Il traffico a SFO è aumentato del 60% tra 2010 e 2016 e oltre la metà è dovuta a Uber e Lyft

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Do transportation network companies decrease or increase congestion?INTRODUCTIONTransportation network companies (TNCs) have grown rapidly in recent years. In 2016, TNCs were 15% of all intra-San Francisco vehicle trips, which is 12 times the number of taxi trips (1), while in New York in 2016, TNC ridership equaled that of yellow cab and doubled annually between 2014 and 2016 (2). TNCs are on-demand ride services where rides are arranged through a mobile app to connect the passenger with a driver, often a private individual driving their personal vehicle (3). The current system is commonly viewed as a bridge technology that may be replaced by fleets of self-driving cars if and when that technology is ready (4, 5). TNCs are one form of shared mobility and one form of Mobility-as-a-Service (MaaS). They have been referred to by several names, including ridesourcing, ride-hailing, and e-hail. Ridesourcing is the preferred international standard (6), but we refer to TNC throughout this text because it is the legal term used in California, where this study was conducted.Because they have the potential to reduce the reliance on private cars, the TNCs themselves present a vision of the future in which they reduce traffic congestion and allow roads to be repurposed to other uses (7, 8). There are several mechanisms by which TNCs could reduce congestion. If TNCs are shared concurrently, a service known as ridesplitting, they could reduce traffic if they replace a trip that would otherwise be in a vehicle with fewer occupants. Simulations show that ridesplitting has substantial potential to reduce congestion (9). TNCs could induce travelers to shift trips from auto to transit by providing better first- and last-mile connections to regional transit, and there is some evidence to suggest that a small portion of travelers may use TNCs in this way (10, 11). Some have speculated that by providing a convenient alternative to owning a car, TNCs could incentivize people to own fewer cars and, by extension, induce them to shift other trips to transit or non-motorized modes, potentially reducing their total vehicle travel (12, 13).Competing with these factors are several mechanisms by which TNCs may increase traffic congestion. Deadheading, or out-of-service movement, is the movement of a vehicle with no passenger. TNCs and taxis deadhead to look for fares or reposition before or after a paid trip. Out-of-service travel is estimated at about 50% of TNC vehicle miles traveled (VMT) in New York (2) and 20% in San Francisco (1). Whether a trip made by TNC adds traffic to the road also depends on which mode would have been used for the trip if TNC was not available. Between 43 and 61% of TNC trips substitute for transit, walk, or bike travel or would not have been made at all (10, 11, 14, 15), adding traffic to the road that otherwise would not have been there. TNC pickups and drop-offs (PUDO) contribute to congestion on urban streets by disrupting traffic flow in the curb lane, similar to the congestion effects found in areas that rely heavily on taxis (16).Transportation planners and policy makers are interested in understanding the congestion effects of TNCs as they face decisions about how to regulate TNCs and how to integrate them into the existing transportation system (17–19). However, studies assessing the net effect of TNCs on congestion have produced mixed results, concluding that TNCs decrease congestion (20), TNCs add to VMT or increase congestion (2, 14, 15), and TNCs “did not drive the recent increase in congestion” (21), or have been inconclusive (10, 11). There is a need for further research to adjudicate these differences, but research on the topic has been hampered by a lack of data (22, 23). We enter this debate to address the question: Do TNCs decrease or increase traffic congestion?We do this for the case of San Francisco while recognizing that the results from a dense and transit-rich city may not translate into many contexts. A data set scraped from the application programming interfaces of the two largest TNCs provides a unique insight into their operations. These data were collected and processed as described by Cooper et al. (23). We further processed the data to associate TNC volumes, pickups, and drop-offs to each road segment in San Francisco by time of day (TOD). These processed data are included in the Supplementary Materials for use by other researchers.This study is structured as a before-and-after assessment between 2010 conditions when TNC activity is negligible and 2016 conditions when it is not, focusing on the change in average weekday conditions. We derived measures of roadway conditions in both years from GPS-based speed data licensed from INRIX. We estimated the relationship between the change in TNC activity and the change in roadway travel time, assuming zero TNCs in 2010.To control for other factors that may also affect congestion over this period, we used San Francisco’s travel demand model, SF-CHAMP, which produces estimates of traffic volumes on all roads in San Francisco and is sensitive to changes in population and demographics, employment, transportation networks, and congestion. Since SF-CHAMP’s initial development (24), it has been further enhanced (25, 26), extensively tested (27), and successfully applied to analyze policy and infrastructure changes (28, 29). The version of SF-CHAMP used in this study was calibrated to 2010 conditions and does not account for TNCs. This means that when the model is run for current-year inputs, it represents a counterfactual case where TNCs do not exist.The relationship between demand and traffic speed is nonlinear such that adding vehicles in already congested conditions has a bigger effect than adding them in uncongested conditions. Therefore, it is not just the total VMT change that matters but when and where that change occurs. We conducted our analysis directionally for segments known as traffic messaging channels (TMCs), which average 0.3 miles long. For each year, we aggregated all data to these TMC links and averaged across days to represent average weekday conditions for five TODs. These link-TOD-year combinations are more detailed than past TNC studies, which are either more aggregate (2, 13, 20, 21) or based on smaller user surveys (10–12, 14, 15) that cannot be expanded to the link level.After estimating the relationships between the change in travel times, TNCs, and control variables, we applied the estimated models to evaluate network performance metrics for 2010, 2016, and a counterfactual 2016 scenario with no TNCs. We compared the congestion levels in these two scenarios to evaluate our research question. The discussion section of this paper addresses how our results relate to those of the studies cited above, how the methods compare across these studies, and the limitations of this study, focusing on other changes that may be occurring over this period.Observations and hypothesesLike New York (2, 21), San Francisco has experienced a notable increase in congestion over the past few years (Fig. 1) (30). The speed data used in this study confirm this trend, showing that the average speed decreases from 25.6 miles per hour (mph) in 2010 to 22.2 mph in 2016 and that the vehicle hours of delay (VHD) increase by 63% over the same period. Delay is defined as the difference between the congested travel time and the travel time under free-flow conditions.

