PUBLIC TRANSPORT ORIGIN-DESTINATION ESTIMATION USING SMART CARD FARE DATA

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1 0 PUBLIC TRANSPORT ORIGIN-DESTINATION ESTIMATION USING SMART CARD FARE DATA Azalden A. Alsger, Corresponding Author School of Civil Engineering, The University of Queensland Building, The University of Queensland, QLD, Australia, 0 Tel: Fax: +---; a.alsger@uq.edu.au Mahmoud Mesbah School of Civil Engineering, The University of Queensland Building, The University of Queensland, QLD, Australia, 0 Tel: +--- Fax: Fax: +---; mahmoud.mesbah@uq.edu.au Luis Ferreira School of Civil Engineering, The University of Queensland Building, The University of Queensland, QLD, Australia, 0 Tel: ; l.ferreira@uq.edu.au Hamid Safi School of Civil Engineering, The University of Queensland Building, The University of Queensland, QLD, Australia, 0 Tel: + ---; h.safi@uq.edu.au Word count: words text + tables/figures x 0 words = words Submission Date: 0//

2 Alsger, Mesbah, Ferreira, Safi ABSTRACT Over the past few years, a number of techniques have been developed to estimate public transport Origin- Destination (O-D) matrices using smart card fare data. Because of a lack of information regarding the alighting stop for a trip, different walking distance and allowable transfer time assumptions have been applied in the past. Such assumptions have the potential to significantly affect the accuracy level of the estimated O-D matrices. There is little evidence of the accuracy level of O-D estimation methods using smart card fare data. The unique smart card fare data from Brisbane, Queensland, gives the opportunity to assess previous methods and their assumptions. This paper investigates the use of South East Queensland (SEQ) data to study the effect of different assumptions on the estimated O-D matrices and provides sensitivity analysis for different parameters. In addition, an algorithm for generating an O-D matrix based on individual user transactions (trip-legs) is proposed here. As a result, about % of the transfer time was found to be non-walking time (wait and short activity time). More than 0% of passengers walk less than minutes to transfer between alighting and the next boarding stop. This represents about % of the allowable transfer time. The change in the assumed allowable transfer time, from to 0 minutes, had a minor effect on the estimated O-D matrices. Within the same day, it has been found that most passengers return to within a 00m of their first origin. Keywords: Public transport demand, O-D estimation, Inter-transaction time, Public transport smart card data

3 Alsger, Mesbah, Ferreira, Safi INTRODUCTION Smart card fare systems have become a valuable source of information for public transport O-D estimation, allowing a better understanding of individual travel patterns and improving strategic public transport planning. The use of these new techniques has been rapidly increasing. They have the potential to reduce the need for traditional transit surveys, e.g. Household Travel Survey (HTS) and on-board passenger surveys, which have been used for decades to explore passengers travel patterns and generate O-D matrices (-). Recently, a number of studies have used different methodologies to infer the O-D matrices for public transport trips using smart card fare data (-). Most Automated Fare Collection (AFC) systems record passenger boarding information, but no alighting information is recorded. The lack of alighting stop details is the result of the installed AFC systems, where passengers are not required to use their cards when alighting. Given the information limitations of many AFC systems, the accuracy level of the estimated O-D matrices is often uncertain. Also, many of the assumptions made in such methods are yet to be tested (). To complete a passenger s travel sequence in the available literature, the trip-chaining method was used to connect trip-legs for each card holder, based on the assumption that it is inconvenient for a passenger to walk a long distance to board at a different public transport stop. The main objective is to create a buffer zone, based on an assumed walking distance around the subsequent boarding stops, to infer the possible alighting stops. The following example explains the trip-chaining method and how the buffer zone assumption can be implemented. Figure shows an example of the trip-chaining method. 0 B and A are the first boarding and alighting transactions, and the time between B and A is the in-vehicle time. FIGURE Example of the trip-chaining method. A passenger has his first trip from first boarding (B ) to first alighting (A ), and then walks to the next boarding stop to start the second trip from second boarding (B ) to second alighting (A ). As no alighting information is recorded, a buffer zone is assumed around (B ) to estimate (A ). If a previous alighting stop (A ) is inside the buffer zone, the alighting stop can be estimated. Most previous studies consider the walking time as the main element in their assumption and do not consider the non-walking time as part of the transfer time. It should be noted that the time between (B ) and (A ) could be short, which is enough for a transfer only, or long, which could involve an activity. According to Wang () and Zhao et al. (), the acceptable walking distance was assumed to be 00 m or five minutes walking time with a speed of. km/hr. Any bus stop within the walking distance was considered as a possible previous alighting stop. Cui () assumed that the walking distance between different bus stops would not exceed,0 m and any bus stop beyond this distance will not be considered. Munizaga and Palma () proposed that the distance a person is willing to walk depends on

