Travel Demand Increment Due to the Use of Autonomous Vehicles

Abstract

Advanced technology available in promising fully autonomous vehicles (AVs) will encourage people to travel more than they have up to now using their regular vehicles. It is believed that the disadvantages of manually driven vehicles, such as driving fatigue, stressful feelings, aging physical skills deterioration, and other unwanted effects, will vanish once AVs are employed. For this purpose, this study presents the results of a large questionnaire performed in Győr, Hungary, about the public perception and acceptance of AVs. In addition, assessing the impact of using such technology on increasing travel demand when people can alter their mode of transport to an AV. The study demonstrates that respondents’ prior knowledge of AVs plays a crucial role in generating a greater number of trips when they are able to use AVs. Furthermore, it has also been found that providing further awareness and education to the population about the meaning of the term autonomous vehicles and widening their insights about the new features provided by these vehicles will result in a higher number of trips. Eventually, this information will act as a considerable indicator to provide a prior understanding of the possible challenges that may impact the sustainability of future transport systems.

1. Introduction

The penetration of recent advanced technology into general aspects of human life has also revolutionized the transport system, which is one of the most important sectors that directly affect our daily life. The continuous development of the vehicle automation inventory, which will alter the conventional vehicles and free drivers from the driving task, aims to achieve faster, safer, and more efficient trips in the future. Many researchers expect that introducing autonomous vehicles as next-generation driving systems will remarkably impact traffic capacity and safety [1,2,3]. Furthermore, AVs are believed to have outstanding advantages in other important parameters such as reducing energy intensity, fuel consumption and emissions, travel time, and congestion [4,5,6]. Therefore, the desire to use this new form of transport has become a globally spread fact, especially in modern societies.

There is a growing body of literature focused on the impact of AVs on travel demand by capturing travelers’ preferences regarding recent transport modes and the future opportunity to use AVs. This literature includes studies highlighting the unwanted efforts by drivers and how AVs might reduce the high amount of physical and cognitive efforts exerted by driving a conventional vehicle [7]. For example, people who prefer not to drive in congested traffic or who struggle to park in narrow spaces will find travel in AVs less stressful [8]. In many cases, travel costs and travel time might be essential travel features favored by travelers when performing their trips. However, travel distance, on the other hand, may affect their final decision [9]. In this regard, AVs are set to be much preferable to long distances trips [10,11], as the passengers make better use of their travel time in a more efficient manner than when they spend it driving [12]. Furthermore, travelers who do not drive now or drive less than they might like will have the desire to replace the non-car trip with a car trip by spending longer travel times inside AVs [13,14,15,16,17].

More recently, attention has been given to the potential increase in the number of trips taken by AVs by researchers who have achieved plausible outcomes from their studies. Hardman et al. [18] have interviewed level 2 automation users to attain their ideas about the impact of automation on travel; results of the study demonstrated that respondents are willing to travel longer distances and they would also shift their transport mode and alter their flights to travel by car instead. The reasons behind this travel increment return to the less stressed and relaxed feeling by the drivers in the vehicle’s autopilot mode. Similarly, Perrine et al. [19] have reported that the introduction of AVs results in a shift of destination choice and leads to increased travel across further distances with personal vehicles, although closer distances are favored across all modes. Lehtonen et al. [20] have conducted a site questionnaire for those who use (SAE level 3) automated cars, either as drivers or as passengers. The authors found a remarkable possibility for travel increments due to the respondents’ willingness to perform leisure activities and feelings of ease with travel during automated driving. Almlof et al. [21] have studied the influence of travel’s purpose on increasing the number of trips. The study found that people who conduct leisure trips will tend to make a larger number of trips compared to those who conduct commuting trips. This might lead to a change in the transport system as non-commuting trips are usually made during off-peak hours.

