# Revealing Motives for Car Use in Modern Cities—A Case Study from Berlin and San Francisco

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## Abstract

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## 1. Introduction

#### 1.1. Motives for Car Use

#### 1.2. Motives for Car Use in Extended Discrete Choice Models

#### 1.3. Comparison of Car Use in Cities

#### 1.4. Motivation and Scope of This Work

- Discovering affective and instrumental motives for car use in modern cities;
- Applying a cost-efficient survey design to measure influences of long-distance travel behavior on everyday car use;
- Revealing the impact of population density and public transit supply on the frequency of car use;
- Giving insights in the decision-making process and explain more of the overall heterogeneity in pure regression model through the integration of motives in the HCM.

## 2. Concept of a Travel Skeleton and Data Collection

## 3. Data Description and Statistical Analysis

## 4. Attitudinal Constructs

## 5. Methodology Approach

#### 5.1. Ordered Hybrid Choice Model

#### 5.2. Construct of Reduced Form Model

## 6. Applied Model Specification of Motives for Car Use

- sociodemographic characteristics (person and household);
- long-distance travel activities;
- everyday travel;
- spatial structure at residential location;
- motives for car use.

## 7. Results of the Ordered Hybrid Choice Model

#### 7.1. Effects on Instrumental and Affective Motives

#### 7.2. Direct Effects on Car Use

#### 7.3. Overall Effects on Car Use

## 8. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Ecke, L.; Chlond, B.; Magdolen, M.; Eisenmann, C.; Hilgert, T.; Vortisch, P. Deutsches Mobilitätspanel (MOP)—Wissenschaftliche Begleitung und Auswertungen Bericht 2017/2018: Alltagsmobilität und Fahrleistung; Karlsruher Institut für Technologie, Institut für Verkehrswesen: Karlsruhe, Germany, 2019. [Google Scholar]
- infas; DLR; IVT Research; infas 360. Mobilität in Deutschland—Ergebnisbericht. 2017. Available online: http://www.mobilitaet-in-deutschland.de/pdf/MiD2017_Ergebnisbericht.pdf (accessed on 27 June 2020).
- Alteneder, W.; Risser, R. Soziologie der Verkehrsmittelwahl: Motive und Bedürfnisse im Zusammenhang mit der Verkehrsmittelwahl. Z. Verk.
**1995**, 41, 77–83. [Google Scholar] - Steg, L.; Vlek, C.; Slotegraaf, G. Instrumental-reasoned and symbolic-affective motives for using a motor car. Transp. Res. Part F4
**2001**, 4, 151–169. [Google Scholar] [CrossRef] - Gardner, B.; Abraham, C. Psychological correlates of car use: A meta-analysis. Transp. Res. Part F Traffic Psychol. Behav.
**2008**, 11, 300–311. [Google Scholar] [CrossRef] - Steg, L. Car use: Lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res. Part A Policy Pract.
**2005**, 39, 147–162. [Google Scholar] [CrossRef] - Hunecke, M. Mobilitätsverhalten Verstehen und Verändern: Psychologische Beiträge zur Interdiszipliniaren Mobilitätsforschung; Springer: Berlin, Germany, 2015. [Google Scholar]
- Bergstad, C.J.; Gamble, A.; Hagman, O.; Polk, M.; Gärling, T.; Olsson, L.E. Affective–symbolic and instrumental–independence psychological motives mediating effects of socio-demographic variables on daily car use. J. Transp. Geogr.
**2011**, 19, 33–38. [Google Scholar] [CrossRef] - Belgiawan, P.F.; Schmöcker, J.-D.; Abou-Zeid, M.; Walker, J.; Lee, T.-C.; Ettema, D.F.; Fujii, S. Car ownership motivations among undergraduate students in China, Indonesia, Japan, Lebanon, Netherlands, Taiwan, and USA. Transportation
**2014**, 41, 1227–1244. [Google Scholar] [CrossRef] [Green Version] - Ellaway, A.; Macintyre, S.; Hiscock, R.; Kearns, A. In the driving seat: Psychosocial benefits from private motor vehicle transport compared to public transport. Transp. Res. Part F Traffic Psychol. Behav.
