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Results of the Mobility Survey 2023

Commuting Behavior at the TU Dortmund 2022

Key data on the survey

From mid-February to mid-March 2023, TU Dortmund University's Sustainability Office conducted a survey on the mobility behavior of members of the university. The aim of the survey was to determine the commuting behavior of university members. To this end, information was collected on the distances traveled during and outside of lecture times, as well as on the preferred means of transport and the time required for this.

A total of 573 (13.7 %) employees (including academic staff) and 897 (2.7 %) students took part in the survey. The total figures for the individual status groups are taken from TU Dortmund University's annual statistical report for 2022. Of the respondents, 48% (58%) of employees (students) are women, 51% (41%) are men and 1% (1%) are diverse.

Four icons. A bicycle at top left. Top right a bus. Bottom left a car. A train on the bottom right. © Icon car, bicycle, bus: fontawesome Icon train: wikimedia

Comments on the Evaluation

The data was analyzed for each question, meaning that the basic populations differed in some cases. Only complete information and plausible answers were analyzed. Due to these quality requirements, in some cases up to 25% of the responses (370 questionnaires) had to be excluded from the analysis. Nevertheless, at least 1000 responses per question could always be taken into account, meaning that the results are still representative.

Commuting Distances, Durations and Frequencies

Interactive graphic. Source: Sustainability Office/TU Dortmund University

A color-coded thematic map of NRW shows the distribution of commuters based on their place of residence. The cities of the federal state are shown. The colors correspond to a logarithmic scale proportional to the number of residences.

  • Green: regions with the highest number of residences (logarithmic value around 3).
  • Light green to yellow: Regions with medium to low residential density.
  • Light yellow: Regions with the lowest values (close to 0 on the log scale).

The map shows Dortmund as the central metropolitan region, marked by the darker colors, while the surrounding cities have a lower density.
Exact figures are listed in the table below:

CityNumber of residences
Dortmund802
Bochum98
Essen48
Witten40
Hagen33
Kamen18
Kamen18
Hamm17
Recklinghausen17
Iserlohn16
Castrop-Rauxel15
Lünen15
Unna15
Schwerte14
Gelsenkirchen13
Duisburg10
Waltrop9
Wuppertal9
Fröndenberg/Ruhr8
Münster8
Hattingen7
Oberhausen7
Werne7
Arnsberg6
Cologne6
Menden6
Selm6
Ennepetal5
Lüdighausen5
Soest5
Werl5
Weather5
Dates4
Dülmen4
Ense4
Herdecke4
Marl4
Oer-Erkenschwick4
Sprockhövel4
Ahlen3
Altena3
Bönen3
Bottrop3
Gevelsberg3
Gladbeck3
Herten3
Schwelm3
Steinfurt3
Ascheberg2
Bielefeld2
Gütersloh2
Halver2
Holzwickede2
Moers2
Remscheid2
Anröchte1
Balve1
Bergkamen1
Bochholt1
Bonn1
Coesfeld1
Dinslaken1
Enningerloh1
Erwitte1
Grevenbroich1
Haltern am See1
Heiden1
Heiligenhaus1
Heinsberg1
Hennef1
Herscheid1
Kaarst1
Kamp-Lintfort1
Kierspe1
Kirchlengern1
Lüdenscheid1
Monheim on the Rhine1
Neuenrade1
Nordkirchen1
Olfen1
Radevormwald1
Ratingen1
Rietberg1
Sendenhorst1
Siegen1
Sundern1
Verl1
Viersen1
Voerde1
Welver1
Wermelskirchen1
Wesel1
Wilich1

A color-coded thematic map of NRW shows the distribution of employees who commute by car to TU Dortmund University based on their place of residence. The cities and districts of the federal state are shown, with the colors following a logarithmic scale proportional to the number of residences:

  • Green: regions with the highest number of residences (logarithmic value around 3).
  • Light green to yellow: Regions with medium to low residential density.
  • Light yellow: Regions with the lowest values (close to 0 on the log scale).

