3.1. Positive Environmental Impacts
Given the considerable number of trips made by ridesourcing services, their role in energy consumption, greenhouse gas emissions, congestion, etc., is not negligible. Previous research shows that these taxis have both positive and negative effects on the environment. When it comes to the positive environmental effects of ridesourcing services, ridesourcing is assumed to be green or environmentally friendly since it can increase the use of pre-existing vehicles and reduce empty drives and idle distances
[12][34].
Comparing the capacity utilization of TNC drivers with traditional taxis, ridesourcing services have a higher capacity utilization and productivity rate
[41][73][8,95]. In comparing taxi and ridesourcing service quality in Los Angeles, Brown and Lavalle
[74][96] notice that TNC users pay 40% lower fares and wait only one-fifth of the time relative to taxis. Nie
[54][79] also shows that TNCs can increase the taxi capability usage rate in the off-peak times in Shenzhen, China. Similar results were obtained in the major U.S. metropolitan cities by Cramer and Krueger
[75][97], who analyze the capacity utilization of UberX drivers based on time and miles. They found that UberX drivers have a 30% higher time utilization rate and a 50% higher miles utilization rate. They list four factors that may explain this difference. Firstly, TNC drivers make use of a technology that suits driver-passenger more effectively. Second, TNCs have a larger scale than taxi companies, which support faster matches. Third, regulations on traditional taxis are inefficient. Finally, the flexible labor supply model of TNCs and their dynamic pricing more closely match supply with demand throughout the day.
It has been argued that the integration of ridesourcing services and public transport can increase the efficiency of the transportation system by serving a niche demand that public transport does not generally serve well
[3][48][1,74]. The positive impact of ridesourcing services on public transit is that they can extend or complement public transit
[76][36]. When ridesourcing serves the routes and operates at the times that public transport does not serve well, it complements public transit. Ridesourcing can extend public transit by solving the first and last mile problem created by the fixed route and fixed schedule of public transit
[3][23][77][1,20,98]. The results of the study of Zgheib et al.
[77][98] show that the integration of ridesourcing and public transport can increase the overall market share of the Beirut BRT by 2%. They further explored that a 50% reduction in TNCs′ fares can lead to a 3.5% increase in the BRT market share in this city. However, it should be noticed that their model was simple and did not consider correlations across error components.
Moreover, individuals in lower-density urban areas typically suffer from a first and last mile problem due to the comparatively lower transit routes. The potential role that ridesourcing services can play in complementing and expanding public transit has prompted transit agencies and local governments to set up on-demand systems that include a multimodal, integrated, and connected transportation system
[78][79][99,100]. For example, the U.S. Federal Transit Administration (FTA, Washington, DC, USA) Sandbox Program funded a range of pilot application-based on-demand projects to provide first/last mile connections to fixed route services
[80][101]. In Canada, the Regional Municipality of Waterloo has launched similar pilot projects in Kitchener, Cambridge, and Waterloo to integrate transit fixed routes with ridesourcing services
[81][102].
Some have speculated that the growth of ridesourcing services is an opportunity to reduce car ownership and automobile dependence
[82][13][25,46]. Some evidence suggests that the entry of ridesourcing services is attributed to a decline in personal car dependence. For instance, after Uber and Lyft left Austin, Texas, Hampshire et al.
[83][103] found that 45% of the TNCs′ users turned to personal cars and 8.9% of this group purchased an additional personal vehicle in response to the suspension. However, it seems that a part of the inclination to personal cars following the disruption may be justified by changes in travel behavior caused by Uber and Lyft operations in the past. The study is also based on the assumption that previous users of Uber and Lyft have switched to a mode of transport, while people may have switched to a mixed use of transport modes.
Some surveys measured the decline in car ownership due to the availability of ridesourcing services. Of the participants in the study of Henao and Marshall
[14][47] in the Denver region, 13% reported that they own fewer vehicles due to the availability of ridesourcing services. They found that restaurants/bars, working trips to the CBD, airport, hotels, and event venues are the most popular locations that people prefer to substitute driving with ridesourcing. Lavieri et al.
[14][47] indicate that 9% of respondents in their study in Austin, Texas, tend to dispose of one or more household cars due to the availability of ridesourcing services.