**2003**, 6, 217–231. [Google Scholar] [CrossRef] - Shiftan, Y.; Outwater, M.L.; Zhou, Y. Transit market research using structural equation modeling and attitudinal market segmentation. Transp. Policy
**2008**, 15, 186–195. [Google Scholar] [CrossRef] - Anable, J. ‘Complacent Car Addicts’ or ‘Aspiring Environmentalists’? Identifying travel behaviour segments using attitude theory. Transp. Policy
**2005**, 12, 65–78. [Google Scholar] [CrossRef] [Green Version] - Kamargianni, M.; Dubey, S.; Polydoropoulou, A.; Bhat, C. Investigating the subjective and objective factors influencing teenagers’ school travel mode choice—An integrated choice and latent variable model. Transp. Res. Part A Policy Pract.
**2015**, 78, 473–488. [Google Scholar] [CrossRef] [Green Version] - Roberts, J.; Popli, G.; Harris, R.J. Do environmental concerns affect commuting choices? Hybrid choice modelling with household survey data. J. R. Stat. Soc. A
**2018**, 181, 299–320. [Google Scholar] [CrossRef] [Green Version] - Hunecke, M.; Haustein, S.; Böhler, S.; Grischkat, S. Attitude-Based Target Groups to Reduce the Ecological Impact of Daily Mobility Behavior. Environ. Behav.
**2010**, 42, 3–43. [Google Scholar] [CrossRef] [Green Version] - Lois, D.; López-Sáez, M. The relationship between instrumental, symbolic and affective factors as predictors of car use: A structural equation modeling approach. Transp. Res. Part A Policy Pract.
**2009**, 43, 790–799. [Google Scholar] [CrossRef] - Sohn, K.; Yun, J. Separation of car-dependent commuters from normal-choice riders in mode-choice analysis. Transportation
**2009**, 36, 423–436. [Google Scholar] [CrossRef] - Sefara, D.; Franek, M.; Zubr, V. Socio-psychological factors that influence car prefernce in undergraduate students: The case of the Czech Republic. Technol. Econ. Dev. Econ.
**2015**, 21, 643–659. [Google Scholar] [CrossRef] - Nalmpantis, D.; Roukouni, A.; Genitsaris, E.; Stamelou, A.; Naniopoulos, A. Evaluation of innovative ideas for Public Transport proposed by citizens using Multi-Criteria Decision Analysis (MCDA). Eur. Transp. Res. Rev.
**2019**, 11. [Google Scholar] [CrossRef] [Green Version] - Ashok, K.; Dillon, W.R.; Yuan, S. Extending Discrete Choice Models to Incorporate Attitudinal and Other Latent Variables. J. Mark. Res.
**2002**, 39, 31–46. [Google Scholar] [CrossRef] - Ben-Akiva, M.; Mcfadden, D.; Gärling, T.; Gopinath, D.; Walker, J.; Bolduc, D.; Börsch-Supan, A.; Delquié, P.; Larichev, O.; Morikawa, T.; et al. Extended Framework for Modeling Choice Behavior. Mark. Lett.
**1999**, 10, 187–203. [Google Scholar] [CrossRef] - Walker, J.L. Extended Discrete Choice Models: Integrated Framework, Flexible Error Structures, and Latent Variables. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2001. [Google Scholar]
- Nurul Habib, K.; Tudela, A.; Carrasco, J.; Idris, A. Incorporating the explicit role of psychological factors on mode choice: A hybrid mode choice model by using data from an innovative psychometric survey. In Proceedings of the Second International Choice Modeling Conference, Leeds, UK, 6 July 2011. [Google Scholar]
- Yáñez, M.F.; Raveau, S.; Ortúzar, J.D.D. Inclusion of latent variables in Mixed Logit models: Modelling and forecasting. Transp. Res. Part A Policy Pract.
**2010**, 44, 744–753. [Google Scholar] [CrossRef] - Johansson, M.V.; Heldt, T.; Johansson, P. The effects of attitudes and personality traits on mode choice. Transp. Res. Part A Policy Pract.
**2006**, 40, 507–525. [Google Scholar] [CrossRef] [Green Version] - Abrahamse, W.; Steg, L.; Gifford, R.; Vlek, C. Factors influencing car use for commuting and the intention to reduce it: A question of self-interest or morality? Transp. Res. Part F Traffic Psychol. Behav.
**2009**, 12, 317–324. [Google Scholar] [CrossRef] - Márquez, L.; Macea, L.F.; Soto, J.J. Willingness to change car use to commute to the UPTC main campus, Colombia: A hybrid discrete choice modeling approach. JTLU
**2019**, 12. [Google Scholar] [CrossRef] [Green Version] - De Vos, J.; Alemi, F. Are young adults car-loving urbanites? Comparing young and older adults’ residential location choice, travel behavior and attitudes. Transp. Res. Part A Policy Pract.
