Managed Lane Choices by Carpools Comprised of Family Members Compared to Non-Family Members

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1 Managed Lane Choices by Carpools Comprised of Family Members Compared to Non-Family Members Mark W. Burris, Ph.D, P.E. Snead I Associate Professor Zachry Department of Civil Engineering Texas A&M University CE/TTI Building Room 0G TAMU College Station, TX - Corresponding author: mburris@tamu.edu Ph: -- Fax: -- Mandeep Singh Pannu, EIT Transportation Planning Analyst Traffic Engineers, Inc. Southwest Freeway, Suite 00 Houston, TX 0 mandeep@trafficengineers.com Ph: -0- Paper Submitted for Publication in Transportation Letters November 00

2 ABSTRACT Carpools can be comprised of family members (fampools), non-family members (non-fampools) or a combination of both. By analyzing the characteristics of fampools and non-fampools, we can better understand both groups and how policies may impact each group differently. One area of particular interest is the impact of managed lanes on the mode choice of fampools and nonfampools. Many managed lanes offer travelers a choice to use the lanes toll-free as a carpool or use the lanes as a single occupant vehicle for a toll. This may influence some carpools to break up, with one hypothesis being fampools would be more likely to stay together. This research found that the majority of fampools and non-fampools are interested in using managed lanes. The analysis of survey data proved that there are many similarities between the fampools and non-fampools but very few differences. Fampools carpooled on a smaller percentage of their weekly trips than non-fampools did. Fampools more often formed carpools for dropping off kids at school or day care, non-fampools formed carpools more often for sharing the vehicle expenses. The simulation study performed to analyze the impact of different travel scenarios available on managed lanes on the mode choice of carpoolers showed that a very small percentage of fampools and non-fampools chose to travel as a single occupant in managed lanes. Both fampools and non-fampools were insensitive towards the toll cost. The analysis of the descriptive statistics and the results from the simulation models confirmed that fampools and non-fampools have similar commute characteristics, interest in managed lanes, and socio economic characteristics and had similar reactions to managed lanes. KEY WORDS Managed Lanes, Carpools, Fampools, mode choice

3 INTRODUCTION Carpooling declined during the 0 s and 0 s, even as the policies were in place to encourage carpooling (Ferguson (); Pisarski (); Pisarski (00); Poole and Balaker (00)). Overall, the carpool mode share has decreased from 0 percent of all commuters in 0 to percent in 000 (Pisarski (00)). The recent census data shows that the overall carpooling mode share decreased to percent in 00 (Liberles (00)). Carpoolers are comprised of two very different groups of travelers: fampools and non-fampools. Additionally, the majority of carpools these days are fampools (Pisarski (); Burris et al. (00); Li et al. (00) ). Alan Pisarski, in his study of Commuting in America II found, Most carpooling today is not carpooling in the sense we knew it just a few years ago: a voluntary arrangement among co-workers or neighbors. That is dying; most of the surviving carpool activity consists of family members with parallel destinations and timing. A term Fampool is used to describe the carpoolers who travel with other household travelers. In major metropolitan areas fampoolers are one third to two third of total carpoolers (Pisarski ()). With the decreased carpool mode share and increased interest in using high occupancy/toll (HOT ) lanes by transportation authorities in the United States, it became crucial to examine the role of HOT lanes in encouraging or discouraging carpools (Parkany ()). Most research has focused on how carpooling mode share changes with time, fuel costs, and socio-economic characteristics like income, economic growth, availability of jobs, etc. A great deal of the research done on carpool mode share has used the National Household Travel Survey (NHTS) dataset which is limited to socio-economic and demographic variables (Pisarski (); Bard (); Dohery et al. (00); Teal ()). But none of these studies examined the behavior of fampools and non-fampools in the vicinity of managed lane options which allow them to travel as a SOV after paying a toll. It is important to understand the impact of toll and travel time savings on mode choice of carpoolers as HOT lanes may negatively impact carpooling. Managed lanes may greatly impact the different carpool groups and the impacts may be considerably different by group. In the case of managed lanes (MLs), there is often the option to pay for travel on the lanes as a single occupant vehicle (SOV) or travel toll-free as a HOV. It would not be surprising if fampools would be more likely to stick together as carpools under this scenario while non-fampools may more frequently take the opportunity afforded by the ML and break up into (toll paying) SOVs. This research investigates the mode choice of fampools and non-fampools in the presence of a ML option and identifies important factors influencing their mode choice through descriptive statistics and discrete choice modeling of survey data. The primary objective of this research is to examine the differences and similarities in the characteristics and mode choice of these two types of carpoolers, including how fampools and non-fampools react to different travel options on the managed lanes. This includes how carpoolers from these two categories may switch their mode if given an option to travel alone on a managed lane for a particular price. This information will be important to planners looking at A high occupancy vehicle (HOV) lane is a lane that restricts usage to people who are driving vehicles with a certain number of occupants (often or more). A high occupancy/toll (HOT) lane is a HOV lane that also allows people to use the lane for a toll if they have too few occupants to travel toll-free. Managed lanes (MLs) is a generic term that encompasses all lanes that are managed (access controlled) by pricing, occupancy, etc. Therefore, HOT lanes are one form of MLs