4 Alsger, Mesbah, Ferreira, Safi the type of person, type of city, weather, gradient and other factors. The walking distance was chosen to be,000 m in the latter study. Nassir et al. () chose the walking distance to be 00 m, and any bus stop located outside this geographical boundary was not considered. This walking distance threshold will be experimentally investigated in the current paper. To estimate a passenger O-D trip by connecting individual trips (trip-legs), different values of allowable transfer time (time threshold) have been assumed in the past. The assumed time threshold ranges from 0 min [() and ()]; to 0 min [() and ()]; and even 0 min (). Different ranges based on the transit mode were assumed by (). This assumption will be tested here, using observed data. The rich and unique recorded data from Translink allowed the current study to investigate and evaluate the effect of different assumptions, such as: (a) transfer time threshold; (b) buffer zones (walking distances), and (c) last destination of a passenger in a given day is the same first boarding for that day. The generated matrices have been compared and evaluated to investigate the effect of different factors on the accuracy level. The remaining sections of this paper are organized as follows. The next section highlights the O-D estimation methodology. This is followed by data description and preparation. Subsequently, a detailed sensitivity analysis and the main results are provided. Finally, conclusions and suggestions for future work are discussed. METHODOLOGY The approach followed here is to develop an O-D estimation algorithm based on the trip-chaining method. Assumptions from past studies are evaluated here. The main factors in the trip-chaining method are examined in detail, using a range of values. The next section gives the definitions of some of the terms used. General Definitions Trip-leg is the in-vehicle time or distance (part of a trip) between a passenger boarding and alighting. O-D Trip is the movement of a passenger from an origin to a destination using public transport services. An O-D trip may have one or multiple trip-legs including transfers between trip-legs. However, it is only the component within the public transport system. Access to public transport is not part of the O-D trip. Transfer Distance is the walking distance to transfer to another public transport service. It is the walking distance between a passenger s alighting and next boarding (the walking distance between subsequence trip-legs). Buffer Zone is an assumed area set around public transport boarding stops to include any possible public transport alighting stops. Trip-Chaining Method Assumptions Different assumptions have been made by different studies based on the trip-chaining method. Some of the typical assumptions include: the last destination of a passenger in a given day is assumed to be the same as the boarding of that passenger s first transaction for that day. In addition, the origin of the trip is assumed to be the boarding stop and the destination is assumed to be the alighting stop. The major assumption to connect trip-legs and obtain a passenger trip is transfer time threshold, which is the allowed Inter Transaction Time (ITT) for transfer. Inter Transaction Time (ITT) ITT is the time between subsequent boarding (e.g. B ) and the previous alighting (e.g. A ) in Figure. ITT is the combination of a passenger walking time and non-walking time. The non-walking time is the combination of waiting time and a possible activity time, as shown in Figure. In order to obtain a passenger s travel pattern, a threshold for the Inter Transaction Time (ITT) needs to be assumed. This time will be set as time intervals for transfer time. If the ITT is less than this