One can demonstrate that a number of previous studies have shown the impact of AVs on travelers’ mobility concerning certain case studies, such as those in Berlin, Paris, Delft, and Victoria [22,23,24,25]. Similarly, Hamadneh et al. [26] investigated the potential impact of AVs on travel behavior in Budapest, Hungary. The study included a simulation scenario for AVs to fully replace conventional transport modes. Other studies have investigated the impact of AVs on land use itself, as alterations in accessibility may reshape where people choose to live and how land is utilized. According to Zakharenko’s research [27], urban areas could potentially expand by approximately 7% towards the outskirts. Litman [28] suggests that the extent of these effects will be influenced by transportation and land use policies, and current policies indicate a potential increase in sprawl ranging from 10% to 30%. Cordera et al. [11] have stated that the positive effect of AVs to increase the capacity of urban and interurban road infrastructures can disappear if it is accompanied by a large increase in the demand for shared journeys by new users (young, elderly) or empty journeys.

A large body of literature believed that the utilization of AVs to lower travel expenses and ensure accessible transportation for individuals who do not drive could result in a rise in trip generation. In this regard, Truong et al. [25] proposed a new methodology for estimating entirely new trips associated with AVs by measuring gaps in travel needed at different life stages. The study results reported that AVs would increase average daily trips by 4.14%. Similarly, Guerra [29], Sivak, and Schoettle [30] and Heinrichs and Cyganski [31] stated that AVs have the potential to enhance vehicle usage by diminishing expenses associated with travel and parking, as well as by enhancing transportation accessibility for both underage individuals and senior citizens. This increase in vehicle travel may be generated by the mode shift from public transport and active modes and by generating entirely new trips. Harper et al. [32], Abraham [33] and Rosenbloom et al. [34] referred to the possibility of travel rising in a completely automated vehicle setting as being because of the increased mobility that non-driving individuals would experience, such as seniors and those with medical conditions that limit their ability to travel. However, existing travel demand modeling studies tend to overlook AVs’ impacts on entirely new trips [12,35]. Therefore, it is necessary to create a strategy for forecasting the potential effects of autonomous vehicles on various age demographics, encompassing the creation of entirely novel journeys.

In general, many of the addressed studies have focused on the willingness to use and acceptance of AVs and their implications on the transport system [36,37,38,39]. However, a few of these studies have distinguished the interrelation of the prior attained information about AVs and the interest in making more trips, regardless of the purpose and mode of transportation. To address this shortcoming, this study has investigated the variables affecting the desire to increase car trips by increasing the public knowledge about AVs through collecting, presenting, and analyzing a dataset obtained from a large-scale survey conducted in Győr, Hungary. Eventually, this method will ascertain the readiness of city road networks to adapt to the expected evolution of transport in the near future.

The remainder of this paper is organized as follows. Section 2 describes the methodology used to analyze the obtained dataset. Section 3 presents the results that have been achieved after a thorough analysis of the factors influencing the number of trips generated using AVs. In Section 4, we discuss the reported results and mention the surrounding circumstances of the conducted work. Section 5 draws some conclusions and talks about future directions. Section 6 includes the study’s limitations and makes future suggestions.

2. Materials and Methods

A total of 5679 persons have been interviewed using a large questionnaire that covered Győr City, which has a population of 122,616 and 70 other villages in the agglomeration (

Table 1. Surveyed villages in the agglomeration.

Village	Population	Village	Population
Abda	3377	Lázi	531
Árpás	268	Lébény	3351
Ásványráró	2157	Lipót	855
Bakonygyirót	178	Mecsér	660
Bakonypéterd	301	Mérges	113
Bakonyszentlászló	1734	Mezőörs	960
Bezi	528	Mórichida	847
Bőny	2264	Mosonszentmiklós	2629
Börcs	1412	Nagybajcs	1137
Csikvánd	505	Nagyszentjános	1934
Darnózseli	1627	Nyalka	522
Dunaszeg	2437	Nyúl	4577
Dunaszentpál	761	Öttevény	3152
Écs	2228	Pannonhalma	3571
Enese	1838	Pázmándfalu	1165
Felpéc	995	Pér	2665
Gönyű	3381	Rábacsécsény	615
Gyarmat	1415	Rábapatona	2677
Gyömöre	1245	Rábaszentmihály	494
Győrasszonyfa	511	Rábaszentmiklós	150
Győrladamér	1876	Ravazd	1282
Győrság	1579	Rétalap	594
Győrsövényház	794	Románd	323
Győrszemere	3583	Sikátor	333
Győrújbarát	7983	Sokorópátka	1147
Győrújfalu	2478	Szerecseny	831
Győrzámoly	3763	Táp	741
Hédervár	1329	Tápszentmiklós	935
Ikrény	1985	Tarjánpuszta	429
Kajárpéc	1316	Tényő	1820
Kisbabot	242	Tét	4186
Kisbajcs	968	Töltéstava	2600
Kóny	2744	Vámosszabadi	3846
Koroncó	2621	Vének	216
Kunsziget	1385	Veszprémvarsány	1069

