0 0 Quantifying the Effects of Fare Media upon Transit Service Quality using Fare-Transaction and Vehicle-Location Data Jason B. Gordon, Corresponding Author Massachusetts Institute of Technology Massachusetts Avenue, Room 0 Cambridge, Massachusetts 0 jay_g@mit.edu Word count:,0 words + figures x 0 words (each) =, TRB Paper number: 0 Submitted August st, 0. Resubmitted with changes November th, 0
Gordon 0 0 ABSTRACT Dwell time constitutes a significant portion of transit vehicles travel times and thereby directly affects the quality of service that customers experience. Although automated fare-collection (AFC) systems are purported to reduce dwell times on services that require onboard fare processing, the benefits of AFC systems (and by comparison, the costs of non-afc transactions) have been difficult to quantify. This paper develops and applies a methodology for estimating the impact of various fare media upon bus and light-rail travel times using archived AFC and automated vehicle-location (AVL) data from the Massachusetts Bay Transportation Authority in Boston. From a baseline distribution of AFC-card transaction durations, marginal transaction durations are estimated for magnetic-stripe tickets and cash payments, yielding an excess farebox-interaction time metric. The metric is compared across services and time periods, and its impact upon each passenger is estimated by building upon previous research that infers individual passenger origins and destinations from AFC and AVL data. Cash fares are estimated to have a significantly higher marginal processing time than other media, yet magnetic-stripe tickets account for approximately one-third of system-wide excess fare-processing time because of their higher usage. Cash durations, however, are more variable, warranting possible further research into their impact upon headway variability. Card and ticket holders are classified by their inferred home locations, and the geographic distribution of riders enduring higher cumulative excess farebox-interaction times is compared to environmental justice data to explore potential equity implications. Keywords: Public Transport, Transit, Bus, Light Rail, AFC, AVL, ODX, Fare Policy, Dwell Time, Service Performance, Environmental Justice,
Gordon 0 0 0 INTRODUCTION Dwell time constitutes a significant portion of transit vehicles travel times (,,, ) and thereby directly affects the quality of service that customers experience. Onboard fare payment in turn affects dwell time, and the reduction of fare-processing time is purported to be among the foremost benefits of automated fare-collection (AFC) systems that use contactless RFID cards in place of cash, tokens, or magnetic-stripe tickets. Iseki et al, however, note that the benefits of AFC implementations have been difficult to quantify, and found the three transit agencies cost benefit analyses that they reviewed to be inconsistent and inconclusive (). Dwell time has been modeled as a function of boardings, alightings, passenger load, crowding, and vehicle characteristics (, ), while some researchers have included fare-media variables as well. Milkovitz, for example, estimates that ticket transactions in Chicago typically add. seconds to dwell time in uncrowded situations but that no difference is discernable on crowded buses (). The Transit Capacity and Quality of Service Manual estimates a marginal dwell time of 0. to.0 seconds for magnetic-stripe tickets and 0.0 to. seconds for exact change (). Fricker uses onboard video to observe 00 bus stop visits and uses the results to test others dwell time models, finding that the results differ significantly from the literature, often due to unanticipated situations and behaviors that the models do not take into account (). It should be expected that the impact of fare media, like other dwell-time predictors, differ between transit agencies due to differences in hardware, fare policy, and rider and operator behavior. This paper describes the development and application of a methodology for estimating the agency-specific impacts of different fare media upon travel times by using stop-level automated vehicle-location (AVL) data and passenger-level origin destination (OD) data. Stop-level vehicle data can often be obtained directly from AVL systems, while passenger OD information must be inferred unless passengers interact with the fare-collection system at both their origins and destinations. The literature contains several methods for inferring passenger origins and destinations by combining AFC and AVL data (,,, 0,,,, ) or by statistical methods when AVL data are unavailable (). The case study in this paper uses data from one such OD-inference implementation utilizing the AFC and AVL data of the Massachusetts Bay Transportation Authority (MBTA) in Boston, but the methodology should be applicable to any agency with observed or inferred passenger-level OD information.