Ridesourcing services can open a window of opportunity for planners to minimize parking supply, create new land uses, and reduce overall vehicle miles traveled (VMT)
[11][23][82][14,20,25]. For many people, parking is the main reason to substitute ridesourcing for personal driving
[25][55]. TNCs can provide a mobility service to and from areas with low parking supply
[84][104] because ridesourcing drivers never have to search for parking. Therefore, they can reduce overall VMT by eliminating wasteful driving, such as the search for parking at the end of trips
[85][36][23,65]. Henao and Marshall
[85][23] indicate that about 26% of TNC riders would have driven if these services did not exist and needed a parking spot in Denver.
The growth of ridesourcing services can also be a step forward in reducing congestion and energy use in cities
[58][13][86][16,46,105]. Erhardt et al.
[11][14], listed several mechanisms where ridesourcing services may reduce congestion. First, if TNCs shared trips based on a ridesplitting behavior, they would replace the trips that could otherwise be in a vehicle with fewer passengers. Second, travelers may use ridesourcing services to address first and last mile connections to regional transportation. As a result, TNCs may allow passengers to replace driving trips with transit. Finally, TNCs can discourage car ownership by offering an appealing alternative to driving. They can lead people to own fewer cars and shift to public transportation or active modes of transport.
Wenzel et al.
[85][23] believe that ridesourcing services can decrease energy use in several ways. First, in the short term, sharing rides with strangers or polling is an opportunity to reduce VMT. It can significantly reduce miles of travel and the energy consumed in several vehicles with fewer occupants. Second, TNC drivers may ignore the initial increase in a more efficient car′s purchase price since the lower fuel costs may offset the cost in the medium run. Finally, in the long term, riders may retire their existing cars to avoid fixed costs for their mobility need and, as a result, may eliminate the trips they made with their vehicle beforehand. Jin et al.
[6][4] further point out that if TNCs exclusively take advantage of electricity powered driverless cars, the prevalence of ridesourcing services could reduce energy use and urban pollution.
Overall, the confirmed positive impact of ridesourcing services is that they are more efficient than traditional taxis. In the reviewed literature, we identified several environmental opportunities, including increasing public transportation efficiency, reducing car ownership, minimizing parking supply, reducing congestion, and energy consumption. However, there is no evidence that these opportunities are yet exploited.
3.2. Negative Environmental Impacts
While the environmental merit of ridesharing is well documented
[69][87][91,106], the environmental influence of ridesourcing is uncertain
[88][107]. Theoretically, TNCs may reduce the overall VMT, congestion, energy consumption, and air pollution by increasing taxis and public transit efficiency. However, there is empirical evidence to reject this idea and characterize these services as detrimental to a city′s sustainable environment, as ridesourcing may add more idle cars to the road and attract some public transit users
[3][2][1,22].
Some research, including Xu et al.
[89][24], poses some doubts about the positive influence of ridesourcing on the public transportation system. It is argued that some passengers make ridesourcing trips which were previously carried out by transit. Some of the trips are also new trips that they might not have otherwise made without the availability of ridesourcing
[90][108]. Based on evidence reported in
Table 1, between 14 and 58% of ridesourcing trips are substituted with public transport trips. Clewlow and Mishra
[25][55] have found that the introduction of TNCs is correlated with a 15% reduction in transit ridership in major U.S. cities. However, they argue that this effect is not the same for all forms of public transport, as public buses and light rail are more impacted, while heavy rail is benefiting from the new generation of taxi services. While it was initially expected that ridesourcing services would be an alternative to conventional taxis, Rayle et al.
[3][1] have found that most ridesourcing trips in San Francisco are substituting for modes other than a taxi and are, therefore, outside the traditional taxi industry. Similarly, in Brazil, de Souza Silva et al.
[51][76] suggest that 30% of riders would travel by public transport if these services were not available as an alternative.
Table 1. The results of studies on the impact of ridesourcing services on VMT/VKT, empty miles rate, transit substitution, walking or bicycling substitution, driving/carpool/taxi substitution.
