOLAEVNA TIKHOMIROVA 2 1, 2 The National Nuclear Research
More informationHighway Capacity and Quality of Service
A3A10: Committee on Highway Capacity and Quality of Service Secretary: Richard G. Dowling, Dowling Associates Highway Capacity and Quality of Service WAYNE K. KITTELSON, Kittelson & Associates, Inc. This
More informationSTATISTICAL PATTERNS OF TRAFFIC DATA AND SAMPLE SIZE ESTIMATION
STATISTICAL PATTERNS OF TRAFFIC DATA AND SAMPLE SIZE ESTIMATION Nezamuddin Graduate Research Assistant E-mail address: nezam@umd.edu Phone: (301) 403-4589 Joshua L. Crunkleton Graduate Research Assistant
More informationRobichaud K., and Gordon, M. 1
Robichaud K., and Gordon, M. 1 AN ASSESSMENT OF DATA COLLECTION TECHNIQUES FOR HIGHWAY AGENCIES Karen Robichaud, M.Sc.Eng, P.Eng Research Associate University of New Brunswick Fredericton, NB, Canada,
More informationIntegrating the I-95 Vehicle Probe Project Data and Analysis Tools into the FAMPO Planning Program
Integrating the I-95 Vehicle Probe Project Data and Analysis Tools into the FAMPO Planning Program I-95 Corridor Coalition Background The I-95 Corridor Coalition is an alliance of transportation agencies,
More informationFleet Size and Mix Optimization for Paratransit Services
Fleet Size and Mix Optimization for Paratransit Services Liping Fu and Gary Ishkhanov Most paratransit agencies use a mix of different types of vehicles ranging from small sedans to large converted vans
More informationTHE SELECTION OF BRIDGE MATERIALS UTILIZING THE ANALYTICAL HIERARCHY PROCESS
THE SELECTION OF BRIDGE MATERIALS UTILIZING THE ANALYTICAL HIERARCHY PROCESS Robert L. Smith Assistant Professor/Extension Specialist, Virginia Tech Robert J. Bush Associate Professor, Virginia Tech and
More informationData Services Engineering Division. Traffic Monitoring System Program
Data Services Engineering Division Traffic Monitoring System Program July 2015 Table of Contents Table of Contents Chapter 1: Introduction... 1-1 Chapter 2: Automatic Traffic Recorders (ATRs)... 2-1 ATRs
More informationMEMORANDUM. Stanley E. Young University of Maryland, Center for Advanced Transportation Technology seyoung@umd.edu 301-792-8180
MEMORANDUM To: From: Vehicle Probe Project Arterial Data Quality Committee Stanley E. Young University of Maryland, Center for Advanced Transportation Technology seyoung@umd.edu 301-792-8180 Date: Subject:
More informationTransportation Impact Assessment Guidelines
Transportation Impact Assessment Guidelines Preface The following TIA Guidelines have been developed jointly by the City s Planning and Growth Management and Public Works and Services departments in an
More informationIntersection Cost Comparison Spreadsheet User Manual ROUNDABOUT GUIDANCE VIRGINIA DEPARTMENT OF TRANSPORTATION
Intersection Cost Comparison Spreadsheet User Manual ROUNDABOUT GUIDANCE VIRGINIA DEPARTMENT OF TRANSPORTATION Version 2.5 i Virginia Department of Transportation Intersection Cost Comparison Spreadsheet
More informationTransportation Infrastructure Investment Prioritization: Responding to Regional and National Trends and Demands Jeremy Sage
FREIGHT POLICY TRANSPORTATION INSTITUTE Transportation Infrastructure Investment Prioritization: Responding to Regional and National Trends and Demands Jeremy Sage Motivation Why do we (and should we)
More informationGPS TRUCK DATA PERFORMANCE MEASURES PROGRAM IN WASHINGTON STATE
Draft Research Report Agreement T4118, Task 31 Truck Performance Measure Research Project GPS TRUCK DATA PERFORMANCE MEASURES PROGRAM IN WASHINGTON STATE by Edward McCormack and Wenjuan Zhao University
More informationAuthor: Hamid A.E. Al-Jameel (Research Institute: Engineering Research Centre)
SPARC 2010 Evaluation of Car-following Models Using Field Data Author: Hamid A.E. Al-Jameel (Research Institute: Engineering Research Centre) Abstract Traffic congestion problems have been recognised as
More informationIllinois Tollway: Development of Incident Management Based Performance Measures. Jeff Hochmuth, PE, PTOE Wilbur Smith Associates
