4 How to solve parking problems in the residential areas?
4.1 Study area description
The first step of this study consisted of selecting a suitable residential neighbourhood to investigate parking issues. The two main selection factors for the study area were (1) that the area should be noticeably affected by parking problems and (2) that it should not be too broad so as to facilitate the data collection. Given that the FIZ Future extensions are mostly affecting the Northern part of current FIZ, residential areas beyond the southern railway line were not considered relevant. Areas directly north of FIZ were also not considered due to a planned trial parking management scheme in the area (Landeshauptstadt München, 2013b) The residential neighbourhood bounded to the north by Permanederstraße, to the south by Troppauerstraße and to the west by Knorrstraße was identified as being the most attractive zone for on-street parking. The area was closed off in this manner firstly due to its close proximity to FIZ, whereby any point of the residential area is accessible within less than 12 minutes walking distance from the FIZ main entrance. Second, because the area is located next to a transport node consisting of the subway station Am-Hart and several bus connections – thereby making it attractive for commuters. The third argument was that the majority (>60%) of residents coming from this specific area took part in the citizen’s discussion about FIZ Future, in which parking was highlighted as a main concern (BMW Group & Landeshauptstadt München, 2013)
Investigations were finally bounded to the east by the small park parallel to Georg-von-May-Straße. The main reason for this choice was the willingness to limit the size of the study area to facilitate the data collection. Assuming that drivers want to park as close as possible to the FIZ main entrance and transport facilities, it also made sense to focus on the western part of the residential area. Another plausible assumption was that drivers were not willing to park beyond the small park, acting as a ‘separator’ between the western and the eastern part.
However, additional investigations revealed afterwards that more distant parking can also be appealing if walking from a parking spot to the destination is attractive enough (e.g., walking through a small park during summer). In this regard, it cannot be excluded that parking
25 problems may reach beyond the small park to the east. Nevertheless, these are likely to be less significant than the ones highlighted within the selected study area.
Figure 16 provides a schematic representation of the investigated residential neighbourhood.
The area is mainly residential, with a rather low population density, consisting of separate large houses, up to three floors, mostly occupied by several families. At present, the few points of interest (namely a bakery, a Volkshochschule and a small factory) are concentrated in the southern part on Troppauerstraße. A school is also currently being built in the north-west on Knorrstraße and opening in September 2016 with 900-1100 (Landeshauptstadt München, 2014).
In terms of accessibility, the area includes several pedestrian paths that enable a good connection between its different parts, through the allotments in the north and to the adjacent residential area in the east. Overall, there is a significant contrast between the two sides of Knorrstraße with FIZ on the west side (tall buildings, high concentration of employees, main generator for activity and traffic) and the residential neighbourhood on the east side (separate houses, rather low population density and residents seeking for a quiet place to live).
This contrast is also noticeable in the different streets that were investigated. These can roughly be classified into three categories. First, Sudetendeutschestraße and Knorrstraße are main transport axes with extensive through traffic. Second, Troppauerstraße presents specific driving and parking patterns with large in-and-out-coming traffic as well as short-term parking, due to the proximity of the shop and the small factory (delivery services, customers etc.). Finally, the remaining streets can be considered as ‘quiet’ residential streets with very little through traffic. In terms of parking regulation, there is a global lack of delineation and signage in the entire area. Parking restriction can be found on the east part of Troppauerstraße, in front of the bakery and the small factory. Additional signs also restrict parking on some sides of Sudetendeutschestraße, in Doeberlstraße and at the end of Georg-von-May-Straße.
26 Legend
Land-Use Transport Facilities
Residential area Highschool under construction
Current BMW FIZ
Allotment (Schrebergarten) Small park
Subway station Bus station Road
Path
Points of interest
Bakery
Volkshochschule
Small factory
Towards FIZ Main Entrance
Streets investigated
Figure 16: Residential area close to FIZ Main Entrance
27 4.2 Data collection and processing
The second important step consisted of developing a methodology to collect and process the relevant parking data so as to grasp the parking situation in the study area. This represented a significant amount of work, in addition to the data collection in itself due to the planning required before going on site and also afterwards to think about suitable solutions to analyse the data.