Gordon 0 THE MBTA Services and Fare Policy The MBTA operates public transport services in the greater Boston metropolitan area, including heavy rail, light rail, bus, ferry, and commuter-rail services. All buses and light-rail vehicles (LRVs) are equipped with fareboxes that accept AFC cards, magnetic-stripe tickets, and cash, while station gates accept cards and tickets only. All light rail services and two bus rapid transit (BRT) services operate partly in gated stations, where fares are paid or validated prior to boarding, and partly at ungated outdoor stops, where fares are paid on board. All other bus routes operate exclusively with onboard fare collection while all heavy rail services are entirely gated. AFC fares are discounted from other fare media to discourage onboard cash and ticket payment and the additional time that they require. But because not all bus stops have fare vending machines nearby, customers are allowed to use fareboxes to add value to their cards, then pay their fares with their cards at the discounted rate. 0 0 Data Sources and Preprocessing The Massachusetts Institute of Technology, in collaboration with the MBTA, maintains a data warehouse of AFC, AVL and other automatically collected transit data. Raw bus AVL data from internal stop announcements, external destination announcements, schedule-adherence records, and 0-second location heartbeats are processed to infer stop visits: the times at which each bus serves a bus stop. Rail GPS, track-circuit, and automatic vehicle-identification (AVI) data are similarly used to synthesize heavy and light-rail stop visits (). While passenger-trip origins are recorded by station gates, the origins of farebox customers are unknown because the AFC and AVL systems are not connected. Furthermore, the MBTA requires fare transactions only upon gate entry or vehicle boarding but not upon exit or alighting, thus passenger destinations are also unknown. A passenger origin-, destination-, and interchange-inference (ODX) model developed by Gordon () was therefore adapted to the MBTA s data by Dumas (), in order to infer farebox origins by matching AFC and AVL data, and to infer destinations and interchanges (transfers) based on a set of spatial and temporal assumptions about the cardholder s other transactions. The ODX model was later redesigned by Sánchez-Martínez () to incorporate a dynamic-programing destination-inference algorithm using a generalized-cost objective function that considers various aspects of transit disutility (riding, waiting, transferring, walking, etc.), in order to better handle passengers who transfer within gated stations or who ride BRT or LRV vehicles between gated and ungated stops and stations. The resultant ODX data contain origins times and locations for % of all card, ticket, and cash customers and destination times and
Gordon locations for 0% of customers. This study uses the complete population of AFC, AVL, and ODX data from October 0, with the exception of one Saturday ( October) and one holiday (Halloween, October) due to incomplete AVL data. 0 0 0 ESTIMATING EXCESS TRANSACTION TIMES BY FARE MEDIUM The duration of fare transactions is not directly recorded by the MBTA s AFC system but is a component of the observed time between consecutive fare transactions on a given farebox. If O is the observed duration between the completion of a fare transaction and the completion of the previous transaction, then the unobserved fare-transaction time, F, is the difference between O and the unobserved inter-arrival time since the previous transaction s completion, or I: F = O I () In this case F is the amount of time that the customer spent interacting with the farebox while I is the amount of time elapsed between the completion of the previous customer s transaction and the beginning of the current customer s transaction: the interval in which the farebox was not being used. We should expect that each fare medium has a unique distribution of fare-transaction durations, but we assume that inter-arrival durations are unrelated to fare medium (although it is possible that some customers anticipating long transaction times for themselves might let their fellow riders board first). Thus, the fare-transaction duration for an AFC card, FCARD is formulated as: FCARD = OCARD I () While this does not definitively reveal the distributions of fare-transaction durations, we can estimate the differences between different media. Using cards as the baseline because we assume (and demonstrate below) that they have the shortest fare-transaction durations, we can calculate the marginal cost of another medium, such as tickets, or MTICKET as: MTICKET = FTICKET FCARD () Which can be substituted as: MTICKET = OTICKET I (OCARD I) () MTICKET = OTICKET OCARD ()
Gordon 0 0 The distribution of observed durations for each fare medium was estimated using all ODXinferred AFC farebox records from the aforementioned -day dataset. Each AFC record was matched to the appropriate stop-visit record, and each observation was classified and recorded as follows: Card validations directly preceded by one or more topup records on the same card ID (where topup refers to a customer adding value to a card with cash at the farebox) were classified as Card topup and validation. The duration is measured as the time elapsed between the transaction preceding the first topup and the time of the topup customer s stored-value fare deduction. All cash and ticket transactions, and all card transactions not directly preceded by topups, are classified as named. Their durations are measured as the time elapsed between the preceding transaction and the current transaction. The first fare transaction at each stop visit is excluded from these calculations in order to reduce the amount of inter-arrival time incurred when a vehicle is in motion or out of service. Any inter-arrival time should therefore be limited to the arrival rate of passengers at the vehicle after at least one customer has interacted with the farebox during the given visit. After omitting the first fare transaction of each stop event, the classification process yielded a sample of approximately. million card validations,,000 ticket validations,,000 card topups with validation, and,000 cash fares. The resultant distributions are shown in Figure, with a histogram of each distribution as well as box plots indicating the first, second, and third quartiles of each distribution, and whiskers indicating. times each distribution s interquartile range (IQR).