4 adding MLs in their jurisdiction as MLs could have a significant, and negative, impact on carpooling. If fampools were more likely to stay together and an area had a very high percentage of fampools than the negative impact on carpooling of adding MLs would be less than in an area predominated by non-fampools (more traditional carpools). REVIEW OF CHARACTERISTICS OF FAMPOOLS AND NON-FAMPOOLS A study analyzed traveler characteristics by mode choice in HOT corridors in Houston (Burris and Figueroa (00)). In this study, particular effort was spent examining the characteristics of QuickRide travelers. In the QuickRide program, HOV+ are allowed to travel for free and HOV carpools are allowed to travel during peak periods for a toll of $ while SOV s are never allowed to travel on the two HOT corridors. This research found in almost 0 percent of carpools, the passengers were family members. QuickRide trips were more likely to be school trips (%), and in % of these trips, travelers were traveling with a child. The issues related to carpooling were explored directly by examining reasons behind people s travel mode choice decision through a survey of travelers in Dallas-Fort Worth and Houston, the two largest metropolitan areas in Texas (Li et al. (00)). In the survey, respondents rated different reasons for carpooling. Fampools rated dropping off kids at school or day care and enjoying travel with others higher than other reasons. While, non-fampools rated other factors including travel time and cost related factors higher on average. Comparing the two groups, non-fampools rated factors including access to HOV lanes, travel time savings, sharing vehicle expenses, reliability of arrival time, splitting tolls on toll roads, encouraged by program at work and preferred parking at work significantly higher than fampools. It also found that percent of carpools were fampools. The commute surveys from and 00 in the San Francisco Bay Area estimated that fampools make up one-third of carpools (Poole and Balaker (00)). Similarly: In California, fampooling increased from percent in to percent in (Southern California Association of Governments ()). In Minneapolis-St. Paul, study found percent of carpoolers were fampools (Poole and Balaker (00)). In Houston, previous studies found that between 0 percent and percent of carpools were fampools (Burris and Figueroa (00)). In a nationwide estimate of all work commute carpools from 0 to 00, an increase of fampools from. percent to percent was found (McGuckin and Srinivasan (00)). Based on the NHTS data, the majority of carpool peak period trips were likely to be fampools. The majority of highest occupancy trips during peak time were not work trips. The highest occupancy trips were for social and family purposes, which also suggested the likelihood of fampooling (Poole and Balaker (00)). SURVEY DATA COLLECTION The survey responses from two Texas metropolitan areas (Houston and Dallas), which have numerous HOV and HOT lanes, were used for this research. Data regarding traveler s commute characteristics, views towards managed lanes, stated preference questions, and socio economic characteristics were collected. Many of the respondents already traveled on a roadway with a toll or an HOV lane, while the remainder traveled on roadways where an HOV, HOT, or managed lane might be added at a future date. The survey was advertised using newspaper articles, TV news, push cards given at tollbooths and links on different websites. In both Houston and Dallas,

5 the majority of the respondents completed the survey online, but this data did not have many respondents from lower income and minority populations. For this reason, a paper and laptop based survey was also conducted at the community centers and department of public safety offices in low income and minority neighborhoods. These surveys were identical to the ones online. (See the project final report at 0_transmittal_00.pdf for additional details regarding survey design and administration). Unfortunately, the 0 total respondents still did not reflect the ethnic and economic makeup of the two cities, so a weighting process was undertaken. The data was weighted to better represent the demographic features of the population. The sampling weights (fixed) were developed using the income (four groups), ethnicity (four groups), and toll-road usage (two groups). Due to the data collection process used for this survey, the sampling design was not simple random sampling (SRS). In this scenario, the use of fixed weights based on the assumption of SRS would imply lower standard error (SE). Thus, using fixed weights may lead to some results from non-srs surveys being found statistically significant when in fact they are not. To address this issue, replicate weights were calculated using fixed weights as the input (Burris et al. (00)). All analyses in this paper use these replicate weights. The survey data from Houston and Dallas were compared to determine if they need to be analyzed separately or can be analyzed as one group. Houston and Dallas respondent data were analyzed based on their response to the stated preference questions. If the respondent selected a managed lane option (SOV or HOV) in at least one of the four stated preference questions then they were categorized as selecting a managed lanes option. The selection of a managed lanes option was fairly consistent between the two cities in most strata (Table ). There was no significant difference in choosing MLs between the two cities, and therefore a model that combined the data from these two cities was developed. DESCRIPTIVE ANALYSIS OF CHARACTERISTICS OF FAMPOOLS AND NON- FAMPOOLS With the decline of traditional carpooling and the increase in fampooling, there is an increasing need for a better understanding of these two groups. This understanding canhelp identify reasons for changes in the carpooling behavior and possible ways to encourage carpooling. This may be even more critical in areas considering HOT lanes and MLs as the reaction to these new lane choices may have considerably different impacts on fampools versus non-fampools. For this purpose, carpoolers were divided into fampool and non-fampool groups. Fampools included all carpools that had only family members regardless of age. This is consistent with HOV lane policy that considers all people, even those too young to drive, as members of the carpool. Similarly, non-fampools were comprised of any set of multiple people in the same vehicle who were not related, again regardless of age. There were a few respondents who had both family members and non-family members in the vehicle, termed mixed carpools. Due to the small sample size of mixed carpools, it was not possible to keep the mixed carpools as an independent group for the descriptive statistics analysis. The mixed carpools were further investigated to decide whether to merge them with the group of fampools or non-fampools. The comparison of the mode choice of all three groups revealed that mixed carpools showed different tendencies than did fampool and non-fampool groups. Hence mixed carpools were excluded from all data analysis in this paper. The detailed