5 Alsger, Mesbah, Ferreira, Safi threshold, the ITT will be considered as a transfer time, otherwise, ITT includes an activity and thus the next trip-leg will be considered as a new trip. O-D Estimation Algorithm The main algorithm for connecting trip-legs for an individual passenger to estimate the O-D matrix and investigate the trip-chaining method assumptions is shown in Figure. FIGURE O-D estimation algorithm. The main part of this algorithm detects transfers so that the trip-legs can be merged to obtain an O-D trip (linked trip-legs). Based on this condition, the boarding of first transaction (trip-leg) for a unique card ID is an origin. The remaining trip-legs for the same card ID will be transfers if ITT is less than the allowable transfer time. If ITT exceeds the allowable transfer time, the last alighting stop is the destination of the passenger s trip and the next boarding stop is the origin of a new trip. If it is the last transaction of the current card ID, choose another card ID and continue the searching algorithm. As the time threshold has an important impact on the estimated O-D matrix, the effect of using different time thresholds is analyzed here. The time interval for the threshold was increased from min to 0 min, in -min intervals. The trip-legs were linked together to estimate the O-D matrix, as shown in the upper dotted box of the methodology chart. The trip-chaining method assumptions, such as transfer time threshold, allowable walking distance (buffer zone), and last destination of a passenger in a given day being the same first boarding for that day, are investigated. The generated matrix based on a 0-min threshold was set as a reference measurement. In Brisbane, a threshold of 0 min is used for fare collection purposes. All other estimated matrices are compared with the reference matrix to quantify the effect of different transfer time threshold assumptions. As a result, the

6 Alsger, Mesbah, Ferreira, Safi 0 likely errors in the estimated matrices associated with different transfer time threshold assumptions have been quantified. Data Description and Preparation The smart card data analyzed here was obtained from Translink, the public transport authority of South East Queensland (SEQ), Australia. Data for one weekday was analyzed, making up, transactions (0,0 card holders) over the SEQ bus, train and ferry network. A transaction record is generated each time a passenger boards or alights. Each transaction contains information such as operation date, run, route, direction, ticket number, smartcard ID, boarding time, alighting time, boarding stop and alighting stop. An important aspect of this system is that it includes both boarding and alighting times and locations, where the passenger gets on or off a public transport vehicle. Transferring activities are not directly obtained. The data was filtered and some transactions are excluded, such as duplicate transactions and when no boarding or alighting stops were recorded. For the one-day data, the initial number of transactions (after cleaning) was,. O-D ESTIMATION Detecting transfer trip-legs is the main part of the algorithm proposed here. Once the ITT threshold is decided, trip-legs can be linked. This ITT threshold includes walking to the next boarding stop, waiting time, and a possible activity for relatively longer ITTs. The main challenge is identifying the ITT, where a trip-leg will be considered as initial (origin of the trip) if the time is greater than ITT, and as transfer if the time is less than ITT. The main results, including a sensitivity analysis based on the proposed algorithm (Figure ) for different ITT, are summarised here. Matrices are generated based on different ITT assumptions. The ITT threshold is varied from to 0 min at -min intervals. Figure presents the total O-D trips and trips with a transfer. A slight decrease in the total number of O-D trips is noticed as ITT increases from to 0 min. On the other hand, the number of trips with a transfer increases as ITT increases. An increase in the percentage of transfer trips (from % to %) occurs as the ITT increases from to min. A slight increase (from % to %) in the transfer trips is noticed as ITT increases from 0 to 0 min. The nature of demand (the low portion of trips with transfers) indicates that the majority of passengers do not transfer. % % % % % % FIGURE Total number of O-D and transfer trips.

7 Alsger, Mesbah, Ferreira, Safi It also indicates that the assumed transfer time threshold does not have a significant impact on the O-D matrices or the number of transfer trips. Figure shows the distribution of trip duration with one, two, and three transfers. Most of the O-D trips have only one transfer. The number of O-D trips that have one transfer increases as the trip duration increases and reaches its maximum at 0 min trip duration. The same can be said about the trip that has two transfers, which reaches its maximum at 0 min trip duration. Few O-D trips that have three transfers occur after 0 min trip duration and reach their maximum at 0- min trip duration. The number of trips with transfer indicates that most of the transfer trips have only one transfer. As the trip duration increases, there is an increase in two and three transfers, although the number is small compared to O-D trips with one transfer. 0 FIGURE Variation of transfer numbers at different trip durations. The estimated O-D trips were aggregated based on the Brisbane Strategic Transport Model (BSTM) zones () to provide an overview of the results. Figures presents the O-Ds geographical analysis of the morning peak ( to a.m.) and the evening peak ( to p.m.), respectively, for the 0-min ITT. Figure (a) shows the morning origins at the zonal level, for 0-min ITT. Figure (b) shows the morning destinations for the 0-min ITT. The morning destination is concentrated around the CBD area, where most of the trips are work trips. Figure (c) shows the evening origins where most of the trips are generated from the CBD area. Figure (d) shows the evening destinations for the 0 min ITT. The morning origins and the evening destinations appear to be symmetric, which indicates that passengers O-D trips begin and end at the same location. On the other hand, the morning destinations and the evening origins seem to be symmetric, especially around the CBD area, which indicates that most of the O-D trips are work trips where passengers go to work in the morning and go home in the evening.