), under the frame of two projects (“IKOP-3.2.0-15-2022-00042 kódszámú projekt közösségi közlekedés fejlesztése elektromos autóbuszok beszerzésével Győrben és gazdasági övezetében” and “IKOP-3.2.0-15-2022-00043 kódszámú projekt Győr elővárosi közlekedés fejlesztése”). The group of villages in the agglomeration will be mentioned later as “Agglomeration”. The questionnaire, which was conducted in November 2022, aimed to glean the people’s expectation of increasing their average number of trips per day if they could use AVs in the next few years. The survey’s instructions included 40 questions representing a quantitative method to extensively attain details on participants’ opinions and experiences for each given question. The first set of questions consists of the sociodemographic background of the participants, including age, gender, education level, employment status, and monthly income. The next set of questions is about the participants’ current transport purposes and transport mode and their desire to make new trips if they were to shift to a vehicle automation mode. The remaining sets of questions include inquiries about the participants’ current knowledge of AVs, which may convince them to make extra trips using AVs. Conversely, if they intend to increase their knowledge by gaining more information about AVs, would this motivate them to make additional trips throughout their daily travel?

The dataset has been intensively gathered, then manipulated, analyzed, and presented using “R Project for Statistical Computing” in the form of well-illustrated pie charts and bar graphs. As the study focuses primarily on travel demand increments due to the existence of AVs, two equations have been formulated to calculate the number of current trips performed by the respondents, as well as the number of extra trips that will be performed by the participants with the existence of AVs, (as explained later). Finally, the travel demand increment is calculated in both scenarios: the extra number of trips by AVs with current participant knowledge and the updated number of additional trips by AVs with the updated knowledge gained by the participants.

In order to better understand the methodology used to find the gradual increment of travel demand using AVs, we include an explanation for the calculation steps that consist of several equations that have been used to calculate the recent and future daily, weekly, and monthly number of trips. The steps of this calculation can be clearly described in Figure 1.

3. Results

The most important variables that affect travel demand during the phase of using autonomous vehicles have been highlighted. The variables then undergo a process of interrelated analysis to achieve reasonable results that predict traffic volume in road networks with the existence of AVs. These variables are listed as follows:

3.1. Participant’s Sociodemographic Background

The sociodemographic variables of the participants are considered essential to the questionnaire, as these variables have a direct relation to the reason behind their travel. Figure 2 and Figure 3 show the percentages of participants’ age, gender, education level, employment status, and monthly income in Győr City and Győr Agglomerations, respectively.

In this survey, a reasonable percentage of the participants are in the age group under 18 years. Some might feel skeptical regarding the feasibility of collecting responses from this age group. However, this age group cannot be ignored as they act as car passengers when conducting their main purpose of travel, mainly attending their schools. The age group of participants 65 years or older, on the other hand, is expected to have an impact on travel demand, possibly because of the advantages of free travel they have using some modes of public transport. Therefore, an indispensable percentage of their participation has been taken into account, and their responses have been carefully analyzed. Regarding the other variables, a balance in the gender of the participants has also been targeted, as shown in the above Figures. Holders of various degrees of education were also interviewed, and different employment statuses, which are common in the country, with a good range of monthly incomes, were selected to attain a quite representative sample for the study.

3.2. Participants’ Interest in AVs

The questionnaire results show a great desire to use AVs by the participants across different age groups. However, respondents in the younger age groups, such as <18 and (18–25), seem to be more interested in AVs than older age groups, especially (60–64) and 65+. This is quite understandable, as younger age groups are considered to have a greater tendency toward recent industrial and technological developments, and they can more easily access information about AVs, especially through the use of social media platforms. The remaining age groups in between demonstrate a comparatively equal level of desire to use AVs. Respondents in these age groups have a more stable financial income which enables them to choose new technological forms of transport and look for new kinds of luxury and entertainment inside vehicles.

