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The Impact of Ride-Hailing Services on Congestion

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Early research shows ride-hailing services’ global development and benefits. Rahul Telang highlights that policy scrutiny is expanding due to evidence of their harmful externalities.

Image courtesy of interviewee. January 16, 2024

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This video video discusses the growing popularity and impact of ride-hailing services like Uber and Ola globally. Over the past decade, these services have become the standard for taxis, prompting questions about their economic effects, employment opportunities, and potential negative consequences such as pollution and traffic congestion.

A key focus is on the congestion caused by ride-hailing services. The analysis examines the impact of a driver strike in India, where Uber and Ola services were temporarily halted, leading to a 10-14% reduction in congestion. This video video finding indicates that ride-hailing services contribute to increased traffic congestion, especially during peak hours.

This video video outlines mechanisms by which ride-hailing services exacerbate congestion, including "deadheading," where drivers drive without passengers while seeking fares, and the effect on route choices. When ride-hailing services are unavailable, lesser-used, smaller routes become more attractive due to reduced congestion, suggesting that these services could, paradoxically, lead to less efficient travel routes.

Furthermore, when ride-hailing services are available, they may deter citizens from using public transport options, shifting them back to these alternatives when ride-hailing is not an option. The discussion emphasizes the complexity of assessing their impact on urban transportation and offers insights into possible policy considerations to mitigate congestion caused by ride-hailing services.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.015 --> 00:02.885
Think most people are familiar that the right hailing,

00:03.135 --> 00:05.645
right hailing services have become really popular

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and popular, not just in the us the world over.

00:09.585 --> 00:11.485
So if you look up any newspaper

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or in, in your experience, you know

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that the right hailing services have become the de factor

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standard for basically taking taxis.

00:21.185 --> 00:23.325
So then the question comes in that, you know,

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the ride healing services have become

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so popular over the last 10 years.

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What has been the impact of these services?

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And when I say impact, you know, the impact is very broad.

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You know, what is the economic benefit?

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What is the employment opportunities?

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What has been the impact on the innovation,

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had the new technology being developed?

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But then the question also comes in that, you know,

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does it lead to pollution?

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Does it lead to congestion?

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What are potentially the social cost

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of these six things happening?

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So, you know, one of those thing, a new service,

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a new technology has become very prevalent.

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Then the question obviously people want to ask is that

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what does the technology technology does to

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whatever context people want to look at?

01:11.625 --> 01:14.445
So congestion is, you know, one very important domain.

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So that is really the, the context of how this,

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how the whole thing basically evolved.

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This whole idea came in because Uber

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and Ola, they were the, one of the more popular

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ride hailing services in India.

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I think in 2017 or 18 or 19, you know, the OLA

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and the Uber driver said, okay, we are gonna have a strike.

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So they said, okay, I will take my, you know, services off

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Delhi Roads or Mumbai Roads or Bangalore roads.

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So the question came in that

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after the services basically stopped stop,

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what impact did it have?

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So at that time we said that what, you know, try

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to look at congestion that do ride hailing services

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or removal of ride hailing services.

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Does it lead to any change in congestion?

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So that was the idea.

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So the focus was that, okay, if you want

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to answer this question, what kind of data do we need?

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Can we get the data?

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Can we write down the good empirical specification

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to be able to estimate the, the question?

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So that was the context in which we basically said, okay,

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this is something we wanna look at.

02:26.445 --> 02:30.255
Then we went about getting the data, analyzing the data,

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writing the paper, and so on and so forth.

02:33.035 --> 02:34.535
So the main result, we found

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that when the ride healing services was basically went off

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the road, the congestion actually went down

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by about 10 to 11%.

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That basically said that the ride healing

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services are causing congestion.

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That is when the Uber

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and O are applying in the city,

02:55.685 --> 02:58.865
it actually raise at the congestion. And again, you

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Know, congestion means what?

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So we looked at the,

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because we were able to measure the traffic at hourly time,

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so we could classify that, is it the peak hour?

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Is the non peak hour?

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Is it like a very heavily traffic road versus not a very

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heavily traffic road?

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So we were able to classify that

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and basically we found that in the peak hour, you know,

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whatever the time when the traffic is the highest,

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that's when the congestion actually,

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after these services went off the road,

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the congestion went down anywhere between 10 to 14%.

03:34.485 --> 03:37.065
The issue that basically become very popular,

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that does the right hearing services cost congestion,

03:41.405 --> 03:43.705
you know, that question is also very unclear.

03:43.805 --> 03:45.745
You know, sometime it is a political question

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where these companies don't want to be classified

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as causing congestion.

03:50.895 --> 03:53.305
Then what do you do if the cost congestion?

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What can the policymaker do?

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You know, you cannot ban Uber, you cannot ban Ola.