Exact figures are listed in the following table:

CityNumber of residences
Dortmund105
none21
Bochum17
Witten16
Essen13
Hagen9
Castrop-Rauxel8
Herne6
Iserlohn5
Hattingen5
Schwerte4
Lünen4
Recklinghausen4
Hamm3
Waltrop3
Dates3
Gelsenkirchen3
Lüdinghausen2
Wickede2
Sprockhövel2
Gevelsberg2
Kamen2
Fröndenberg/Ruhr2
Oberhausen2
Oer-Erkenschwick2
Selm2
Unna2
Bönen1
Kirchlengern1
Mülheim an der Ruhr1
Olfen1
Marl1
Holzwickede1
Willich1
Münster1
Menden1
Werl1
Bocholt1
Kamp-Lintfort1
Anröchte1
Weather1
Herdecke1
Neuenrade1
Dülmen1
Gladbeck1
Wuppertal1
Monheim on the Rhine1
Gütersloh1
Oberaden1
Arnsberg1
Remscheid1
Steinfurt1

A color-coded thematic map of NRW shows the distribution of students commuting by car to TU Dortmund University based on their place of residence. The cities and districts of the federal state are shown, with the coloring following a logarithmic scale proportional to the number of residences:

  • Green: regions with the highest number of residences (logarithmic value around 3).
  • Light green to yellow: Regions with medium to low residential density.
  • Light yellow: Regions with the lowest values (close to 0 on the log scale).

Exact figures are listed in the following table:

CityNumber of residences
Dortmund63
Hagen13
Witten10
none10
Bochum9
Iserlohn9
Recklinghausen7
Essen7
Gelsenkirchen6
Unna6
Schwerte5
Castrop-Rauxel4
Ennepetal4
Werl4
Herne4
Fröndenberg/Ruhr4
Weather3
Schwelm3
Wuppertal3
Menden3
Waltrop3
Altena3
Lünen2
Wickede2
Werne2
Herdecke2
Oer-Erkenschwick2
Marl2
Dülmen2
Hemer2
Herten2
Cologne2
Mülheim an der Ruhr2
Arnsberg2
Ense2
Kamen2
Sprockhövel2
Gladbeck2
Sundern1
Hennef1
Gevelsberg1
Haltern am See1
Siegen1
Sendenhorst1
Lippetal1
Selm1
Halver1
Krefeld1
Oberhausen1
Lüdenscheid1
Rietberg1
Moers1
Radevormwald1
Bönen1
Ennigerloh1
Verl1
Coesfeld1
Steinfurt1
Erwitte1
Wermelskirchen1
Nordkirchen1
Herscheid1
Soest1
Wesel1
Heiligenhaus1
Dates1
Bottrop1
Voerde1

A color-coded thematic map of NRW shows the distribution of non-car commuters based on their place of residence. The cities and districts of the federal state are shown, with the coloring following a logarithmic scale proportional to the number of residences:

  • Green: regions with the highest number of residences (logarithmic value around 3).
  • Light green to yellow: Regions with medium to low residential density.
  • Light yellow: Regions with the lowest values (close to 0 on the log scale).

Exact figures are listed in the following table:

CityNumber of residences
Dortmund557
Bochum67
Essen25
Witten11
Kamen11
Hamm11
Hagen10
Herne9
Duisburg8
Lunen8
Mülheim an der Ruhr7
Münster7
Unna5
Recklinghausen5
Werne5
Cologne4
Soest4
Gelsenkirchen4
Wuppertal4
Schwerte4
Ahlen3
Castrop-Rauxel3
Lüdinghausen3
Hemer2
Beckum2
Wickede2
Arnsberg2
Iserlohn2
Bielefeld2
Waltrop2
Bottrop2
Selm2
Ascheberg2
Ense2
Kierspe1
Kaarst1
Bonn1
Fröndenberg/Ruhr1
Gütersloh1
Bergkamen1
Hattingen1
Steinfurt1
Ennepetal1
Ratingen1
Oberhausen1
Heiden1
Remscheid1
Bönen1
Halver1
Dinslaken1
Menden1
Dülmen1
Holzwickede1
Welver1
Herdecke1
Marl1
Herten1
Viersen1
Balve1
Heinsberg1

A color-coded thematic map of NRW shows the distribution of car-only commuters based on their place of residence. The cities and districts of the federal state are shown, with the coloring following a logarithmic scale proportional to the number of residences:

  • Green: regions with the highest number of residences (logarithmic value around 3).
  • Light green to yellow: Regions with medium to low residential density.
  • Light yellow: Regions with the lowest values (close to 0 on the log scale).