6 descriptive statistics were generated for the commute characteristics, interest in managed lanes, and socio-economic characteristics for fampools and non-fampools (Table ). There were very few significant differences found between the two groups. Fampools carpooled a on a smaller percentage of their weekly trips than non-fampools. Approximately percent of fampools continued to their final destination after dropping off their passenger in comparison to percent of non-fampools. This makes sense as the majority of non-fampools had the same destination. These were the only significant differences (p=0.0) between the two groups. Many interesting similarities were found with fampools and non-fampools. Both fampools and non-fampools were most likely to be on a commute trip. The amount of carpooling increased for both fampools and non-fampools with increased travel distance. Fampools had an average carpool formation time of. minutes in comparison to. minutes for non-fampools. Overall, fampools and non-fampools had very similar characteristics. In addition to examining the similarities and differences between the characteristics of fampools and non-fampools, their reaction toward the use of managed lanes was analyzed. After a brief description of MLs, survey respondents were asked if they would be interested in using MLs. The majority of respondents in both groups indicated an interest in using managed lanes. To explore further, many different factors for the interest or disinterest in using managed lanes were also examined. The respondents ranked different factors on a scale from to, with rank as the least important and as the most important. Both fampools and non-fampools stated travel time reliability as the most important factor for their interest in using managed lanes. Able to travel faster than GPL was the second most important reason for travelers from both groups. When asked about the reason for their disinterest in using managed lanes, fampools rated other the highest, followed by not interested in paying a toll. Because other was so important, the text that respondents entered corresponding to other was examined. The vast majority of the typed notes were anti-toll. Non-fampools were not interested in using managed lanes because of the toll and also because they were not interested in driving alone. Note that despite these small differences in rankings, the difference was not statistically significant and there were no significant differences between the two groups found regarding their interest or disinterest in using MLs. The important reasons for the formation of the respondent s current carpool were also examined to find any differences between fampools and non-fampools. Fampools stated dropping off kids at school or day care was more important to them than non-fampools. Nonfampools rated sharing vehicle expenses higher than fampools. These differences were significant at a percent level of confidence. Overall, fampools rated relaxation while traveling as the most important while Preferred parking at work and encouraged by program at work were the least important factors in the formation of their current carpool. Non-fampools indicated access to HOV lanes was the most important reason while dropping off kids at school or day care was the least important reason in the formation of their current carpool (see Table ). For further analysis, fampools were split into two sub-groups: with children and adults only. The three groups were examined to see if fampools with a child(ren) were different than fampools of adults-only or non-fampools and if fampools of adults-only were most similar to non-fampools. It was found that non-fampools carpooled more regularly in a week in comparison to the two fampool groups. Within fampool subgroups, fampools of adults-only carpooled more often than fampools with a child(ren). The difference was significant at a percent level of confidence. When comparing their average carpool formation times, fampools with a child(ren)