**2020**, 132, 986–998. [Google Scholar] [CrossRef] - Van, H.T.; Fujii, S. A cross Asian country analysis in attitudes toward car and public transport. J. East. Asia Soc. Transp. Stud.
**2011**, 9, 411–421. [Google Scholar] - Institute for Mobility Research. Urban Mobility in China. 2017. Available online: https://www.bmwgroup.com/content/dam/grpw/websites/bmwgroup_com/company/downloads/de/2017/2017-BMW-Group-IFMO-Publikation-Juni.pdf (accessed on 27 June 2020).
- von Behren, S.; Minster, C.; Magdolen, M.; Chlond, B.; Hunecke, M.; Vortisch, P. Bringing travel behavior and attitudes together: An integrated survey approach for clustering urban mobility types. In Proceedings of the 97th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 7–11 January 2018; 2018. [Google Scholar]
- von Behren, S.; Minster, C.; Esch, J.; Hunecke, M.; Vortisch, P.; Chlond, B. Assessing car dependence: Development of a comprehensive survey approach based on the concept of a travel skeleton. Transp. Res. Procedia
**2018**, 32, 607–616. [Google Scholar] [CrossRef] - Kuhnimhof, T.; Wulfhorst, G. The Reader’s Guide to Mobility Culture. Chapter 3. In Megacity Mobility Culture—How Cities Move on in a Diverse World; Institute for Mobility Research, Ed.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 55–64. ISBN 978-3-642-34734-4. [Google Scholar]
- Klinger, T.; Lanzendorf, M. Moving between mobility cultures: What affects the travel behavior of new residents? Transportation
**2015**, 43, 243–271. [Google Scholar] [CrossRef] - San Francisco Municipal Transportation Agency. Travel Decisions Survey. Summary Report. 2017. Available online: https://www.sfmta.com/sites/default/files/reports/2017/Travel%20Decisions%20Survey%20Summary%20Report%202017_Accessible.pdf (accessed on 27 June 2020).
- Senatsverwaltung für Umwelt, Verkehr und Klimaschutz. Mobilität der Stadt. Berliner Verkehr in Zahlen. 2017. Available online: https://www.berlin.de/senuvk/verkehr/politik_planung/zahlen_fakten/download/Mobilitaet_dt_komplett.pdf (accessed on 27 June 2020).
- Bhat, C.R.; Dubey, S.K. A new estimation approach to integrate latent psychological constructs in choice modeling. Transp. Res. Part B Methodol.
**2014**, 67, 68–85. [Google Scholar] [CrossRef] [Green Version] - Vij, A.; Walker, J.L. How, when and why integrated choice and latent variable models are latently useful. Transp. Res. Part B Methodol.
**2016**, 90, 192–217. [Google Scholar] [CrossRef] - CMC. CMC Choice Modelling Code for R; Choice Modelling Centre, University of Leeds: Leeds, UK, 2017. [Google Scholar]
- Niklas, U.; von Behren, S.; Eisenmann, C.; Chlond, B.; Vortisch, P. Premium factor—Analyzing usage of premium cars compared to conventional cars. Res. Transp. Bus. Manag.
**2020**, 25, 100456. [Google Scholar] [CrossRef] - Neue Mobilität Berlin. NEUE MOBILITÄT BERLIN—Innovative Mobilitätsentwürfe für Berlin. Available online: http://neue-mobilitaet.berlin/?lang=en (accessed on 6 March 2020).
- Sucha, M.; Viktorova, L.; Risser, R. Can an Experience with No Car Use Change Future Mode Choice Behavior? Sustainability
**2019**, 11, 4698. [Google Scholar] [CrossRef] [Green Version] - Esztergár-Kiss, D.; Koppányi, Z.; Lovas, T. Mobility Mapping Based on a Survey from the City of Berlin. Period. Polytech. Transp. Eng.
**2016**, 44, 35–41. [Google Scholar] [CrossRef] [Green Version] - Domarchi, C.; Tudela, A.; González, A. Effect of attitudes, habit and affective appraisal on mode choice: An application to university workers. Transportation
**2008**, 35, 585–599. [Google Scholar] [CrossRef] - Chen, C.-F.; Chao, W.-H. Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit. Transp. Res. Part F Traffic Psychol. Behav.
**2011**, 14, 128–137. [Google Scholar] [CrossRef] - Nguyen, N.T.; Miwa, T.; Morikawa, T. Switching to Public Transport Modes for Commuting Trips: Considering Latent Motivations in Ho Chi Minh City. Asian Transp. Stud.
**2018**, 5, 117–136. [Google Scholar] [CrossRef]

