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PFS method for pedestrian origin-destination surveys of enclosed areas
ScienceDirect Transportation Research Procedia 00 (2017) 000–000
Available online at www.sciencedirect.com
www.elsevier.com/locate/procedia
ScienceDirect Transportation Research Procedia 27 (2017) 680–687 www.elsevier.com/locate/procedia
20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary
PFS method for pedestrian origin-destination surveys of enclosed areas Tamás Soltésza*, Attila Abaa, Miklós Bánfia, Miklós Kózela a
BME Department of Transportation Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary
Abstract Although examination of pedestrian origin-destination (OD) relations of enclosed areas such as pedestrian zones, underpasses and transport hubs is a key point in designing such infrastructure, a very few efficient methods are known in literature. This paper introduces a novel survey technique for this purpose called PFS (Pedestrian Following Survey). The main principle of PFS method is that survey personnel follow randomly chosen pedestrians without any disturbance and record their routes through the investigated area (as well as some additional data). After the observed pedestrian leaves the area (at an egress point) the surveyor chooses a new pedestrian to follow from a nearby access-point. Collected data form a sample for pedestrian movements, similarly to floating car data in road transport. After comparing PFS method to well-known survey types the mathematic model of relation between the sample and total OD matrices is described. Then results of completed surveys applying PFS method are presented. These surveys were organized at Móricz Zsigmond circus which is a major transport hub in Budapest, Hungary, and were carried out by university students. Calculated OD matrices are validated by traffic volumes of particular public transport stops and cross-sections collected from other surveys. Experience shows that PFS method can be applied with success and can provide data efficiently compared to other OD survey types. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting.
* Corresponding author. Tel.: +36-1-463-1052. E-mail address: [email protected]
2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting. 10.1016/j.trpro.2017.12.082
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Tamás Soltész et al. / Transportation Research Procedia 27 (2017) 680–687 Soltész-Aba-Bánfi-Kózel / Transportation Research Procedia 00 (2017) 000–000
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Keywords: traffic survey; pedestrian traffic; origin-destination survey; OD matrix
1. Introduction, objectives The main goal of the paper is to introduce the Pedestrian Following Survey (PFS) method as a novel OD (origindestination) survey technique of open – but well defined – areas with medium or high pedestrian traffic. In general the aim of OD measurements is to determine origin and destination points of particular trips as well as to provide detailed information about traveler and their behavior (e.g. age, gender, motivation). Especially pedestrian origin-destination surveys can have further purposes besides general aims. On the one hand results of such surveys can be used for designing (scaling) pedestrian infrastructure elements (width of passages, number of elevators, etc.) and transport hubs (shortest paths between public transport stops, etc.) at a microscopic level. On the other hand nature of transfers (main transport connections, flows between public transport modes, etc.) and traveler’s behavior can also be examined at a macroscopic level. According to literature review and comparison to well-known survey types PFS method is a brand new measurement technique. Based on several years of experience (completed surveys) and validation authors can declare that PFS method is an exact (cannot be misunderstand like a questionnaire), resource-saving method which respects the privacy rights and can be used at large, physically open areas. Owing to the mathematic model and the measurement technique described in details adaptation and application of PFS is easy for transport planners. 2. Description of PFS method Pedestrian Following Survey is a novel survey technique based on the idea of observing traffic by a moving observer. Godfrey in 1969 is executed a moving observation to examine speed-density relationships, of course for road network. (Godfrey, 1969). This method – also known as floating car data (FCD) – was later extended and now it uses a wide range of raw data provided by fleet management systems. On the other hand, calibration of pedestrian simulations also needs walking related parameters which are hard to examine. A measurement in the Netherlands used the idea of pedestrian moving observation to examine walking times, and other times such as waiting, buying ticket, etc. However, this research only observed one platform of a train station and did not focus on origin destination flows. (Daamen et al., 2004). PFS is based on two simultaneous measurements. The first group of personnel follows randomly chosen pedestrians on their route through the enclosed area while the second group measures the traffic volume of access points to determine population. The survey happens simultaneously with traffic (not separated like questionnaire sent by post) but without disturbance. Other surveys with the same goals are all based on interaction with pedestrians, e.g. questionnaire on the spot, OD survey with cards. Produced datasets can be primarily used for examining pedestrian and public transport passenger flows (origindestination connections) at relatively large, open (but easily definable) areas. The origin-destination matrix can be calculated between the access and egress points of the area. Further on during the following of pedestrians various other information can be recorded. Access and egress points of the area can be public transport stops (PuT - bus, trolley, tram, metro, railway), sidewalks of connected streets, buildings in the enclosed area (both internal and bordering – usually several buildings grouped), and other traffic generating points (e.g. public bike stations, parking cars, car-sharing). Pedestrian following survey personnel starts recording first at a randomly selected access point, afterwards at a point where the previously followed pedestrian left the area. Very important, that the selection of pedestrians should always be random (first person who appears). If waiting for next pedestrian takes a while, a neighboring access point could be selected (but sample size also has to be considered: this movement is not suggested at low-traffic access points). Some public transport stop works as a terminus; either only alighting passengers appear (as access point) or only boarding passengers leave (as egress point). These termini should be paired with each other even if they do not belong to the same line. In PuT stops survey personnel should also ensure representativeness in space by selecting pedestrians from different doors of vehicle (front/middle/back randomly chosen).
