Preferred citation style. Schüssler, N. (2009) Challenges of route choice models derived from GPS, Workshop on Discrete Choice Models, EPF
|
|
|
- Nicholas Hubbard
- 5 years ago
- Views:
Transcription
1 Preferred citation style Schüssler, N. (2009) Challenges of route choice models derived from GPS, Workshop on Discrete Choice Models, EPF Lausanne, Lausanne, August 2009.
2 Challenges of route choice models derived from GPS observations Nadine Schüssler IVT ETH Zürich August 2009
3 Motivation & Objectives GPS data offers more accurate and reliable information about: Chosen routes Times Participant burden can be reduced substantially But extensive data processing is required to derive suitable input data for route choice modelling: Derivation of modes and trips (Schüssler and Axhausen, forthcoming) Identification of chosen routes (Schüssler and Axhausen, 2009) Choice set generation 3
4 Choice set generation Choice set generation for 500 car trips on the Swiss Navtec network (408,636 nodes and 882,120 unidirectional links) Choice set generation procedures tested: Random Walk (Frejinger, 2007) Branch & Bound (Prato and Bekhor, 2006) Stochastic Choice Set Generation (SCSG) Breadth First Search on Link Elimination (BFS-LE) Detailed description in Schüssler, Balmer and Axhausen (2009) 4
5 The Breadth First Search on Link Elimination 5
6 BFS-LE performance optimisation Two approaches for performance optimisation 1. Randomly shuffle the processing order in each tree-level 2. Toplologically equivalent network reduction 6
7 Computational Performance BFS-LE(P02) SCSG 7
8 Choice set structure (1) Chosen route reproduction (choice set size 100) BFS-LE: 73% SCSG: 75% Distribution of road types Distace travelled on SCSG BFS-LE Chosen routes Motorway 16% 17% 12% Extra-urban main roads 18% 18% 16% Urban main roads 34% 35% 36% Local roads 32% 31% 36% 8
9 Choice set structure (2) BFS-LE(P02) SCSG 9
10 Model estimations: setup Model estimations for 1500 OD pairs; SCSG and BFS-LE; 20, 60 and 100 alternatives Testing the influence of Travel time, road types, Sampling Correction (Bovy et al., 2009) Treatment of route overlap Path Size (Ben-Akiva and Bierlaire, 1999) Path Size Correction (Bovy et al., 2008) Commonality Factor (Cascetta et al., 1996) Road type specific Path Size factor (based on Hoogendoorn- Lanser and Bovy (2007)) Subnetworks (Frejinger and Bierlaire, 2007) 10
11 Sampling Correction (SC) and Path Size Correction (PSC) Sampling Correction where Q in = SC j in f = ln Q in in PSin exp PS exp C n jn ( c / b ) in ( c / b) jn Path Size Correction PSC in = a Γ i l L a i ln j C n δ aj 11
12 The road type specific Path Size Formulation 1: RTPS1 irn = 1 L ir a Γ ir l N a na Formulation 2: RTPS2 irn = 1 L i a Γ ir l N a na 12
13 Model results: Basic model Sampling Correction for SCSG models highly significant and considerably improves model fit Road type and travel times B20 B60 B100 S20 S60 S100 Travel time motorway * * Travel time extra-urban * * * Travel time urban main Travel time local road Percentage MW * Percentage UM 0.04 * 0.04 * 0.29 * Percentage LR * Sampling Correction Adj. rho quare
14 Model results: Similarity Factors B100 S100 Value Adj rho 2 Value Adj rho 2 PS PSC CF RTPS1 motorway RTPS1 extra-urban * RTPS1 urban main RTPS1 local road * RTPS2 motorway RTPS2 extra-urban * RTPS2 urban main RTPS2 local road σ_subnetwork * 0.272
15 Influence of similarity factors on travel time parameters B100 S100 Travel time motorway Travel time extra-urban Travel time urban main Travel time local road Percentage MW * 3.15 * 5.48 * Percentage UM 0.46 * 0.77 * * * 5.08 * Percentage LR * 5.15 * 6.31 * PS PSC RTPS1 motorway * RTPS1 extra-urban * RTPS1 urban main RTPS1 local road 0.07 * 0.41 Sampling Correction Adj. rho quare
16 Outlook Reduction of the choice set size and comparison to the generated choice sets of the corresponding size Randomly Similarity-based Rule-based Panel effects Subnetwork combined with similarity factors Transformations of the similarity parameters (e.g. BoxCox) Test the network-free approach by BierlaireFrejinger (2008) 16
17 References (1) Ben-Akiva, M. E. and M. Bierlaire (1999) Discrete choice methods and their applications to short-term travel decisions, in R. Hall (ed.) Handbook of Transportation Science, chap. 2, 5 34, Kluwer, Dordrecht. Bierlaire, M. and E. Frejinger (2008) Route choice modeling with network-free data, Transportation Research Part C: Emerging Technologies, 16 (2) Bierlaire, M., E. Frejinger and J. Stojanovic (2006) A latent route choice model in Switzerland, paper presented at European Transport Conference, Strasbourg, September Bovy, P. H. L., S. Bekhor and C. G. Prato (2008) The factor of revisited path size: Alternative derivation, Transportation Research Record, 2076, Bovy, P. H. L., S. Bekhor and C. G. Prato (2009) Route sampling correction for stochastic route choice set generation, paper presented at the 88th Annual Meeting of the Transportation Research Board, Washington, D.C., Jan, Oliver
