Impact of Individual Daily Travel Pattern on Value of Time

Transcription

1 Impact of Individual Daily Travel Pattern on Value of Time Rajesh Paleti, Peter Vovsha (Parsons Brinckerhoff) Danny Givon, Yehoshua Birotker (Jerusalem Transportation Masterplan Team)

2 Value of Time (VOT) Traveller s VOT is inferred from the relative values of time and cost coefficients in utility functions of mode and/or time-of-day (TOD) choice models VOT Utils / Hour Utils / $ Time Time / Cost Cost $ Hour

3 Is Value of Time (VOT) Constant? Not true! Earlier studies Systematic variation of VOT travellers by Different demographic characteristics (for instance, low versus high income) Broad trip characteristics (for instance, work versus non-work)

4 Variation of VOT by TOD Not many attempts to capture the situational variation of VOT over the course of the day Differential schedule constraints & time pressures imposed by the daily travel pattern of the individual

5 How to Capture VOT Variation by TOD? Simplified approach Parameterize time/cost coefficients by TOD Implication VOT might be higher during one TOD period versus another purely based on trip departure time Not appealing behaviorally! Innovations in Travel Modeling, 2014

6 Is there a Better Explanation? Differences in the situational time pressures that the person is subject to during different TOD periods Understand the impact of the daily travel pattern of an individual on his/her VOT after controlling for key factors such as income and car occupancy

7 Two Approaches Tested Non-Parametric approach with a very disaggregate trip segmentation by purpose and daily activity pattern Parameterization of time coefficient by different measures of time pressure

8 Underlying Models & Data Joint mode and TOD choice models estimated using pooled RP & SP data RP Component Jerusalem Household Travel Survey 2010 (8,000 households) SP Component pivoted off actual trips in the RP survey 3 additional premium transit alternatives Express Bus, BRT, LRT Toll & managed lane alternatives In each SP experiment, one peak period was defined Asked to choose a combination of mode and time period Design variables travel time, travel cost, fare, parking cost, toll, transit frequency, access & egress times, probability of experiencing delay, & probability of finding a seat Innovations in Travel Modeling, 2014

9 Features of the underlying Models Account for Scale Heterogeneity (SP versus RP) Bias/ State Dependence (SP alternative versus RP pivot choice) Serial Correlation/Panel Effect across SP games Nesting structure to account for similarities in mode and TOD alternatives Innovations in Travel Modeling, 2014

10 Non-Parametric Approach Considering all kinds of trip sequences, tour types, and person characteristics: Leads to an explosion in the number of segments Alternate way: Look at the overall daily activity pattern Identify specific time periods as they relate to the mandatory activity Innovations in Travel Modeling, 2014

11 Typical Daily Travel Patterns Before Mandatory During Mandatory After Mandatory Primary Mandatory Activity Start Time Primary Mandatory Activity End Time Typical Mandatory Daily Travel Pattern Innovations in Travel Modeling, 2014

12 Typical Daily Travel Patterns Tour 1 Tour 2 Tour 3 Departure time from home Arrival time back home Typical Non-Mandatory Daily Travel Pattern Innovations in Travel Modeling, 2014

13 Time Pressure Variation Mandatory travel patterns are different from non-mandatory travel patterns Mandatory patterns are subject to substantial spatial and temporal constraints imposed by the primary mandatory activity Constraints can also vary depending on whether the time period under consideration is before, during, or after the primary mandatory activity Innovations in Travel Modeling, 2014

14 Definition of Primary Mandatory Activity Full-time and part-time workers First arrival time at usual work location Last departure time from usual work location For full-time university and school students, First arrival time at university/school Last departure time from university/school Innovations in Travel Modeling, 2014

15 Model Segmentation Mandatory Patterns Segmented by 3 time periods and combination of the origin and destination trip purposes Non-mandatory Patterns Segmented by combination of origin and destination trip purposes. In some cases, multiple segments were combined either due to data limitations or due to insignificant differences in estimation results Innovations in Travel Modeling, 2014

16 Variation of VOT by Trip Purpose & Travel Pattern Shopping Maintenance Eating Out After Mandatory Shopping During Mandatory Shopping Before Mandatory Shopping Non-Mandatory Shopping After Mandatory Eating Out 0.00 During 0.50 Mandatory 1.00 Eating Out Before Mandatory Eating Out Non-Mandatory Eating Out Discretionary After Mandatory Discretionary During Mandatory 4.00 Discretionary Visit Before Mandatory Discretionary Non-Mandatory Discretionary After Mandatory Maintenance During Mandatory Maintenance Before Mandatory Maintenance Non-Mandatory Maintenance After Mandatory Visit During Mandatory Visit Before Mandatory Visit Non-Mandatory Visit

17 Non-Parametric Approach Results confirm that two trips of the same person and for the same purpose can have different VOT Overall travel pattern (mandatory versus non-mandatory) Relative time period in the mandatory travel pattern (BPM, DPM, or APM) Innovations in Travel Modeling, 2014

18 Does VOT vary by trip/tour? T1 T2 T4 T3

19 Parametric Approach Explicitly account for time pressure instead of completely segmenting the models by time window Active time period: 6 am to 11 pm Length of the time window within the active time period (TW) Number of trips during each time window & within the active time period (N) Time available per trip during each time window (TW/N) V Time Time TW N VOT Time Cost Cost 1 2 Income Car Occupancy Income Car Occupancy Cost TW N 1 Innovations in Travel Modeling, 2014

20 Minimal Segmentation Fully segmented models for trips to work, university, & school Single parametric model for all trips for other purposes

21 Trips to Work Income=11000 NIS/month or $3,080 per month Alpha = 0.2 Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR)

22 Trips Before Primary Mandatory Income=11000 NIS/month or $3,080 per month Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR)

23 Trips During Mandatory Activity Maintenance Income=11000 NIS/month or $3,080 per month Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR) Very few shopping, discretionary, and visit trips in DM period

24 Trips During Mandatory Activity Trips to Mandatory activity Income=11000 NIS/month or $3,080 per month Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR) Very few shopping, discretionary, and visit trips in DM period

25 Trips After Primary Mandatory Income=11000 NIS/month or $3,080 per month Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR)

26 Trips in Non-Mandatory Pattern Income=11000 NIS/month or $3,080 per month Time Available per trip (minutes) IVTT VOT($/HR) Car occupancy OVT VOT ($/HR)

27 Conclusion Results demonstrate dependence of VOT on travel pattern! Parametric approach: More compact Yields consistent & behaviorally intuitive parameter estimates Compatible with advanced ABM structures VOT from highest to lowest: Maintenance trips During Mandatory Activity Before Mandatory period Trips to mandatory activity During Mandatory Activity Trip to the primary mandatory activity After Mandatory period Non-mandatory pattern