In general, Figure 4 and Figure 5 show that an average of 67.4% and 62.6% of the respondents are interested in using AVs in Győr City and Győr Agglomerations, respectively, which proves the recent growth of public trust and willingness to consider vehicle automation. Therefore, AVs’ impact on travel demand is thoroughly investigated in the next subsections of the study.

The respondents in Győr City have been asked for their opinion about the safety of currently used regular vehicles (RVs), and we investigated their desire to exchange these vehicles for AVs. Although 57% of the respondents accept the current safety of RVs, they still demonstrate interest and willingness to use AVs. However, 10.2% of respondents who are unhappy with the current safety level of vehicles would like to use safer vehicles in their transport. Similarly, in the Győr Agglomerations, 52% of the respondents who expressed interest in using AVs have no doubt about the safety of current vehicles; possibly they look forward to the leisurely experience of riding in AVs. At the same time, 10.7% aim to increase travel safety. The details are shown in Figure 6 and Figure 7.

3.3. Recent Transport Purposes Impact on Future AV Trips

During the survey, participants were asked about their purpose for travel, which is considered a crucial factor that affects travel demand. The distribution of participants’ transport purposes is divided into “Work”, “School”, Shopping”, “Healthcare”, and “Other”, as shown in Figure 8 and Figure 9. According to their age groups, the participants responded with various percentages for the reasons behind their daily travel. For example, a large percentage of the younger age group perform their trips for the purpose of attending their schools. Older age groups, on the other hand, represent most of the respondents who travel for the purpose of “Healthcare” and Shopping”. These facts might be basic information but are very important to better understand the reasons behind the respondents’ desire to make more trips with the existence of AVs in light of their transport purpose. Figure 8 illustrates that 66% of people who need “Healthcare”, among which elderly persons represent the majority, are ready to use AVs to conduct much more trips to drive them to hospitals and healthcare centers in Győr City. In addition, more than half of respondents who selected either “Work”, “School”, or “Shopping” as a transport purpose would prefer to travel more using AVs when conducting their trips. Similarly, considerable rates represented by 77%, 76%, 79%, and 75% of the participants who responded for “Healthcare” “Shopping”, “School”, and “Work” transport purposes, respectively, would like to make more trips using AVs in the Agglomeration, as shown in Figure 9.

3.4. Recent Transport Mode Impact on Future AV Trips

As autonomous vehicles are set to become a future mode of transport, due to the technological penetration in transport systems, it is very important to investigate whether people will increase their rates of travel once they alter their currently used modes of transport in favor of AVs. In this regard, attitude and other psychological factors might be the main reasons influencing public interest in using AVs instead of more recently used transport modes [40]. However, the promised technology and the availability of special computers in driverless vehicles, which allow them to be remotely controlled and achieve door-to-door transport service, would generate great competition, especially with regular cars and public transport. Thus, if the respondents already have this clear definition of an AV, they will have prior ideas about how those vehicles work, and accordingly, this will mitigate the risk and confusion in using it.

In our study, Figure 10 and Figure 11 illustrate the recent transport modes used by the respondents, according to their age groups, during the time of the survey in both study areas. Participants who have their own cars and those who use public transport are of equal rates among different age groups, and they represent most of the respondents. Lower rates of participation were in evidence for travel by train, taxi, and scooter. Furthermore, in Figure 10 and Figure 11, the respondents’ tendency to increase trips when replacing a particular transport mode with an AV has also been clearly distributed. It is found that an average of 52% of respondents in Győr City have the desire to increase their travel distances using AVs. However, the results show an indispensable deviation from the respondents who use trains for their travel than for those who use taxis and motorcycles, while car owners and public transport are in the middle point. On the other hand, a higher percentage, around 80% of respondents in the Győr Agglomerations, showed a huge acceptance of vehicle automation in their desire to travel more when they can change from their old mode of transport to an AV. This may persuade them to return to the long distances they recently traveled to arrive in larger cities; thus, they might think that AVs will simplify their transport and save their efforts. A slight deviation could be noticed in the data between the rates of those making more trips among different transport modes.

