Veröffentlichung / Publication
Keeping Passenger Surveys up-to-date A Fuzzy Approach
Autoren / Authors: Markus Friedrich PTV AG, Karlsruhe
Klaus Nökel PTV AG, Karlsruhe Peter Mott PTV AG, Karlsruhe
Veröffentlicht in / Published in: Friedrich, M., Nökel, K., Mott, P. (2000): Keeping Passenger Surveys up-to-date: A Fuzzy Approach, Transportation Research Records, No. 1735, p. 35-42.
Universität Stuttgart Institut für Straßen- und Verkehrswesen Lehrstuhl für Verkehrsplanung und Verkehrsleittechnik www.uni-stuttgart.de/isv/vuv/
Keeping Passenger Surveys up-to-date A Fuzzy Approach
recommended for presentation th
at the 79 Annual Meeting of the Transport Research Board Washington, January 2000
PTV AG Stumpfstr. 1 D-76131 Karlsruhe Tel.: +49-721-9651-0 Fax: +49-721-9651-299 Email:
[email protected]
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Keeping Passenger Surveys up-to-date – A Fuzzy Approach
Authors: Markus Friedrich, PTV AG Stumpfstr. 1, D-76135 Karlsruhe, Germany Phone ++49-721-9651316, fax ++49-721-9651299, email
[email protected] Peter Mott, PTV AG Stumpfstr. 1, D-76135 Karlsruhe, Germany Phone ++49-721-9651203, fax ++49-721-9651299, email
[email protected] Klaus Noekel, PTV AG Stumpfstr. 1, D-76135 Karlsruhe, Germany Phone ++49-721-9651328, fax ++49-721-9651299, email
[email protected]
Abstract: The knowledge of travel demand is an essential prerequisite for analyzing and planning the transport supply. Obtaining travel demand data for a transit system requires passenger surveys which combine counts and interviews. As a matter of fact passenger surveys have two unpleasant characteristics. They are expensive and the results of such studies tend to lose their validity fairly rapidly. For this reason, the development of techniques which reduce survey costs and keep demand matrices up to date are gaining increasing interest. The paper will give details of a technique for computer-aided processing of passenger surveys and present a method for continuous updating of demand matrices. Since traffic surveys only represent a snapshot situation the proposed updating method employs a Fuzzy approach to consider that traffic volumes vary within a certain bandwidth.
Keywords: Passenger survey, matrix correction, Fuzzy theory
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INTRODUCTION
The knowledge of travel demand is an essential prerequisite for analyzing and planning the transport supply. In transit networks with an integrated fare system precise information on travel demand is also crucial, if travel demand serves as a key for distributing revenues onto lines of individual operators. Obtaining travel demand data for a transit system requires passenger surveys which combine counts and interviews. As a matter of fact passenger surveys have two unpleasant characteristics. They are expensive and the results of such studies tend to lose their validity fairly rapidly. For this reason, the development of techniques which reduce survey costs and keep demand matrices up to date are gaining increasing interest. This paper will give details of a technique for computer-aided processing of passenger surveys and present a method for continuous updating of demand matrices. Figure 1 outlines the data flow within the procedure: • Base year: Computer-aided processing of passenger survey data can help to reduce survey costs and save time. In order to compute an o-d matrix and traffic volumes for the base year it is necessary to check the survey data for plausibility, to determine projection factors and to assign the surveyed travel demand onto the network. • Current year: The updating process corrects the base matrix by employing current passenger count data from manual or automatic counts. Since traffic surveys only represent a snapshot situation the proposed updating method TFlowFuzzy applies a Fuzzy approach to consider that traffic volumes vary within a certain bandwidth.