Howev
. |
Author(s) |
City/Region |
Period |
Method |
Impact |
Target Population |
Sample Size |
Data Size |
Direction 1 |
VMT/VKT |
[91][19] |
Denver, Colorado |
2016 |
Survey |
+83.5% |
Lyft/Uber drivers |
416 rides |
- |
Negative |
[11][14] |
San Francisco |
2010–2016 |
Modeling- regression |
+7% |
San Francisco Bay Area residents |
- |
- |
Negative |
[92][109] |
Paris Region |
2017 |
Survey |
No effect |
TNC users |
1966 |
- |
Non |
Empty miles rate |
[75][97] |
5 US cities |
2014–2015 |
Modeling |
+36% to 45% |
UberX drivers |
- |
- |
Negative |
[85][23] |
Austin Texas |
June 2016 to April 2017 |
Modeling |
+45% |
RideAustin drivers |
- |
1.5 million rides |
Negative |
Car sale |
[83][103] |
Austin Texas |
2016 |
Survey |
+8.9% |
Uber and/or Lyft users |
1840 |
- |
Positive |
[92][109] |
Paris Region |
2017 |
Survey |
No effect |
TNC users |
1966 |
- |
Non |
New trip generation |
[25][55] |
7 major US cities |
2014–2016 |
Survey |
+22% |
Urban residents |
4094 |
- |
Negative |
[3][1] |
San Francisco |
2014 |
Survey |
+8% |
TNC users |
380 |
- |
Negative |
[91][19] |
Denver, Colorado |
2016 |
Survey |
+12% |
Lyft/Uber drivers |
416 rides |
- |
Negative |
[93][110] |
California |
2015 |
Survey |
+8% |
Residents of California |
2400 |
1975 |
Negative |
[38][67] |
Santiago, Chile |
2017 |
Modeling |
+3% |
Uber users |
1600 |
- |
Negative |
[94][111] |
Santiago, Chile |
2017 |
survey |
5.4% |
Santiago residents |
1500 |
- |
Negative |
Transit substitution |
[25][55] |
7 major US cities |
2014–2016 |
Survey |
15% |
Urban residents |
4094 |
- |
Negative |
[3][1] |
San Francisco |
2014 |
Survey |
33% |
TNC users |
380 |
- |
Negative |
[91][19] |
Denver, Colorado |
2016 |
Survey |
22.2% |
Lyft/Uber drivers |
416 rides |
- |
Negative |
[95][112] |
7 US cities |
2016 |
Survey |
14% |
Mobility users |
4500 |
- |
Negative |
[48][74] |
New York |
2009–2016 |
Regression model |
58.54% |
Taxi trips |
1458 |
143,926 |
Negative |
[93][110] |
California |
2015 |
Survey |
22% |
Residents of California |
2400 |
1975 |
Negative |
[51][76] |
Brazilian cities |
2017 |
Logistic regression model |
30% |
Brazilian Uber users |
500 |
384 |
Negative |
[6][4] |
New York |
2014 |
Spatial cross-correlation |
Mixed effects |
Uber pickup records |
- |
74394 pickup records |
Non |
[38][67] |
Santiago, Chile |
2017 |
Modeling |
34% |
Uber users |
1600 |
- |
Negative |
[94][111] |
Santiago, Chile |
2017 |
survey |
37.6% |
Santiago residents |
1500 |
- |
Negative |
[81][102] |
Waterloo, Ontario, Canada |
2018–2019 |
Descriptive analysis |
74% |
TNC rides |
585 |
- |
Negative |
[96][113] |
Bogotá, Colombia |
2019 |
Discrete Choice Models |
33% |
Uber trips |
- |
50,760 queries |
Negative |
[97][114] |
Chengdu, China |
2016 |
Modeling |
33% |
DiDi trip data |
- |
181,172 trips |
Negative |
Transit extending or complementing |
[37][66] |
US cities |
2017 |
Descriptive analysis |
27% |
National Household Travel Survey (NHTS) |
- |
- |
Positive |
Walking or bicycling substitution |
[25][55] |
7 major US cities |
2014–2016 |
Survey |
24% |
Urban residents |
4094 |
- |
Negative |
[3][1] |
San Francisco |
2014 |
Survey |
21.0% |
TNC users |
380 |
- |
Negative |
[95][112] |
7 US cities |
2016 |
Survey |
18% |
Mobility users |
4500 |
- |
Negative |
[93][110] |
California |
2015 |
Survey |
20% |
|
|
|
Negative |
[91][19] |
Denver, Colorado |
2016 |
Survey |
12% |
Lyft/Uber drivers |
416 rides |
- |
Negative |
[38][67] |
Santiago, Chile |
2017 |
Modeling |
4% |
Uber users |
1600 |
- |
Negative |
[81][102] |
Waterloo, Ontario, Canada |
2018–2019 |
Descriptive analysis |
26% |
TNC rides |
585 |
- |
Negative |
[35][64] |
Tehran, Iran |
2017 |
Chi-square test |
19.7% |
Urban residents |
2377 |
- |
Negative |
[35][64] |