Gordon 0 FIGURE Distribution of inter-transaction durations by fare medium Card validations exhibit a median and mode of seconds, with a long but shallow tail that extends beyond the graph. Assuming that card transaction time is extremely short, the peak at seconds is corroborated by Zografos and Levinson (), who find that base boarding time with no fare transactions takes approximately seconds. The tail likely includes fare transactions that were preceded by non-zero inter-arrival times transactions in which the cardholder was not queued at the farebox. AFC cards show the least variability, with an IQR (between the first and third quartiles) of seconds. The distribution of ticket validations has a median and mode of seconds and exhibits a wider distribution than cards, with an inter-quartile range of seconds. Farebox ticket readers occasionally read and eject a ticket multiple times, presumably to read a partially damaged magnetic strip, which may cause of some of this variation. Cash exhibits a much wider distribution that cards and tickets, with a median of seconds but no clear mode: the distribution shows a broad rise centered near seconds with significant
Gordon 0 0 0 peaks and and. The broad spread of the cash distribution, with an IQR of. seconds, likely represents the variability with which bills are unfolded and inserted into fareboxes, while the multiple peaks may indicate some mechanical or procedural artifact. For example, many cash transactions are flagged as cash short, indicating that an operator accepted a partial fare payment. This often occurs when students, who are entitled to a discount, use regular storedvalue cards instead of reduced-fare student cards. The multiple peaks might therefore correspond to distinct numbers of bills, or periods of operator interaction with the farebox. Card topup and validation has the greatest spread of the four distributions, with a mode of approximately seconds, a median of, and an IQR of.. This longer duration is expected, as the customer must tap her card on the reader, wait for a response, insert bills, tap again to load the value, and finally tap to pay her fare. Because we cannot directly measure the duration of any one fare transaction, and because the distributions of non-card fare transactions exhibit significant variation, further analysis requires that we model the unobserved excess farebox-interaction duration for each observed non-card fare transaction. While a simulation model could be given some measure of center and of variability for each medium s distribution in Figure, the non-simulated analysis in this case study seeks a single measure of excess time for each of the four media, for which we use the average. Although we might assume that outliers in the above distributions are due to inter-arrival time and are therefore similar across fare media, the noise induced by the tails of the above distributions (due to sampling zeros, the rounding of the data to whole seconds, and the high relative magnitude of the outliers) requires that they be removed. The whiskers in Figure suggest truncating card, ticket, cash, and topup distributions at,,, and seconds respectively, but doing so may bias the results by allowing different ratios of fare-transaction to inter-arrival durations among the four categories. Averages were therefore taken on the domain [0, ] minutes, to allow a similar amount of inter-arrival time in each tail while eliminating the portions of the tails with significant numbers of zero-frequency observations. The resultant calculations yield an average observed duration, O, of.0 seconds for card transactions, 0. seconds for ticket transactions,. seconds for cash transactions, and. seconds for card topup and validation. The average card-transaction duration of.0 is significantly higher than the median and mode of seconds, presumably due to the inter-arrival time in the tail, I. By assuming similar inter-arrival times in each distribution and applying equation to subtract the card average from each of the other media s averages, we arrive at the estimated excess farebox-interaction durations, F, of. seconds for tickets,. seconds for cash, and. seconds for card topup and validation. The excess farebox-interaction duration for