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The following of a pedestrian should be accomplished without disturbing the subject (pedestrian), from a discreet distance, because several information can be recorded after egress. Multiple pedestrians walking together can be managed as a group, indicating the number of persons, possible meeting/parting points and times. For all followed pedestrian the access point, all the passed cross-sections and the egress point should be recorded with time stamps. In case of PuT stops, the time of egress is the arriving time to the stop not the boarding time in order to be able to calculate walking speeds. Waiting time is irrelevant in this survey from both macroscopic and microscopic standpoint. If more lines use the same PuT stop, survey personnel should wait with the pedestrian to specify which line was chosen. Pedestrians walking to PuT stops to meet someone arriving by PuT should be handled as their destination is the PuT stop not one of the public transport lines. Some pedestrian would use prohibited routes while survey personnel should obviously abide traffic rules. In this case pedestrian should be tracked by eye as long as possible, if it is impossible, a new pedestrian from up close should be recorded. Various properties of pedestrians can be also recorded in order to create more diverse and representative datasets. These properties can be demographical (age group, gender, etc.), related to pedestrian behavior (respect to traffic rules, usage of ear/headphones, usage of mobile device, etc.) or any additional information (e.g. baggage/type). In order to determine the (later described) population which forms the base of traffic ratios the number of accessing pedestrians at all cross-sections/points should be measured. Egress point and internal cross-section measuring can be used as validation, if necessary. In this case the internal cross-sections should be recorded by the pedestrian following personnel. The main output of PFS method is an origin-destination matrix for the enclosed area (and macroscopically for the related transport network) and Sankey diagrams of the pedestrian flows. Other possible outputs can be the ratio of destination and transferring traffic, walking distances (and their distribution), walking speeds (and their distribution), alternative routes between same origins and destinations demographic distributions, pedestrian behavior/habits. To describe the main mathematical features of the sample, the following nomenclature is introduced. Nomenclature fij sij xi
traffic flow between access point i and egress point j (element of the origin-destination matrix of the area) number of followed pedestrians between access point i and egress point j (element of the sample matrix) sampling rate of an access point
As a consequence of the sampling of PFS method (survey personnel chooses pedestrian from a point where the previous one left the area), sample sizes are nearly the same for each point’s two directions (access and egress traffic) (1). (The slight difference is caused by the initial and last followed pedestrians during the measurement.)
s s i
ij
j
(1)
ij
However, the OD matrix of the examined area usually different. This illustrates well that sample matrix is not representative for the whole OD matrix itself but only for the access points (where followed pedestrians are chosen randomly). If a point has large destination traffic then it will have a large sample size even it has low origin traffic. In other words, sample size depends only on the destination (egress) traffic of a point therefore sampling rates will be different for each access point. Thus, to calculate the OD matrix, the sample matrix can be divided by the sampling rate row by row (2).
f ij
sij xi
, where xi
s
ij
f
ij
j
j
(2)
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3. Comparing method to well-known pedestrian OD-survey types In transport engineering practice various methods apart from PFS are used to define an enclosed area’s origindestination matrix. The simplest method is to measure passing pedestrians in both directions at numerous internal cross-sections. From these sets of sectional measurements the main traffic flows of the area (on the selected network links) can be deduced, principally the selected pedestrian routes but no OD matrix can be calculated. This measurement can be used the best to verify pedestrian area design and to locate bottlenecks or spare areas. For macroscopic modelling questionnaires on the spot are commonly used. Pedestrians are asked at egress points of the area and population is given by cross-section measurements on egress points. Willingness to answer affects this method significantly. It is better suited to determine public transport transfers as waiting passengers are easier to be asked. Questionnaires are also used as complementary surveys to any other measurements. One of the commonly used OD surveys is to give special (colored, marked, etc.) cards to pedestrians on access points and to collect these cards at egress points. This method fits to closed areas with limited leaving/entry points, such as pedestrian subways. Not all pedestrians take away and give back cards, the sample size is usually around 50%. Therefore population should be measured by cross-section counting for both access and egress points. With the increasing usage of electronic technologies new survey methods can also be taken into account. Basically, two technologies are used. Based on near-field communication technologies travel (smart) card data can be collected automatically by public transport validators. A disadvantage is that origin and destination traffic is out of observation, only public transport transfers are detected (if the fare collection system requires ticket validation at transfers). (Jang, 2010) Based on mid-range communication, signals of mobile devices (e.g. Wi-Fi, Bluetooth) can be tracked by appropriately installed detectors (at every OD points) – so the origin and destination of device’s owner can also be described. Additional counting is also needed at access and egress points as usage is quite similar to card survey, but the sample size is significantly affected by pedestrians’ mobile device usage behavior. (Bhaskar et al. 2013) Table 1. Comparison of PFS method and other OD-survey types Set of sectional measurements Disturbance of traffic
Questionnaire on the spot
OD-survey with cards
OD-survey by electronic devices
PFS method
No
Yes
Yes
No
No
Simple
Simple
Medium
Complex
Simple
High
Moderate
High
Low
Moderate
No
Yes (on egress points)
Yes (both directions)
Yes (both directions)
Yes (on access points)
Main output
Traffic volumes on network links
Sample OD-matrix
Sample OD-matrix
Sample OD-matrix
Sample OD-matrix
Provided sampling rate
Full (on selected links)
Medium (usually 5-15%)
High (around 50%)
Medium
Medium (usually 5-15%)
Representativity of sample
(not sample)
For egress points, but willingness to answer affects it
For both access and egress points
For both access and egress points, but usage affects it
For access points only
-
Any other data (depends on questions)
-
Speed
Route choice (in the area), Demographics, Speed, etc.
Pedestrian areas with limited no. of paths (if OD matrix is not needed)
Public transport hubs (if mainly macroscopic data are needed)
Closed areas with limited no. of leaving/entry points (e.g. ped. subways)
Closed areas with limited no. of leaving/entry points (e.g. ped. subways)
Open areas with high destination traffic (e.g. squares in city centers)
Equipment need No. of survey personnel Need for simultaneous traffic survey
Additional output
Typical appliance area
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4. Realization of the method In general, PFS method is described in section 2, at this point a case study is going to be introduced. Since 2014 the survey has been implemented five times. All these surveys were organized at Móricz Zsigmond circus (as an enclosed area) which is a major transport hub in Budapest, Hungary. The article represents the results of the fourth and fifth measurements (in 2015 and 2016). Figure 1 shows the layout of the investigated area.
