Rajesh Paleti (corresponding), Parsons Brinckerhoff

Transcription

1 Paper Author (s) Rajesh Paleti (corresponding), Parsons Brinckerhoff Peter Vovsha, PB Americas, Inc. Danny Givon, Jerusalem Transportation Masterplan Team, Israel Yehoshua Birotker, Jerusalem Transportation Masterplan Team, Israel Paper Title & Number Impact of Individual Daily Travel Pattern on Value of Time [ITM # 30] Abstract The objective of this study is to understand the impact of the daily travel pattern of an individual on his/her Value of Time (VOT) after controlling for key factors such as income and car occupancy. To accomplish this, a trip segmentation framework that allows testing the hypothesis of variation of VOT by TOD and daily pattern structure was developed. Each of the model components in the new framework takes a form of an integrated mode and trip departure TOD choice model. These models were estimated using both Revealed Preference (RP) and Stated Preference (SP) data from a large-scale GPS-assisted Household Travel Survey undertaken in Jerusalem, Israel in To our knowledge, this is one of the first attempts to develop a rigorous modeling framework for capturing a situational variation of VOT. The developed models are incorporated in the operational Activity-Based travel demand Model (ABM) for the Jerusalem Region. Statement of Financial Interest The authors do not have any direct financial interest with regard to this work. Statement of Innovation A traveler's willingness to pay for travel time savings depends on his/her socio-economic characteristics, travel purpose, and situational factors such as time pressure under which the travel is undertaken. Earlier literature on Value of Time (VOT) analysis focused mostly on the first two factors but did not examine the last factor thoroughly. However, in the real world we expect that (at least in most cases) a worker would be willing to pay more during the before-work period than during the after-work period since most of the worker should reach his/her work place by a certain time while the after-work schedule in general should be more relaxed. The additional time pressure during the before-work period makes time more valuable, thus increasing VOT. In some particular cases, where a worker with a flexible schedule has a high-priority post-work activity with a fixed schedule (for example tickets to a concert) the situation can be reversed. The current study aims to capture such impacts of daily activity patterns on a person s VOT using a comprehensive trip segmentation framework that comprises of several integrated mode and trip departure TOD choice models. Each of these integrated models was estimated using both Revealed Preference (RP) and Stated Preference (SP) data from a large-scale GPS-assisted

2 Household Travel Survey undertaken in Jerusalem, Israel. The results not only confirm the long-held hypothesis about variation of VOT by socio-economic factors and trip purpose but also shed light on the variation of VOT with daily travel patterns. To our knowledge, this is the first attempt to develop a rigorous modeling framework for capturing variation of VOT as a function of the individual daily activity pattern. An additional feature of the proposed approach is that it was practically implemented within the framework of an applied Activity-Based Model (ABM).

3 Impact of Individual Daily Travel Pattern on Value of Time Rajesh Paleti Parsons Brinckerhoff One Penn Plaza, Suite 200 New York, NY Phone: Peter Vovsha Parsons Brinckerhoff 1 Penn Plaza, 3 rd Floor New York, NY Phone: vovsha@pbworld.com Danny Givon Jerusalem Transportation Masterplan Team (JTMT) Clal Building, First Offices Floor, 97 Jaffa Rd Jerusalem, Israel Phone: danny_g@jtmt.gov.il Yehoshua Birotker Jerusalem Transportation Masterplan Team (JTMT) Clal Building, First Offices Floor, 97 Jaffa Rd Jerusalem, Israel Phone: birotker@jtmt.gov.il Paper size: 2,079 words + 3 figures ( 250) = 2,829 words Submitted for presentation at 5th Transportation Research Board Conference on Innovations in Travel Modeling (ITM) December 2, 2013 Page 1 of 8