4.2.1 Objectives
The investigation of the residential neighbourhood consisted of several on-site visits, which allowed for both a quantitative and qualitative understanding of parking issues occurring in the area. In this regard, several objectives were set-up:
Identify different on-street parking user groups, based on their parking behaviour and on-site observations
Estimate the on-street parking capacity of the area
Study the on-street parking capacity utilisation over time and street by street
Identify and locate critical parking issues occurring in the area (illegal parking, conflict situations, etc)
Gain insight into how residents are affected by the current parking situation and FIZ Future (brief interviews during data collection)
4.2.2 On-site investigations
On-site visits, mainly consisting of license plate recording and observations, were performed at times considered strategic for the parking study:
Sunday 11th January 2015: 19:30 – 20:30
Wednesday 21st January 2015: 6:30 – 9:30
Wednesday 21st January 2015: 15:30 – 17:30
Going on a weekday and during the weekend was particularly important for understanding the parking pressure experienced by the residents. On Sunday evening, it is reasonable to assume that most residents are at home and that cars parked in the area mostly belong to them and not to commuters. Specific situations are also likely to happen, such as residents being away with their car for vacation, people parking in the neighbourhood to visit their relatives or even to take public transport to the airport (B. Grüber, personal communication, November 26, 2014). Nevertheless, the on-street parking situation on Sunday evening can be considered as a reference state, excluding the influence of BMW FIZ, surrounding facilities and other points of interests that would motivate non-residents to park in the area.
28 Collecting data at three different time periods (Sunday evening, Wednesday morning and Wednesday evening) was also necessary to identify characteristic parking patterns over time, so as to find out which car belonged to whom. The data collected on Sunday evening provides information about cars likely to belong to residents. During weekdays, data collection in the morning and in the late afternoon 10 days after the initial Sunday collection also provides information to differentiate between residents and commuters. Indeed, a working resident is likely to leave in the morning and come back in the evening, while a typical commuter comes in the morning and leaves in the late afternoon.
Investigating both Wednesday morning and Wednesday afternoon was also necessary to witness potential parking conflicts occurring at different times of day. For example, conflicts with delivery services are more likely to happen in the morning; while residents usually face problems to find a parking place when they come back from work in the late afternoon, and not when they leave home in the morning.
4.2.3 License plate numbers collection
Parking data collection was performed using a smart phone application. This application was configured to record (1) which car was parked, (2) where and (3) when. As shown in Figure 17, the street name and the license plate number can be easily entered, while the application automatically records the time and date. Further information can also be entered concerning the brand of the car and if the car is parked in a residential garage.
Figure 17: Screenshot of the application for license plate counting
With two team members involved in the data collection, it took approximately one hour to record the license plate numbers of all cars parked in the ten streets constituting the study area, starting from Troppauerstraße and ending at Permanederstraße. For data aggregation and processing, each one-hour lasting data collection round is considered to be a ‘snapshot’
of the parking situation during a given time span. For example, the first round of data collection on Wednesday morning lasted from about 6:30 to 7:30.
29 In this way, the database corresponding to the first snapshot starts from the first license plate number recorded on Troppauerstraße close to 6:30 until the last license plate number recorded at around 7:30 on Permanederstraße. Due to this way of collection, it is not exactly accurate to talk about a ‘snapshot’ of the parking situation since additional cars may have parked on Troppauerstraße after the street was counted. Nevertheless, this assumption of a
‘snapshot’ strongly facilitated data processing and displaying.
In this regard, five distinct time spans were defined: numbers. Table 8 shows an extract of the Excel table containing the data collected. Given the large amount of data, it was necessary to develop a simple method to visualise and identify typical parking patterns.