Fig. 1. Unified layout of case study at Móricz Zsigmond circus.
The enclosed area is split into public transport stops (red and green dots), sidewalks (green arrows) and spaces (block of buildings and parts of the square in claret). These three elements – as access and egress points – and their IDs represent all the possibilities, where pedestrians (or passengers) can leave (or enter to) the investigated area. 16 public transport stops are located at the circus; “B” means direction towards city center, “K” means direction toward outskirts, “L” and “F” are termini (with alighting or boarding passengers only) while “E”, “D” and “L” are the exits of metro station. The numbers next to dots show the sign of lines serving that particular stop (“+” means further lines). The circus can be approached or left via 13 sidewalks along both sides of connecting roads and streets. The circus is bounded by 7 blocks of buildings. The square itself is represented by T0. The cross-sections (blue) play dual role. On the one hand they are validation measurement points. On the other hand they describe the accurate route of followed pedestrians. 4 major zebra crossings (“Z1-Z4”), the subway (“A”) and 3 additional points represent the cross-sections. The surveys were carried out by university students in different parts of the day. In 2015, 25 students were involved, the measurement started at 10AM, and lasted for 1,5 hours; 60 minutes follow-up and 30 minutes of validation measurement. In 2016, 80 survey personnel worked for 30-30 minutes in morning peak as well as in low traffic period. The recorded parameters were the following (by groups): Access attributes: ID of access point, sign of the line from where the pedestrian got off, time of access (start of follow-up). Pedestrian attributes: gender, age groups (3), baggage type (if belongs), head and ear phones (if belongs), number of group members. Route attributes: passed cross-sections (ID-s), respect to traffic rules (violation), comments. Egress attributes: ID of egress point, sign of the line where the pedestrian got on, time of egress (end of followup). This unified parameter list makes repeating surveys comparable.
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5. Results In the case-study there were 37 access and egress points, which would result a huge OD-matrix if we regarded all of them separately. As pedestrian data has much less entries than the number of elements in this original matrix, many OD-connections would be 0 in the sample. Thus, some of the points have to be merged. The basis of merging points can be different, depending on the objective of the survey. If the area is examined in a microscopic level (pedestrian routes, flows, speeds are important within the area), then nearby points should be merged (based only on vicinity). If macroscopic connections are examined (transfers between PuT lines), then merging should be based on functions: PuT stops (toward the same directions) and local points (for destination traffic) separately. In the case-study, the second method was used. Points were merged into 6 public transport zones (5 zones for surface PuT lines – according to their directions and one for the metro) and 4 local zones. As a result, the size of sample matrix decreased to 100 elements. During the survey carried out in October 2015, 460 pedestrian data entries were recorded. After processing them, the values of the sample matrix elements varied from 0 (9 elements, 5 of them in the main diagonal) to 14. Traffic volume data were recorded at almost every access points, thus sampling rate could be calculated for 9 points out of 10. These rates varied from 4.4% to 13.6%. Traffic of surrounding buildings is very complicated to measure, so it was not recorded during this survey. However, sampling rate can be estimated for this zone, as well. As the average rate was 7.15% for the other local zones (and 7.05% for the PuT zones, which is very close to the former), it was supposed that there is no large difference between local zones, so calculation were made with the same 7.15% rate for this zone, as well. From the sample matrix and sample rates the OD matrix of the area (for one hour) was calculated and can be seen in Table 2. According to zone types (local or PuT), the matrix can be divided into four quarters. As “C1”, “C2”, “C3” and “T” are the local zones1, the first quarter contains only pedestrian traffic: people who walk through the area or who walk from or to a surrounding building. (This is nearly 29% of the whole traffic; examination of this kind of traffic would be very difficult with other survey types.) The second and fourth quarters contain the traffic between local zones and PuT stops, while the third one shows the transfers between PuT directions. Table 2. OD matrix of the investigated area for one hour Zone from\to
C1
C2
C3
T
É
B
K
D
N
Σ
M
C1
195
179
179
212
65
49
114
49
81
16
1140
C2
79
52
39
118
79
13
26
39
26
131
602
C3
111
55
11
133
66
22
11
22
11
11
454
T
111
83
111
194
69
83
42
69
56
111
931
B
52
52
74
103
7
81
7
22
15
15
428
É
160
46
46
91
69
0
0
137
160
137
846
K
41
51
41
41
31
31
0
62
92
21
410
D
49
0
33
131
33
82
16
0
33
0
376
N
32
49
129
65
113
97
32
113
0
65
696
M
169
68
51
85
34
118
0
34
34
0
592
Σ
999
635
714
1173
566
577
249
548
508
507
6475
1 Area (dashed line circles) of zone C1 represents B1, B2, K1, K2, and S according to Figure 1. Area (dashed line circles) C2 represents V1, V2, H, B3, and B4 according to Figure 1. Area (dashed line circles) C3 represents F1+F2+L according to Figure 1. Area (dashed line rectangle) T represents T0, T1, T2, T3, T4, T5, T6, and T7 according to Figure 1.
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Based on Table 2, Figure 2 shows the OD flows of pedestrians who cross the enclosed area in 2015. “C1”, “C2”, “C3” are those sidewalks where pedestrians can leave (or enter to) the investigated area. “T” is merging all the surrounding buildings and the square itself. By each zone bold numbers show the total amount of origin traffic while italic numbers represent the total amount of destination traffic. The width of each arrows is proportional to the number of pedestrians in an hour between particular zones. The rectangles after the three arrows represent the traffic inside the zone (length is proportional to the volume). It can be seen that the highest flow (212 pedestrian/hour) origins from zone “C1” and ends in “T”. As a consequence many pedestrian cross the square as well as have local purposes.