18 References (2) Cascetta, E., A. Nuzzola, F. Russo and A. Vitetta (1996) A modified logit route choice model overcoming path overlapping problems: Specification and some calibration results for interurban networks, in J. B. Lesort (ed.) Proceedings of the 13th International Symposium on Transportation and Traffic Theory, , Pergamon, Oxford. Frejinger, E. (2007) Random sampling of alternatives in a route choice context, paper presented at European Transport Conference, Leeuwenhorst, October Frejinger, E. and M. Bierlaire (2007) Capturing correlation with subnetworks in route choice models, Transportation Research Part B: Methodological, 41 (3) Hoogendoorn-Lanser, S. and P. H. L. Bovy (2007) Modeling overlap in multi-modal route choice by inclusion of trip part specific path size factors, Transportation Research Record, 2003,
19 References (3) Marchal, F., J. K. Hackney and K. W. Axhausen (2005) Efficient map matching of large Global Positioning System data sets: Tests on speedmonitoring experiment in Zurich, Transportation Research Record, 1935, Prato, C.G. and S. Bekhor (2006) Applying branch-and-bound technique to route choice set generation, Transportation Research Record, 1985, Schüssler, N. and K. W. Axhausen (2009) Efficient map-matching of GPS points on highresolution navigation networks, Working Paper, 568, IVT, ETH Zurich, Zurich. Schüssler, N. and K. W. Axhausen (forthcoming) Processing GPS raw data without additional information, Transportation Research Record. Schüssler, N., M. Balmer and K. W. Axhausen (2009) Route choice sets for very highresolution data, Working Paper, 567, IVT, ETH Zurich, Zurich. 19
20 Appendix
21 Commonality Factor and Path Size Commonality Factor CF in = β 0 ln j C n L L i ij L j γ Path Size PS in = a Γ i la L i k C n δ 1 ak L * C L k n
22 Subnetworks A continuous sequence of links that are easily identifiable and behaviorally relevant is defined as a subnetwork Here: The Motorway network Utility function where U in = f (,xin ) β + η δ + ν imw η n is a normal distributed coefficient with zero mean δ imw = 1 if alternative i used the motorway and δ imw = 0 otherwise ν in is an i.i.d. Gumbel distributed error term n in
Route choice models: Introduction and recent developments
Route choice models: Introduction and recent developments Michel Bierlaire transp-or.epfl.ch Transport and Mobility Laboratory, EPFL Route choice models: Introduction and recent developments p.1/40 Route
Route Choice Analysis: Data, Models, Algorithms and Applications Emma Frejinger Thesis Supervisor: Michel Bierlaire
p. 1/15 Emma Frejinger Thesis Supervisor: Michel Bierlaire p. 2/15 Which route would a given traveler take to go from one location to another in a transportation network? Car trips (uni-modal networks)
Capturing Correlation in Route Choice Models using Subnetworks
Capturing Correlation in Route Choice Models using Subnetworks Emma Frejinger and Michel Bierlaire Transport and Mobility Laboratory (TRANSP-OR), EPFL Capturing Correlation with Subnetworks in Route Choice
A sampling of alternatives approach for route choice models
Seminar, Namur, February 2009, A sampling of alternatives approach for route choice models p. 1/34 A sampling of alternatives approach for route choice models Emma Frejinger Centre for Transport Studies,
The Network GEV model
The Network GEV model Michel Bierlaire January, 2002 Discrete choice models play a major role in transportation demand analysis. Their nice and strong theoretical properties, and their flexibility to capture
Hiroshima University 2) Tokyo University of Science
September 23-25, 2016 The 15th Summer course for Behavior Modeling in Transportation Networks @The University of Tokyo Advanced behavior models Recent development of discrete choice models Makoto Chikaraishi
We can now formulate our model as showed in equation 2:
Simulation of traffic conditions requires accurate knowledge of travel demand. In a dynamic context, this entails estimating time-dependent demand matrices, which are a discretised representation of the
City form and its macroscopic fundamental diagram
Research Collection Presentation City form and its macroscopic fundamental diagram Author(s): Axhausen, Kay W. Publication Date: 2018-06 Permanent Link: https://doi.org/10.3929/ethz-b-000267750 Rights
Preferred citation style for this presentation
Preferred citation style for this presentation Vitins, B.J. (2010) Grammar-Based Network Construction, presented at the Seminar Modeling Complex Socio-Economic Systems and Crises 5, ETH Zurich, Zurich,
ROUTE CHOICE MODELING USING GPS DATA
ROUTE CHOICE MODELING USING GPS DATA By NAGENDRA S DHAKAR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR
A path choice approach to ac,vity modeling with a pedestrian case study
A path choice approach to ac,vity modeling with a pedestrian case study Antonin Danalet Sta,s,que, transport et ac,vités, Grenoble November 5, 2013 Presenta,on outline Motivation: Why pedestrian activities?