4 Objectives, Motivations, and Innovation Traditionally, most studies that focused on capturing the Value of Time (VOT) of travellers have segmented the time and cost coefficients in the generalized cost function either by different demographic characteristics (for instance, low versus high income group) or broad trip characteristics (for instance, work versus non-work). However, there are not many studies that attempted to capture the situational variation of VOT over the course of the day owing to the differential time pressures imposed by the daily travel pattern of an individual. A simplified approach that parameterizes time/cost coefficients by Time-of-Day (TOD) is not appealing behaviorally since there is no direct consideration why VOT might be higher during one TOD period versus another purely based on trip departure time. Any such variation in the VOT of the same individual can be better explained on the basis of differences in the situational time pressures that the person is subject to during different TOD periods. The objective of this study is to 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. To accomplish this, a trip segmentation framework that allows testing the hypothesis of variation of VOT by TOD and daily pattern structure was developed. Each of the model components in the new framework takes a form of an integrated mode and trip departure TOD choice model. These models were estimated using both Revealed Preference (RP) and Stated Preference (SP) data from a largescale GPS-assisted Household Travel Survey undertaken in Jerusalem, Israel in All the resulting models not only have a rich specification of socio-economic factors (both from the RP and SP components) but also a wide range of policy variables such as tolling, parking cost etc (mainly from the SP component). Several important econometric aspects associated with joint RP-SP data analysis were accounted for in the model specification and estimation thus increasing the reliability of the model results. To our knowledge, this is one of the first attempts to develop a rigorous modeling framework for capturing a situational variation of VOT. The developed models are incorporated in the operational Activity-Based travel demand Model (ABM) for the Jerusalem Region. Methodology VOT can vary by the characteristics of the person. Also, VOT can vary with the trip purpose, direction of travel (inbound versus outbound), and overall individual activity-travel pattern. Ideally VOT studies should consider impact of both person characteristics and daily activitytravel patterns. However, considering all kinds of trip sequences, tour types, and person characteristics can lead to an explosion in the number of segments. Thus, instead of using tour or trip as the segment of analysis, the idea is to look at the overall patterns and specific time periods for each individual as they relate to the mandatory activity pursued during the day. The underlying intention is to capture the impact of varying time pressures during different time periods relative to the primary mandatory activity. For workers and students, there is a primary mandatory activity that is normally pursued regularly at the same location and also during usual time windows compared to other non-mandatory activities. So, mandatory travel patterns are different from non-mandatory travel patterns in that the traveler is subject to substantial spatial and temporal constraints imposed by the primary mandatory activity. Moreover, these constraints can also vary depending on whether the time period under consideration is before, during, or after the primary mandatory activity. Page 2 of 8

5 Given that workers can have multiple work places either due to variable work places or multiple jobs, it is necessary to define the primary mandatory activity time window for workers. In this study, the primary mandatory activity time window for full-time and part-time workers is defined from the first departure time from home to the usual work location and to the last departure time from usual work location, respectively. For full-time university and school students, the first departure time from home to university/school and last departure time from university/school to home define the boundaries of the primary mandatory activity time window. Subsequently, three time periods are identified for each mandatory pattern: 1=Before Primary Mandatory (BPM), 2=During Primary Mandatory (DPM), and 3=After Primary Mandatory (APM). All mode and TOD choice models for non-primary mandatory and non-mandatory trips in a mandatory pattern are segmented by the three time periods and combination of the origin and destination trip purposes. Similarly, all models for non-mandatory trips in a non-mandatory pattern (NMP) are segmented by the combination of origin and destination trip purposes. However, it is important to note that the mode and TOD choice model is not necessarily segmented at the same level. In some cases, multiple segments were combined either due to data limitations or due to insignificant differences in estimation results. We do not present the methodology and model results for all segments in this paper due to the word limitation. To describe briefly, an integrated mode and TOD choice model with nested logit model structure was adopted to account for a) similarity in the mode and TOD alternatives, and b) scale differences in the unobserved factors in RP and SP data. The reader is referred to Paleti et al. (2013) for a complete overview of the modeling framework and estimation results for the To Work trip segment. The modeling framework and the interpretation of results for other trip segments are similar to the work segment. Major Results While the results presented in the paper do demonstrate the merits of our study, we would like to note that this research is a work in progress and the results presented in this paper will most likely be updated by the date of the conference. Given that time and cost coefficients vary by income level and car occupancy, we focus on one car occupancy level and household income level to demonstrate the differences in the VOT by travel patterns. Specifically, we used income level of 3,500 NIS (New Israeli Shekel; 1 NIS = $0.28) and car occupancy level of 1 for VOT comparisons in Figures 1 and 2. Also, VOT depends on whether travel is in-vehicle or out-ofvehicle (OVTT). For brevity, in-vehicle times are used for comparison across different travel patterns. Variation of VOT by Trip Purpose Figure 1 presents the VOT results for different trip purposes in the same time period. All VOT values in the figures are equivalent NIS per hour. It can be seen from Figure 1a that the VOT for work trips is higher in the outbound direction (to work) compared to the outbound direction (from work). However, the directional effect was reversed for university trips. However, no significant difference in VOT between directions of travel was observed for escorting trips. Among different trip purposes during the BPM period, Other mandatory trips were found to have the highest VOT (about $12 NIS/hour). This category comprises of all mandatory trips other than the primary mandatory activity. However, maintenance trips have higher VOT than any other trips during the DPM period. Any such differences were not observed across different trip purposes in the AW period. Moreover, VOT during the APM period was much lower than Page 3 of 8