3.5. Knowledge and Information about AVs Impact on Future AV Trips

This section includes an analysis of the part of the dataset which represents the public response to certain survey questions, such as the respondents’ current performed trips, their current knowledge about AVs, their desire to learn more about AVs, and whether they plan to make additional trips with the existence of AVs. The responses are thoroughly analyzed and discussed to discover a final trend among the public to increase travel demand by using AVs.

It has been found from the questionnaire responses that the current number of trips made by different age groups in Győr City is 1554 trips/day. This result has been calculated using Equation (1) by gathering the number of trips made daily, weekly, three times a week, monthly, and three times a month, as shown in Figure 12.

Based on these the current trips per month are:

$$C{T}_m=\Sigma (T_d\times d_f)+\Sigma (T_w\times w_f)+\Sigma (T_m\times m_f)+\Sigma (T_{3w}\times 3\times w_f)+\Sigma (T_{3m}\times 3\times m_f),$$

(1)

where

• $T_d$ is Number of respondent with daily trips
• $d_f$ is Daily trip factor
• $T_w$ is Number of respondent with weekly trips
• $w_f$ is Weekly trip factor
• $T_m$ is Number of respondent with monthly trips
• $m_f$ is Monthly trip factor
• $T_{3w}$ is Number of respondent with 3 trips weekly
• $T_{3m}$ is Number of respondent with 3 trips monthly

$$C{T}_m=\Sigma (1316\times 30)+\Sigma (114\times 4)+\Sigma (34\times 1)+\Sigma (552\times 3\times 4)+\Sigma (8\times 3\times 1)=\mathrm{46,618}\mathrm{trips}/\mathrm{month},$$

(2)

$$C{T}_d=\frac{\mathrm{46,618}}{30}=1554\mathrm{trips}/\mathrm{day},$$

(3)

However, a larger sample has been interviewed in the Agglomeration, as it covers wider areas and includes various transport modes and longer travel distances. The same steps for calculating the current number of trips per day in Győr City in Equation (1) have been used for the Győr Agglomerations as well. Figure 13 shows the distribution of 1830 trips/day among different age groups, which are currently occurring in the Agglomeration.

Based on these the current trips per month and day are:

$$C{T}_m=\Sigma (1362\times 30)+\Sigma (312\times 4)+\Sigma (399\times 1)+\Sigma (1472\times 3\times 4)+\Sigma (242\times 3\times 1)=\mathrm{54,897}\mathrm{trips}/\mathrm{month},$$

(4)

$$C{T}_d=\frac{\mathrm{54,897}}{30}=1830\mathrm{trips}/\mathrm{day},$$

(5)

One of the key questions included in this survey is whether the participants have prior knowledge about the general concept of vehicle automation technology. Due to the importance of this topic [41], responses including participants’ knowledge about AVs have been amply analyzed, as this knowledge has a clear relationship to the desire to make more trips in the future when AVs exist. Figure 14 and Figure 15 show that 28.6% and 27.6% of the participants have knowledge about AVs in the Győr City and Győr Agglomerations.

In the next step, participants with the knowledge status shown above were asked about their desire to make more trips, given the availability of AVs. A large proportion of those participants answered “Yes”, and thus, they generated an additional number of trips, which have been distributed according to their travel needs as follows: 1–2 extra trips per day, 1–2 extra trips per week, and 1–2 extra trips per month. Equation 6 has been used to calculate a total number of 226 and 647 extra trips/day for Győr City and the Agglomeration, respectively. These extra trips have been divided by the current trips to obtain a percentage of increase in travel demand equal to 14.5% and 35.3%, respectively. Figure 16 shows the distribution of the extra trips regarding the participants’ age groups. Figure 17 shows the distribution of the extra trips according to the participants’ knowledge about AVs.

$$ETA{V}_m=\Sigma (E{T}_d\times 1.5\times d_f)+\Sigma (E{T}_w\times 1.5\times w_f)+\Sigma (E{T}_m\times 1.5\times m_f),$$

(6)

where

• $E{T}_d$ is Number of respondents intending to make 1–2 extra trips per day
• $d_f$ is Daily trip factor
• $E{T}_w$ is Number of respondents intending to make 1–2 extra trips per week
• $w_f$ is Weekly trip factor
• $E{T}_m$ is Number of respondents intending to make 1–2 extra trips per month
• $m_f$ is Monthly trip factor