Exact figures are listed in the following table:

CityNumber of residences
Dortmund168
Bochum26
Witten26
Hagen22
Essen20
Iserlohn14
Castrop-Rauxel12
Recklinghausen11
Herne10
Gelsenkirchen9
Schwerte9
Unna8
Waltrop6
Lünen6
Fröndenberg/Ruhr6
Werl5
Hattingen5
Wuppertal4
Sprockhövel4
Ennepetal4
Oer-Erkenschwick4
Wickede4
Weather4
Kamen4
Menden4
Dates4
Hamm3
Herdecke3
Mülheim an der Ruhr3
Marl3
Arnsberg3
Schwelm3
Dülmen3
Selm3
Gladbeck3
Altena3
Gevelsberg3
Oberhausen3
Lüdinghausen2
Herten2
Bönen2
Hemer2
Werne2
Steinfurt2
Ense2
Cologne2
Haltern am See1
Hennef1
Siegen1
Bocholt1
Kamp-Lintfort1
Münster1
Holzwickede1
Willich1
Sundern1
Ennigerloh1
Halver1
Krefeld1
Olfen1
Lüdenscheid1
Rietberg1
Kirchlengern1
Heiligenhaus1
Verl1
Anröchte1
Herscheid1
Nordkirchen1
Radevormwald1
Sendenhorst1
Moers1
Monheim on the Rhine1
Remscheid1
Bottrop1
Neuenrade1
Soest1
Wermelskirchen1
Oberaden1
Gütersloh1
Erwitte1
Wesel1
Coesfeld1
Lippetal1
Voerde1

A color-coded thematic map of NRW shows the preferred commuting behavior of students and employees based on their place of residence. The cities and districts of the federal state are shown, with the colors following a logarithmic scale proportional to the preferred commuting method:

  • Green: regions more likely to be car commuters.
  • Light green to yellow: regions where car and non-car commuters are balanced.
  • Light yellow: Regions that are more likely to be non-car commuters.

Exact figures are listed in the following table:
(Figures of car-only and non-car are in %)

CityCar-only[%]Non-car [%]Residences
Food425248
Mülheim an der Ruhr307010
Oberhausen43147
Remscheid50502
Wuppertal44449
Cologne33676
Bottrop33673
Gelsenkirchen693113
Münster12888
Dülmen75254
Lüdinghausen40605
Castrop-Rauxel802015
Herten67333
Marl75254
Recklinghausen652917
Waltrop67229
Steinfurt67333
Gütersloh50502
Bochum276898
Dortmund2169802
Hagen673033
Hamm186517
Herne504520
Ennepetal80205
Hattingen71147
Herdecke75254
Witten652840
Arnsberg50336
Halver50502
Hemer50504
Iserlohn881216
Menden67176
Ense50504
Soest20805
Wickede67336
Bönen67333
Fröndenberg/Ruhr75128
Holzwickede50502
Kamen226118
Lunen405315
Schwerte642914
Selm50336
Unna533315
Werne29717
Duisburg08010
Ratingen01001
Grevenbroich001
Kaarst01001
Viersen01001
Dinslaken01001
Bonn01001
Heinsberg01001
Heiden01001
Ascheberg01002
Ahlen01003
Beckum01002
Bielefeld01002
Balve01001
Kierspe01001
Welver01001
Bergkamen01001
Heiligenhaus10001
Monheim on the Rhine10001
Grevenbroich001
Willich10001
Kamp-Lintfort10001
Moers5002
Voerde10001
Wesel10001
Radevormwald10001
Wermelskirchen10001
Hennef10001
Bocholt10001
Coesfeld10001
Nordkirchen10001
Olfen10001
Dates10004
Gladbeck10003
Haltern am See10001
Oer-Erkenschwick10004
Ennigerloh10001
Sendenhorst10001
Rietberg10001
Verl10001
Kirchlengern10001
Gevelsberg10003
Schwelm10003
Sprockhövel10004
Weather8005
Sundern10001
Altena10003
Herscheid10001
Lüdenscheid10001
Neuenrade10001
Siegen10001
Anröchte10001
Erwitte10001
Lippetal10001
Werl10005

The figure above shows that the city of Dortmund forms the center of TU Dortmund University's catchment area. The data also shows that many commuters also come from surrounding cities and municipalities, particularly along the Ruhr axis. On average, both employees and students travel 19 km (median: 10 km) per commute. Students (employees) need an average of 39 (33) minutes to cover this distance. While the commuting frequency for employees hardly differs between the lecture period with 3.8 (median: 4.0) days per week and the lecture-free period with 3.6 (median: 4.0) days per week, students are at the university on average 3.7 (median: 4.0) days per week during the lecture period and 2 (median: 2) days per week during the lecture-free period.

The survey showed that people tend to use their car instead of public transport or cycling, especially in residential areas north and south of Dortmund. There is less car use along the east-west axis. A more detailed breakdown can be seen in the adjacent figure.