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Figure 1: Overview
processing of survey data plausibility check, projection, “direct” assignment
Program
base matrix Result
Input
supply data base year • stops • lineroutes + timetables
1 2 3
1 0 12 20
base matrix as weighting matrix
volumes for base year 2 10 0 15
3 18 14 0
supply data current year • stops • lineroutes + timetables
passenger counts • link / stop counts • fluctuation ranges
TFlowFuzzy
Program
matrix correction with entropy maximation
updated matrix Results 1 2 3
1 0 14 20
base year
passenger survey data • boarding counts • interviews
2 11 0 16
volumes for current year 3 19 14 0
current year
Input
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COMPUTER-AIDED PROCESSING OF PASSENGER SURVEYS
A frequent approach for passenger surveys in urban transit networks suggests to interview the passengers onboard of the transit vehicle. Surveyors travel on selected service trips of a transit line (= survey line), where they perform two central tasks: 1. At each stop they count the number of boarding passengers. This results in one boarding-count record for each stop of a service trip. 2. After each stop they attempt to interview as many of these passengers as possible. The surveyors are requested to record four stops: (1) the boarding stop where the passenger entered the survey line, (2) the stop where the passenger intends to alight the survey line, (3) the origin and (4) the destination stop of the passenger’s trip. Depending on the survey design the passengers may also be asked to provide additional information, e.g. their ticket-type or trip purpose. Each interview creates one interview record. Application of software can help to reduce costs and save time during all phases of a passenger survey: • Preparation of survey: In the preparation phase software may be applied to obtain a sample of service trips. Service trips are usually selected with a stratified random sampling method [1] which subdivides all service trips into homogeneous strata, e.g. all service trips of one line during peak-hours. Software can also improve duty schedules for the surveyors by minimizing the duty time with crew scheduling algorithms. • During interview: Computer-aided interviewing using a palmtop computer allows online validation by comparing a passenger’s route data to the network and timetable data stored in the computer. Unfortunately this method has two weak points limiting its value for passenger surveys: it is slower than the conventional paper & pencil method and it is more expensive, if many surveyors need to be equipped simultaneously. • After interview: In case of paper & pencil surveys, software can speed up the process of transferring paper data into digital databases by scanning the questionnaires. PTV has successfully applied this approach in numerous surveys in Germany ranging from 10.000 to 500.000 interviews. One machine can process approximately 3.600 questionnaires per hour. • Data processing: Once the survey records exist in digital form, three main tasks need to be completed before the survey data are ready for analysis: (1) plausibility check and completion of the routes obtained from the interviews, (2) projection of passenger trips based on the boarding counts, (3) assignment of the trips onto the network. The following sections of this chapter present the processing method of survey data as it is implemented in the comprehensive transportation model VISUM [2] (see Figure 2). In addition to checking and projecting the survey data records, VISUM is capable of assigning the projected interview
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records as travel demand onto the network similar to o-d matrices during standard transit assignment. This special assignment process is called “direct” assignment. Figure 2: Processing of survey data interview data ⇒ core information on passenger trips
supply data • stops • line routes • timetables
count data • boarding counts
supply data • stops • line routes • timetables
plausibility check and data completion ⇒ complete routes
projection ⇒ routes with projected volumes
„direct“ assignment ⇒ traffic volumes ⇒ service indicators ⇒ o-d matrix
?
? S
S
S
S
S
S
S
S
S
S
S
S
50 S
S
50 S
S
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Plausibility Check and Completion of Routes
A data record obtained from a passenger interview according to the design described above will contain the following attributes: • name or identifier of survey line, • number of boarding stop into survey line, • departure time at the boarding stop, • number of alighting stop from survey line, • number of origin stop or origin zone, • number of destination stop or destination zone. These attributes hold the core information on a passenger’s trip. This data set needs to be checked for plausibility and completed, as it does not include information on the lines and service trips the passenger used prior to or after the survey line. It also does not contain information on the departure and arrival times at origin stop, destination stop or transfer stops which is important for calculating travel and transfer times. A network model [2]containing detailed data describing the transit lines, i.e. the line routes and timetables, serves a basis for a plausibility check. The plausibility check compares the stated transit stops, lines and departure times with the data of the network model. After the plausibility check VISUM tries to complete each interview record to a whole route which entirely describes a passenger’s trip with all transit lines used and their related departure / arrival times. This is achieved by using the survey line as an anchor from where to look back to the origin and ahead to the destination stop. The objective of this step is to identify suitable preceding and succeeding service trips which were most likely used by the passenger (see Figure 3). Identification of preceding service trips starts at the boarding stop (S2). Knowing the boarding time it is possible to determine suitable service trips in two ways: • direct connections: all service trips which provide a direct connection between origin stop (S1) and boarding stop (S2) are suitable, as long as they arrive before the stated departure time at S2. • connection search: all connections (i.e. a sequence of service trips) which can be calculated by a connection search procedure are appropriate, if they arrive in time at S2. From the set of possible connections the procedure selects the connection which assures the latest departure time at the origin stop. Identification of succeeding service trips starts at the alighting stop (S3) and works likewise.
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Figure 3: Completion of routes preceding lines
succeeding lines
?