6 the VOT values in the BPM and DPM periods. The sample size was not adequate for uncovering the travel time and travel cost sensitivity of discretionary trips undertaken during the BPM and DPM periods. For non-mandatory travel patterns, significant differences were observed across trip purposes. For instance, VOT of maintenance trips is nearly twice the VOT of trips of other purposes. The results confirm the long-held hypothesis that VOT varies by trip purpose for nonmandatory travel patterns as well as mandatory travel patterns. Variation of VOT by Travel Pattern Figure 2 presents the VOT results comparing VOT for the same trip purpose across different travel patterns. It can be clearly seen from the figure that VOT for any given trip purpose varies significantly by travel pattern and the time period during which the trip is undertaken. For instance, shopping and eating out trips undertaken during BPM period have much higher VOT than shopping and eating out trips undertaken during other time periods. Also, among all time periods and travel patterns, maintenance and visiting trips have highest VOT during DPM and BPM time periods, respectively. Moreover, VOT of trips in the non-mandatory travel patterns is consistently lower than the VOT of trips in the BPM and DPM periods of mandatory travel patterns. Furthermore, the VOT values in non-mandatory patterns are in the same range as those of the trips undertaken during the APM period in mandatory travel patterns. This makes sense intuitively since the APM period in mandatory travel patterns is similar to non-mandatory travel pattern in terms of the time constraints on the trips undertaken during these periods. Overall, the results confirm that two trips of the same person and for the same purpose can have different VOT depending on the overall travel pattern (mandatory versus non-mandatory) as well as the relative time period in the mandatory travel pattern (BPM, DPM, or APM). This suggests that the traditional method of segmenting trips by purpose alone will not be sufficient to capture the differences in the time and cost sensitivities of trips made by the same individual. In this regard, VOT segmentation that reflects individual daily activity-travel pattern type represents a promising avenue where advantages of a microsimulation ABM can be taken. Page 4 of 8

7 Figure 1a Figure 1b Figure 1c Figure 1d Figure 1e Figure 1 Variation of VOT by Trip Purpose Page 5 of 8

8 Figure 2a Figure 2b Figure 2c Figure 2d Figure 2e Figure 2 Variation of VOT by Travel Patterns Page 6 of 8

9 Figure 3a Figure 3b Figure 3c Figure 3 Variation of VOT by Car Occupancy, Income, & Type Page 7 of 8

10 Variation of VOT by Car Occupancy, Income, and Type (IVTT versus OVTT) Figure 3 presents the VOT results for different car occupancy levels, income levels, and type. It can be seen from Figure 3a that VOT values gradually increase with car occupancy levels for all trip purposes, travel periods, and travel patterns. Also, similar increasing trend was observed with respect to income with high income households having higher VOT for the same trip type compared to low income households (see Figure 3b). Lastly, out-of-vehicle times (OVT) have approximately twice the VOT of in-vehicle times (IVT) which is intuitive given that walk access and egress are physically more onerous compared to IVTs. Implications for the science and/or practice of travel modeling VOT is one of the most widely researched topics in travel demand modeling. However, there is no consensus either in the research or practice community on VOT segmentation and values. The results have varied significantly between very high VOTs to negative VOTs (i.e., positive time/cost coefficient for some segments; see Cirillo and Axhausen (2004)). While some of these results are implications of the underlying model specification (for instance, mixed logit model with normal mixing distribution always assigns a non-zero probability to negative VOT estimates), in some cases it is an implication of the difficulties associated with uncovering time and cost sensitivities using RP data. The current research attempts to capture differences in VOT using integrated mode and time-of-day (TOD) choice models estimated using both RP and SP data. Furthermore, unlike earlier studies that only examined VOT variation with different socioeconomic factors and broad trip purposes, this study captures the impact of daily travel patterns on VOT. In the real world, it is very common to observe people of the same socio-economic group subject to significantly different time constraints depending on their travel pattern. For instance, maintenance trips made by workers before work are normally more constrained than maintenance trips after work since most of the workers are under pressure of reaching their work place by a certain time during the before-work period. Ignoring such situational differences in VOT valuation can have significant policy implications. For instance, an individual s willingness to pay tolls under congestion pricing schemes might vary significantly depending on whether the person undertakes the trip before work, during, or after work period. Thus, forecasts of toll revenues would be biased if the analyst assumes constant VOT for any given trip purpose and socio-economic segment. We believe that research on deeper understanding of the situational variation of VOT is timely and important to improve our understanding of travel behavior and modeling practices. References The provided references only serve the purpose of supporting the main statements in the brief. The full paper includes a substantial literature review and summary of VOTs. Cirillo, C. and K.W. Axhausen (2004) Evidence on the distribution of values of travel time savings from a six-week diary, Arbeitsbericht Verkehrs- und Raumplanung, 212, IVT. ETH Zürich, Zürich. Paleti, R., P. Vovsha, D. Givon, and Y. Birotker (2013) Joint Modeling of Trip Mode and Departure Time Choices using Revealed and Stated Preference Data. Technical Paper, Parsons Brinckerhoff. Page 8 of 8