Fig. 2. Crossing pedestrian’s OD flow.
From other traffic surveys (one of the metro station in April 2015 – zone “M”, and one of tram line 6 in November 2015 – zone “N”), some PuT traffic volumes are known as a reference. Zone “M” represents traffic of metro line M4 (points M4D, M4É and M4L on Figure 1) and zone “N” represents traffic of tramline 6 (point 6 on Figure 1). Volumes of the same hour of the day are compared with the results of the case-study in Table 3. Table 3. Known traffic volumes (passenger/hour) from other surveys Zone
Reference PuT surveys
Case study
Relative difference
Alighting
Boarding
A/B Rate
Alighting
Boarding
A/B Rate
Alighting
Boarding
A/B Rate
M
738
600
1.23
507
592
1.17
-15.6%
-19.8%
-5.0%
N
644
462
1.39
508
696
1.37
+9.9%
+8.1%
-1.7%
Results show that there are relatively high differences (10-20%) in traffic volumes. Regarding this, we have to add that the metro line was opened only in 2014. Before that Móricz Zsigmond circus was a major public transport hub, and surface level PuT network changed gradually, so traffic habits may have changed between the surveys (as the square lost of its importance). As a survey experience traffic volumes on the same place can vary by up to 10-15% between similar days, without any obvious reason. On the other hand, difference values are very similar between boarding and alighting traffic in the same PuT stop. Rates of alighting and boarding volumes are also calculated for both surveys: these vary not more than 5% at these PuT stops. As alighting traffic was measured, while boarding volumes were derived from the OD matrix, this can be seen as a validation of OD matrix calculation. A similar survey was carried out in April 2016. A detailed description of the results of this would exceed the limitations of this paper, thus we focus on the differences to the 2015 survey. The number of survey personnel was
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larger this year, and as a result, 840 data entries were recorded. This would allow a more detailed examination (with more zones), but this way of comparison would be difficult, so the same merging procedure were applied. In the 2016 survey, local zones were not measured, thus sampling rates could not be estimated on the same basis. On the other hand, two control cross-sections were measured, the sampling rates of which were used. The overall traffic volumes were 12% higher than 2015, but the survey was carried out in an earlier hour of the day (closer to peak hour). Several elements of OD matrix were changed, from which the most interesting is the decrease in the traffic of zone “É” (PuT from or to the northwest). Tramlines of this zone had had termini in 2015, but they were extended in January 2016 (towards south, into zone “D”), thus the number of transfers from this zone fell (e.g. traffic flow from zone “É” to “D” totally disappeared, and from “D” to “É” decreased by 80%). The differences of traffic volumes compared to the same reference PuT surveys varied from -17.2% (alighting at the metro station, similarly to 2015) to +2.9%. 6. Experience and conclusion After literature review and comparison to well-known survey types PFS method can be assumed as a novel survey technique of enclosed areas. Mathematic model and measurement technique described in details as well as a case study is introduced. Key findings of this article are that PFS is a new, validated, exact, resource-saving method, which respects the privacy rights and can be used at large areas. Calculated OD matrices are validated by traffic volumes of control public transport stops and cross-sections collected from other surveys. Unlike to conventional questionnaires observation (follow-up) provides accurate (exact) information about origin and destination of each trips. Number of survey personnel is moderate and special devices are not required. PFS respects the privacy rights due to the fact that pedestrians (passengers) not have to be upheld and asked personally. Another advantage is the method works well in relatively large, open pedestrian areas, as well. For possible further developments the followings can be taken into consideration. Appropriate selection of control cross-sections and public transport stops for simultaneous data collection (as the basis of validation) is important. Clarification and update of access and egress points of enclosed area in accordance with layout modifications (e.g. relocation of public transport stops, modification of public transport line routes and signs) is necessary. Detailed motivation of particular trips (as a traveler behavior) can be also expressed and examined if survey personnel does not finish following-up the pedestrian who reaches an egress point (as an “intermediate stop” of its trip); if follow-up continues towards a “real” egress point (where the pedestrian physically leaves the investigated area) the motivation of intermediate stop (e.g. eating some meals) is derivable. Parking cars can be handled either as a destination flow (like a “final” egress point) or – as it stated above – starting car movements (from a parking lot as an “intermediate” egress point) towards “real” egress point can be followed. As a conclusion PFS method can be defined as an efficient measure to provide OD dataset and information about traveler’s behavior for planning procedure on microscopic and macroscopic level as well. According to our detailed description transport planners can easily adapt and apply PFS method for their purposes. References Godfrey, J.W., 1969. The mechanism of a road network. In: Traffic Engineering and Control, Vol. 11 (7), 1969, pp. 323-327. Daamen, W., Hoogendoorn, SP., 2004. Pedestrian traffic flow operations on a platform: observations and comparison with simulation tool SimPed. In: Allen, J., Brebbia, C.A., Hill, R.J., Sciutto, G., Sone, S., (Eds.), Computers in railways IX (Congress Proceedings of CompRail 2004), Dresden, Germany, May 2004 (pp. 125-134). Southampton: WIT Press Jang, W., 2010. Travel Time and Transfer Analysis Using Transit Smart Card Data. In: Transportation Research Record: Journal of the Transportation Research Board Dec 2010, Vol. 2144, pp. 142-149 Bhaskar, A., Kieu, L. M., Qu, M., Nantes, A., Miska, M., Chung, E., 2013. On the use of Bluetooth MAC Scanners for live reporting of the transport network. In: 10th International Conference of Eastern Asia Society for Transportation Studies, 9-12 September 2013, Taipei, Taiwan.