S2
S3
2.2
?
alighting stop
boarding stop
origin stop
point of observation
S4
destination stop
S1
survey line
Projection of Passenger Trips
Since a passenger survey will normally capture only a sample of all passengers it is necessary to determine a projection factor for each interview record. This projection factor stands for the number of travelers which are represented by one interview record. The projection process in VISUM consists of three steps (Figure 4): 1. Projection of interviewed passengers to all boarding passengers: This projection considers the fact that surveyors may fail to interview all boarding passengers or that passengers may refuse to answer. The projection factor PFac1 is obtained by comparing the number of boarding passengers from the boarding count with the number of interviewed passengers. 2. Projection of surveyed vehicle section to whole vehicle: If a passenger survey does not cover all sections of the surveyed vehicle, i.e. not all seats of a vehicle or train, a projection factor PFac2 is
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needed for adjustment. This factor can be derived from the number of seats in the surveyed section and the total number of seats on the survey line. 3. Projection of surveyed services to all services: As a passenger survey may only inspect selected transit lines or selected service trips of a line, a projection factor PFac3 is required to consider the entire transit supply. Projection factor PFac3 can be determined by finding surveyed service trips which match the non-surveyed service trips, i.e. which show a comparable line route and operation time. Figure 4: Projection of passenger trips for one interview record 1. Projection of interviewed passengers to all boarding passengers Input:
S
• number of boarding passengers per stop BPass • number of interviewed passengers per stop IPass Output: • Projectionfactor1 = BPass / IPass
e.g.
BPass = 4, IPass = 2 BPass / IPass = 2
2. Projection of surveyed vehicle section to whole vehicle Input: • total vehicle seats TSeat • surveyed vehicle seats SSeat Output: •
e.g. total seats / surveyed seats = 4
Projectionfactor2 = TSeat / SSeat
3. Projection of surveyed services to all services Input: • surveyed service trips • non-surveyed service trips Output: •
Projectionfactor3 = specific weighting factor for each service trip
6:00 7:00
service trip1 1,5
8:00
service trip2 0,0 service trip3 1,5
In a comprehensive passenger survey which includes transit lines of the entire network, a passenger using several lines may be interviewed more than once during his trip. To take this into consideration, two approaches are possible:
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• The surveyors may only interview passengers on the first transit line, i.e. after boarding at the origin stop. Thus it can be avoided to interview the same passenger twice. • The projection includes an additional factor taking into account the number of transit lines used for a passenger’s trip. The number of transit lines used is equal to the number of transfers + 1. Thus the following projection factor can be determined for each interview record:
Projectionfactor PFac =
PFac1 × PFac2 × PFac3 number of transfers + 1
Using the data from Figure 4 an interview record containing three transit lines, i.e. two transfers, for example, would obtain a projection factor of 4 (= 2 × 4 × 1.5 / 3). Thus this interview record represents four passenger trips which must be assigned onto the transit network.
2.3
Assigning Surveyed Passenger Trips onto the Network
Standard transit assignment distributes passenger trips stored in an o-d matrix onto the lines of a transit network. “Direct” assignment of passenger trips from interview records differs from standard assignment in a number of aspects: • Direct assignment does not require a route or connection search procedure. The routes of the passengers are already identified during they step “plausibility check and completion of routes” (see 2.1). • Direct assignment has a lower degree of freedom, since interview records provide more information than o-d matrices. In addition to the origin and destination zones, interview records hold information on transfer stops and departure times. Traffic volumes calculated under consideration of these constraints are generally very close to cross-sectional traffic counts. • Whilst an o-d matrix contains exactly one demand value for any o-d pair, there may exist several interview records for one o-d pair, each describing a specific route. The projection factor PFac of the interview record corresponds to the demand value of the o-d matrix. Despite these differences both, standard and direct assignment, produce the same types of result: traffic volumes, service indicators (e.g. travel time), travel demand indicators (e.g. passenger-kilometers) and routes for analysis of traffic flows on selected links and stops. Within VISUM direct assignment is implemented as an alternative assignment method. This permits users to analyze and display the results of standard and direct assignment in the same manner. It has been found that the results of direct assignment are beneficial calibrating the assignment parameters of standard assignment, e.g. transfer penalties and sensitivity to marginal changes in travel time.