**Figure 1.**Motives for car use. The 1–5 Likert-scaled questions as described in Table 1.

Indicators | Psychological Questions |
---|---|

${I}_{1}^{AM}$ | I feel free and independent when I drive a car. |

${I}_{2}^{AM}$ | I like to drive a car. |

${I}_{3}^{AM}$ | Driving a car means fun and passion for me. |

${I}_{4}^{AM}$ | Driving a car means freedom to me. |

${I}_{5}^{AM}$ | Being able to use my driving skill when driving a car is fun for me. |

${I}_{6}^{AM}$ | When I sit in the car I feel safe and protected. |

${I}_{7}^{AM}$ | The make of a car is important to me. |

${I}_{1}^{IM}$ | The functioning of a car is more important to me than the make of a car. |

${I}_{2}^{IM}$ | A car is primarily a means to an end for me. |

${I}_{3}^{IM}$ | I only use a car to get from A to B. |

${I}_{4}^{IM}$ | It doesn’t matter to me what vehicle type I drive. |

${I}_{5}^{IM}$ | I constantly have to be mobile in order to comply with my everyday obligations. |

${I}_{6}^{IM}$ | My everyday organization requires a high degree of mobility. |

Person Characteristics | ||||

Yes | No | |||

Age over 30 years | 78.23% | 21.77% | ||

Fulltime job | 57.30% | 42.70% | ||

Male | 52.27% | 47.73% | ||

Own bicycle | 48.92% | 51.08% | ||

Always | Sometimes | Never | ||

Share of car disposal | 46.66% | 23.20% | 30.14% | |

Household Characteristics | ||||

Yes | No | |||

Premium car in household | 20.45% | 79.55% | ||

Household from Berlin | 47.73% | 52.27% | ||

<$2500 | $2500–$5000 | $5001–$8000 | >$8000 | |

Share of income classes | 16.75% | 32.54% | 31.34% | 24.76% |

Type 1 | Type 2 | Type 3 | Type 4 | |

Share of household type | 31.22% | 18.30% | 25.72% | 24.76% |

Mobility and Car Use Characteristics | ||||

Yes | No | |||

Commuting by car | 73.09% | 26.91% | ||

Monomodal behavior | 29.19% | 70.81% | ||

Large number of daytrips | 4.07% | 95.93% | ||

Large number of vacation trips | 8.73% | 91.27% | ||

Long-distance trips by car | 23.56% | 76.44% | ||

Spatial Characteristics | ||||

Yes | No | |||

High population density | 39.71% | 60.29% | ||

High public transit accessibility | 25.84% | 74.16% | ||

N = 836 |

Factors | ||
---|---|---|

Affective Motive (AM) | Instrumental Motive (IM) | |

Indicators in PFA | ||

${I}_{1}^{AM}$ | 0.84496 | 0.04663 |

${I}_{2}^{AM}$ | 0.82794 | 0.08091 |

${I}_{4}^{AM}$ | 0.80952 | 0.01878 |

${I}_{5}^{AM}$ | 0.76738 | 0.07639 |

${I}_{3}^{AM}$ | 0.76463 | −0.04576 |

${I}_{6}^{AM}$ | 0.62815 | −0.01329 |

${I}_{7}^{AM}$ | 0.54755 | −0.23227 |

${I}_{3}^{IM}$ | −0.03506 | 0.69449 |

${I}_{2}^{IM}$ | −0.13355 | 0.64428 |

${I}_{1}^{IM}$ | 0.09295 | 0.58612 |

${I}_{4}^{IM}$ | −0.15331 | 0.49222 |

${I}_{6}^{IM}$ | 0.21989 | 0.27218 |

${I}_{5}^{IM}$ | 0.23032 | 0.26416 |

High factor loadings (> 0.3) are bolded.; n = 836 |

**Table 4.**Main parameter estimates. (A) Parameters of the structural equation of the choice model; (B) Parameters of the structural equations of the latent variables.