03:58.775 --> 04:01.425
They're generating so much value to the customer.

04:01.925 --> 04:04.345
So then the first question basically become,

04:04.805 --> 04:08.785
so our finding our aim at some level was to actually show

04:08.895 --> 04:11.625
that, hey, these services is causing

04:11.765 --> 04:13.385
so much congestion in the city.

04:14.255 --> 04:16.345
Then the question is that, okay, what do you do with that?

04:16.775 --> 04:18.185
Then, you know, then you can think

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of all the potential mechanism that one can do

04:20.605 --> 04:23.545
and remember what we can only suggest,

04:23.685 --> 04:24.985
we can't actually do it.

04:25.405 --> 04:27.625
So we kind of, you know, our study at some level.

04:27.765 --> 04:30.665
So one of the things that we try to highlight

04:30.695 --> 04:32.745
that why are they causing congestion?

04:34.135 --> 04:37.635
So, you know, one can think of this as a very academic world

04:37.645 --> 04:39.795
where basically you can say that, you know what,

04:40.215 --> 04:43.515
if the ride hailing service become available to you,

04:44.775 --> 04:46.875
you'll not use your personal car

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but actually use ride hailing service.

04:49.415 --> 04:51.035
So then the thought will be that, hey,

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why should the congestion go up?

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Because if there was no ride healing services,

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you would've used your own car.

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So congestion would not have changed.

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So then, you know, we show that no,

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the congestion is actually going up.

05:03.295 --> 05:05.355
So then the question is that why is it going up?

05:05.695 --> 05:07.635
So one of the idea that we discuss

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and we kind of show what the, what the,

05:10.915 --> 05:13.995
what the ride healing services do is what is called the,

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you know, dead headache.

05:15.515 --> 05:17.835
That is dead headache that the driver

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is driving on the road without the passenger

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because they're actually trying to find somebody else

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or trying to find a customer.

05:27.695 --> 05:29.515
So the, the ride on the road

05:29.735 --> 05:33.275
and the cost congestion, so that's a me one

05:33.275 --> 05:34.435
of the mechanisms

05:34.435 --> 05:37.035
through which the congestion basically goes up

05:37.535 --> 05:40.435
and we try to actually show in our data that hey,

05:40.905 --> 05:43.635
ride hailing services actually causing congestion.

05:44.255 --> 05:47.235
The other element that we actually show that, you know,

05:47.235 --> 05:50.075
when the ride healing services basically go away,

05:52.045 --> 05:56.645
some rules, they become actually less congested.

05:57.105 --> 05:58.125
And what that means is

05:58.125 --> 06:01.725
that when they become less congested, people are willing

06:01.825 --> 06:04.525
to take this route because they're actually smaller.

06:04.825 --> 06:06.845
So you are actually traveling less,

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but the congestion is less so you would take it.

06:09.945 --> 06:12.645
But when the ride healing services are in operation,

06:13.275 --> 06:16.445
even though those routes are smaller, smaller,

06:16.445 --> 06:18.085
because the congestion is so high,

06:18.415 --> 06:20.485
you'd rather take a longer route.

06:20.985 --> 06:22.045
So we show that, you know,

06:22.045 --> 06:24.045
ride Healing service has caused this as well.

06:24.065 --> 06:26.885
So, you know, there's various mechanism we were able

06:26.885 --> 06:29.125
to highlight that what happened.

06:29.345 --> 06:31.805
You know, the other element is that, you know, the public

06:32.445 --> 06:34.565
transport, you know, when there's a ride hailing service

06:34.595 --> 06:36.485
available, people say,

06:36.645 --> 06:39.365
I will take ride hailing services versus taking

06:39.525 --> 06:40.605
a train or a metro.

06:41.435 --> 06:43.965
When the ride hailing services actually go away,

06:44.475 --> 06:46.605
then people say, okay, I'm gonna take a Subway

06:46.945 --> 06:48.125
or I'm gonna take a Metro.

06:48.545 --> 06:52.925
So we were able to identify all these potential causes,

06:52.995 --> 06:54.925
poor potential congestion,

06:55.345 --> 06:57.245
and then the question is that, what can we do?

06:57.385 --> 07:00.365
And then one can only speculate what can we do?

07:00.385 --> 07:02.725
But really the goal was to be able to say

07:02.725 --> 07:03.765
that this is happening

07:03.785 --> 07:05.685
and at least identify the mechanism

07:05.785 --> 07:07.125
that's causing the congestion.