Cairo, Egypt |
2017 |
Chi-square test |
19.3% |
Urban residents |
2011 |
- |
Negative |
[94][111] |
Santiago, Chile |
2017 |
survey |
1.6% |
Santiago residents |
1500 |
- |
Negative |
Driving/taxisubstitution |
[25][55] |
7 major US cities |
2014–2016 |
Survey |
46% |
Urban residents |
4094 |
- |
Positive |
[3][1] |
San Francisco |
2014 |
Survey |
46% |
TNC users |
380 |
- |
Positive |
[91][19] |
Denver, Colorado |
2016 |
Survey |
52.1% |
Lyft/Uber drivers |
416 rides |
- |
Positive |
[95][112] |
7 US cities |
2016 |
Survey |
42% |
Mobility users |
4500 |
- |
Positive |
[83][103] |
Austin Texas |
2016 |
Survey |
45% |
Uber and/or Lyft users |
1840 |
- |
Positive |
[38][67] |
Santiago, Chile |
2017 |
Modeling |
52% |
Uber users |
1600 |
- |
Positive |
[96][113] |
Bogotá, Colombia |
2019 |
Discrete Choice Models |
30% |
Uber trips |
- |
50,760 queries |
Positive |
[94][111] |
Santiago, Chile |
2017 |
survey |
68% |
Santiago residents |
1500 |
- |
Positive |
There have been optimistic views regarding the role of ridesourcing services in reducing wasteful driving and congestion in cities. However, some studies warn that the current ridesourcing system contributes to the growth of VMT in cities. For instance, Henao and Marshall
[91][19] estimate that ridesourcing in the Denver region in the U.S., accounts for 83.5% more VMT compared to when it was not available. Schaller
[98][115] focuses on the major American cities and reported that ridesourcing services put 2.8 VMT on the road compared to every mile of private car travel. It should be noted that the focus has been on large cities and there is a paucity of evidence about medium and small-sized cities. The literature suggests several reasons for this increase in VMT. First, a group of TNC drivers living outside major cities usually commute relatively long distances to begin and end their driving shift. Second, sharing ridesourcing trips is not yet popular and the trips have lower occupancy rates compared to ridesharing services
[39][68]. Third, some TNC drivers do not park their car after a ride and select circulating while waiting to be matched with the next passenger. Forth, ridesourcing services induce new trips that would not be made if these services were not available
[99][116]. Fifth, as a result of increasing the number of part-time drivers, TNCs may increase the average number of rides provided per driver or vehicle, which influences the overall VMT and traffic congestion in cities
[85][23].
Regarding the first reason, Cramer and Krueger
[75][97] calculate that the empty miles rate of Uber drivers in Seattle is about 45% and that it is about 36% for Los Angeles. However, Wenzel et al.
[85][23] note that Cramer and Krueger failed to include the empty commuting miles at the beginning and end of shifts. Wenzel et al.
[85][23] estimate that this commuting distance is about 19% of the total VMT for RideAustin drivers. Besides, they estimate that TNC drivers travel 21% longer distances to pick up passengers in Austin, Texas, and drive 55% more miles between the end of a trip and the next ride. There are three reasons why some TNC drivers opt to circulate rather than parking immediately after a ride: (1) TNC drivers often cannot quickly and accurately locate the waiting positions of riders. Therefore, cruising on the road helps them to find a new request in a shorter time
[100][117]; (2) Drivers mainly search for riders based on their self-interest and experiences and, therefore, those uncoordinated searching strategies lead to longer idle driving
[52][77]; and (3) In downtown, there are a restricted number of places for drivers to park. Therefore, vacant taxis can only cruise on roads while awaiting their next ride
[89][24].