WiFi- Based Marauder's Map, or where are members of a campus and why?
WiFi- Based Marauder's Map, or where are members of a campus and why? Antonin Danalet Workshop on pedestrian models 2014 April 11, 2014 Presenta=on outline Motivation: Why pedestrian activities? Detection:
MEZZO: OPEN SOURCE MESOSCOPIC. Centre for Traffic Research Royal Institute of Technology, Stockholm, Sweden
MEZZO: OPEN SOURCE MESOSCOPIC SIMULATION Centre for Traffic Research Royal Institute of Technology, Stockholm, Sweden http://www.ctr.kth.se/mezzo 1 Introduction Mesoscopic models fill the gap between static
Supplementary Technical Details and Results
Supplementary Technical Details and Results April 6, 2016 1 Introduction This document provides additional details to augment the paper Efficient Calibration Techniques for Large-scale Traffic Simulators.
Divers aspects de la modélisation de la demande en transport
Divers aspects de la modélisation de la demande en transport Michel Bierlaire Laboratoire Transport et Mobilité, Ecole Polytechnique Fédérale de Lausanne Divers aspects de la modélisation de la demande
A Link-Based Day-to-Day Traffic Assignment Model
University of Windsor Scholarship at UWindsor Odette School of Business Publications Odette School of Business 21 A Link-Based Day-to-Day Traffic Assignment Model Xiaozheng He Xiaolei Guo University of
Normalization and correlation of cross-nested logit models
Normalization and correlation of cross-nested logit models E. Abbe, M. Bierlaire, T. Toledo Lab. for Information and Decision Systems, Massachusetts Institute of Technology Inst. of Mathematics, Ecole
Safety of users in road evacuation: supply and demand-supply interaction models for users
Urban Transport XIII: Urban Transport and the Environment in the 21st Century 783 Safety of users in road evacuation: supply and demand-supply interaction models for users A. Vitetta, G. Musolino & F.