Impact of Ride-Hailing Services on Congestion

00:00:00 - Think most people are familiar that the right hailing, right hailing services have become really popular and popular, not just in the us the world over. So if you look up any newspaper or in, in your experience, you know that the right hailing services have become the de factor standard for basically taking taxis. So then the question comes in that, you know, the ride healing services have become so popular over the last 10 years. What has been the impact of these services?
00:00:34 - You know, what is the economic benefit? What is the employment opportunities? What has been the impact on the innovation, had the new technology being developed? But then the question also comes in that, you know, does it lead to pollution? Does it lead to congestion? What are potentially the social cost of these six things happening? So, you know, one of those thing, a new service, a new technology has become very prevalent.
00:01:05 - what does the technology technology does to whatever context people want to look at? So congestion is, you know, one very important domain. So that is really the, the context of how this, how the whole thing basically evolved. This whole idea came in because Uber and Ola, they were the, one of the more popular ride hailing services in India. I think in 2017 or 18 or 19, you know, the OLA
00:01:38 - So they said, okay, I will take my, you know, services off Delhi Roads or Mumbai Roads or Bangalore roads. So the question came in that after the services basically stopped stop, what impact did it have? So at that time we said that what, you know, try to look at congestion that do ride hailing services or removal of ride hailing services. Does it lead to any change in congestion?
00:02:07 - So the focus was that, okay, if you want to answer this question, what kind of data do we need? Can we get the data? Can we write down the good empirical specification to be able to estimate the, the question? So that was the context in which we basically said, okay, this is something we wanna look at. Then we went about getting the data, analyzing the data, writing the paper, and so on and so forth. So the main result, we found that when the ride healing services was basically went off
00:02:42 - by about 10 to 11%. That basically said that the ride healing services are causing congestion. That is when the Uber and O are applying in the city, it actually raise at the congestion. And again, you Know, congestion means what? So we looked at the, because we were able to measure the traffic at hourly time, so we could classify that, is it the peak hour?

Impact of Ride-Hailing on Traffic Congestion

00:03:11 - Is it like a very heavily traffic road versus not a very heavily traffic road? So we were able to classify that and basically we found that in the peak hour, you know, whatever the time when the traffic is the highest, that's when the congestion actually, after these services went off the road, the congestion went down anywhere between 10 to 14%. The issue that basically become very popular, that does the right hearing services cost congestion,
00:03:43 - You know, sometime it is a political question where these companies don't want to be classified as causing congestion. Then what do you do if the cost congestion? What can the policymaker do? You know, you cannot ban Uber, you cannot ban Ola. They're generating so much value to the customer. So then the first question basically become, so our finding our aim at some level was to actually show that, hey, these services is causing
00:04:14 - Then the question is that, okay, what do you do with that? Then, you know, then you can think of all the potential mechanism that one can do and remember what we can only suggest, we can't actually do it. So we kind of, you know, our study at some level. So one of the things that we try to highlight that why are they causing congestion? So, you know, one can think of this as a very academic world where basically you can say that, you know what, if the ride hailing service become available to you,
00:04:46 - but actually use ride hailing service. So then the thought will be that, hey, why should the congestion go up? Because if there was no ride healing services, you would've used your own car. So congestion would not have changed. So then, you know, we show that no, the congestion is actually going up. So then the question is that why is it going up? So one of the idea that we discuss and we kind of show what the, what the, what the ride healing services do is what is called the, you know, dead headache.
00:05:19 - is driving on the road without the passenger because they're actually trying to find somebody else or trying to find a customer. So the, the ride on the road and the cost congestion, so that's a me one of the mechanisms through which the congestion basically goes up and we try to actually show in our data that hey, ride hailing services actually causing congestion. The other element that we actually show that, you know,
00:05:52 - some rules, they become actually less congested. And what that means is that when they become less congested, people are willing to take this route because they're actually smaller. So you are actually traveling less, but the congestion is less so you would take it. But when the ride healing services are in operation, even though those routes are smaller, smaller, because the congestion is so high,

Impact of Ride Hailing on Public Transport

00:06:20 - So we show that, you know, ride Healing service has caused this as well. So, you know, there's various mechanism we were able to highlight that what happened. You know, the other element is that, you know, the public transport, you know, when there's a ride hailing service available, people say, I will take ride hailing services versus taking a train or a metro. When the ride hailing services actually go away, then people say, okay, I'm gonna take a Subway or I'm gonna take a Metro.
00:07:05 - poor potential congestion, and then the question is that, what can we do? And then one can only speculate what can we do? But really the goal was to be able to say that this is happening and at least identify the mechanism that's causing the congestion.

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Human Rights and Corruption: Problems and Potential of Individualising a Systemic Problem

A statistical approach to law school citation rankings

High school change

What is constitutional ideology?

Politics, Identity, and Class Certification on the U.S. Courts of Appeals

The City as a Growth Machine: Toward a Political Economy of Place

The Role of Education in Reducing Racial Inequality

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