Table 8: Extract of license plate numbers database
Date Street License
Plate BMW Private
Garage Time Time ID
Sun 11/01/2015 Bruno-Hofer-Platz DGFW5226 True False 19:47:56 T0
Sun 11/01/2015 Troppauer Str. DHIS201 False False 19:13:12 T0
Wed 21/01/2015 Rockingerstr. DONN333 False False 16:12:21 T4
Wed 21/01/2015 Rockingerstr. DONN333 False False 17:26:58 T5
Wed 21/01/2015 Troppauer Str. DV546AH False False 8:04:15 T2
Wed 21/01/2015 Troppauer Str. DV546AH False False 16:50:16 T5
Wed 21/01/2015 Troppauer Str. DV546AHITALY False False 6:47:27 T1
30
Wed 21/01/2015 Troppauer Str. DV546AHITALY False False 9:01:10 T3
Wed 21/01/2015 Troppauer Str. DV546AHITALY False False 15:53:31 T4
Wed 21/01/2015 Georg-von-May-Str. EBEAF300 False False 7:13:13 T1
Wed 21/01/2015 Georg-von-May-Str. EBEAF300 False False 9:24:04 T3
Wed 21/01/2015 Georg-von-May-Str. EBEAF300 False False 16:14:52 T4
Wed 21/01/2015 Georg-von-May-Str. EBEAF300 False False 17:12:35 T5
Wed 21/01/2015 Schafhäutlstr. EBEDK102 False False 7:28:42 T1
Wed 21/01/2015 Schafhäutlstr. EBEDK102 False False 8:24:37 T2
Wed 21/01/2015 Schafhäutlstr. EBEDK102 False False 9:14:36 T3
Wed 21/01/2015 Knorrstr. EBET926 True False 7:48:23 T1
Wed 21/01/2015 Knorrstr. EBET926 True False 8:50:10 T2
Wed 21/01/2015 Knorrstr. EBET926 True False 9:35:42 T3
Wed 21/01/2015 Knorrstr. EBET926 True False 16:25:16 T4
Wed 21/01/2015 Knorrstr. EBET926 True False 17:37:23 T5
4.2.4 Representation of parking behaviour patterns
The parking behaviour of each car was represented by a so-called ‘parking behaviour code’, which is a six-digit long combination of zeros and ones, according to the presence of the car at a certain time. As illustrated in Figure 18, the first digit corresponds to T0 (Sunday from 19:30 to 20:30), the second digit to T1 (Wednesday from 6:30 to 7:30), the third digit to T2 (Wednesday from 7:30 to 8:30), etc. If the license plate was recorded at a given time, the corresponding digit was equal to 1; otherwise it was equal to 0.
Figure 18: Representation of a ‘parking behaviour code’
Based on the data collected, the ‘parking behaviour code’ was computed for each license plate recorded. This code makes it easy to visualise when a given car was parked in the area and also to estimate when it came and left. Table 9 illustrates the concept by showing the results obtained for the nine license plates displayed in Table 8.
31 Table 9: Examples of parking behaviour
License Plate Parking Behaviour Description
DGFW5226 1 000 00 Only present on Sunday evening. Not recorded on Wednesday.
DHIS201 1 111 11 Present on Sunday evening and during the entire Wednesday.
DONN333 0 011 11 Neither present on Sunday evening, nor on early
Wednesday morning. Came at around T2 and parked during the rest of the day.
DV546AH 0 01 001 Neither present on Sunday evening, nor on early
Wednesday morning. Came at T2, left and came back at T5.
DV546AHITALY 0 101 10 Not present on Sunday evening. Present on early Wednesday morning, left, came back from T3 to T4, left again in the evening.
EBEAF300 0 101 11 Not present on Sunday evening. Present on early
Wednesday morning, left, came back at T3 and stayed park afterwards.
EBEDK102 0 111 00 Not present on Sunday evening. Present on the whole Wednesday morning, left in the afternoon.
EBET926 0 111 11 Not present on Sunday evening. Present during the entire Wednesday.
4.2.5 Identification of parking user groups
There is a complex mix of users parking in the residential neighbourhood. This includes not only residents and commuters (not necessarily FIZ employees), but also other parking users, all with relatively complex parking patterns (e.g., short term parkers, FIZ visitors, people visiting relatives, etc). Under this assumption, three different parking user groups were defined: (1) residents (2) commuters and (3) unknown. Assigning a car to a specific category was based on a range of underlying assumptions and the data available. All assumptions (seven in total) are described below and summed-up in Table 10, ordered from the most probable [0] to the most questionable [6] (see ‘Classification Step’ in Table 10).
First, the identification of residents was performed based on objective facts and easily recognisable parking behaviour. In this regard, any car matching one of the following criteria was considered to belong to a resident (with the probability of such an assumption being effectively true considered to be high):
The car was recorded at least once in a private parking garage (Private Garage = TRUEin Table 8). [0]
32
The car was recorded on Sunday evening and at least once on Wednesday. Indeed, why would an external parker be first recorded on Sunday evening and then again on a weekday 10 days after? [1]
In case the car was only recorded on Sunday evening (1 000 00), the identification becomes more complex. On Sunday evening, most cars parked in the area are likely to belong to residents. Nevertheless, it is also possible that some external parkers are present in the area (e.g., people coming exceptionally to visit relatives). To reflect this situation, the assumption was made that if the license plate number of the car is from the city of Munich (starting with M), the car belongs to a resident. All cars coming from other locations are put in the category unknown. [2]
On Wednesday, if a car leaves the area and then comes back, it is reasonable to assume that this car belongs to a resident. [3]
Furthermore, several cars were put into the unknown category [4]. This was due to a lack of data that would not make it possible to clearly differentiate between residents and commuters. Particular attention was also paid to the cars with an unknown parking behaviour code so as to make sure that the majority were able to be eventually categorised. In addition, several commuters could be identified under the assumption that a commuter is likely to arrive in the morning and to leave in the evening (e.g., 0 111 10) [5].