7 reported the highest time of. minutes in comparison to the. minutes for fampools with adults-only and. minutes for non-fampools. However, this difference was not significant and there were few other differences found. The data were analyzed further to examine the characteristics of travelers from these three groups by dividing them into sub-groups based on their current number of passengers. However, none of the differences between the groups were found to be significant. This was partially due to the similarities between the groups and partially due to lower sample sizes (and higher standard error). The descriptive statistics analysis proved that there were many similarities between the fampools and non-fampools but very few differences. The differences found in the descriptive statistics analysis did not provide sufficient information to conclude any specific reasons for the mode switching by different groups of carpoolers. Overall, this information was only minimal help in determining what variables should most likely be included in the mode choice model (discussed below). DISCRETE CHOICE MODELING Another objective of this research was to estimate the mode choice of fampools and nonfampools on managed lanes. The reaction of carpoolers from both groups were examined for different toll and travel time options available on managed lanes based on their survey responses to ML stated preference questions. The mode choice models were developed for fampools and non-fampools using Nlogit (NLogit (00)). Different random parameter logit models were developed to predict traveler s responses to the GPL and ML options. Different variables were used as dummy variables in the model built to predict mode use of the six options given in the stated preference section of the survey. These options were: SOV on the MLs HOV on the MLs HOV+ on the MLs SOV on the GPLs HOV on the GPLs HOV+ on the GPLs In the next step a market segmentation approach was used was to decide upon whether to use a pooled logit model (a single model for both fampool and non-fampool respondents) or a separate model for respondents from each group (Koppelman and Bhat (00)). Market segmentation approach can be used to determine whether the impact of the variables is different among population groups. Models were estimated for the sample associated with each segment and compared to the pooled model (all segments represented by a single model) to determine if there are statistically significant and important differences among the market segments. Based on the test statistic, it was found that two separate models better predicted the mode choice of fampools and non-fampools. Mode Choice Model for Fampools As described above, a random parameter logit model was estimated to better understand the factors influencing the mode choice of fampools. Various utility equations with different variables were tested. The utility functions given in Table were found to have best fit and

8 explanatory ability. All the variables were significant at a percent confidence level. Driving alone on the GPLs was the base mode. The utility equations for the mode chosen by fampools revealed relatively few surprises. To begin, the only positive alternative specific coefficient (ASC) was for HOV on the GPLs. Exact interpretation of the coefficient was difficult since we could not be sure if the result was more due to the fact that it was a fampool or the mode chosen. For example, fampools on a commute trip were less likely to choose HOV on MLs. It was hard to be sure if that was because fampools were not on commute trips or if they avoided HOV on MLs. In any case, the results appeared reasonable and will be compared to the non-fampool results for additional clarity. The overall model provided an acceptable adjusted rho square value of 0.. The estimated VTTS was $.0 per hour for the fampools. Mode Choice Model for Non-fampools Similar to the development of the fampool mode choice model, a random parameter logit model was developed for non-fampools. All the variables used in the non-fampool model were significant at a percent confidence level (Table ) except for income in the final mode (HOV+ on the GPLs). The overall model provided an acceptable adjusted rho square value of 0.. It was interesting to note that time was not a significant variable in this model. It implied nonfampools were not very sensitive to travel time. Note that this is only valid for the range of tolls displayed in the survey. However, this range covered all reasonable toll levels as most respondents were shown tolls between $0. and $, but a small percentage of respondents were shown tolls up to $. As time was not a significant variable, according to the mode choice model in Table, any changes in travel time did not impact the mode choice of non-fampools. To investigate this unusual result the survey data was analyzed further. Data was analyzed based on the mode choice by the non-fampools in the stated preference section of the survey and travel time savings offered by the managed lanes for a specific toll. It was found that even with the large travel time savings non-fampools were equally likely to choose between MLs and GPLs (Figure ). It confirmed that non-fampools were not sensitive to the time and were equally likely to choose between MLs and GPL. The findings from the descriptive statistics analysis also confirm that non-fampools were more sensitive to the toll cost in comparison to the fampools. Simulation Results for the Fampools and Non-fampools Next, to analyze the impact of travel time and tolls on the mode choice of fampools and nonfampools, many different managed lane travel scenarios were simulated. The mode choice models described in the previous section were used to estimate the percentage of travelers willing to use managed lanes under realistic toll and travel time savings scenarios. The main focus was on to analyze any mode switching by carpool groups. The simulation included several assumptions that resemble some typical ML scenarios found in the US: The trip length was assumed to be 0 miles The managed lane speed was assumed to be mph The speed on general purpose lanes (GPLs) was assumed to be 0 mph The toll varied between $0.00 and $.00 in increments of $0.0 General purpose lanes (GPLs) refer to the lanes that are open to all traffic without tolls or occupancy restrictions. They are the typical freeway lanes, where the MLs are the unusual, managed, lanes.