Fig. 1 The p.m. peak period roadway level-of-service (LOS) in San Francisco (30).(A) 2009 conditions; (B) 2017 conditions. LOS grades roadways by vehicle delay, from LOS A representing free flow to LOS F representing bumper-to-bumper conditions. Data and an interactive mapping tool are available at
This change corresponds to the period in which TNCs emerged. Figure 2 shows the distribution of the TNC PUDO for an average Wednesday in fall 2016. The data show that TNCs are concentrated in the downtown area, consistent with findings elsewhere (11, 13), and in the locations where level-of-service deterioration is worst.

Fig. 2 Daily TNC pickups and drop-offs for an average Wednesday in fall 2016 (1).Darker colors represent a higher density of TNC activity. Data and an interactive mapping tool are available at
Several other changes may also affect congestion. Between 2010 and 2016, San Francisco population grew from 805,000 to 876,000 (31) and employment grew from 545,000 to 703,000 (32). Important network changes include a rebuild of the Presidio Parkway, the introduction of turn restrictions on Market Street, several “road diets,” and bus improvements (33). We account for these changes through SF-CHAMP. In addition, we reviewed a list of active construction projects during the 2016 analysis period to evaluate whether they were associated with disproportionate speed decreases, and did not find that they were.The data do not show the share of ridesplitting in San Francisco, but it is between 13% and 20% elsewhere (14, 15), with some of those trips carrying no additional passengers (3, 15). Rail ridership grows substantially over this period and bus ridership does not (34), consistent with other findings that TNCs may complement rail and compete with bus (11, 35). We do not observe a meaningful change in car ownership, with an average of 1.08 cars per household in 2010 and 1.10 cars per household in 2016 (36).In addition to the 20% of TNC VMT that is out-of-service, 70% of San Francisco TNC drivers live outside the city (1). While we do not explicitly track it in this study, the drivers’ commutes into the city may add more VMT to the network. Our data do not provide a direct observation of what TNC users otherwise would have done, so they cannot speak directly to modal substitution. The data do allow us to infer the PUDO locations and associate those locations with specific directional roadways.Some argue that TNCs have little effect on traffic operations because they occur in the evening when congestion is less severe (12, 13). Our data show not only that 43% of TNC VMT occurs between 6:30 p.m. and 3 a.m. but also that 26% of TNC VMT occurs in the 3-hour a.m. or p.m. peak periods compared to 40% for 4-hour peaks in Boston (15).Given these observations, we suggest that the gap between the background changes predicted by SF-CHAMP and the observed change in travel times is an indicator of TNC impact. Specifically, we hypothesize:1) If TNCs have no effect on congestion, the background changes should reasonably predict the observed travel time changes.2) If TNCs decrease congestion, then the observed change in travel time should be better than the background changes would predict.3) If TNCs increase congestion, then the observed change in travel time should be worse than the background changes would predict. We expect the gap to be biggest for times and locations with high levels of TNC activity.