Once these assumptions were made, there were only four parking behaviour codes without classification remaining. However, this was particularly critical given that these codes were the most frequent, accounting for 22, 39, 43 and 66 cars; this corresponded to more than 40% of the total number of cars recorded. The final classifications were the outcome of various group discussions and remain the most subjective ones. Explanations are provided in Table 10 under the classification step [6].
Table 10: Different parking behaviours recorded and their categorization Classification
Resident Present on Sunday and on another weekday High chance to be a resident
33 Unlikely to be a commuter. Can be a resident coming back in the evening but also a short-term parker.
Can be a resident leaving in the morning and coming back home late. Can also be a short-term parker (dropping off relatives for example)
[4] 5 0 110 00 Unknown Lack of data: recorded only once
Can be a resident leaving in the morning and coming back home late. Can also be a
short-34
[6] 22 0 011 11 Commuter Arrives in the morning between approximately 7:30 and 8:30 and stays parked in the area until the late afternoon.
Likely to be a commuter
[6] 66 0 111 00 Unknown Present in the morning, absent in the evening Can be a commuter arriving and leaving OR a resident leaving home and coming back late
[6] 43 0 111 10 Commuter Present in the morning and leaving in the evening (not evidence that the car arrived in the morning)
Little probably that a resident leaves his house at 16:30 on a Wednesday evening (43 cars is a high number)
Likely to be a commuter arriving and leaving early
[6] 39 0 111 11 Unknown Present the whole day
Not likely to be a commuter (coming before 6:30 and leaving after 17:30 would mean more than 10 working hours!)
Can be a resident but also an external parker
4.2.6 Critical analysis
Having developed a methodology to assign a category to each recorded parker, it wasalso necessary to take a step back and evaluate not only the advantages but also the possible future improvements for further investigations.
The approach of comparing weekends and weekdays as well as mornings and afternoons has proven to be an efficient way of analysing parking-related issues in the residential neighbourhood. The smart phone application was also a very convenient solution to collecting the data and then easily transferring it into Excel. The representation of parking behaviour using a combination of zeros and ones has also shown to be an effective way of quickly visualizing specific parking patterns. The identification of parking user groups in itself remains relatively subjective, but the more data available, the more precise the identification can be.
35 Nevertheless, the methodology involves some limitations and several points should be taken into account for future investigations:
Data collection was only performed on a Wednesday
Similar results would almost certainly be obtained by collecting data on a Thursday – given that the number of BMW employees present at FIZ during Wednesday and Thursday is practically the same (see Figure 8). However, parking issues may become more critical on Tuesday, when the number of FIZ employees present is the highest.
Monday and Friday have significantly less employees present than the other days.
Nevertheless, BMW representatives highlighted the most typical problems occurring on Monday morning and Friday evening, when commuters, especially those renting a flat in Munich to work there during the week, travel back home during the weekend (B. Grüber, personal communication, January 28, 2015). An in-depth study of parking issues in the study would therefore require proceeding with data collection during different weekdays.
Data collection was only performed from 6:30 to 9:30 and from 15:30 to 17:30 The data provided by BMW shows that many FIZ employees arrive before 6:30 and leave after 17:30 (see Figure 7). This was also confirmed during the on-site observations with the residential neighbourhood being already relatively full at 6:30.
The study area was considerably less crowded at 17:30; however, it could be that most residents are still working at this time (or gone out for shopping) and that most cars actually belong to commuters.
In this regard, there is a clear lack of data about the arrival time in the early morning and the departure time in the evening, which would be particularly useful to differentiate between residents and commuters. The gap between 9:30 and 15:30 also appears to be problematic because a significant amount of information is lost about cars leaving/coming within that time span (see the most observed parking behaviour:
0 111 00). For further study, it would be necessary to start the data collection before 6:30 and finish in the evening between 17:30-20:30. However, this would require a lot of personnel resources and also a good coordination to manage breaks.
0 111 00). For further study, it would be necessary to start the data collection before 6:30 and finish in the evening between 17:30-20:30. However, this would require a lot of personnel resources and also a good coordination to manage breaks.