GPS en verplaatsingsgedrag
 

GPS en verplaatsingsgedrag

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    GPS en verplaatsingsgedrag GPS en verplaatsingsgedrag Presentation Transcript

    • GPS Data CollectionHarry TimmermansEindhoven University of Technology6/18/2013
    • The Survey Method• Conventional survey methods for activity-travel diarydata• Application of new data collection methods– GPS logger (original traces)– User participation• Social demographic information• Personal profile• Downloading and uploading data• Validating activity-trip agendas– Web-based prompt recall survey• Embedded in TraceAnnotator
    • The Prompt Recall
    • Validation of Activities/Trips
    • Survey Management• Time horizon– 4 waves, each wave takes 3 months– Each individual is invited for 3 months continuously• Location– Rijnmond and Eindhoven regions• Respondents– People living in area– Companies recruit their own panels• Statistics followed will use the data from Rijnmondregion as an example
    • User Participation (# of days)19%6%11%5%59%0%10%20%30%40%50%60%70%0~7 8~14 15~31 32~60 60~PercentageoftheparticipationNumber of daysUser participation: Rijmond area• 300 of 434 respondents are fully or partly involved in the survey
    • 0%10%41%26%23%0%5%10%15%20%25%30%35%40%45%~16yr 17~30yr 31~55yr 56~65yr 66~yrAgeAge of RespondentsThe percentage of respondents whoare older than 55 is almost 50%.No children
    • Frequency of Activities/TripsMissing daysHigh frequency is due to the shortevents, which needs to be filtered.Single activity
    • Frequency of Activity Type
    • Ave. Activity Duration by Type
    • Frequency of Transport ModeMany short walking trips
    • Approach• Classification of transport modes and activity episode– Bayesian Belief Network (BBN)• Replaces ad hoc rules• A graphical representation of probabilistic causal informationincorporating sets of probability conditional tables;• Represents the interrelationship between spatial and temporalfactors (input), and activity-travel pattern (output), i.e.transportation modes and activity episode;• Learning-based improved accuracy if consistent evidence isobtained over time from more samples;
    • Framework6/18/2013 Feng&Timmermans 13• Transportation mode• Activity episodePersonalDataGPS dataGeographicalData
    • Conditional Probabilities
    • Theoretical support and applications• Accuracy of the algorithm– Limited sample and transportation modes– Full sample and full transportation modes• Comparison of different imputation algorithms• Improve the imputed activity/trip sequence• Map matching between GPS traces and road networks• Impact of equity of travel time uncertainty
    • Accuracy of the AlgorithmSource: Anastasia, et al., (2010) Semi-Automatic Imputation of Activity-Travel Diaries Using GPS Traces, Prompted Recall and Context-Sensitive Learning Algorithms. Journal of Transportation Research Record, 2183.
    • Accuracy of the AlgorithmActivity Walking Running Cycling Bus Motorcycle Car Train Metro Tram Light railActivity 84% 4% 0% 0% 0% 0% 1% 9% 2% 0% 0%Walking 2% 97% 0% 0% 1% 0% 0% 0% 0% 0% 0%Running 0% 0% 98% 0% 1% 0% 1% 0% 0% 0% 0%Cycling 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0%Bus 1% 0% 0% 0% 87% 0% 0% 0% 0% 12% 0%Motorcycle 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0%Car 0% 0% 0% 0% 1% 0% 98% 0% 0% 0% 1%Train 0% 0% 0% 0% 0% 0% 5% 58% 36% 0% 0%Metro 1% 0% 0% 0% 0% 0% 0% 1% 98% 0% 0%Tram 0% 0% 0% 0% 0% 0% 2% 0% 0% 98% 0%Light rail 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 98%GPS OnlyActivity 84%Walking 97%Running 98%Cycling 100%Bus 87%Motorcycle 100%Car 98%Train 58%Metro 98%Tram 98%Light rail 98%Source: Feng, T and Timmermans, H. (2012) Recognition oftransportation mode using GPS and accelerometer data. InternationalConference of IATBR, Toronto, Canada, 15-20, July, 2012.