Gordon 0 tickets is greater than the marginal dwell time of. seconds estimated by Milkovitz () but within the range of 0. to.0 seconds specified in TCRP (). MEASURING IMPACTS System-Perspective Impacts Researchers and practitioners have proposed methods for measuring system performance using AFC and AVL data (, ) and several of these measures, such as travel speed, travel-time reliability (0), and stop-level headway variance can be impacted by excess fare-transaction duration. These excess durations should be considered the average upper bound, as not all excess fare time may contribute directly to delays. For example, a cash customer might pay when a bus is being held at a traffic signal, or an operator may depart a stop and allow a customer to top up his card while the bus is in motion. Impacts by time period The potential impact of fare medium upon travel time varies by time of day, as shown in Figure. AFC cards are used for more than 0% of transactions during peak periods, when the system sees its highest ridership and provides the highest service frequencies, but decreases farther from the peak period especially at night. Card validation Ticket validation Cash payment Card topup and validation Sunrise (a a) Early AM (a a) AM Peak (a a) Midday (a :0p) School (:0p p) PM Peak (p :0p) Evening (:0p 0p) Late Evening (0p a) Night (a a) Saturday (all day) Sunday (all day) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 00% Figure Distribution of fare media by time period
Gordon 0 0 0 Removing the card medium from Figure and weighting the remaining three media by their associated excess farebox-interaction durations yields the proportional contribution of each ticket medium to excess farebox-interaction time (EFT), as illustrated in Figure. The two cash types (cash payment and card topup & validation) exert a significant influence, especially during the weekday inter-peak period, when they account for more than % of EFT. Nonetheless, with tickets being so widely used on the MBTA, they account for roughly one third of EFT averaged across each weekday despite their lower marginal impact. The -day study period saw approximately,000 cash and ticket farebox payments and 0,000 card topups with validation. Extrapolating this over a year yields approximately million cash fares, each paying approximately 0 cents more than card fares and resulting in roughly. million dollars of additional fare revenue. This revenue should be weighed against the additional EFT caused by card topup and validation, and the resultant capacity reductions and service-quality degradations. Pricing cash and cards equally would obviate the need for onboard topups, potentially reducing system-wide EFT by % if all onboard-topup customers become cash customers. However, such a policy is likely to cause some existing card users or new customers to use cash, which could lessen or outweigh such EFT savings. Alternatively, providing more card-topup opportunities throughout the MBTA service area (whether through MBTA fare vending machines or at third-party points of sale) could provide customers with the means of adding stored value offboard, so that the onboard topup policy could be eliminated.