This resulted in Győr City:

$$ETA{V}_m=\Sigma (119\times 1.5\times 30)+\Sigma (152\times 1.5\times 4)+\Sigma (343\times 1.5\times 1)=6782\mathrm{trips}/\mathrm{month},$$

(7)

$$ETA{V}_d=\frac{6782}{30}=226\mathrm{trips}/\mathrm{day},$$

(8)

So, the travel demand increment (TDI) for Győr City is:

$$TDI=\frac{226}{1554}=14.5\%,$$

(9)

This resulted in the Agglomeration:

$$ETA{V}_m=\Sigma (350\times 1.5\times 30)+\Sigma (447\times 1.5\times 4)+\Sigma (659\times 1.5\times 1)=\mathrm{19,421}\mathrm{trips}/\mathrm{month},$$

(10)

$$ETA{V}_d=\frac{\mathrm{19,421}}{30}=647\mathrm{trips}/\mathrm{day},$$

(11)

So, the travel demand increment (TDI) for the Agglomeration:

$$TDI=\frac{647}{1830}=35.3\%,$$

(12)

On the questionnaire, another important question was directed toward the participants, whether they have the intention to obtain more information and know more about AVs. Such questions aim to increase public understanding about the daily continuous development of the automation industry and, in particular, to enhance their willingness to use AVs in the near future. Therefore, we observed a new group of participants who answered “Yes” to signal interest in “learning more about AVs” will receive updated information and knowledge about AVs. Accordingly, a significant change from “do not know about AVs status” to “know about AVs status” occurred, and the latter group increased to 52.2% and 59% in Győr City and Győr Agglomerations, respectively. See Figure 18 and Figure 19.

To further analyze the impact of modified knowledge of AVs on travel demand, it is believed that attaining updated knowledge will encourage participants to lean toward taking more trips, as it has been proven in previously calculated figures for additional trips by AVs. As a result, new additional trips were generated and added to the previously calculated extra trips, which together represent an updated number of extra trips by AVs. Using Equation 6, the updated number of extra trips has been calculated to obtain a total number of 435 and 854 trips/day in Győr City and the Győr Agglomerations, respectively. Figure 20 illustrates the distribution of the updated extra trips regarding the age groups of the respondents. Figure 21 illustrates the modified percentage of knowledge of AVs, which eventually resulted in new rates of travel demand increments. These increment rates of travel demand are equal to 28% and 46.6%, to be achieved by the introduction of AVs in both study areas.

This resulted in Győr City:

$$ETA{V}_m=\Sigma (229\times 1.5\times 30)+\Sigma (293\times 1.5\times 4)+\Sigma (661\times 1.5\times 1)=\mathrm{13,055}\mathrm{trips}/\mathrm{month},$$

(13)

$$ETA{V}_d=\frac{\mathrm{13,055}}{30}=435\mathrm{trips}/\mathrm{day},$$

(14)

So, the travel demand increment (TDI) for Győr City is:

$$TDI=\frac{435}{1554}=28\%,$$

(15)

This resulted in the Agglomeration:

$$ETA{V}_m=\Sigma (465\times 1.5\times 30)+\Sigma (589\times 1.5\times 4)+\Sigma (869\times 1.5\times 1)=\mathrm{25,628}\mathrm{trips}/\mathrm{month},$$

(16)

$$ETA{V}_d=\frac{\mathrm{25,628}}{30}=854\mathrm{trips}/\mathrm{day},$$

(17)

So, the travel demand increment (TDI) for the Agglomeration:

$$TDI=\frac{854}{1830}=46.6\%,$$

(18)

Table 2. Summary of the Current Trips based on the Participant’s Responses.

	Current Trips in Győr City	Current Trips in the Agglomeration
Daily	1316	1162
Weekly	114	312
Monthly	34	399
3 Trips a Week	552	1472
3 Trips a Months	8	242
Total Trips/Months	46,618	54,897
Average Trips/Day	1554	1830

,

Table 3. Summary of the Extra Trips based on the Participant’s Responses.