Modeling Road Traffic Congestion by Quasi-Dynamic Traffic Assignment
Modeling Road Traffic Congestion by Quasi-Dynamic Traffic Assignment GAETANO FUSCO CHIARA COLOMBARONI STEFANO LO SARDO Department of Civil, Constructional and Environmental Engineering Sapienza University
A nested recursive logit model for route choice analysis
Downloaded from orbit.dtu.dk on: Sep 09, 2018 A nested recursive logit model for route choice analysis Mai, Tien; Frejinger, Emma; Fosgerau, Mogens Published in: Transportation Research Part B: Methodological
Simulation on a partitioned urban network: an approach based on a network fundamental diagram
The Sustainable City IX, Vol. 2 957 Simulation on a partitioned urban network: an approach based on a network fundamental diagram A. Briganti, G. Musolino & A. Vitetta DIIES Dipartimento di ingegneria
Practical note on specification of discrete choice model. Toshiyuki Yamamoto Nagoya University, Japan
Practical note on specification of discrete choice model Toshiyuki Yamamoto Nagoya University, Japan 1 Contents Comparison between bary logit model and bary probit model Comparison between multomial logit
Structural estimation for a route choice model with uncertain measurement
Structural estimation for a route choice model with uncertain measurement Yuki Oyama* & Eiji Hato *PhD student Behavior in Networks studies unit Department of Urban Engineering School of Engineering, The
ANALYSING ROUTE CHOICE DECISIONS ON METRO NETWORKS
ANALYSING ROUTE CHOICE DECISIONS ON METRO NETWORKS ABSTRACT Sebastián Raveau Department of Transport Engineering and Logistics Pontificia Universidad Católica de Chile Avenida Vicuña Mackenna 4860, Santiago,
Spatial tessellations of pedestrian dynamics
Spatial tessellations of pedestrian dynamics Marija Nikolić Michel Bierlaire Bilal Farooq TRANSP-OR, Ecole Polytechnique Fédérale de Lausanne heart2013 -- 2nd Symposium of the European Association for
Activity path size for correlation between activity paths
Workshop in Discrete Choice Models Activity path size for correlation between activity paths Antonin Danalet, Michel Bierlaire EPFL, Lausanne, Switzerland May 29, 2015 Outline Motivation: Activity-based
Normalization and correlation of cross-nested logit models
Normalization and correlation of cross-nested logit models E. Abbe M. Bierlaire T. Toledo August 5, 25 Abstract We address two fundamental issues associated with the use of cross-nested logit models. On
Normalization and correlation of cross-nested logit models
Normalization and correlation of cross-nested logit models E. Abbe M. Bierlaire T. Toledo Paper submitted to Transportation Research B Final version November 2006 Abstract We address two fundamental issues
Estimation of mixed generalized extreme value models
Estimation of mixed generalized extreme value models Michel Bierlaire michel.bierlaire@epfl.ch Operations Research Group ROSO Institute of Mathematics EPFL Katholieke Universiteit Leuven, November 2004
Activity space: Concept, measurement and first results
Activity space: Concept, measurement and first results Stefan Schönfelder IVT ETH Zürich June 2003 Introduction Principle question How may locational choice and the intensity of individual usage of urban
Pedestrian multi-class speed-density relationship: evaluation of integrated and sequential approach
heart 2017-6 th Symposium of the European Association for Research in Transportation Pedestrian multi-class speed-density relationship: evaluation of integrated and sequential Marija Nikoli, Michel Bierlaire,
Analyzing how travelers choose scenic routes using route choice models
Analyzing how travelers choose scenic routes using route choice models Majid Alivand, Hartwig Hochmair, Sivaramakrishnan Srinivasan Abstract Finding a scenic route between two locations is a common trip
A Dynamic Programming Approach for Quickly Estimating Large Scale MEV Models
A Dynamic Programming Approach for Quickly Estimating Large Scale MEV Models Tien Mai Emma Frejinger Mogens Fosgerau Fabien Bastin June 2015 A Dynamic Programming Approach for Quickly Estimating Large
Travel Parameter Modelling for Indian Cities- Regression Approach
Travel Parameter Modelling for Indian Cities- Regression Approach 1 V.M.Naidu, 2 CSRK Prasad, 3 M.Srinivas 1 Assistant Professor, 2,3 Professor 1,3 Civil Engineering Department-GVPCOE, Visakhapatnam, India
Investigating the Empirical Existence of Static User Equilibrium
Investigating the Empirical Existence of Static User Equilibrium J. Raimbault 1,2 juste.raimbault@parisgeo.cnrs.fr 1 UMR CNRS 8504 Géographie-cités 2 UMR-T IFSTTAR 9403 LVMT EWGT 2016 - Istanbul Session
URBAN TRANSPORTATION SYSTEM (ASSIGNMENT)
BRANCH : CIVIL ENGINEERING SEMESTER : 6th Assignment-1 CHAPTER-1 URBANIZATION 1. What is Urbanization? Explain by drawing Urbanization cycle. 2. What is urban agglomeration? 3. Explain Urban Class Groups.
DO TAXI DRIVERS CHOOSE THE SHORTEST ROUTES?