Gordon Ticket validation Cash payment Card topup and validation Sunrise (a a) Early AM (a a) AM Peak (a a) Midday (a :0p) School (:0p p) PM Peak (p :0p) Evening (:0p 0p) Late Evening (0p a) Night (a a) Saturday (all day) Sunday (all day) 0 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 00% Figure Distribution of excess farebox-interaction time by time period Impacts by service The observed farebox transactions for October 0 were merged with AVL data for each vehicle trip, and each fare transaction was weighted by the average EFT for its fare medium to arrive at an EFT measure for each stop visit. Each vehicle trip was then aggregated by route, direction, and time period to summarize the effects of EFT by service. Of the, services (route/direction/time-period combinations) that included more than 0 observed vehicle trips in the sample, most had an EFT per vehicle-trip of less than one minute, with approximately 0 services having a median EFT of to minutes. This measure includes light rail services, which have fewer farebox transactions because much of their alignment operates within the gated subway system. Nonetheless, there are far more bus routes than light rail, and EFT is not likely to be significant at the route level on average because of the relatively small numbers of cash, ticket, or card-topup customers on any one vehicle trip. Any significant impacts of EFT are likely due to the temporal variability of non-card transactions, especially cash and topup transactions, which may cause significant delays at specific stop visits and which could in turn degrade the quality of service on high frequency routes if they induce bus bunching or other headway irregularities (0, ). While the variance of individual fare-transaction durations might
Gordon 0 0 be tested using a simulation model, the variability in the frequency of these transactions per trip can be explored using the current data set. For example, the SL is a bus route providing high-frequency service between downtown Boston and Dudley Square. miles to its southwest. In October 0, trips were run in the outbound direction during the :0 p.m. to :00 p.m. School time period, with an average running time of : and a standard deviation of :. The median EFT per vehicle trip was seconds approximately % of the median running time while the 0 th percentile EFT per trip was seconds, or % of median running time. Figure shows the observed outbound headways at the intersection of Washington Street and Union Park Street, the midpoint of the route, for trips starting between :0 and :00 p.m. The average headway across all observations is : with a standard deviation of :. With an 0 th percentile headway of :0, a rider boarding at this stop every weekday should expect to experience such a headway approximately once per week (or assuming a random arrival process, an approximately -minute estimated wait). Regardless of whether EFT is a significant cause of this headway irregularity, it is apparent that EFT will disproportionately accumulate on those trips that experience greater headways if passengers arrivals are randomly distributed in time. For example, when calculating the number of riders onboard during each stop visit in this example, we find that % of the service s riders are onboard a trip having an 0 th percentile EFT or greater. 0/0/0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0/0/0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0/0/0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 0//0 :00 : :0 : :00 : :0 : :00 FIGURE Observed bus arrivals at midpoint of route SL
Gordon 0 To compare EFT across services of different lengths or durations, EFT can be normalized by time or distance. But because EFT contributes to travel time, normalizing by travel time would underrepresent the effects of EFT. Normalizing by distance instead enables the disutility of EFT to be measured against the utility of moving toward the customer s desired location in space. In the case of the SL outbound between :0 and :00 p.m., the median EFT per mile (EFTM) across vehicle trips is seconds, while the 0 th percentile EFTM is seconds. In other words, we should expect that one in five rides on this service will incur approximately one minute of excess travel time per mile due to EFT, yielding approximately. additional minutes of running time. Rider-Perspective Impacts 0 0 While excess farebox-interaction time may affect service performance, passenger-trip-level OD data enables measures to be tested from the passenger s perspective as well. The ODX model infers origins for card, ticket, and cash passengers, but because cash transactions cannot be referenced to other transactions made by the same rider, destinations can only be inferred for card and ticket holders. This means that the impacts of cash fare payment can be recorded but the impacts of those payments can only be measured for card and ticket customers although we can approximate the impact on cash customers (with some amount of bias) if we assume that they have similar OD distributions to card and ticket customers. To measure the impacts of excess farebox-interaction time upon riders, ODX data are used to determine the vehicle, trip, and start and end times and locations for each cardholder or ticketholder ride, where a ride is defined as the travel of a single passenger on a single vehicle. Using the EFT-weighted stop visits discussed in the previous section, the EFT of each stop visit that a passenger experiences is allocated to her ride, yielding a per-ride EFT score which can serve as a proxy for the amount of time that the passenger could have saved if all riders boarding at all stop visits that she experienced had used AFC cards. It is also possible that EFT affects downstream riders who have not yet boarded a specific bus, as their wait time may thereby be increased. Yet in other cases EFT may delay a bus by enough time to enable a rider to board, when that rider would otherwise have had to wait for the next bus. Such downstream effects are excluded from this study but may be discernable in a simulation model. The population of MBTA bus rides during the -day study period exhibits a median EFT of. seconds per mile, with 0 th, 0 th, and th percentile EFTM values of.,., and. seconds respectively. Over 0 percent of bus rides have no EFT, meaning that those bus rides