	Extra Trips in Győr City	Extra Trips in the Agglomeration
1–2 extra trips/day	119	350
1–2 extra trips/week	152	447
1–2 extra trips/month	343	659
Total Trips/Months	6782	19,421
Average Trips/Day	226	647
Demand Increment	14.5%	35.3%

and

Table 4. Summary of the Updated Extra Trips based on the Participant’s Responses.

	Extra Trips in Győr City	Extra Trips in the Agglomeration
1–2 extra trips/day	229	462
1–2 extra trips/week	293	589
1–2 extra trips/month	661	869
Total Trips/Months	13,055	25,628
Average Trips/Day	435	854
Demand Increment	28.0%	46.6%

summarize the participants’ responses that have been gathered as well as the calculated average trips per day and the travel demand increment percentages.

As a final step, the average number of trips per day has been calculated for the whole population of the studied areas. By using the simple method of ratio and proportion between the number of interviewed persons and the whole population, an expanded number of trips has been obtained, as shown in the following:

Number of trips for the whole population based on the sample rate of the household interviews in Győr City

$$\mathrm{Current}\mathrm{Trips}=\mathrm{Current}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=1554\times \frac{\mathrm{122,616}}{2024}=\mathrm{94,143}\mathrm{Trips}/\mathrm{day},$$

(19)

$$\mathrm{Extra}\mathrm{Trips}=\mathrm{Extra}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=226\times \frac{\mathrm{122,616}}{2024}=\mathrm{13,691}\mathrm{Trips}/\mathrm{day},$$

(20)

$$\mathrm{Updated}\mathrm{Extra}\mathrm{Trips}=\mathrm{Updated}\mathrm{Extra}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=209\times \frac{\mathrm{122,616}}{2024}=\mathrm{12,661}\mathrm{Trips}/\mathrm{day},$$

(21)

Number of trips for the whole population based on the sample rate of the household interviews in the Agglomeration

$$\mathrm{Current}\mathrm{Trips}=\mathrm{Current}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=1830\times \frac{\mathrm{116,765}}{3655}=\mathrm{58,462}\mathrm{Trips}/\mathrm{day},$$

(22)

$$\mathrm{Extra}\mathrm{Trips}=\mathrm{Extra}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=647\times \frac{\mathrm{116,765}}{3655}=\mathrm{20,669}\mathrm{Trips}/\mathrm{day},$$

(23)

$$\mathrm{Updated}\mathrm{Extra}\mathrm{Trips}=\mathrm{Updated}\mathrm{Extra}\mathrm{Trips}\mathrm{for}\mathrm{the}\mathrm{sample}\times \frac{\mathrm{Population}}{\mathrm{Sample}\mathrm{size}}=207\times \frac{\mathrm{116,765}}{3655}=6613\mathrm{Trips}/\mathrm{day},$$

(24)

It should be noticed that the updated number of extra trips has been broken down into two parts, as per the above calculations, in order to obtain a clear illustration of the gradual steps of increments in travel demand due to increasing knowledge of AVs within a larger scale that includes the whole population, as shown in Figure 22. In addition, it has been found that the total number of trips in Győr City became larger than the number of trips in the Agglomerations, as the expanding factor for Győr City is almost double the expanding factor for the latter.

4. Discussion

The adoption of AVs has the potential to significantly impact travel demand, leading to an increase in the number of trips taken by individuals. Factors such as convenience, reduced cost, desire to use AVs, and people’s prior knowledge and experience with AV technology contribute to this increment. As AV technology continues to advance, policymakers, transportation planners, and researchers must consider these factors to develop strategies that maximize the benefits of AVs while addressing potential challenges associated with increased travel demand. In addition, conducting a questionnaire survey on people’s opinions about autonomous vehicles and their potential impact on increasing the number of trips in the future can be limited by respondents’ awareness and understanding. As autonomous vehicles are still a relatively new concept for many people, there is a possibility of facing perceived disadvantages among those with less awareness of their capabilities and implications. Respondents may lack sufficient knowledge of autonomous vehicles, which could lead to biased or uninformed responses.