DO TAXI DRIVERS CHOOSE THE SHORTEST ROUTES? A Large-Scale Analysis of Route Choice Behavior of Taxi Drivers Using Floating Car Data in Shanghai Junyan Li Superviors: Juliane Cron M.Sc. (Technische Universität
Data-driven fundamental models for pedestrian movements
Workshop on Transportation Network and Management Data-driven fundamental models for pedestrian movements Marija Nikoli EPFL, March 9, 2017 Congestion Research challenges Understand, describe and predict
Binary choice. Michel Bierlaire
Binary choice Michel Bierlaire Transport and Mobility Laboratory School of Architecture, Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne M. Bierlaire (TRANSP-OR ENAC EPFL)
ANALYSIS OF CAR BEHAVIOR IN MATSUYAMA CITY
ANALYSIS OF CAR BEHAVIOR IN MATSUYAMA CITY (USING PROBE PERSON DATA) TEAM G 16-Sep 2018 T h e U n i v e r s i t y o f To k yo, Jap a n & T h e U n i v e r s i t y o f C e n t r a l P u n j a b, L a h o
SP Experimental Designs - Theoretical Background and Case Study
SP Experimental Designs - Theoretical Background and Case Study Basil Schmid IVT ETH Zurich Measurement and Modeling FS2016 Outline 1. Introduction 2. Orthogonal and fractional factorial designs 3. Efficient
PLANNING TRAFFIC SAFETY IN URBAN TRANSPORTATION NETWORKS: A SIMULATION-BASED EVALUATION PROCEDURE
PLANNING TRAFFIC SAFETY IN URBAN TRANSPORTATION NETWORKS: A SIMULATION-BASED EVALUATION PROCEDURE Michele Ottomanelli and Domenico Sassanelli Polytechnic of Bari Dept. of Highways and Transportation EU
Stability and innovation of human activity spaces
Stability and innovation of human activity spaces http://www.ivt.ethz.ch/vpl/publications/reports/ab258.pdf Stefan Schönfelder * IVT - Institute for Transport Planning and Systems ETH - Swiss Federal Institute
Accessibility in the Austria and the United States: Influences of the Automobile and Alternative Transport Modes on Household Activity Patterns
Accessibility in the Austria and the United States: Influences of the Automobile and Alternative Transport Modes on Household Activity Patterns Paper presented at the Conference on Social Change and Sustainable
Yu (Marco) Nie. Appointment Northwestern University Assistant Professor, Department of Civil and Environmental Engineering, Fall present.
Yu (Marco) Nie A328 Technological Institute Civil and Environmental Engineering 2145 Sheridan Road, Evanston, IL 60202-3129 Phone: (847) 467-0502 Fax: (847) 491-4011 Email: y-nie@northwestern.edu Appointment
CIV3703 Transport Engineering. Module 2 Transport Modelling
CIV3703 Transport Engineering Module Transport Modelling Objectives Upon successful completion of this module you should be able to: carry out trip generation calculations using linear regression and category
Spitsmijden Reward Experiments. Jasper Knockaert VU University Amsterdam
Spitsmijden Reward Experiments Jasper Knockaert VU University Amsterdam Overview Spitsmijden? Experimental design Data collection Behavioural analysis: departure time choice (and trade off with (reliability
Encapsulating Urban Traffic Rhythms into Road Networks
Encapsulating Urban Traffic Rhythms into Road Networks Junjie Wang +, Dong Wei +, Kun He, Hang Gong, Pu Wang * School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan,
Path and travel time inference from GPS probe vehicle data
Path and travel time inference from GPS probe vehicle data Timothy Hunter Department of Electrical Engineering and Computer Science University of California, Berkeley tjhunter@eecs.berkeley.edu Pieter
A macroscopic loading model for dynamic, multi-directional and congested pedestrian flows
HKSTS Post-Conference Workshop A macroscopic loading model for dynamic, multi-directional and congested pedestrian flows Flurin S. Hänseler, William H.K. Lam, Michel Bierlaire Hong Kong, December 16, 2014
A Machine Learning Approach to Trip Purpose Imputation in GPS-Based Travel Surveys
A Machine Learning Approach to Trip Purpose Imputation in GPS-Based Travel Surveys Yijing Lu 1, Shanjiang Zhu 2, and Lei Zhang 3,* 1. Graduate Research Assistant 2. Research Scientist 2. Assistant Professor
METHODOLOGICAL ISSUES IN MODELING TIME-OF-TRAVEL
METHODOLOGICAL ISSUES IN MODELING TIME-OF-TRAVEL PREFERENCES Moshe Ben-Akiva (corresponding author) Massachusetts Institute of Technology 77 Massachusetts Avenue, Room -8 Cambridge, MA 0239 Tel. 67-253-5324
Introduction to mixtures in discrete choice models
Introduction to mixtures in discrete choice models p. 1/42 Introduction to mixtures in discrete choice models Michel Bierlaire michel.bierlaire@epfl.ch Transport and Mobility Laboratory Introduction to
(Behavioral Model and Duality Theory)
2018 (Behavioral Model and Duality Theory) fukuda@plan.cv.titech.ac.jp 2 / / (Rational Inattention) 6 max u(x 1,x 2 ) x 1,x 2 s.t. p 1 x 1 + p 2 x 2 = I u = V (p 1,p 2,I) I = M(p 1,p 2, ū) x H j (p 1,p
Appendix BAL Baltimore, Maryland 2003 Annual Report on Freeway Mobility and Reliability
(http://mobility.tamu.edu/mmp) Office of Operations, Federal Highway Administration Appendix BAL Baltimore, Maryland 2003 Annual Report on Freeway Mobility and Reliability This report is a supplement to:
INTRODUCTION TO TRANSPORTATION SYSTEMS
INTRODUCTION TO TRANSPORTATION SYSTEMS Lectures 5/6: Modeling/Equilibrium/Demand 1 OUTLINE 1. Conceptual view of TSA 2. Models: different roles and different types 3. Equilibrium 4. Demand Modeling References:
Development and estimation of a semicompensatory model with a flexible error structure
Development and estimation of a semicompensatory model with a flexible error structure Sigal Kaplan, Shlomo Bekhor, Yoram Shiftan Transportation Research Institute, Technion Workshop on Discrete Choice
A nested recursive logit model for route choice analysis
A nested recursive logit model for route choice analysis Tien Mai a, Mogens Fosgerau b and Emma Frejinger a a Department of Computer Science and Operational Research Université de Montréal and CIRRELT,
A route map to calibrate spatial interaction models from GPS movement data
A route map to calibrate spatial interaction models from GPS movement data K. Sila-Nowicka 1, A.S. Fotheringham 2 1 Urban Big Data Centre School of Political and Social Sciences University of Glasgow Lilybank
Urban Link Travel Time Estimation Using Large-scale Taxi Data with Partial Information
Urban Link Travel Time Estimation Using Large-scale Taxi Data with Partial Information * Satish V. Ukkusuri * * Civil Engineering, Purdue University 24/04/2014 Outline Introduction Study Region Link Travel
Beyond Normality: A Distributionally Robust Stochastic User Equilibrium Model
Beyond Normality: A Distributionally Robust Stochastic User Equilibrium Model Selin Damla Ahipasaoglu Rudabeh Meskarian Thomas L. Magnanti Karthik Natarajan June 15, 2014 Abstract The Stochastic User Equilibrium
Simulation-based travel time reliable signal control
Simulation-based travel time reliable signal control Paper accepted for publication in Transportation Science Xiao Chen School of Highway, Chang an University, Xi an, China Carolina Osorio Department of
ecompass eco-friendly urban Multi-modal route PlAnning Services for mobile users ecompass TR 045 Checking the Stationarity of the Datasets
Project Number 288094 ecompass eco-friendly urban Multi-modal route PlAnning Services for mobile users STREP Funded by EC, INFSO-G4(ICT for Transport) under FP7 ecompass TR 045 Checking the Stationarity
Objective: Support decision making when designing monitoring schemes
TRIMM is supported by funding from the 7 th Framework Programme Call: SST.2011.5.2-2. Theme: Advanced and cost effective road infrastructure construction. management and maintenance Wim Courage TNO Objective:
Extracting mobility behavior from cell phone data DATA SIM Summer School 2013
Extracting mobility behavior from cell phone data DATA SIM Summer School 2013 PETER WIDHALM Mobility Department Dynamic Transportation Systems T +43(0) 50550-6655 F +43(0) 50550-6439 peter.widhalm@ait.ac.at
A path choice approach to activity modeling with a pedestrian case study
Waiting for the train Having a coffee Buying a ticket s e 7:35-7:40 7:40-7:45 7:45-7:50 7:50-7:55 7:55-8:00 8:00-8:05 8:05-8:10 8:10-8:15 A path choice approach to activity modeling with a pedestrian case
Adynamicnetworkloadingmodel for anisotropic and congested pedestrian flows
heart 2015 Adynamicnetworkloadingmodel for anisotropic and congested pedestrian flows Flurin S. Hänseler, William H.K. Lam, Michel Bierlaire, Gael Lederrey, Marija Nikolić September 9, 2015 Unsteady, anisotropic
Destination Choice Model including panel data using WiFi localization in a pedestrian facility
Destination Choice Model including panel data using WiFi localization in a pedestrian facility Loïc Tinguely, Antonin Danalet, Matthieu de Lapparent & Michel Bierlaire Transport and Mobility Laboratory
Regression Methods for Spatially Extending Traffic Data
Regression Methods for Spatially Extending Traffic Data Roberto Iannella, Mariano Gallo, Giuseppina de Luca, Fulvio Simonelli Università del Sannio ABSTRACT Traffic monitoring and network state estimation
Modelling Backward Traveling Holes in Mixed Traffic Conditions using an Agent Based Simulation
Modelling Backward Traveling Holes in Mixed Traffic Conditions using an Agent Based Simulation Amit Agarwal 1 Gregor Lämmel 2 Kai Nagel 1 1 Transport Systems Planning and Transport Telematics Technische
An exploration of the relationship between mode choice and complexity of trip chaining patterns
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2004 An exploration of the relationship between mode choice and complexity of trip chaining patterns Xin, Ye
Estimation of the Subjective Topological Structure of Action Spaces using Learning Agents
Estimation of the Subjective Topological Structure of Action Spaces using Learning Agents Ed Manley Department of Civil, Environmental and Geomatic Engineering University College London ucesejm@ucl.ac.uk
Capacity Drop. Relationship Between Speed in Congestion and the Queue Discharge Rate. Kai Yuan, Victor L. Knoop, and Serge P.