8 Alsger, Mesbah, Ferreira, Safi (Origin) (Destination) (a) (b) (c) (d) FIGURE Geographical analysis of (a) morning origins, (b) morning destinations, (c) evening origins, and (d) evening destinations. Testing the first assumption: Transfer Time Threshold Figure shows the distribution of the average ITT, walking time, and non-walking time for passengers who made more than one trip. The walking time is calculated based on the direct distance between alighting stop and next boarding stop, assuming an average walking speed of km/hr. Most of the transactions for passengers who made more than one trip-leg took place during an inter transaction time of 0 min. However, when a transfer occurs, most of the inter transaction time is nonwalking time. As the inter transaction time increases, the walking and non-walking times increase, as shown in Table.

9 Alsger, Mesbah, Ferreira, Safi FIGURE Distribution of ITT, walk, and non-walk time. Table shows the average and the percentage of walking and non-walking times, based on different ITT intervals. For the average ITT between 0 and min, passengers walk around one min to get to the next boarding stop and have a non-walking time of around min. For an ITT between and 0 min, the average walking time is around. min and the average of non-walking time is around 0 min to start the next trip. The average walking time as a percentage of the total ITT decreases from.% to.% as the ITT increases from 0 to 0 min. TABLE Walking and Non-walking Components vs. ITT ITT (min) Walk * (min) Walk/ITT (%) Walk * (m) Non-walk * (min) Non-walk/ITT (%) * Average values. Figure shows the cumulative distribution of the number of transactions, in terms of walking time (Walk), non-walking time (Non-Walk), and inter transaction time (ITT). More than 0% of passengers are willing to walk less than min to transfer between bus stops or train stations. In the case of long ITTs, when an activity is being undertaken, the walking time between the alighting stop and next boarding stop is still very short. In addition, about 0% of passengers have an ITT between 0 and min. The 0-min transfer time set by Translink does not have a significant impact on travel patterns. The investigated transfer time threshold indicates that most of this time is waiting time, as the walking time is relatively short.

10 Alsger, Mesbah, Ferreira, Safi FIGURE Cumulative distribution of ITT, walk time and non-walk time. Testing the second assumption: Transfer Walking Distance Table shows the average walking distance for different ITT. Passengers walk a short distance if they have less time to transfer to their next boarding stop, which is about 0m for ITT less than 0 min. As the ITT exceeds min, only a marginal change in average walking distance can be noticed (see Table ). Figure shows the cumulative distribution of walking distances for different ITT. The distribution of the walking distance is not significantly affected by ITT, as the walking distances are short. About 0% of passengers walk less than 0 m to transfer between stops at different ITT thresholds. About % of passengers walk less than 0 m to get to their next boarding stop at different ITT thresholds. FIGURE Cumulative distribution of walking distance for different ITT. Figure compares the results of using different assumptions from the literature related to walking buffer zones, 00m ( and ), 00m (), 00m (), and 00m (), based on different ITT. The majority of transfers happen within a short walking distance, for example, more than % of the transactions are