Finally, it is worth mentioning that the recently used new cars may include some automation features. However, our study targeted only fully autonomous vehicles by recording the respondents’ opinions about the number of new trips that may be generated using these vehicles. Therefore, the results of the household-interview-based calculations on trip increments are only a stated preference study due to the fact that respondents had never experienced autonomous vehicles. Although we can state that the era of AVs will bring new travel demands, it would be a huge challenge to operate sustainable transport systems, especially in big cities.

5. Conclusions

This study presents the results of travel demand increments caused by AV use through a large and dense questionnaire conducted in Győr City and the Győr Agglomerations. The survey was taken by a total number of 5679 persons to gather as much extensive and representative data as possible with the aim of understanding the possible challenges that may impact the sustainability of future transport systems.

The study results have revealed a significant increase in the number of trips to be performed using AVs in both studied areas. This would not be achieved unless there was a great desire among participants to use this new transport technology; in fact, almost two-thirds of the participants’ responses showed an interest in using AVs once they exist. The study also linked the public’s tendency to travel more with AVs to the purpose of the travel itself. For example, 66% and 75% of Győr City and the Agglomeration participants who selected “Healthcare” as a purpose of travel would like to use AVs to perform their trips to hospitals and healthcare centers. This clearly demonstrates the perceived usefulness of AVs for those respondents, among whom elderly persons might represent the majority. Transport mode, on the other hand, was found to be a crucial variable in travel demand increment during the study when people had the opportunity to replace their currently used transport mode with an AV. However, results showed that respondents from the Győr Agglomerations seem to show more willingness to shift to AVs, as they proportionally feel more travel fatigue due to the longer distances that they travel to arrive the major cities. Thus, they are motivated to increase their comfort and reduce driving stress through the use of vehicle automation.

The study revealed that one of the most important variables affecting travel demand is participants’ knowledge of AVs. The obtained results have shown that participants with current knowledge of AVs aim to increase travel demand by 14.5% and 35.3% in Győr City and in the Agglomeration. This will be achieved by conducting daily, weekly, or monthly additional trips to cope with currently unmet travel needs. In addition, it was found that further updates to participants’ knowledge of AVs through widening their insights into the new features provided by vehicle automation will result in 28% and 46.6% travel demand increments in both studied areas, respectively. Thus, these results reflect respondents’ trust and satisfaction with promising automated transport technologies.

Overall, the fact of increasing rates of travel demand caused by using AVs once they exist gave the impression that people really have an astonishing tendency to explore the delightful experience of riding such a mode of transport. For this purpose, it is important to regularly conduct surveys that study the public’s acceptance of AV technology, as it has become a highly developed industry in recent years. Eventually, these studies will be useful for obtaining prior indicators to examine the operation of driverless vehicles in our road networks and their impact on transport systems’ stability.

6. Limitations and Future Suggestions

When we were conducting the questionnaire to investigate travel demand increments using autonomous vehicles, there were several limitations that should be considered. Additionally, there are some future suggestions that can help improve the study design and address these limitations. Some of the limitations include the fact that respondents may provide answers they perceive as socially desirable rather than their true opinions or behaviors. This bias can affect the accuracy of the data collected and lead to overestimation or underestimation of travel demand increments. In addition, a lack of real-world experience presents limitations, as autonomous vehicles are still in the early stages of development and deployment, with limited real-world experience for most individuals. Without personal encounters with autonomous vehicles, respondents may struggle to provide concrete opinions or predictions based on actual experiences. Furthermore, predicting the future impact of autonomous vehicles on trip numbers is inherently uncertain. Many factors, including regulations, infrastructure development, consumer adoption, and societal changes, can influence the outcome. Survey respondents may find it challenging to provide accurate predictions due to the complexity and uncertainty surrounding these factors.

Future suggestions that may overcome these stated limitations include emphasizing the importance of collaboration with industry. This includes collaboration with autonomous vehicle manufacturers, transportation companies, or research institutions to access real-world data or conduct field experiments. This collaboration could provide valuable insight and bridge the gap between hypothetical scenarios and actual travel demand increments. Another important suggestion consists of conducting longitudinal studies instead of a cross-sectional questionnaire to ensure continuous improvements, as the longitudinal study follows respondents over time. This approach can capture changes in travel demand increments as autonomous vehicle technology evolves and becomes more accessible.