    • Comparison of Imputation AlgorithmsId Algorithms1 Bayesian Network (BN)2 Naive Bayesian (NB)3 Logistic regression (LR)4 Multilayer Perception (MP)5 Decision Table (DT)6 Support Vector Machine (SVM)7 C4.5 (C45)8 CART (CART)AlgorithmsTraining Data Test DataCCI (%) ICI (%) Kappa CCI (%) ICI (%) KappaBN 99.805 0.195 0.997 99.474 0.526 0.993NB 86.966 13.034 0.822 86.648 13.352 0.818LR 94.865 5.135 0.926 94.510 5.490 0.921MP 97.118 2.882 0.958 96.816 3.184 0.954DT 98.886 1.114 0.984 98.100 1.900 0.973SVM 94.667 5.333 0.923 94.458 5.542 0.920C45 99.825 0.175 0.998 99.309 0.691 0.990Table 3 Prediction accuracy and model performance• Training data and test data• We use the indicators of the correctly classifiedinstances (CCI), incorrectly classified instances (ICI) andKappa value (Kappa).• Data are for each time epoch- WCTRS 2013Count PercentageTraining data 39,942 75%Test data 13,316 25%Total 53,258 100%Training and test datasets
    • Comparison of Imputation AlgorithmsTable 4 Hit ratios by transportation mode and activity episodeNote: A-Activity episode; B-Train; C-Walking; D-Bike; E-Car; F-Bus; G-Motorbike; H-Running; I-Tram; J-Metro• BN and C45 may perform more stable than others• The hit ratios for the test data do not have to be lower than that for thetraining data, except the BN and C45.• The level of the hit ratio of BN model is comparable with other methods.Training Data A B C D E F G H I JBN 0.997 0.997 0.999 1 0.999 0.999 1 0.999 1 1NB 0.848 0.969 0.934 0.799 0.836 0.926 0.949 0.98 1 0.983LR 0.989 0.991 0.818 0.928 0.891 0.758 0.947 0.76 1 1MP 0.998 0.974 0.916 0.926 0.965 0.743 0.989 0.985 1 1DT 0.999 0.971 0.958 0.985 0.979 0.99 0.991 0.974 0.982 0.98SVM 0.987 0.999 0.76 0.925 0.876 0.888 0.971 0.654 1 1C45 1 0.999 0.993 0.997 0.997 0.994 0.998 0.999 0.996 0.99Test Data A B C D E F G H I JBN 0.996 0.993 0.988 0.997 0.994 0.977 0.999 1 1 0.983NB 0.849 0.964 0.942 0.789 0.826 0.9 0.946 0.963 1 0.975LR 0.99 0.994 0.815 0.915 0.882 0.733 0.935 0.752 1 1MP 0.998 0.976 0.896 0.926 0.962 0.708 0.987 0.974 1 1DT 0.998 0.948 0.939 0.973 0.97 0.973 0.982 0.963 0.892 0.959SVM 0.987 0.998 0.763 0.931 0.869 0.844 0.968 0.641 0.985 1C45 0.998 0.998 0.974 0.992 0.987 0.98 0.991 0.956 1 0.992
    • Superimposing the activity/trip sequenceL1 = L4HOMEL2WorkL3ShopSportTrip 2Trip 3Trip 4123Trip 1Trip 5Trip 6L5Restaurant • Method 1o The frequency of the transportation mode whichhas the highest probability is identified for eachtrip episode separately. The transportation modewhich has the highest frequency for all trips isselected.• Method 2o The frequencies of all transportation modes of alltrip episodes which belong to the same tour areput together. Then, the one which has thehighest frequency with highest probabilities isselected to replace others.• Method 3o In case of three or more trips within a sametour, we identify the transportation mode usingMethod 1 for all trips excluding the first and thelast trips. Then, we use the confirmed mode asthe replacement of the first and last trips.- NTTS2013