11 Alsger, Mesbah, Ferreira, Safi made at ITT less than 0 min for different walking distance assumptions (00, 00, and 00m). For the 00m walking distance, about % of transfers are made at ITT less than 0 min. 0 FIGURE Transfer percentage at different walking distances and ITTs. There is no change in the number of transfers when walking distance is assumed to be equal or greater than 00m for different ITTs. As the ITT increases, the number of transfers increases, for different assumptions related to walking distances. This means there is no need to increase the assumed walking distance beyond 00m, since there is no significant change in the results. Testing the Third Assumption: Last Destination is the same as the First Origin Previous estimation methods assumed that last destination of a passenger in a given day is the same as the first boarding for that day, as no alighting information was recorded. The unique SEQ data allows for testing of this assumption as boarding and alighting information is recorded. The 0-min O-D matrix (the reference matrix) was used to investigate this assumption. Some of the O-D trips were excluded as they only have one trip-leg. From the total number of, O-D trips, % of passengers returned to their first boarding (first origin) of that day; 0% returned to their first boarding within 00m walking distance; and % within 00m walking distance. It should be noted that the distance between the alighting stop and the boarding stop is different from the distance between the alighting/boarding stop and the actual destination (place of activity). The typical 00m access distance in the literature is between the alighting/boarding stop and the destination. Figures (a) and (d) show the morning origins and the evening destinations for the 0 min ITT. As expected, the results indicate that most passengers returned to their first origin in that given day. ACCURACY LEVEL The GEH statistic is used to evaluate the accuracy level of each matrix based on different ITT thresholds. The GEH Statistic is a widely used formula in traffic engineering and traffic modelling to compare two sets of traffic volumes. As the GEH formula is applied to every single pair of the estimated O-D matrix, the pitfalls that occur when using simple percentage formulas to compare two O-D matrices will be avoided. The GEH statistic indicates a good fit when GEH is less than (). The GEH formula is presented as following:

12 Alsger, Mesbah, Ferreira, Safi GEH = Where: ( ) sampled measurement and ( ) reference measurement. The generated O-D matrix for a 0-min ITT was considered as a reference (to be compatible with Translink 0-min transfer time). Figure shows the relationship between the errors in each estimated matrix (compared to 0-min O-D matrix) and its related ITT. Expectedly, the error in the estimated matrix is minimum at around 0 min and increases when ITT is differ greatly from 0 min (e.g. min). Nevertheless, the highest average GEH for all matrices is less than 0., which means that there is no significant difference in the estimated matrices as ITT changes from to 0 min. It also indicates that the decreasing of ITT (from 0 to min) has more effect on the estimated matrices than increasing ITT (from 0 to 0 min). 0 FIGURE Relative errors (compared to 0 min) in O-D estimation with various thresholds CONCLUSIONS O-D matrices provide a critical foundation for public transportation analysis, design, and management. Such data gives information on the number of travellers between different zones of a region, which can be used in transportation planning to determine the infrastructure and service demand. As a result, the accuracy level of estimated O-D matrices is a very important issue. Smart card fare data was used to assess the validity of trip-chaining assumptions on the estimated public transport O-D matrix. Transfer time threshold, transfer walking distance, and last destination of a passenger in a given day, were the major assumptions investigated here. Most of the ITT is non-walking time (waiting and short activity time). Changing the transfer time set by Translink does not have a visible impact on passengers travel patterns. More than ninety percent of passengers are willing to walk less than min to transfer between two services. An algorithm for estimating O-D matrices was applied using different assumptions for ITT. As a result, a slight change in the estimated matrices was observed as the ITT increased from to 0 min. The increased ITT allowed more trip-legs to be joined together, which increased the number of transfer trips from fifteen to twenty-three percent, as the ITT increased from to 0 min. Ninety-one percent of the transfer trips have only one transfer (for ITT=0 min). This indicates that the assumed allowable transfer time does not have a significant impact on the O-D matrices or the number of transfer trips.