9 Total travel time was the combined time of the actual travel time plus carpool formation time The time required to pick up and drop off the passenger was calculated from the reported carpool formation time by the respondents in the survey. HOVs reported an average carpool formation time of.0 minutes and HOV+s reported an average carpool formation time of. minutes. This time was added to the actual travel time to get the total travel time for the simulation runs. To analyze the mode switching behavior of fampols and non-fampools, three different scenarios were simulated. In many ML situations higher occupancy vehicles receive some price discount or free travel so these scenarios include such price discount. In the first scenario, the SOV toll was varied from the $0.0 to $.00, the HOV toll was kept at half of the SOV toll, and HOV+ were allowed to travel for free. As the toll increased from $0.0 to $.00 for SOVs (and to $.0 for HOVs), the decrease in SOVs and HOVs on MLs was compensated by an increase in all GPL modes (Figure, ). To summarize, with the increased toll, the majority of travelers who switched chose SOV or HOV mode on GPLs. The percentage switching to HOVs on GPLs was slightly higher for fampools. While for non-fampools, SOVs on GPLs observed the highest percentage switch. Overall, the carpool mode share decreased for both fampools and non-fampools by. percent and. percent respectively. In the second scenario the removal of the toll discount for HOVs on managed lanes was investigated. The SOV toll on the managed lanes was kept constant at $ but the HOV toll on the managed lanes varied from $0.00 to.0. HOV+s were still allowed to use the managed lanes for free. For both fampools and non-fampools, as the toll increased, a very sharp decrease was observed for HOVs on MLs (Figure, ). The majority of travelers, who switched, chose SOV and HOV modes on GPLs. For fampools, the highest percentage increase was observed in HOV mode travelers on GPLs while the highest percentage of non-fampools switched to SOV mode on GPLs. With the increased toll, there was a similar decrease observed in the overall carpool mode share for both fampools and non-fampools of. percent and. percent respectively. Third scenario examined the removal of free travel for HOV+s on managed lanes. The HOV toll varied from $0.0 to $.0 but the SOV toll was constant at $ and the HOV toll was also kept constant at $.0. For both fampools and non-fampools, as the toll increased, the decrease in HOV travelers on the MLs was compensated by the increase in all other modes (Figure, ). There was an overall decrease observed in the total number of HOVs of 0. percent for the fampools and. percent for the non-fampools. From the simulations, it was observed that both fampools and non-fampools switched their modes to GPLs in response to the increased toll on the MLs. To examine the extent of this mode switching, the elasticities were calculated. Elasticities were calculated for scenario to test the reasonableness of fampools and non-fampools switching to the SOV and HOV modes on ML for every $0.0 increase in the toll value. For fampools, SOV elasticity varied between -0.0 and -0. and for HOVs elasticity varied between -0.0 and -0.. For non-fampools, SOVs elasticity varied between and -0., and the HOVs elasticity varied between -0.0 and According to the literature these values seemed reasonable and demand elasticity was fairly inelastic (Matas and Raymond, 00).

10 Comparison between Fampools and Non-fampools Both fampools and non-fampools were not very sensitive towards the cost of the toll. For both fampools and non-fampools, as the toll increased, the decrease in the number of travelers on the MLs was compensated by an increase of travelers in the GPLs. The majority of travelers switched to SOV and HOV modes on the GPLs. The percentage of fampool and non-fampool travelers switching to SOV and HOV modes on GPLs were too close to reach any concrete conclusion regarding their mode switching behavior although fampools remained HOVs slightly more often. The results from the mode choice analysis agreed with the findings from the descriptive data analysis. For fampools, the percentage of travelers who switched to HOV on GPLs was slightly higher. These results confirmed the finding that dropping off kids at school or day care was more important reason for fampools than non-fampools for the formation of their current carpool, hence were more likely to stay with their current carpool. For non-fampools, the percentage of travelers switching to SOV mode on GPLs was slightly higher. The descriptive statistics analysis found that sharing vehicle expenses was more important to non-fampools than fampools for the formation of their current carpools. This could be one reason for nonfampools switching to SOVs on GPL. However, this percentage shift was too small to validate it as a conclusion. Overall, in the mode choice analysis, there were no contrasting differences observed between fampools and non-fampools. CONCLUSIONS To better understand the mode choice of fampools and non-fampools, this research examined survey data collected from travelers in Houston and Dallas, Texas. The majority of both fampools and non-fampools were interested in using managed lanes. There were very few significant differences found in the commute characteristics of fampools as compared to nonfampools. Fampools carpooled a smaller percentage of their weekly trips than non-fampools. Fampools indicated that dropping off kids at school or day care was the most important reason for their current carpool formation in comparison to the non-fampools. Sharing vehicle expenses was more important to the non-fampools than the fampools. This was one of the few clues behind any possible differences in how the two groups may change mode in response to MLs. There were almost no significant differences found in the socio-economic characteristics and reasons for interest or disinterest in using managed lanes of fampools and non-fampools. To further investigate how these travelers may react to MLs, mode choice models were developed for the fampool and non-fampool carpool groups. The value of travel time for the fampools was estimated to be $.0 per hour. Non-fampools were not sensitive to travel time. Next a simulation study was done using these models to estimate the percentage of fampools and non-fampools that would likely use managed lanes under different travel scenarios. A very small percentage of fampools and non-fampools chose SOV mode on ML. Both fampools and nonfampools were insensitive to the tolls. With an increased toll, some travelers from both groups switched to SOV and HOV modes on GPLs. However, this shift was very small and so similar that nothing concrete could be concluded about differences in the mode switching behavior of fampools versus non-fampools based on these findings. This also indicates that factors other than price are influencing their choice to carpool and the most important reasons for carpooling are found in Table. Although the survey sample size was large, the number of travelers in some categories, for example HOV+ non-fampool, was small. A considerable variation in the characteristics of the