Log-likelihood | −12,116.04 | ||||||

Log-likelihood of choice component | −557.26 | ||||||

Log-likelihood of null model (choice component) | −918.44 | ||||||

McFadden pseudo-${R}^{2}$ | 0.39 | ||||||

N | 836 | ||||||

Thresholds of the choice component | |||||||

${\tau}^{\left(1\right)}$ | 1.814 | ||||||

${\tau}^{\left(2\right)}$ | 3.075 | ||||||

(A) | (B) | ||||||

Parameter | Value | Parameter$\alpha $from variable | on latent variable | ||||

affective motive | instrumental motive | ||||||

${\beta}_{age>30}$ | 0.472 | *** | Age > 30 years | −0.194 | ** | 0.172 | * |

${\beta}_{fulltime}$ | 0.148 | Fulltime job | 0.210 | *** | 0.079 | ||

${\beta}_{male}$ | 0.003 | Male | 0.398 | *** | −0.375 | *** | |

${\beta}_{bicycle}$ | −0.078 | ||||||

${\beta}_{car-sometimes}$ | 0.183 | Car disposal - sometimes | 0.660 | *** | 0.286 | ** | |

${\beta}_{car-always}$ | 1.214 | *** | Car disposal - always | 1.126 | *** | 0.294 | *** |

${\beta}_{premiumcar}$ | 0.235 | ** | Premium car in household | 0.664 | *** | −0.035 | |

${\beta}_{berlin}$ | 0.097 | From Berlin | 0.014 | 0.980 | *** | ||

${\beta}_{lowincome}$ | −0.322 | ** | Low income | 0.308 | *** | 0.290 | ** |

${\beta}_{hhtype3}$ | 0.320 | *** | |||||

${\beta}_{hhtype4}$ | 0.200 | * | |||||

${\beta}_{highdaytrips}$ | 0.533 | * | |||||

${\beta}_{highvacation}$ | −0.085 | ||||||

${\beta}_{commuting\_car}$ | 1.000 | ||||||

${\beta}_{long-distance\_car}$ | 0.304 | *** | |||||

${\beta}_{monomodal}$ | −0.194 | * | |||||

${\beta}_{density}$ | −0.047 | ||||||

${\beta}_{pt\_accessibility}$ | −0.112 | ||||||

${\gamma}_{affective}$ | 0.398 | *** | |||||

${\gamma}_{instrumental}$ | 0.020 | ||||||

$\sigma $ | 0.970 | *** |

Variable | Direct Effect | Effect via LV Affective Motive | Effect via LV Instrumental Motive | Effect via LVs Combined | Overall, Effect | Overall, Effect in Ordered Probit |
---|---|---|---|---|---|---|

Age > 30 years | 0.472 | −0.077 | 0.003 | −0.074 | 0.398 | 0.347 |

Fulltime job | 0.148 | 0.000 | 0.002 | 0.002 | 0.233 | 0.206 |

Male | 0.003 | 0.158 | −0.007 | 0.151 | 0.154 | 0.139 |

Own bicycle | −0.078 | −0.078 | −0.146 | |||

Car disposal - sometimes | 0.183 | 0.262 | 0.006 | 0.268 | 0.451 | 0.355 |

Car disposal - always | 1.214 | 0.448 | 0.006 | 0.454 | 1.668 | 1.414 |

Premium car in household | 0.235 | 0.264 | −0.001 | 0.263 | 0.498 | 0.434 |

From Berlin | 0.097 | 0.006 | 0.019 | 0.025 | 0.122 | 0.121 |

Low income | −0.322 | 0.122 | 0.006 | 0.128 | −0.194 | −0.157 |

Household type 3 | 0.320 | 0.320 | 0.233 | |||

Household type 4 | 0.200 | 0.200 | 0.228 | |||

High daytrips | 0.533 | 0.533 | 0.559 | |||

High vacation trips | −0.085 | −0.085 | −0.135 | |||

Commuting by car | 1.000 | 1.000 | 1.000 | |||

Long-distance trips by car | 0.304 | 0.304 | 0.305 | |||

Monomodal behavior | −0.194 | −0.194 | −0.180 | |||

High population density | −0.046 | −0.046 | 0.022 | |||

High public transit accessibility | −0.112 | −0.112 | −0.159 |

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## Share and Cite

**MDPI and ACS Style**

von Behren, S.; Bönisch, L.; Niklas, U.; Chlond, B.
Revealing Motives for Car Use in Modern Cities—A Case Study from Berlin and San Francisco. *Sustainability* **2020**, *12*, 5254.
https://doi.org/10.3390/su12135254

**AMA Style**

von Behren S, Bönisch L, Niklas U, Chlond B.
Revealing Motives for Car Use in Modern Cities—A Case Study from Berlin and San Francisco. *Sustainability*. 2020; 12(13):5254.
https://doi.org/10.3390/su12135254

**Chicago/Turabian Style**

von Behren, Sascha, Lisa Bönisch, Ulrich Niklas, and Bastian Chlond.
2020. "Revealing Motives for Car Use in Modern Cities—A Case Study from Berlin and San Francisco" *Sustainability* 12, no. 13: 5254.
https://doi.org/10.3390/su12135254