Available online at www.sciencedirect.com
www.elsevier.com/locate/procedia
ScienceDirect Transportation Research Procedia 27 (2017) 680–687 www.elsevier.com/locate/procedia
20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary
PFS method for pedestrian origin-destination surveys of enclosed areas Tamás Soltésza*, Attila Abaa, Miklós Bánfia, Miklós Kózela a
BME Department of Transportation Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary
Abstract Although examination of pedestrian origin-destination (OD) relations of enclosed areas such as pedestrian zones, underpasses and transport hubs is a key point in designing such infrastructure, a very few efficient methods are known in literature. This paper introduces a novel survey technique for this purpose called PFS (Pedestrian Following Survey). The main principle of PFS method is that survey personnel follow randomly chosen pedestrians without any disturbance and record their routes through the investigated area (as well as some additional data). After the observed pedestrian leaves the area (at an egress point) the surveyor chooses a new pedestrian to follow from a nearby access-point. Collected data form a sample for pedestrian movements, similarly to floating car data in road transport. After comparing PFS method to well-known survey types the mathematic model of relation between the sample and total OD matrices is described. Then results of completed surveys applying PFS method are presented. These surveys were organized at Móricz Zsigmond circus which is a major transport hub in Budapest, Hungary, and were carried out by university students. Calculated OD matrices are validated by traffic volumes of particular public transport stops and cross-sections collected from other surveys. Experience shows that PFS method can be applied with success and can provide data efficiently compared to other OD survey types. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting.
* Corresponding author. Tel.: +36-1-463-1052. E-mail address: [email protected]
2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting. 10.1016/j.trpro.2017.12.082
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Keywords: traffic survey; pedestrian traffic; origin-destination survey; OD matrix
1. Introduction, objectives The main goal of the paper is to introduce the Pedestrian Following Survey (PFS) method as a novel OD (origindestination) survey technique of open – but well defined – areas with medium or high pedestrian traffic. In general the aim of OD measurements is to determine origin and destination points of particular trips as well as to provide detailed information about traveler and their behavior (e.g. age, gender, motivation). Especially pedestrian origin-destination surveys can have further purposes besides general aims. On the one hand results of such surveys can be used for designing (scaling) pedestrian infrastructure elements (width of passages, number of elevators, etc.) and transport hubs (shortest paths between public transport stops, etc.) at a microscopic level. On the other hand nature of transfers (main transport connections, flows between public transport modes, etc.) and traveler’s behavior can also be examined at a macroscopic level. According to literature review and comparison to well-known survey types PFS method is a brand new measurement technique. Based on several years of experience (completed surveys) and validation authors can declare that PFS method is an exact (cannot be misunderstand like a questionnaire), resource-saving method which respects the privacy rights and can be used at large, physically open areas. Owing to the mathematic model and the measurement technique described in details adaptation and application of PFS is easy for transport planners. 2. Description of PFS method Pedestrian Following Survey is a novel survey technique based on the idea of observing traffic by a moving observer. Godfrey in 1969 is executed a moving observation to examine speed-density relationships, of course for road network. (Godfrey, 1969). This method – also known as floating car data (FCD) – was later extended and now it uses a wide range of raw data provided by fleet management systems. On the other hand, calibration of pedestrian simulations also needs walking related parameters which are hard to examine. A measurement in the Netherlands used the idea of pedestrian moving observation to examine walking times, and other times such as waiting, buying ticket, etc. However, this research only observed one platform of a train station and did not focus on origin destination flows. (Daamen et al., 2004). PFS is based on two simultaneous measurements. The first group of personnel follows randomly chosen pedestrians on their route through the enclosed area while the second group measures the traffic volume of access points to determine population. The survey happens simultaneously with traffic (not separated like questionnaire sent by post) but without disturbance. Other surveys with the same goals are all based on interaction with pedestrians, e.g. questionnaire on the spot, OD survey with cards. Produced datasets can be primarily used for examining pedestrian and public transport passenger flows (origindestination connections) at relatively large, open (but easily definable) areas. The origin-destination matrix can be calculated between the access and egress points of the area. Further on during the following of pedestrians various other information can be recorded. Access and egress points of the area can be public transport stops (PuT - bus, trolley, tram, metro, railway), sidewalks of connected streets, buildings in the enclosed area (both internal and bordering – usually several buildings grouped), and other traffic generating points (e.g. public bike stations, parking cars, car-sharing). Pedestrian following survey personnel starts recording first at a randomly selected access point, afterwards at a point where the previously followed pedestrian left the area. Very important, that the selection of pedestrians should always be random (first person who appears). If waiting for next pedestrian takes a while, a neighboring access point could be selected (but sample size also has to be considered: this movement is not suggested at low-traffic access points). Some public transport stop works as a terminus; either only alighting passengers appear (as access point) or only boarding passengers leave (as egress point). These termini should be paired with each other even if they do not belong to the same line. In PuT stops survey personnel should also ensure representativeness in space by selecting pedestrians from different doors of vehicle (front/middle/back randomly chosen).