Illinois Tollway: Development of Incident Management Based Performance Measures Jeff Hochmuth, PE, PTOE Wilbur Smith Associates Tollways and Data Toll authorities have always been data heavy Need to verify
More informationCHALLENGES TO EFFECTIVE ARTERIAL TRAFFIC MONITORING: LESSONS FROM THE I-95 CORRIDOR COALITION S VEHICLE PROBE PROJECT ABSTRACT
CHALLENGES TO EFFECTIVE ARTERIAL TRAFFIC MONITORING: LESSONS FROM THE I-95 CORRIDOR COALITION S VEHICLE PROBE PROJECT Stanley E. Young. P.E., Ph.D. Research Engineer, University of Maryland 2200 Technology
More informationENGINEERING REPORT. College Street: Interstate 85 to Donahue Drive Traffic Signal System Feasibility Study Auburn, Alabama
ENGINEERING REPORT College Street: Interstate 85 to Donahue Drive Traffic Signal System Feasibility Study Auburn, Alabama Prepared for: The City of Auburn Prepared by: 3644 Vann Road Suite 100 Birmingham,
More informationRisk assessment of hazardous material transportation routes in the City of New Haven FINAL REPORT. Nicholas Lownes and Ashrafur Rahman
Risk assessment of hazardous material transportation routes in the City of New Haven FINAL REPORT by Nicholas Lownes and Ashrafur Rahman University of Connecticut October 2013 INTRODUCTION The City of
More informationThis paper outlines the focus of the analysis thus far, preliminary results from the current methodology and directions for future
THE FLUIDITY OF THE CANADIAN TRANSPORTATION SYSTEM: A COMMERCIAL TRUCKING PERSPECTIVE Alexander Gregory 1, Transport Canada Kristina Kwiatkowski 1, Transport Canada Introduction As part of its effort to
More information1 FIXED ROUTE OVERVIEW
1 FIXED ROUTE OVERVIEW Thirty transit agencies in Ohio operate fixed route or deviated fixed route service, representing a little less than half of the 62 transit agencies in Ohio. While many transit agencies
More informationMeasurement Information Model
mcgarry02.qxd 9/7/01 1:27 PM Page 13 2 Information Model This chapter describes one of the fundamental measurement concepts of Practical Software, the Information Model. The Information Model provides
More informationWorkshop: Using Spatial Analysis and Maps to Understand Patterns of Health Services Utilization
Enhancing Information and Methods for Health System Planning and Research, Institute for Clinical Evaluative Sciences (ICES), January 19-20, 2004, Toronto, Canada Workshop: Using Spatial Analysis and Maps
More informationCrash Analysis. Identify/Prioritize. Gather Data. Analyze Crashes and Identify Improvements. Review Funding Options. Implement Improvements
Crash Analysis Identify/Prioritize 1. Find high crash areas 2. Review citizen input 3. Access City/County Safety Improvement Candidate Locations Gather Data 1. Use DOT-provided crash analysis programs
More informationFirst Transit Contra Flow Lane in Downtown San Francisco
Introduction First Transit Contra Flow Lane in Downtown San Francisco Javad Mirabdal, Bond Yee Traffic congestion places a tremendous burden on transit vehicles in cities worldwide. San Francisco, located
More information9988 REDWOOD AVENUE PROJECT TRAFFIC IMPACT ANALYSIS. April 24, 2015
9988 REDWOOD AVENUE PROJECT TRAFFIC IMPACT ANALYSIS April 24, 2015 Kunzman Associates, Inc. 9988 REDWOOD AVENUE PROJECT TRAFFIC IMPACT ANALYSIS April 24, 2015 Prepared by: Bryan Crawford Carl Ballard,
More informationSplit Lane Traffic Reporting at Junctions
Split Lane Traffic Reporting at Junctions White paper 1 Executive summary Split Lane Traffic Reporting at Junctions (SLT) from HERE is a major innovation in real time traffic reporting. The advanced algorithm
More informationIncident Detection via Commuter Cellular Phone Calls
Incident Detection via Commuter Cellular Phone Calls Bruce Hellinga Abstract Rapid and reliable incident detection is a critical component of a traffic management strategy. Traditional automatic incident
More informationCapacity planning for fossil fuel and renewable energy resources power plants