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UPDATING DEMAND MATRICES
3.1
Basic Principles
For some 20 years now, primarily in English-speaking countries, so-called matrix correction (or matrix update) techniques have been used to produce a current travel demand matrix from an earlier travel demand matrix (base matrix) using current traffic count values. Based on research by VAN ZUYLEN / WILLUMSEN [3, 4], BOSSERHOFF [5] and ROSINOWSKI [6] which focuses on matrices for private transport, PTV has extended the application of these techniques to public transport. Starting point of the classic approach is the travel demand tij for o-d pairs. Travel demand is usually described as a matrix, but for our purposes a vector representation containing all non-zero o-d trips is more suitable:
⎛0 ⎜ ⎜ t 21 ⎜t ⎜ 31 ⎜ M ⎜t ⎝ n1
t12 0
t13 L t 23 L
t 32
0
L
M
M
tn2
t n3
O L
⎛ t12 ⎞ ⎛ t1 ⎞ ⎜ ⎟ ⎜ ⎟ ⎜ t13 ⎟ ⎜ t 2 ⎟ ⎜ M ⎟ ⎜t ⎟ 3 t1n ⎞ ⎜ ⎟ ⎜ ⎟ ⎟ ⎜ t1n ⎟ ⎜ M ⎟ t 2n ⎟ ⎜ ⎟ ⎜ ⎟ tk t t 3n ⎟ = ⎜ 21 ⎟ = ⎜ ⎟ ⎟ ⎜t ⎟ ⎜ M ⎟ M ⎟ ⎜ 23 ⎟ ⎜ ⎟ M M 0 ⎟⎠ ⎜ ⎟ ⎜⎜ ⎟⎟ ⎜ t 2n ⎟ M ⎜ ⎟ ⎜ ⎟ ⎜ t 31 ⎟ ⎜ M ⎟ ⎜ M ⎟ ⎜t ⎟ ⎝ ⎠ ⎝ p⎠
where: travel demand between zone i and j tij... p ...
number of non-zero o-d pairs
This vector describes the travel demand of an earlier state. Vector value tk describes the number of trips for the kth o-d pair with non-zero trips. Index p gives the total number of non-zero o-d pairs Considering the current travel demand, it is assumed that no o-d specific information is available but only traffic counts. For public transport such traffic counts may be available either as counts of boarding and alighting passengers at transit stops or as link counts. In case of boarding/alighting counts it is important to note, that only initial boardings at the origin stops and final alightings at the destination stop may be employed to update a matrix, i.e. the counts must not include transferring passengers. The following vector v denotes traffic counts at m locations:
v T = (v 1
v2
v3 L vl L vm )
where: m ... number of traffic count locations
The trips of any o-d pair contribute a certain share to each traffic count. In case of boarding and alighting passengers the marginal sums of the demand matrix are known. In case of link counts the
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counted volumes correspond to the sum of all o-d trips travelling on this link. In general there is a linear relation between the demand on the o-d pairs and the traffic counts:
⎛ a11 L a1 p ⎞ ⎟ ⎜ ⎜ M O M ⎟ ⋅ t= A ⋅ t= v ⎟ ⎜a ⎝ m1 L a mp ⎠ A is called the share-matrix. The number of columns of this share-matrix refers to the number of nonzero o-d pairs, the number of rows corresponds to the number of traffic counts. Each element alk of this share-matrix expresses the share of trips of one o-d pair k which uses link l. The share-matrix A has an exceptionally regular form in case of boarding and alighting counts. The following example shows the share-matrix A for a network with 3 zones (n = 3) and 6 counts (m = 6), i.e. 3 boarding and 3 alighting counts: ⎛1 ⎜ ⎜0 ⎜0 ⎜ ⎜0 ⎜1 ⎜ ⎜0 ⎝
1 0 0 0 0 ⎞⎛ t12 ⎞ ⎛ board1 ⎞ ⎟ ⎟⎜ ⎟ ⎜ 0 1 1 0 0 ⎟⎜ t13 ⎟ ⎜ board 2 ⎟ 0 0 0 1 1 ⎟⎜ t 21 ⎟ ⎜ board 3 ⎟ ⎟ ⎟⎜ ⎟ = ⎜ 0 1 0 1 0 ⎟⎜ t 23 ⎟ ⎜ alight1 ⎟ ⎜ ⎟ ⎜ ⎟ 0 0 0 0 1 ⎟⎟⎜ t 31 ⎟ ⎜ alight 2 ⎟ 1 0 1 0 0 ⎟⎠⎜⎝ t 32 ⎟⎠ ⎜⎝ alight 3 ⎟⎠
For boarding and alighting counts the share-matrix A does not depend on the transit supply, i.e. the line routes and timetables. In case of link counts, however, the share-matrix A needs to consider the route choice of passengers which is affected by the transit supply. To create a share-matrix for link counts one can assign any demand matrix, e.g. the outdated base matrix, onto the current network. It is also possible to combine boarding/alighting counts and link counts. The chief problem of matrix correction methods results from the fact that typically m