Capacity Drop Relationship Between in Congestion and the Queue Discharge Rate Kai Yuan, Victor L. Knoop, and Serge P. Hoogendoorn It has been empirically observed for years that the queue discharge rate
Risk-Averse Network Design with Behavioral Conditional Value-at-Risk for Hazardous Materials Transportation
Risk-Averse Network Design with Behavioral Conditional Value-at-Risk for Hazardous Materials Transportation Liu Su and Changhyun Kwon Department of Industrial and Management Systems Engineering, University
LUTDMM: an operational prototype of a microsimulation travel demand system
LUTDMM: an operational prototype of a microsimulation travel demand system Min. Xu 1, Michael. Taylor 2, Steve. Hamnett 2 1 Transport and Population Data Centre, Department of Infrastructure, Planning
Sensitivity of estimates of travel distance and travel time to street network data quality
Sensitivity of estimates of travel distance and travel time to street network data quality Paul Zandbergen Department of Geography University of New Mexico Outline Street network quality Approaches to
Discrete panel data. Michel Bierlaire
Discrete panel data Michel Bierlaire Transport and Mobility Laboratory School of Architecture, Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne M. Bierlaire (TRANSP-OR ENAC
Combining SP probabilities and RP discrete choice in departure time modelling: joint MNL and ML estimations
Combining SP probabilities and RP discrete choice in departure time modelling: joint MNL and ML estimations Jasper Knockaert Yinyen Tseng Erik Verhoef Jan Rouwendal Introduction Central question: How do
Improving the travel time prediction by using the real-time floating car data
Improving the travel time prediction by using the real-time floating car data Krzysztof Dembczyński Przemys law Gawe l Andrzej Jaszkiewicz Wojciech Kot lowski Adam Szarecki Institute of Computing Science,
Advanced information feedback strategy in intelligent two-route traffic flow systems
. RESEARCH PAPERS. SCIENCE CHINA Information Sciences November 2010 Vol. 53 No. 11: 2265 2271 doi: 10.1007/s11432-010-4070-1 Advanced information feedback strategy in intelligent two-route traffic flow
Accounting for inertia in modal choices:
Workshop on Discrete Choice Models EPFL, Lausanne - August 2010 Accounting for inertia in modal choices: Some new evidence using RP/SP dataset Content of the presentation Backgrounds Modal choice model
STRC 1st. Comparison of capacity between roundabout design and signalised junction design. Jian-an TAN, Citec ingénieurs conseils SA
Comparison of capacity between roundabout design and signalised junction design Jian-an TAN, Citec ingénieurs conseils SA Conference paper STRC 2001 Session Engineering STRC 1st Swiss Transport Research
Incorporating Spatial Dependencies in Random Parameter Discrete Choice Models
Incorporating Spatial Dependencies in Random Parameter Discrete Choice Models Abolfazl (Kouros) Mohammadian Department of Civil and Materials Engineering University of Illinois at Chicago 842 W. Taylor
Matthias Schubert. Jochen Köhler. Michael H. Faber
[ 1 International Probabilistic Symposium 2006, Ghent, 28-29 November 2007 Analysis of Tunnel accidents by using Bayesian Networks Matthias Schubert ETH Zürich, Institute for Structural Engineering, Group
SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, BERLIN, GERMANY, D AMER CHEMICAL SOC, TH ST, NW, WASHINGTON, USA, DC, 20036
SCIENCE CITATION INDEX POLYMER SCIENCE JOURNAL LIST Total journals: 41 1. ADVANCES IN POLYMER SCIENCE Irregular ISSN: 0065-3195 SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, BERLIN, GERMANY, D-14197 2.