Capacity planning for fossil fuel and renewable energy resources power plants S. F. Ghaderi *,Reza Tanha ** Ahmad Karimi *** *,** Research Institute of Energy Management and Planning and Department of
More informationMethod for route selection of transcontinental natural gas pipelines
Method for route selection of transcontinental natural gas pipelines Fotios G. Thomaidis 1 National and Kapodistrian University of Athens Department of Informatics and Telecommunications fthom@kepa.uoa.gr
More informationUsers Perceptive Evaluation of Bus Arrival Time Deviations in Stochastic Networks
Users Perceptive Evaluation of Bus Arrival Time Deviations in Stochastic Networks Users Perceptive Evaluation of Bus Arrival Time Deviations in Stochastic Networks Nikolaos G. Daskalakis, Anthony Stathopoulos
More informationSHRP 2 Reliability Project L38C. Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Florida
SHRP 2 Reliability Project L38C Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Florida SHRP 2 Reliability Project L38C Pilot Testing of SHRP 2 Reliability Data and Analytical Products:
More informationAlison Hayes November 30, 2005 NRS 509. Crime Mapping OVERVIEW
Alison Hayes November 30, 2005 NRS 509 Crime Mapping OVERVIEW Geographic data has been important to law enforcement since the beginning of local policing in the nineteenth century. The New York City Police
More informationStudy and Calculation of Travel Time Reliability Measures
Center for Applied Demography & Survey Research University of Delaware Study and Calculation of Travel Time Reliability Measures by David P. Racca Daniel T. Brown Prepared for the Delaware Department of
More informationTracking System for GPS Devices and Mining of Spatial Data
Tracking System for GPS Devices and Mining of Spatial Data AIDA ALISPAHIC, DZENANA DONKO Department for Computer Science and Informatics Faculty of Electrical Engineering, University of Sarajevo Zmaja
More informationHow should we prioritise incident management deployment?
Marschke, Ms Kath and Ferreira, Dr Luis and Bunker, Dr Jonathan (2005) How should we prioritise incident management deployment?. In Proceedings Australasian Transport Research Forum, Sydney 2005. How should
More informationPRIORITIZATION PROCESSES
PROJECT SELECTION & PRIORITIZATION PROCESSES STIP Workshop Presented by: Bill Lawrence April 2011 Purpose and Review Overview of Project Selection Process Review Various Prioritization Processes Tk Take
More informationReview of Modern Techniques of Qualitative Data Clustering
Review of Modern Techniques of Qualitative Data Clustering Sergey Cherevko and Andrey Malikov The North Caucasus Federal University, Institute of Information Technology and Telecommunications cherevkosa92@gmail.com,
More informationUse of a Web-Based GIS for Real-Time Traffic Information Fusion and Presentation over the Internet
Use of a Web-Based GIS for Real-Time Traffic Information Fusion and Presentation over the Internet SUMMARY Dimitris Kotzinos 1, Poulicos Prastacos 2 1 Department of Computer Science, University of Crete
More informationThe Fresno COG Travel Demand Forecasting Model How the Pieces Fit Together
The Fresno COG Travel Demand Forecasting Model How the Pieces Fit Together Mike Bitner PE, Senior Transportation Planner Council of Fresno County Governments 1 COG Modeling Staff Mike Bitner Kathy Chung
More informationBUILDING CONGESTION INDEXES FROM GPS DATA: DEMONSTRATION
BUILDING CONGESTION INDEXES FROM GPS DATA: DEMONSTRATION Louiselle Sioui (louiselle.sioui@polymtl.ca) Catherine Morency (cmorency@polymtl.ca) Civil Engineering, École Polytechnique de Montréal, 2500 chemin
More informationTRAVEL TIME DATA COLLECTION AND SPATIAL INFORMATION TECHNOLOGIES FOR RELIABLE TRANSPORTATION SYSTEMS PLANNING
TRAVEL TIME DATA COLLECTION AND SPATIAL INFORMATION TECHNOLOGIES FOR RELIABLE TRANSPORTATION SYSTEMS PLANNING Srinivas S. Pulugurtha, Ph.D., P.E. Venkata R. Duddu, Ph.D., E.I. The University of North Carolina
More informationEM Clustering Approach for Multi-Dimensional Analysis of Big Data Set
EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin
More informationWord Count: Body Text = 5,500 + 2,000 (4 Figures, 4 Tables) = 7,500 words
PRIORITIZING ACCESS MANAGEMENT IMPLEMENTATION By: Grant G. Schultz, Ph.D., P.E., PTOE Assistant Professor Department of Civil & Environmental Engineering Brigham Young University 368 Clyde Building Provo,
More informationSU R F A C E T R A N S P O R T A T I O N I N T H E U N I T E D S T A T E S I S A
TRAFFIC CONGESTION AND GREENHOUSE GA SES B Y M AT T H E W B A R T H A N D K A N O K B O R I B O O N S O M S I N SU R F A C E T R A N S P O R T A T I O N I N T H E U N I T E D S T A T E S I S A LARGE source
More informationAnalytic Hierarchy Process (AHP) :
Analytic Hierarchy Process (AHP) : ITS APPLICATION IN FTS BUSINESS MODEL ASSESSMENT Athens University of Economics and Business TRANsportation Systems and LOGistics Laboratory Professor Konstantinos G.
More informationThe use of automated vehicle location systems in solid waste collection operations
The use of automated vehicle location systems in solid waste collection operations CONTACT Bruce G. Wilson, Dept. of Civil Engineering, University of New Brunswick Thuy T.T. Nguyen, Dept. of Civil Engineering,
More informationDevelopment and Application of a Mobility Monitoring Process and Guidebook for Small to Medium-sized Communities
Eisele and Crawford 1 Development and Application of a Mobility Monitoring Process and Guidebook for Small to Medium-sized Communities by William L. Eisele, Ph.D., P.E. 1 Research Engineer Texas Transportation
More informationInformation Security and Risk Management
Information Security and Risk Management by Lawrence D. Bodin Professor Emeritus of Decision and Information Technology Robert H. Smith School of Business University of Maryland College Park, MD 20742
More informationPublic Transport Capacity and Quality Development of an LOS-Based Evaluation Scheme
Public Transport Capacity and Quality Development of an LOS-Based Evaluation Scheme Hermann Orth, IVT, ETH Zürich Robert Dorbritz, IVT, ETH Zürich Ulrich Weidmann, IVT, ETH Zürich Conference paper STRC
More informationINDOT 2000-2025 Long Range Plan
Chapter 9 INDOT 2000-2025 Long Range Plan Highway Needs Analysis Overview The statewide transportation planning process provides for the identification of highway needs through a comprehensive process
More informationCAPACITY AND LEVEL-OF-SERVICE CONCEPTS
CHAPTER 2 CAPACITY AND LEVEL-OF-SERVICE CONCEPTS CONTENTS I. INTRODUCTION...2-1 II. CAPACITY...2-2 III. DEMAND...2-2 IV. QUALITY AND LEVELS OF SERVICE...2-2 Service Flow Rates...2-3 Performance Measures...2-3
More informationSOFTWARE FOR THE OPTIMAL ALLOCATION OF EV CHARGERS INTO THE POWER DISTRIBUTION GRID
SOFTWARE FOR THE OPTIMAL ALLOCATION OF EV CHARGERS INTO THE POWER DISTRIBUTION GRID Amparo MOCHOLÍ MUNERA, Carlos BLASCO LLOPIS, Irene AGUADO CORTEZÓN, Vicente FUSTER ROIG Instituto Tecnológico de la Energía
More informationBANGKOK TRAFFIC MONITORING SYSTEM
BANGKOK TRAFFIC MONITORING SYSTEM Sorawit NARUPITI Associate Professor Transportation Research Laboratory, Department of Civil Engineering, Chulalongkorn University Phayathai Road, Bangkok 10330 Thailand
More informationContent-Based Discovery of Twitter Influencers
Content-Based Discovery of Twitter Influencers Chiara Francalanci, Irma Metra Department of Electronics, Information and Bioengineering Polytechnic of Milan, Italy irma.metra@mail.polimi.it chiara.francalanci@polimi.it
More informationProject Risk Management
Support of Project Risk Management Development of Risk Based Contingency Values for a Baseline Project Budget Estimate Panama Canal 3rd Lane Locks Atlantic and Pacific Locks, Pacific Access Channel, and
More informationChapter 3 - GPS Data Collection Description and Validation
Chapter 3 - GPS Data Collection Description and Validation The first step toward the analysis of accuracy and reliability of AVI system was to identify a suitable benchmark for measuring AVI system performance.