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The following of a pedestrian should be accomplished without disturbing the subject (pedestrian), from a discreet distance, because several information can be recorded after egress. Multiple pedestrians walking together can be managed as a group, indicating the number of persons, possible meeting/parting points and times. For all followed pedestrian the access point, all the passed cross-sections and the egress point should be recorded with time stamps. In case of PuT stops, the time of egress is the arriving time to the stop not the boarding time in order to be able to calculate walking speeds. Waiting time is irrelevant in this survey from both macroscopic and microscopic standpoint. If more lines use the same PuT stop, survey personnel should wait with the pedestrian to specify which line was chosen. Pedestrians walking to PuT stops to meet someone arriving by PuT should be handled as their destination is the PuT stop not one of the public transport lines. Some pedestrian would use prohibited routes while survey personnel should obviously abide traffic rules. In this case pedestrian should be tracked by eye as long as possible, if it is impossible, a new pedestrian from up close should be recorded. Various properties of pedestrians can be also recorded in order to create more diverse and representative datasets. These properties can be demographical (age group, gender, etc.), related to pedestrian behavior (respect to traffic rules, usage of ear/headphones, usage of mobile device, etc.) or any additional information (e.g. baggage/type). In order to determine the (later described) population which forms the base of traffic ratios the number of accessing pedestrians at all cross-sections/points should be measured. Egress point and internal cross-section measuring can be used as validation, if necessary. In this case the internal cross-sections should be recorded by the pedestrian following personnel. The main output of PFS method is an origin-destination matrix for the enclosed area (and macroscopically for the related transport network) and Sankey diagrams of the pedestrian flows. Other possible outputs can be the ratio of destination and transferring traffic, walking distances (and their distribution), walking speeds (and their distribution), alternative routes between same origins and destinations demographic distributions, pedestrian behavior/habits. To describe the main mathematical features of the sample, the following nomenclature is introduced. Nomenclature fij sij xi
traffic flow between access point i and egress point j (element of the origin-destination matrix of the area) number of followed pedestrians between access point i and egress point j (element of the sample matrix) sampling rate of an access point
As a consequence of the sampling of PFS method (survey personnel chooses pedestrian from a point where the previous one left the area), sample sizes are nearly the same for each point’s two directions (access and egress traffic) (1). (The slight difference is caused by the initial and last followed pedestrians during the measurement.)
s s i
ij
j
(1)
ij
However, the OD matrix of the examined area usually different. This illustrates well that sample matrix is not representative for the whole OD matrix itself but only for the access points (where followed pedestrians are chosen randomly). If a point has large destination traffic then it will have a large sample size even it has low origin traffic. In other words, sample size depends only on the destination (egress) traffic of a point therefore sampling rates will be different for each access point. Thus, to calculate the OD matrix, the sample matrix can be divided by the sampling rate row by row (2).
f ij
sij xi
, where xi
s
ij
f
ij
j
j
(2)
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3. Comparing method to well-known pedestrian OD-survey types In transport engineering practice various methods apart from PFS are used to define an enclosed area’s origindestination matrix. The simplest method is to measure passing pedestrians in both directions at numerous internal cross-sections. From these sets of sectional measurements the main traffic flows of the area (on the selected network links) can be deduced, principally the selected pedestrian routes but no OD matrix can be calculated. This measurement can be used the best to verify pedestrian area design and to locate bottlenecks or spare areas. For macroscopic modelling questionnaires on the spot are commonly used. Pedestrians are asked at egress points of the area and population is given by cross-section measurements on egress points. Willingness to answer affects this method significantly. It is better suited to determine public transport transfers as waiting passengers are easier to be asked. Questionnaires are also used as complementary surveys to any other measurements. One of the commonly used OD surveys is to give special (colored, marked, etc.) cards to pedestrians on access points and to collect these cards at egress points. This method fits to closed areas with limited leaving/entry points, such as pedestrian subways. Not all pedestrians take away and give back cards, the sample size is usually around 50%. Therefore population should be measured by cross-section counting for both access and egress points. With the increasing usage of electronic technologies new survey methods can also be taken into account. Basically, two technologies are used. Based on near-field communication technologies travel (smart) card data can be collected automatically by public transport validators. A disadvantage is that origin and destination traffic is out of observation, only public transport transfers are detected (if the fare collection system requires ticket validation at transfers). (Jang, 2010) Based on mid-range communication, signals of mobile devices (e.g. Wi-Fi, Bluetooth) can be tracked by appropriately installed detectors (at every OD points) – so the origin and destination of device’s owner can also be described. Additional counting is also needed at access and egress points as usage is quite similar to card survey, but the sample size is significantly affected by pedestrians’ mobile device usage behavior. (Bhaskar et al. 2013) Table 1. Comparison of PFS method and other OD-survey types Set of sectional measurements Disturbance of traffic
Questionnaire on the spot
OD-survey with cards
OD-survey by electronic devices
PFS method
No
Yes
Yes
No
No
Simple
Simple
Medium
Complex
Simple
High
Moderate
High
Low
Moderate
No
Yes (on egress points)
Yes (both directions)
Yes (both directions)
Yes (on access points)
Main output
Traffic volumes on network links
Sample OD-matrix
Sample OD-matrix
Sample OD-matrix
Sample OD-matrix
Provided sampling rate
Full (on selected links)
Medium (usually 5-15%)
High (around 50%)
Medium
Medium (usually 5-15%)
Representativity of sample
(not sample)
For egress points, but willingness to answer affects it
For both access and egress points
For both access and egress points, but usage affects it
For access points only
-
Any other data (depends on questions)
-
Speed
Route choice (in the area), Demographics, Speed, etc.
Pedestrian areas with limited no. of paths (if OD matrix is not needed)
Public transport hubs (if mainly macroscopic data are needed)
Closed areas with limited no. of leaving/entry points (e.g. ped. subways)
Closed areas with limited no. of leaving/entry points (e.g. ped. subways)
Open areas with high destination traffic (e.g. squares in city centers)
Equipment need No. of survey personnel Need for simultaneous traffic survey
Additional output
Typical appliance area
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4. Realization of the method In general, PFS method is described in section 2, at this point a case study is going to be introduced. Since 2014 the survey has been implemented five times. All these surveys were organized at Móricz Zsigmond circus (as an enclosed area) which is a major transport hub in Budapest, Hungary. The article represents the results of the fourth and fifth measurements (in 2015 and 2016). Figure 1 shows the layout of the investigated area.