Accommodating spatial correlation in local-interaction formation model under a heavy rain disaster
Accommodating spatial correlation in local-interaction formation model under a heavy rain disaster The University of Tokyo Junji URATA Summer School for Advanced activity model and assignment theory BinN
Fare Planning for Public Transport
Konrad-Zuse-Zentrum für Informationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Germany RALF BORNDÖRFER MARIKA NEUMANN MARC E. PFETSCH Fare Planning for Public Transport Supported by the DFG Research
Integration of a capacity-constrained workplace choice model Recent developments and applications for an agent-based simulation in Singapore
Research Collection Journal Article Integration of a capacity-constrained workplace choice model Recent developments and applications for an agent-based simulation in Singapore Author(s): Vitins, Basil
ANALYSING THE CAPACITY OF A TRANSPORTATION NETWORK. A GENERAL THEORETICAL APPROACH
European Transport \ Trasporti Europei (2013) Issue 53, Paper n 8, ISSN 1825-3997 ANALYSING THE CAPACITY OF A TRANSPORTATION NETWORK. A GENERAL THEORETICAL APPROACH 1 Gastaldi M., Rossi R., Vescovi R.
Searching for Parking in GPS Data
Searching for Parking in GPS Data Lara Montini Andreas Horni Nadine Rieser-Schüssler Kay W. Axhausen Travel Behaviour Research: Current Foundations, Future Prospects 13th International Conference on Travel
APPENDIX C-3 Equitable Target Areas (ETA) Technical Analysis Methodology
APPENDIX C-3 Equitable Target Areas (ETA) Technical Analysis Methodology Contents Introduction... 1 ETA Index Methodology... 1 ETA Index Development... 1 Other EJ Measures... 4 The Limited English Proficiency
The determinants of transport modal choice in Bodensee-Alpenrhein region
The determinants of transport modal choice in Bodensee-Alpenrhein region Seyedeh Ashrafi University of Vienna Energie Innovation, February 2018 Modal choice is a decision process to choose between different
Gaussian Processes for Big Data. James Hensman
Gaussian Processes for Big Data James Hensman Overview Motivation Sparse Gaussian Processes Stochastic Variational Inference Examples Overview Motivation Sparse Gaussian Processes Stochastic Variational
Trip Distribution Modeling Milos N. Mladenovic Assistant Professor Department of Built Environment
Trip Distribution Modeling Milos N. Mladenovic Assistant Professor Department of Built Environment 25.04.2017 Course Outline Forecasting overview and data management Trip generation modeling Trip distribution
Lecture 1. Behavioral Models Multinomial Logit: Power and limitations. Cinzia Cirillo
Lecture 1 Behavioral Models Multinomial Logit: Power and limitations Cinzia Cirillo 1 Overview 1. Choice Probabilities 2. Power and Limitations of Logit 1. Taste variation 2. Substitution patterns 3. Repeated
Overview of Seismic PHSA Approaches with Emphasis on the Management of Uncertainties
H4.SMR/1645-29 "2nd Workshop on Earthquake Engineering for Nuclear Facilities: Uncertainties in Seismic Hazard" 14-25 February 2005 Overview of Seismic PHSA Approaches with Emphasis on the Management of
Modifications of asymmetric cell transmission model for modeling variable speed limit strategies
Modifications of asymmetric cell transmission model for modeling variable speed limit strategies Josep Maria Torné* CENIT Center for Innovation in Transport, Technical University of Catalonia (UPC) Barcelona,
Simulation-based travel time reliable signal control
Simulation-based travel time reliable signal control Carolina Osorio Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA239, USA, osorioc@mit.edu,
Bayesian tsunami fragility modeling considering input data uncertainty
Bayesian tsunami fragility modeling considering input data uncertainty Raffaele De Risi Research Associate University of Bristol United Kingdom De Risi, R., Goda, K., Mori, N., & Yasuda, T. (2016). Bayesian
A Land Price Prediction Model using Multi-scale Data and UrbanSim in Seoul
A Land Price Prediction Model using Multi-scale Data and UrbanSim in Seoul April. 27, 2012 Hyejung Kwon, Yongjin Joo, Chulmin Jun Dept. Geoinformatics, The University of Seoul CONTENTS Introduction Data