TNC Volume

Starting from the employment change scenario, the TNC volume scenario accounts for the net effect of adding TNC vehicles to the network. There are three related components to this effect. Deadhead or out-of-service TNC vehicles purely add traffic to the network. In- service TNC trips (those carrying a passenger) also add traffic to the network, but if they substitute for taxi or car trips, there would be a corresponding reduction in traffic generated by those modes. If in-service TNC trips substitute for transit, walk or bike trips, then there is no corresponding reduction in traffic by other modes. The same is true if the TNC trips represent induced demand, meaning that they would not have occurred if TNCs did not exist. To understand the net effect of in-service TNC trips on traffic volumes, it is necessary to estimate which modes those trips would have used, if TNC were not available. SF-CHAMP does not, on its own, account for TNCs as a travel mode. One important reason for this is that data were not previously

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available with which to calibrate a model. For this study, the newly available scraped TNC data to evaluate the TNC effects is used.

3.3.5.1 Processing TNC volume data

For this study, those data was further processed to associate out-of-service TNC volumes with directional links in the SF-CHAMP road network. The said data were also processed to create an observed TAZ-to-TAZ trip table of TNC trips. Both represent average weekday, non-holiday conditions and are limited to trips with both ends in San Francisco. The TNC data were collected over a six-week period in November and December 2016.

SF-CHAMP uses a multi-class user-equilibrium traffic assignment for each of five times-of- day (TODs): 6:00-9:00 AM, 9:00 AM-3:30 PM, 3:30 PM-6:30 PM, 6:30 PM-3:00 AM and 3:00-6:00 AM. Both the TNC out-of-service volumes and in-service trip tables were segmented by these same five TODs. The out-of-service TNC vehicles are accounted for by including them as a pre-loaded volume in the traffic assignments. The TNC in-service vehicles were accounted for by including the trip tables as an additional class in the traffic assignments. To estimate how much non-TNC vehicle demand should be reduced due to substitution with TNC trips, some additional processing was conducted as described below. Because the geographic scope of the data collection method used was limited to the San Francisco County, only TNC trips with both ends in San Francisco were considered for the purpose of this research.

Prior to carrying out the traffic assignment, the simulated trips from SF-CHAMP are compiled into TAZ-to- TAZ person trip tables, segmented by mode and TOD. This was begun by converting the observed in-service TNC vehicle trip tables to person trips, assuming an average occupancy of 1.49 passengers (excluding the driver) per vehicle.

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This average occupancy is calculated from the occupancy rates reported in a survey of TNC users in Boston (Gehrke, Felix, and Reardon 2018). The study data of this research do not reveal the demographic or socio-economic characteristics of TNC users, nor do they directly reveal what TNC users otherwise would have done if TNC were unavailable. Therefore, a simple assumption is made to estimate what otherwise would have happened: it is assume that within a zone pair and a TOD, the introduction of a new mode (TNC) draws from all other modes proportionally to their existing mode share. This is equivalent to the well-known independence of irrelevant alternatives (IIA) property of the multinomial logit model. For example, if a zone pair previously contained 90 car trips and 10 transit trips, and the data for this research show 10 TNC person trips for that zone pair, it is assumed that 9 of those trips substitute for car, and one substitutes for transit, leaving 81 car trips, 9 transit trips and 10 TNC trips for the same total person trips. If the total TNC person trips in a zone pair exceeds the total number of trips on other modes, it is not allowed that the non-TNC trips turn negative. Instead, it is assume that TNC trips first substitute for all available non-TNC trips, and any excess TNC trips are added as “non-shifted” trips. These non-shifted TNC trips could theoretically represent induced demand, but it is also possible that they occur simply because of imperfect data in either the modeled trip tables or the TNC trip tables in a detailed zone system.

The end result of this process is a modified set of person trip tables by mode and TOD, with fewer trips than the original trip tables due to some of those trips shifting to TNC. Table 13 summarizes the change in intra-San Francisco person trips that is output from this process. The results show that 26% of TNC trips substitute for car trips, 1%

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substitute for taxi, 14% for transit and 44% for walk or bike. The remaining 15% of TNC trips are “non-shifted” and are not substituted for another mode. In terms of the change to existing trips by mode, the results show that introducing TNCs reduces the number of car trips by 5.1%, taxi trips by 8.1%, transit trips by 6.1% and walk and bike trips by 7.5%. These results are based on the existing mode shares in those zone pairs at the appropriate time-of-day, so they suggest that TNCs are more likely to occur in zone pairs with a high walk, bike or transit mode share than car.

Table 13. Change in Intra-San Francisco Person Trips

Table 13 Change in Intra-San Francisco Person Trips

Mode Person Trips

without TNCs Person Trips with TNCs Difference Percent Difference Percent of TNC Trips Car 1,269,769 1,205,143 -64,626 -5.1% 26.1% Taxi 33,008 30,334 -2,674 -8.1% 1.1% Transit 556,407 522,492 -33,916 -6.1% 13.7%

Walk & Bike 1,440,941 1,332,261 -108,680 -7.5% 44.0%

TNC 0 247,267 247,267 N/A 100.0%

Total Trips 3,300,125 3,337,496 37,371 1.1% N/A

The person trip tables are converted to vehicle trips by dividing by the average occupancy: 1 for drive alone, 2 for shared ride 2, and 3.5 for shared ride 3+. The original TNC trip table is in vehicle trips already and does not require further conversion. Table

14 shows the change in vehicle trips when TNCs are introduced using this method.

Within San Francisco, 166,000 TNC trips are added to the network. This is partially offset by a reduction of 48,000 car trips and 1,600 taxi vehicle trips. These results suggest that about 70% of TNC trips are new vehicle trips that add traffic to the network, adding a net of 116,000 vehicle trips to the network, which is a 12% increase in intra-San Francisco vehicle trips.

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Table 14 Change in Intra-San Francisco Vehicle Trips

Mode Vehicle Trips

without TNCs Vehicle Trips with TNCs Difference Percent Difference Percent of TNC Trips Car 946,197 897,721 -48,476 -5.1% 29.2% Taxi 19,884 18,273 -1,611 -8.1% 1.0% TNC 0 165,951 165,951 N/A 100.0% Total Trips 966,082 1,081,945 115,863 12.0% 69.8%

To generate the estimates for the TNC Volume scenario for the purpose of this research study, these modified car and taxi vehicle trip tables were assigned to the network, along with the TNC in-service vehicle trip table and the TNC out-of-service preloaded volumes.