GPS and Multi-Week Data Collection of Activity-Travel Patterns Harry Timmermans Eindhoven University of Technology 4/19/2015

The Survey Method Conventional survey methods for activity-travel diary data Application of new data collection method – GPS logger (original traces) – User participation Personal profile Downloading en uploading data Validating activity-trip agendas – Web-based prompt recall Embedded with TraceAnnotator

Framework 4/19/2015Feng&Timmermans3 Transportation mode Activity episode Personal Data GPS data Geographical Data it appears more than reasonable to expect that a traveler‟s decision to acquire travel information is to some extent dependent on the availability of a telework-option, and vice versa. Take for example the situation where a traveler chooses to work from home after having received travel information that her route from home to work is severely congested. Or

Approach Classification of transport modes and activity episode – Bayesian Belief Network (BBN) Replaces ad hoc rules A graphical representation of probabilistic causal information incorporating sets of probability conditional tables; Represents the interrelationship between spatial and temporal factors (input), and activity-travel pattern (output), i.e. transportation modes and activity episode; Learning-based improved accuracy if consistent evidence is obtained over time from more samples;

Conditional Probabilities

The Prompt Recall

Validation of Activities/Trips

Accuracy of the Algorithm Source: 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 Algorithm ActivityWalkingRunningCyclingBusMotorcycleCarTrainMetroTramLight rail Activity84%4%0% 1%9%2%0% Walking2%97%0% 1%0% Running0% 98%0%1%0%1%0% Cycling0% 100%0% Bus1%0% 87%0% 12%0% Motorcycle0% 100%0% Car0% 1%0%98%0% 1% Train0% 5%58%36%0% Metro1%0% 1%98%0% Tram0% 2%0% 98%0% Light rail0% 2%0% 98% GPS Only Activity84% Walking97% Running98% Cycling100% Bus87% Motorcycle100% Car98% Train58% Metro98% Tram98% Light rail98% Source: Feng, T and Timmermans, H. (2012) Recognition of transportation mode using GPS and accelerometer data. International Conference of IATBR, Toronto, Canada, 15-20, July, 2012.

Survey Management Time horizon – 1 st wave ( April 30 ~ August 30) – 2 nd wave ( August 13 ~ November 18) Location – Rotterdam area (NTS NIPO) Communications – TU/e and NTS NIPO communicated closely to give responses/solutions to all type of problems

User Participation (# of days) 1 st wave: 24 (of 55) participants 2 nd wave: 109 (of 155) participants (in progress, around 80 days)

Age of Respondents The percentage of respondents who are older than 55 is 45.7%. No children

Frequency of Activities/Trips Data of missing days were filled by full-day single activities. High frequency is due to the short events, which needs to be filtered further.

Frequency of Activity Type

Activity Duration by Type

Frequency of Transport Mode Many short walking trips

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)

Current Data Problems Number of respondents was not enough, less than half in-take people kept active; Number of days is low, around 50% active participants had more than a month data; Slightly high proportion of elder people; Post processing: data in missing days, missing information (parking, expenditure, etc.)

Thanks for your attention.