    • Morning peak Evening peakOriginal imputed 60,50% 71,1%Method 1 65,8% 76,3%Method 2 76,3% 65,4%Method 3 63,2% 68,4%• Hit ratios of car mode (AM vs. PM)BIKE BUS CAR METRO TRAIN TRAM WALKINGOriginal BIKE 4,3% - 6,4% 4,8% - 5,6% 20,9%BUS 4,3% - 34,6% 9,5% - - 21,3%CAR 4,3% 42,9% 2,3% 6,3% 57,1% - 24,4%METRO - - 0,5% 27,0% - - 2,2%RUNNING 48,9% - 0,3% - - - 12,5%TRAIN - 4,8% 42,7% 34,9% 28,6% - 17,2%TRAM - 47,6% 1,8% - - 79,6% 0,9%WALKING 38,3% 4,8% 11,5% 17,5% 14,3% 14,8% 0,6%Method 1 BIKE 34,0% - 2,8% 4,8% - 1,9% 14,1%BUS 4,3% 4,8% 22,6% 9,5% - - 9,7%CAR - 28,6% 26,2% 11,1% 85,7% - 44,4%METRO - 4,8% 0,8% 23,8% - - 1,9%RUNNING 34,0% - 0,3% - - - 5,3%TRAIN - 9,5% 28,8% 33,3% - - 14,4%TRAM - 38,1% 1,3% - - 72,2% 3,4%WALKING 27,7% 14,3% 17,3% 17,5% 14,3% 25,9% 6,9%Method 2 BIKE 19,1% - 3,1% 4,8% - 1,9% 15,0%BUS 4,3% - 19,6% 9,5% - - 7,8%CAR 2,1% 33,3% 26,7% 11,1% 71,4% - 44,1%METRO - - 0,8% 20,6% - - 1,6%RUNNING 34,0% - 0,3% - - - 6,3%TRAIN - 9,5% 31,6% 36,5% 14,3% - 14,4%TRAM - 47,6% 2,0% - - 77,8% 2,5%WALKING 40,4% 9,5% 16,0% 17,5% 14,3% 20,4% 8,4%Method 3 BIKE 17,0% - 4,8% 4,8% - 1,9% 13,8%BUS 4,3% - 23,2% 9,5% - - 14,4%CAR 2,1% 38,1% 13,7% 6,3% 57,1% - 29,7%METRO - - 1,3% 27,0% - - 1,6%RUNNING 29,8% - 0,3% - - 5,6% 10,6%TRAIN - 9,5% 34,4% 36,5% 28,6% - 16,3%TRAM - 38,1% 0,8% - - 68,5% 2,5%WALKING 46,8% 14,3% 21,6% 15,9% 14,3% 24,1% 11,3%Total 100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0%• Confusion matrix of original imputed data and new methods• The confusion matrix showsthat the suggested algorithmcould substantially improvethe accuracy of theimputation;• As shown in the hit ratio, allimproved methods lead toincreased accuracy formorning peak trips relative tooriginally imputed data;• Method 1 is better than theother two methods, especiallyfor the prediction ofmotorized commute tripsduring peak times.
    • Feedbacks from Respondents• Problems during the survey– Problems of using BT747• Different windows system (64b system)• Internet browser (Firefox sometimes has problems)• Can’t download data (complex reasons)• Can’t upload data (wrong data file or data format)– Problems of website• Small bugs of website program (improved)• Multiple persons in a same household (user account specific)• Long processing time (Not cleaning data)– Missing days• Forget GPS logger or problematic data (view as a schedule)
    • Other Issues• Enough number of respondents• Monitor and remind respondents• Completeness of personal profile data (socialdemography)• Post data processing
    • Thanks for your attention.