11 travelers sometimes resulted in large standard deviations for the descriptive statistics of the groups. This, combined with the fact that respondent answers from fampools and non-fampools were similar meant little difference between the two groups. ACKNOWLEDGEMENTS The authors wish to thank the Texas Department of Transportation for funding the Texas Transportation Institute (TTI) project entitled The Role of Preferential Treatment for Carpools in Managed Lane Facilities, from which the data are used for this research. However, the contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or polices of the Texas Department of Transportation. The authors would like to thank the TTI research team led by Ginger Goodin as research supervisor for conducting the survey to collect data and helping with the data analysis. Special thanks go to the TxDOT project director, Matt MacGregor, P.E., for his leadership and guidance. We would especially like to mention Dr. David Ungemah for programming the online survey and Maneesh Mahlawat for the initial data cleaning. REFERENCES Bard, E. A. Transit and Carpool Commuting and Household Vehicle Trip Making. In Transportation Research Record: Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D.C.,, PP. -. Burris, M. W., D. H. Ungemah, M. Mahlawat and M.S. Pannu. Investigating the Impact of Tolls on HOVs Using MLs. In Transportation Research Record: Journal of the Transportation Research Board, No. 0, Transportation Research Board of the National Academies, Washington, D.C., 00, pp. -. Burris, M. and C. Figueroa. Analysis of Traveler Characteristics by Mode Choice in HOT Corridors. Journal of the Transportation Research, Vol., Issue, 00, pp. 0-. Chen, T. D. Compendium of Student Papers: 00 Undergraduate Transportation Engineering Fellowship Program: Improving Houston Quickride Through an Investigation of Former Users. Texas Transportation Institute, Texas A&M University, College Station, Texas, 00. Doherty, S.T., M. Gosselin, K. Burns and J. Andrey. Household Activity Rescheduling in Response to Automobile Reduction Scenarios. In Transportation Research Record: Journal of the Transportation Research Board, No. 0, Transportation Research Board of the National Academies, Washington, D.C., 00, pp. -. Ferguson, E. The Rise and Fall of the American Carpool: 0-0. Transportation, Vol.,, pp. -. Koppelman, F. S., Bhat, C. A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models. University of Texas, Austin, Texas, 00.

12 0 0 Li, J., P. Embry, S. P. Mattingly, K. F. Sadabadi, I. Rasmidatta, and M.W. Burris. Who Chooses to Carpool and Why?. In Transportation Research Record: Journal of the Transportation Research Board, No. 0, Transportation Research Board of the National Academies, Washington, D.C., 00, pp. 0-. Liberles, J. Riding Along Together: Carpooling. Accessed September, 00. Matas, A. and J. Raymond. The Demand Elasticity on Tolled Motorways. Journal of Transportation and Statistics, Vol., Numbers /, 00, pp. -0. McGuckin, N. and N. Srinivasan. The Journey-to-Work in the Context of Daily Travel. Accessed November 0, 00. Nlogit. Modeling Individual Choice with NLOGIT: Model Estimation, Accessed July, 00. Parkany, E. Can HOT Lanes Encourage Carpooling? A Case Study of Carpooling Behavior on the Express Lanes. Institute of Transportation Studies, University of California, Irvine, California,. Pisarski, A. E. Commuting in America II, the Second National Report on Commuting Patterns and Trends. Urban Land Institute, Washington D. C.,. Pisarski, A. E. Commuting in America III, the Third National Report on Commuting Patterns and Trends. Transportation Research Board, Washinton D. C., 00. Poole, R. W. and T. Balaker. Virtual Exclusive Busways: Improving Urban Transit while Relieving Congestion. Policy Study. Reason Foundation. Accessed July 0, 00. Southern California Association of Governments. State of the Commute Report. Los Angeles. Accessed February 0, 00. Teal, R. F. Carpooling: Who, How and Why. Transportation Research Part A, Vol., Issue,, pp

13 0 TABLES TABLE Respondents Choosing a Managed Lanes Option Characteristic Percentage Choosing a Managed Lane Option Dallas (n=,0) Houston (n=,) Proportion Std. Proportion Std. p-value Error Error Household Income Less than $,000.%.%.%.% 0. $,000 - $0,000.%.%.%.% 0.0 $0,000 - $00,000.%.%.%.% 0. Greater than.%.%.%.% 0. $00,000 Ethnicity Caucasians.%.%.%.% 0.0 Afro-American.%.%.%.% 0. Hispanic.%.%.%.% 0. Others.%.%.%.% 0.0 Trip Purpose Commute 0.%.%.%.% 0. Recreational.%.%.%.% 0. Work.%.% 0.%.% 0. School.%.%.%.% 0. Other.0%.%.0%.% 0. Mode SOV.%.%.0%.% 0. HOV-.%.0%.%.% 0. HOV-+ 0.%.%.%.% 0. Transit.%.%.0%.% 0. Motorcycle.%.%.0%.% 0.0 Vanpool.%.%.%.% 0. Total.%.0%.%.% 0. Note that the number of respondents (n) varied slightly from question to question as not all respondents answered every question.