Fig. 1. Unified layout of case study at Móricz Zsigmond circus.
The enclosed area is split into public transport stops (red and green dots), sidewalks (green arrows) and spaces (block of buildings and parts of the square in claret). These three elements – as access and egress points – and their IDs represent all the possibilities, where pedestrians (or passengers) can leave (or enter to) the investigated area. 16 public transport stops are located at the circus; “B” means direction towards city center, “K” means direction toward outskirts, “L” and “F” are termini (with alighting or boarding passengers only) while “E”, “D” and “L” are the exits of metro station. The numbers next to dots show the sign of lines serving that particular stop (“+” means further lines). The circus can be approached or left via 13 sidewalks along both sides of connecting roads and streets. The circus is bounded by 7 blocks of buildings. The square itself is represented by T0. The cross-sections (blue) play dual role. On the one hand they are validation measurement points. On the other hand they describe the accurate route of followed pedestrians. 4 major zebra crossings (“Z1-Z4”), the subway (“A”) and 3 additional points represent the cross-sections. The surveys were carried out by university students in different parts of the day. In 2015, 25 students were involved, the measurement started at 10AM, and lasted for 1,5 hours; 60 minutes follow-up and 30 minutes of validation measurement. In 2016, 80 survey personnel worked for 30-30 minutes in morning peak as well as in low traffic period. The recorded parameters were the following (by groups): Access attributes: ID of access point, sign of the line from where the pedestrian got off, time of access (start of follow-up). Pedestrian attributes: gender, age groups (3), baggage type (if belongs), head and ear phones (if belongs), number of group members. Route attributes: passed cross-sections (ID-s), respect to traffic rules (violation), comments. Egress attributes: ID of egress point, sign of the line where the pedestrian got on, time of egress (end of followup). This unified parameter list makes repeating surveys comparable.
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5. Results In the case-study there were 37 access and egress points, which would result a huge OD-matrix if we regarded all of them separately. As pedestrian data has much less entries than the number of elements in this original matrix, many OD-connections would be 0 in the sample. Thus, some of the points have to be merged. The basis of merging points can be different, depending on the objective of the survey. If the area is examined in a microscopic level (pedestrian routes, flows, speeds are important within the area), then nearby points should be merged (based only on vicinity). If macroscopic connections are examined (transfers between PuT lines), then merging should be based on functions: PuT stops (toward the same directions) and local points (for destination traffic) separately. In the case-study, the second method was used. Points were merged into 6 public transport zones (5 zones for surface PuT lines – according to their directions and one for the metro) and 4 local zones. As a result, the size of sample matrix decreased to 100 elements. During the survey carried out in October 2015, 460 pedestrian data entries were recorded. After processing them, the values of the sample matrix elements varied from 0 (9 elements, 5 of them in the main diagonal) to 14. Traffic volume data were recorded at almost every access points, thus sampling rate could be calculated for 9 points out of 10. These rates varied from 4.4% to 13.6%. Traffic of surrounding buildings is very complicated to measure, so it was not recorded during this survey. However, sampling rate can be estimated for this zone, as well. As the average rate was 7.15% for the other local zones (and 7.05% for the PuT zones, which is very close to the former), it was supposed that there is no large difference between local zones, so calculation were made with the same 7.15% rate for this zone, as well. From the sample matrix and sample rates the OD matrix of the area (for one hour) was calculated and can be seen in Table 2. According to zone types (local or PuT), the matrix can be divided into four quarters. As “C1”, “C2”, “C3” and “T” are the local zones1, the first quarter contains only pedestrian traffic: people who walk through the area or who walk from or to a surrounding building. (This is nearly 29% of the whole traffic; examination of this kind of traffic would be very difficult with other survey types.) The second and fourth quarters contain the traffic between local zones and PuT stops, while the third one shows the transfers between PuT directions. Table 2. OD matrix of the investigated area for one hour Zone from\to
C1
C2
C3
T
É
B
K
D
N
Σ
M
C1
195
179
179
212
65
49
114
49
81
16
1140
C2
79
52
39
118
79
13
26
39
26
131
602
C3
111
55
11
133
66
22
11
22
11
11
454
T
111
83
111
194
69
83
42
69
56
111
931
B
52
52
74
103
7
81
7
22
15
15
428
É
160
46
46
91
69
0
0
137
160
137
846
K
41
51
41
41
31
31
0
62
92
21
410
D
49
0
33
131
33
82
16
0
33
0
376
N
32
49
129
65
113
97
32
113
0
65
696
M
169
68
51
85
34
118
0
34
34
0
592
Σ
999
635
714
1173
566
577
249
548
508
507
6475
1 Area (dashed line circles) of zone C1 represents B1, B2, K1, K2, and S according to Figure 1. Area (dashed line circles) C2 represents V1, V2, H, B3, and B4 according to Figure 1. Area (dashed line circles) C3 represents F1+F2+L according to Figure 1. Area (dashed line rectangle) T represents T0, T1, T2, T3, T4, T5, T6, and T7 according to Figure 1.
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Based on Table 2, Figure 2 shows the OD flows of pedestrians who cross the enclosed area in 2015. “C1”, “C2”, “C3” are those sidewalks where pedestrians can leave (or enter to) the investigated area. “T” is merging all the surrounding buildings and the square itself. By each zone bold numbers show the total amount of origin traffic while italic numbers represent the total amount of destination traffic. The width of each arrows is proportional to the number of pedestrians in an hour between particular zones. The rectangles after the three arrows represent the traffic inside the zone (length is proportional to the volume). It can be seen that the highest flow (212 pedestrian/hour) origins from zone “C1” and ends in “T”. As a consequence many pedestrian cross the square as well as have local purposes.