More informationEXPLORING SPATIAL PATTERNS IN YOUR DATA
EXPLORING SPATIAL PATTERNS IN YOUR DATA OBJECTIVES Learn how to examine your data using the Geostatistical Analysis tools in ArcMap. Learn how to use descriptive statistics in ArcMap and Geoda to analyze
More informationUsing Analytic Hierarchy Process (AHP) Method to Prioritise Human Resources in Substitution Problem
Using Analytic Hierarchy Process (AHP) Method to Raymond Ho-Leung TSOI Software Quality Institute Griffith University *Email:hltsoi@hotmail.com Abstract In general, software project development is often
More informationStudy of Lightning Damage Risk Assessment Method for Power Grid
Energy and Power Engineering, 2013, 5, 1478-1483 doi:10.4236/epe.2013.54b280 Published Online July 2013 (http://www.scirp.org/journal/epe) Study of Lightning Damage Risk Assessment Method for Power Grid
More informationSTATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI
STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members
More informationDiagrams and Graphs of Statistical Data
Diagrams and Graphs of Statistical Data One of the most effective and interesting alternative way in which a statistical data may be presented is through diagrams and graphs. There are several ways in
More information3. FORECASTING TRAVEL DEMAND IN THE CORRIDOR
3. FORECASTING TRAVEL DEMAND IN THE CORRIDOR Travel demand forecasting was undertaken to assess future travel demands and to identify existing and future system congestion within the Study Area. A key
More information0.0 Curb Radii Guidelines Version 1.0.2
Background In early 2014, Transportation Services initiated a review of the Division's design guidelines and standards to move our organization in a direction consistent with the transportation departments
More informationThe partnership has selected three intersections where enforcement, education, and engineering initiatives are being implemented to improve safety:
Hamilton-Finn Suite 310 Tel. (403) 207-6000 Road Safety 3016 5th Avenue N.E. Fax. (403) 273-3440 Consultants Ltd. Calgary, Alberta dawatt.dawatt.com www.hamiltonfinn.ca January 19, 2005 Mr. Don Szarko,
More informationESTIMATION OF FREE-FLOW SPEEDS FOR MULTILANE RURAL AND SUBURBAN HIGHWAYS
ESTIMATION OF FREE-FLOW SPEEDS FOR MULTILANE RURAL AND SUBURBAN HIGHWAYS Pin-Yi TSENG Professor Department of Traffic Science Central Police University 56, Shu Jen Road, Kwei Shan, Taoyuan, 33334, Taiwan
More informationFuzzy decision support system for traffic control centers
Delft University of Technology Fac. of Information Technology and Systems Control Systems Engineering Technical report bds:00-08 Fuzzy decision support system for traffic control centers A. Hegyi, B. De
More informationGIS DRIVEN URBAN TRAFFIC ANALYSIS BASED ON ONTOLOGY
GIS DRIVEN URBAN TRAFFIC ANALYSIS BASED ON ONTOLOGY Tazin Malgundkar 1,Madhuri Rao 2 and Dr. S.S. Mantha 3 1 Student, Computer Engineering, Thadomal Shahani Engineering College, Bandra, Mumbai, India.