14 TABLE Descriptive Statistics of Fampools and Non-fampools Characteristics Fampools Non-fampools Interested in using ML Proportion Std Error Proportion Std Error P- Value Yes.%.%.%.% 0. No.%.%.%.% 0. Trip Purpose Commute.%.%.%.0% 0. Recreational.0%.%.%.% 0. Work related.%.0%.% 0.% 0. School.%.% 0.%.0% 0. Other.%.%.%.% 0. Total (N) Typical Trip Length Short (0- miles).%.%.%.% 0.0 Medium (- miles).%.% 0.%.% 0. Long (0-0 miles).%.% 0.% 0.% 0. Very Long (more than miles).%.%.%.% 0. Average trip distance (miles) Total (N) Number of Trips per Week or.%.%.%.% 0. From to.%.%.0%.0% 0. From to.%.%.%.% 0. 0.%.%.%.% 0. more than 0 0.0%.%.%.% 0. Average number of trips Total (N)

15 TABLE (Continued) Descriptive Statistics of Fampools and Non-fampools Characteristics Fampools Non-fampools Proportion Std Error Proportion Std Error P- Value Carpool Trips per Week Percentage of trips carpooled.%.%.0%.0% 0.0** Total (N) 0 Pay Toll Yes.%.% 0.%.% 0. Total (N) 0 Carpool Formation Time Average Time (min) Total (N) 0 Age From to years old.% 0.%.% 0.% 0. From to years old.%.% 0.%.% 0. From to years old.%.%.%.% 0.0 From to years old.% 0.%.%.% 0. From to years old.%.%.%.% 0. More than years old.%.% 0.% 0.% 0. Total (N) Gender Male.% 0.%.%.% 0. Female.% 0.% 0.%.% 0. Total (N) Number of Vehicles One or None.% 0.%.%.% 0. Two.%.%.% 0.0% 0. Three or more.%.%.%.% 0. Total (N) 0

16 TABLE (Continued) Descriptive Statistics of Fampools and Non-fampools Characteristics Fampools Non-fampools Proportion Std Error Proportion Std Error P- Value Household Type Single adult.%.%.%.% 0. Unrelated adults (e.g., roommates).%.%.%.% 0. Married without child.%.%.%.% 0. Married with child(ren).%.%.%.0% 0. Single parent 0.%.%.%.% 0. Other.%.%.0%.% 0. Total (N) Household Size One.%.%.%.% 0. Two 0.0%.% 0.%.%.00 Three.%.% 0.% 0.% 0. Four.%.%.%.0% 0. Five or more.%.0%.0%.% 0. Total (N) 0 Ethnicity Caucasian.%.0%.%.% 0. Afro-American.%.%.% 0.0% 0. Hispanic.%.%.%.% 0. Other.% 0.0%.%.% 0. Total (N) Income Less than $, 0.%.%.%.% 0. From $,000 to $, 0.%.%.%.0% 0. From $0,000 to $, 0.%.%.%.% 0. From $,000 to $,.%.0%.0%.% 0. More than $00,000.%.%.%.0% 0.0 Total (N) 0

17 TABLE (Continued) Descriptive Statistics of Fampools and Non-fampools Characteristics Fampools Non-fampools Proportion Std Error Proportion Std Error P- Value Road Houston: Beltway (only Houston toll road in list).% 0.% 0.% 0.% 0. Houston: All other roads listed.%.%.%.% 0. Dallas: George Bush Turnpike and Dallas North Tollway (only Dallas toll roads in list).%.%.%.% 0. Dallas: All other roads listed.%.%.%.% 0. No road selected.%.%.%.% 0. Total (N) 0 Occupation Administrative.%.%.%.% 0. Sales, service, manufacturing, student, and self-employed.%.%.% 0.% 0. Stay-home, unemployed, others.%.% 0.%.% 0.0 Total (N) Education High school graduate or less.%.%.%.% 0. Some college/vocational.%.%.%.% 0. College graduate.%.%.%.% 0. Postgraduate degree.%.%.%.% 0. Total (N) Pay to Park at Destination Yes.%.%.%.% 0.0 No.%.%.%.% 0.0 Total (N) After Passenger Drop Off Driver / Passenger have Same Destination 0.%.%.%.% 0. Continue to Final Destination.% 0.%.%.% 0.0** Pick up Additional Passengers.%.% 0.% 0.% 0. Perform Errands.%.%.%.% 0. Other.%.% 0.%.% 0. Total (N) ** significantly different at 0 percent level of confidence These are the responses from the survey, often the driver of the fampool/non-fampool. So each respondent basically represents carpool.