Fig. 2. Crossing pedestrian’s OD flow.
From other traffic surveys (one of the metro station in April 2015 – zone “M”, and one of tram line 6 in November 2015 – zone “N”), some PuT traffic volumes are known as a reference. Zone “M” represents traffic of metro line M4 (points M4D, M4É and M4L on Figure 1) and zone “N” represents traffic of tramline 6 (point 6 on Figure 1). Volumes of the same hour of the day are compared with the results of the case-study in Table 3. Table 3. Known traffic volumes (passenger/hour) from other surveys Zone
Reference PuT surveys
Case study
Relative difference
Alighting
Boarding
A/B Rate
Alighting
Boarding
A/B Rate
Alighting
Boarding
A/B Rate
M
738
600
1.23
507
592
1.17
-15.6%
-19.8%
-5.0%
N
644
462
1.39
508
696
1.37
+9.9%
+8.1%
-1.7%
Results show that there are relatively high differences (10-20%) in traffic volumes. Regarding this, we have to add that the metro line was opened only in 2014. Before that Móricz Zsigmond circus was a major public transport hub, and surface level PuT network changed gradually, so traffic habits may have changed between the surveys (as the square lost of its importance). As a survey experience traffic volumes on the same place can vary by up to 10-15% between similar days, without any obvious reason. On the other hand, difference values are very similar between boarding and alighting traffic in the same PuT stop. Rates of alighting and boarding volumes are also calculated for both surveys: these vary not more than 5% at these PuT stops. As alighting traffic was measured, while boarding volumes were derived from the OD matrix, this can be seen as a validation of OD matrix calculation. A similar survey was carried out in April 2016. A detailed description of the results of this would exceed the limitations of this paper, thus we focus on the differences to the 2015 survey. The number of survey personnel was
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larger this year, and as a result, 840 data entries were recorded. This would allow a more detailed examination (with more zones), but this way of comparison would be difficult, so the same merging procedure were applied. In the 2016 survey, local zones were not measured, thus sampling rates could not be estimated on the same basis. On the other hand, two control cross-sections were measured, the sampling rates of which were used. The overall traffic volumes were 12% higher than 2015, but the survey was carried out in an earlier hour of the day (closer to peak hour). Several elements of OD matrix were changed, from which the most interesting is the decrease in the traffic of zone “É” (PuT from or to the northwest). Tramlines of this zone had had termini in 2015, but they were extended in January 2016 (towards south, into zone “D”), thus the number of transfers from this zone fell (e.g. traffic flow from zone “É” to “D” totally disappeared, and from “D” to “É” decreased by 80%). The differences of traffic volumes compared to the same reference PuT surveys varied from -17.2% (alighting at the metro station, similarly to 2015) to +2.9%. 6. Experience and conclusion After literature review and comparison to well-known survey types PFS method can be assumed as a novel survey technique of enclosed areas. Mathematic model and measurement technique described in details as well as a case study is introduced. Key findings of this article are that PFS is a new, validated, exact, resource-saving method, which respects the privacy rights and can be used at large areas. Calculated OD matrices are validated by traffic volumes of control public transport stops and cross-sections collected from other surveys. Unlike to conventional questionnaires observation (follow-up) provides accurate (exact) information about origin and destination of each trips. Number of survey personnel is moderate and special devices are not required. PFS respects the privacy rights due to the fact that pedestrians (passengers) not have to be upheld and asked personally. Another advantage is the method works well in relatively large, open pedestrian areas, as well. For possible further developments the followings can be taken into consideration. Appropriate selection of control cross-sections and public transport stops for simultaneous data collection (as the basis of validation) is important. Clarification and update of access and egress points of enclosed area in accordance with layout modifications (e.g. relocation of public transport stops, modification of public transport line routes and signs) is necessary. Detailed motivation of particular trips (as a traveler behavior) can be also expressed and examined if survey personnel does not finish following-up the pedestrian who reaches an egress point (as an “intermediate stop” of its trip); if follow-up continues towards a “real” egress point (where the pedestrian physically leaves the investigated area) the motivation of intermediate stop (e.g. eating some meals) is derivable. Parking cars can be handled either as a destination flow (like a “final” egress point) or – as it stated above – starting car movements (from a parking lot as an “intermediate” egress point) towards “real” egress point can be followed. As a conclusion PFS method can be defined as an efficient measure to provide OD dataset and information about traveler’s behavior for planning procedure on microscopic and macroscopic level as well. According to our detailed description transport planners can easily adapt and apply PFS method for their purposes. References Godfrey, J.W., 1969. The mechanism of a road network. In: Traffic Engineering and Control, Vol. 11 (7), 1969, pp. 323-327. Daamen, W., Hoogendoorn, SP., 2004. Pedestrian traffic flow operations on a platform: observations and comparison with simulation tool SimPed. In: Allen, J., Brebbia, C.A., Hill, R.J., Sciutto, G., Sone, S., (Eds.), Computers in railways IX (Congress Proceedings of CompRail 2004), Dresden, Germany, May 2004 (pp. 125-134). Southampton: WIT Press Jang, W., 2010. Travel Time and Transfer Analysis Using Transit Smart Card Data. In: Transportation Research Record: Journal of the Transportation Research Board Dec 2010, Vol. 2144, pp. 142-149 Bhaskar, A., Kieu, L. M., Qu, M., Nantes, A., Miska, M., Chung, E., 2013. On the use of Bluetooth MAC Scanners for live reporting of the transport network. In: 10th International Conference of Eastern Asia Society for Transportation Studies, 9-12 September 2013, Taipei, Taiwan.