The survey also shows that a significant proportion of people living in Dortmund also travel to the university by car. Despite the good public transport connections and possible cycle paths, around 21% of respondents use the car as their primary means of transportation.

Average Distances and Typical Commuting Routes

Average distances can be calculated from the survey depending on the type of mobility. These can be seen in the adjacent graph. It is striking that the shortest distances are covered by bicycle/pedestrian transport (4.2 km), followed by local public transport (19.4 km) and private motorized transport (26.5 km). The longest distances are covered by long-distance rail passenger transport (SPFV, 41.6 km).

Average distances traveled per type of mobility

If a typical, average commute for students (TU employees) is calculated on the basis of the survey using the actual kilometers driven, then 7.4 (10.8) km would be driven by car in the morning, 0.7 (0.7) km would be covered after changing to public transport, and then 9.5 (5.3) km would be covered by public transport. The last part of the journey of 1.1 (2.0) km would be covered by bicycle or on foot. This is clearly illustrated in the diagram above.

Mobility Behavior

Interactive graphic. Source: Sustainability Office/TU Dortmund University

Of all the people who took part in the survey, 38% use a car or private car as their primary means of transportation. However, a breakdown by status group reveals differences: 49% of employees travel by car, while the proportion of students is around 30%.

The picture is reversed for public transport. Here, the proportion of employees is around 30%, while just under 67% of students use public transport. 52% of employees and 45% of students travel by bicycle/foot.

It is striking that employees tend to use private motorized transport more often for their daily commute, while students mostly choose public transport. Nevertheless, around a third of them also regularly use the car to get to the university.

 

The stacked bar chart shows the distance travelled per mobility type (normalized to the average distance) for the status groups (non-)scientific employee and students.
The individual mobility types are color-coded:

  • Yellow: bicycle/ on foot
  • Green: Public transport (local public transport)
  • Blue: Train, long-distance transport
  • Dark blue: Motorized private transport (car, motorcycle, etc.)

Exact figures can be found in the following table:
(in km)

  (Non-) scientific employee Student
Bicycle / on foot 1,97 1,08
Public transport 5,28 9,51
Train / long-distance traffic 0,67 0,74
Motorized private transport 10,82 7,42

The average number of modes of transport used on the way to university is 1.6, with a median of 1.0, meaning that most participants mainly use one mode of transport, in contrast to the hypothetical commute shown above, where four modes of transport were used.

Carpooling

It is not possible to evaluate this question as it was asked too unspecifically. It is unclear whether the question refers to the people in the vehicle or to the number of additional people in the vehicle.

Of the 519 people who came to the university by car, 52 people (10 %) stated that they were traveling in a carpool. These figures suggest that the proportion of people who carpool to TU Dortmund University may be higher at TU Dortmund University than the national average: according to the Federal Environment Agency, the Federal Ministry of Transport's "Mobility in Germany 2008" report shows that around five percent of car journeys to work are made as passengers and around 65 percent of journeys to work are made as drivers. In comparison, TU Dortmund University appears to have a carpooling rate that is twice as high.

The TU in Comparison

The figures collected in the survey are in line with the results of other mobility surveys. A comparison can be made with the InnaMo Ruhr survey from 2021. In this survey, members of the University Alliance Ruhr (UA-Ruhr, consisting of Ruhr-Universität Bochum, TU Dortmund University and the University of Duisburg-Essen) were asked about their mobility behavior. As Ruhr-Universität Bochum and the University of Duisburg-Essen are very similar to TU Dortmund University, both are also located in the Ruhr region, are similar in size and connected to a highway, offer similar capacities for motorized private transport and are similarly easily accessible by public transport, they are well suited for a comparison.

The survey showed that on average 31% of university members use their car to get to university before the coronavirus lockdown and 39% during the lockdown. This is in line with the 38% from this survey. The InnaMo Ruhr survey also shows that 49% of people traveled to the university by public transport before the lockdown. This also matches the results of this survey, in which 67% of all students and 30% of all employees used public transport.

Another mobility survey conducted by the Frankfurt University of Applied Sciences (UAS) shows similar ratios to the present survey, but differences in location should be taken into account, as the UAS is significantly smaller (in terms of area, number of students) than the UA-Ruhr universities and is located fairly centrally in Frankfurt. Despite the central location of UAS, 38% of all university members come to the university by car and 36% by public transport.

Other mobility surveys from, for example, the Bochum University of Applied Sciences show some clear differences in the use of the individual modality types, although similar trends in the use of modality types between employees and students are also evident here.