18 0 0 TABLE Important Factors in the Formation of Current Carpool by Fampools and Nonfampools Factor Fampools Non-fampools Std Std P- #Obs Mean Error #Obs Mean Error value Drop off Kids at School/Day Care * Access to HOV Lanes Relaxation while Traveling Help Environment and Society Travel Time Saving Enjoy Travel with Others Sharing Vehicle Expenses * Reliability of Arrival Time Splitting Tolls on Toll Roads Get Work done while Traveling Carpool Partner Matching Program Encouraged by Program at Work Preferred Parking at Work Other * significantly different at percent level of confidence

19 TABLE Random Parameter Logit Model for Fampools Mode Variable Coefficient P-Value All SOV on the MLs HOV on the MLs HOV+ on the MLs SOV on the GPLs HOV on the GPLs HOV+ on the GPLs Travel Time (min) Toll Cost ($) Alternative Specific Coefficient On a Commute Trip Paying a Toll for Current Trips Alternative Specific Coefficient On a Work Trip Household Income between $,000 and $0, Education: Post Graduate Degree Alternative Specific Coefficient Household Income between $,000 and $, Traveling in Evening Peak Base Mode Alternative Specific Coefficient Ethnicity: Hispanic Married with No Child(ren) Alternative Specific Coefficient Education: Post Graduate Degree Travel Time (min) Standard Deviation Toll Cost ($) Summary Number of Observations Log Likelihood - Rho Square 0. Percent Estimated Correctly.% Utility Equation Models: V TT 0. Toll 0. TPCommute.0 PayToll V V TT 0. Toll 0. TPWork 0. Income 0. Education V TT 0. Toll. Income 0. EveningPeak V 0.0 TT 0. Toll V TT 0. Toll. Hispanic 0. MarriedWithoutChildrenV V TT 0. Toll. Education

20 0 TABLE Random Parameter Logit Model for Non-fampools Mode Variable Coefficient P-Value All Toll Cost ($) SOV on the MLs HOV on the MLs HOV+ on the MLs SOV on the GPLs HOV on the GPLs Alternative Specific Coefficient Number of Weekly Trips between to Number of Weekly Trips between to Alternative Specific Coefficient On a Work Trip Household Type: Married with No Children Trip Distance Alternative Specific Coefficient Household Income between $,000 and $0, Traveling in Evening Peak On a Work Trip Base Mode Alternative Specific Coefficient Household Type: Married with Children Number of Weekly Trips between to HOV+ on the Alternative Specific Coefficient GPLs Household Income between $,000 and $0, Standard Deviation Toll Cost ($) Summary Number of Observations Log Likelihood -0 Rho Square 0. Percent Estimated Correctly.% Utility Equation Models: V Toll. TripWeek. TripWeek V. 0. Toll. TPWork. HHTypeMnoChild 0.0 TripDist V. 0. Toll. Income. EveningPeak. TPWork V 0. Toll V Toll 0. HHTypeMwithChild 0. TripWeek V V Toll 0. Income

21 0 FIGURES FIGURE Mode Choice of Non-fampools Based on Travel Time Savings. FIGURE Mode Choice of Fampools: Scenario. FIGURE Mode Choice of Non-fampools: Scenario. FIGURE Mode Choice of Fampools: Scenario. FIGURE Mode Choice of Non-fampools: Scenario. FIGURE Mode Choice of Fampools: Scenario. FIGURE Mode Choice of Non-fampools: Scenario. FIGURE Mode Choice of Non-fampools Based on Travel Time Savings.

22 0% Percentage in Each Mode 0% 0% 0% 0% 0% 0% Toll (SOV ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Fampools: Scenario. HOV Toll = 0% HOV Toll = $0

23 0% 0% Percentage in Each Mode 0% 0% 0% 0% 0% 0% Toll (SOV ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Non-fampools: Scenario. HOV Toll = 0% HOV Toll = $0

24 0% 0% Percentage in Each Mode 0% 0% 0% 0% 0% 0% Toll (HOV ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Fampools: Scenario. SOV Toll = $ HOV Toll = $0

25 0% Percentage in Each Mode 0% 0% 0% 0% 0% 0% Toll (HOV ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Non-fampools: Scenario. SOV Toll = $ HOV Toll = $0

26 0% Percentage in Each Mode 0% 0% 0% 0% 0% 0% 0% Toll (HOV+ ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Fampools: Scenario. SOV Toll = $ HOV Toll = $.

27 0% 0% Percentage in Each Mode 0% 0% 0% 0% 0% Toll (HOV+ ML) SOV ML HOV ML HOV+ ML All GPL FIGURE Mode Choice of Non-fampools: Scenario. SOV Toll = $ HOV Toll = $.