13 Alsger, Mesbah, Ferreira, Safi Most passengers return to their first origin in a given day. This assumption includes some errors as some passengers might use other travel modes (private cars) or they return to different destinations. The accuracy level of the estimate matrices is still a concern as the actual O-D matrix is not known. The GEH statistic was used to evaluate the accuracy level in each matrix based on different ITT. The generated 0- min O-D matrix was set as a reference to calculate the relevant errors. The calculated relative error indicates that there is no significant effect on the estimated matrix as ITT changes from to 0 min. The results shown here suggest that the public transport fare policy could be to increase the allowable transfer time, since this increase does not have a significant impact on the number of O-D trips and thus revenue. On the other hand, the increased allowable transfer time encourages more people to use public transport services. Each of the previous assumptions (walking distance, allowable transfer time, and last destination is the same as first origin) includes some errors, which is quantified here. This work can be extended to compare other estimation methods that use smart card system data in different cities. Previous estimation methods and their related assumptions can be applied to the public transport system in SEQ and the results can be evaluated and compared to the results from this study. ACKNOWLEDGEMENTS The authors are grateful for Translink (the public transport authority of South East Queensland, Australia) for providing the data for this research. REFERENCES. Agard, B., C. Morency, and M. Trépanier (0). Mining Public Transport User Behaviour from Smart Card Data. th IFAC Symposium on Information Control Problems in Manufacturing- INCOM.. Bagchi, M., and P. White (0). The Potential of Public Transport Smart Card Data. Transport Policy (): pp. -.. Utsunomiya, M., J. Attanucci, and N. Wilson (0). Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning. In Transportation Research Record: Journal of the Transportation Research Board, No., pp. -.. Morency, C., M. Trépanier, and B. Agard (0). Analysing the Variability of Transit Users Behaviour with Smart Card Data. Intelligent Transportation Systems Conference, 0. ITSC'0. IEEE, IEEE.. Morency, C., M. Trépanier, and B. Agard (0). Measuring Transit Use Variability with Smart- Card Data. Transport Policy (): pp. -.. Munizaga, M., C. Palma, and P. Mora (). Public Transport OD Matrix Estimation from Smart Card Payment System Data. Proceedings from th World Conference on Transport Research, Lisbon, Paper.. Wang, W. (). Bus passenger Origin-Destination Estimation and Travel Behavior using Automated Data Collection Systems in London, UK, Massachusetts Institute of Technology.. Zhao, J., A. Rahbee, and N. Wilson (0). Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems. Computer Aided Civil and Infrastructure Engineering (), pp. -.. Cui, A. (0). Bus passenger Origin-Destination Matrix Estimation Using Automated Data Collection Systems. Massachusetts Institute of Technology.. Munizaga, M. A., and C. Palma (). Estimation of a Disaggregate Multimodal Public Transport Origin Destination Matrix from Passive Smart Card Data from Santiago, Chile. Transportation Research Part C: Emerging Technologies (0), pp. -.. Nassir, N., A. Khani, S. G. Lee, H. Noh, and M. Hickman (). Transit Stop-Level Origin- Destination Estimation Through Use of Transit Schedule and Automated Data Collection System. In Transportation Research Record: Journal of the Transportation Research Board, No. (- ), pp. 0-0.

14 Alsger, Mesbah, Ferreira, Safi. Farzin, J. M. (0). Constructing an Automated Bus Origin-Destination Matrix Using Farecard and Global Positioning System Data in Sao Paulo, Brazil. In Transportation Research Record: Journal of the Transportation Research Board, No. (), pp Barry, J. J., R. Newhouser, A. Rahbee, and S. Sayeda (0). Origin and Destination Estimation in New York City with Automated Fare System Data. In Transportation Research Board (), pp. -.. Munizaga, M. A., F. Devillaine, C. Navarrete, and D. Silva (). Validating travel behavior estimated from smartcard data. Transportation Research Part C, Emerging Technologies, pp Bagchi, M., and P. White (0). What role for smart-card data from bus systems? Municipal Engineer (), pp. -.. Kieu, L., A. Bhaskar, and E. Chung (). Mining temporal and spatial travel regularity for transit planning. Australasian Transport Research Forum (ATRF), th,, Brisbane, Queensland, Australia.. Ma, X., Y. J. Wu, F. Chen, and J. Liu (). Mining smart card data for transit riders travel patterns. Transportation Research Part C, Emerging Technologies, pp. -.. Hofmann, M., and M. O'Mahony. Transfer journey identification and analyses from electronic fare collection data. Intelligent Transportation Systems, 0. Proceedings IEEE, 0.. Seaborn, C., J. Attanucci, and N. Wilson (0). Analyzing multimodal public transport journeys in London with smart card fare payment data. In Transportation Research Record: Journal of the Transportation Research Board, No. (), pp. -.. BSTM User s Guide: BSTM Multi-Modal Model Development, Network and Zoning, BSTM, Queensland Transport & Main Roads, 0, pp. -.. Hollander, Y., and R. Liu (0). The principles of calibrating traffic microsimulation models. Transportation., 0, pp. -.

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