More informationModeling Network Traffic for Planning Applications in a Small Community
Modeling Network Traffic for Planning Applications in a Small Community Ming S. Lee 1 ; Anthony Chen 2 ; Piya Chootinan 3 ; Walter Laabs 4 ; and Will Recker 5 Abstract: A procedure is developed to model
More informationSUPPLIER SELECTION IN A CLOSED-LOOP SUPPLY CHAIN NETWORK
SUPPLIER SELECTION IN A CLOSED-LOOP SUPPLY CHAIN NETWORK Satish Nukala, Northeastern University, Boston, MA 025, (67)-373-7635, snukala@coe.neu.edu Surendra M. Gupta*, Northeastern University, Boston,
More informationSystems Features Analysis (SFA) and Analytic Hierarchy Process (AHP) in Systems Design and Development
Systems Features Analysis (SFA) and Analytic Hierarchy Process (AHP) in Systems Design and Development Felipe P. Vista IV 1, a and Kil To Chong 1, 2, b, * 1 Department of Electronic Engineering, Jeonbuk
More informationSimulating Traffic for Incident Management and ITS Investment Decisions
1998 TRANSPORTATION CONFERENCE PROCEEDINGS 7 Simulating Traffic for Incident Management and ITS Investment Decisions MICHAEL D. ANDERSON AND REGINALD R. SOULEYRETTE UTPS-type models were designed to adequately
More informationStatistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
More informationTransportation Research Board 2015 Summer Midyear Meeting - Freight Systems and Marine Groups Paul Bingham, Economic Development Research Group
Transportation Research Board 2015 Summer Midyear Meeting - Freight Systems and Marine Groups Paul Bingham, Economic Development Research Group Washington, DC June 25, 2015 Activities enhanced or newly
More informationComparing Arterial Speeds from Big-Data Sources in Southeast Florida (Bluetooth, HERE and INRIX)
Comparing Arterial Speeds from Big-Data Sources in Southeast Florida (Bluetooth, HERE and INRIX) Sujith Rapolu Ashutosh Kumar TRB National Transportation Planning Applications Conference (Atlantic City,
More informationMap Matching and Real World Integrated Sensor Data Warehousing
Map Matching and Real World Integrated Sensor Data Warehousing www.nrel.gov/tsdc www.nrel.gov/fleet_dna Evan Burton Data Engineer (Presenter) Jeff Gonder Vehicle System Analysis Team Lead Adam Duran Engineer/Analyst
More informationHERS_IN. HIGHWAY ECONOMIC REQUIREMENTS SYSTEM (for) INDIANA. AASHTO Transportation Estimator Association Conference October 16, 2001
AASHTO Transportation Estimator Association Conference October 16, 2001 HERS_IN HIGHWAY ECONOMIC REQUIREMENTS SYSTEM (for) INDIANA OVERVIEW HERS Background System Planning Tool Development HERS_IN Structure
More informationDOT HS 809 360 October 2001
U.S. Department of Transportation National Highway Traffic Safety Administration DOT HS 809 360 October 2001 Technical Report This document is available to the public from the National Technical Information
More informationMonitoring Program Results and Next Steps
8 Monitoring Program Results and Next Steps The improving economy and greater levels of employment observed in 2014 have generally resulted in higher demands on the transportation network. This is apparent
More informationTOol using STAcked DAta Final report PRC 14-27-F
TOol using STAcked DAta Final report PRC 14-27-F TOol using STAcked DAta Texas A&M Transportation Institute PRC 14-27-F October 2014 Authors David Schrank Tim Lomax 2 Table of Contents List of Figures...
More informationTransportation Policy and Design Strategies. Freight Intensive. Level of Freight Presence
Appendix G Transportation Policy and Design Strategies CONTEXT SENSITIVE SOLUTIONS To address the need to describe freight systems which account for the population distress which may result, an analysis
More information