Gordon 0 occurred without the passenger or any of his fellow passengers making a non-card transaction. Yet approximately 0% of bus rides are classified as card transactions, meaning that the remaining 0% of non-card transactions add EFT to more than half of all observed bus rides. EFT for specific services can also be weighted by the number of riders onboard during each non-card fare transaction, yielding a more meaningful measure of EFT for a given service from the passenger s perspective. Returning to the example of the :0 :00 p.m. weekday SL outbound, the passenger-weighted EFT across all observed vehicle trips exhibits a median of seconds and an 0 th percentile of, seconds. In other words, one in five vehicle trips on the service exhibits at least combined passenger minutes of estimated excess wait time due to ticket, cash, and card-topup transactions. 0 0 Geographic Distribution Using ODX data to approximate each card or ticket holder s place of residence, the impacts of EFTM are estimated across the greater Boston metropolitan area. First, for each day of October 0, each cardholder s first farebox-boarding or station-entry location is recorded, and the location having the highest count for each rider is assumed to be a proxy for that rider s home location. All riders having five or more farebox transactions during the month are then included in their proxy location s sample (grouping nearby stops and stations to derive each proxy location) and EFTM is calculated for the group of riders constituting each location s assumed population. The map in Figure illustrates the distribution of EFTM across estimated home locations. As a proxy for reliability due to variability, the 0 th percentile EFTM is taken from among each cardholder s rides over the month, and the result for each cardholder is averaged over all cardholder s in each stop cluster. EFTM impacts appear low near the light rail services to the west of downtown Boston and slightly higher in the Dorchester and Roxbury neighborhoods to the south of downtown, especially between the Orange and Red Lines (two heavy rail lines running roughly southwest and southeast of downtown, respectively). EFT also appears higher along portions of the Red and Orange Lines themselves, possibly due to riders who begin their daily travels at a heavy rail station but later experience farebox EFT on feeder or other bus routes. The high-ridership Silver Line BRT routes, including the aforementioned SL, constitute a portion of the high-eft area just southwest of downtown. EFTM is also notably high in the towns of Chelsea to the northeast of downtown, Lynn further to the northeast (west of Swampscott), and Quincy to the southeast. Although a comprehensive spatial analysis was not performed, the distribution of higher- EFTM locations appears to correspond to census blocks classified by the Massachusetts
Gordon Executive Office for Administration and Finance as environmental justice zones, in relation to Title VI of the Civil Rights Act of (). More specifically, these correspond to environmental-justice zones having significant portions of both low-income households and significant ethnic minority populations. The map may warrant further research into the findings of Dumas (), who estimated that commuters from Boston s predominantly Black and African- American census tracts tend to have slower average transit speeds than commuters from predominantly white census tracts.
Gordon FIGURE Excess farebox-interaction time endured, by proxy home location
Gordon 0 CONCLUSION The MBTA s acceptance of cash fares and magnetic-stripe tickets on bus and light-rail fareboxes as well as the agency s policy of adding stored value at fareboxes is estimated to increase fare processing times beyond those of AFC cards by an average of. seconds per ticket,. seconds per cash payment, and. seconds when adding value to a smart card before validating it. Not all of this excess time may be recaptured by eliminating cash and ticket fares, however, as some fare payments occur while vehicles are in motion or are stopped for other reasons. Nonetheless, it may be advisable for the MBTA to explore strategies for eliminating the onboard loading of stored-value AFC cards (perhaps by offering more off-board topup locations), as their durations are typically longer and more variable than cash payments while yielding less revenue. While the average impact upon the system is low in general, crowded services incur more excess fare-processing time, which is thereby experienced by more riders. Furthermore, the high variability of cash and topup fares may have impacts upon headway variability especially on high-frequency services and future simulation models of such services might benefit from taking these time distributions into account. A cursory geographic analysis of excess farebox-interaction time by riders estimated home locations was inconclusive, but suggests potential equity issues which might be explored further. 0 ACKNOWLEDGEMENT This research was sponsored by the Massachusetts Bay Transportation Authority. REFERENCES 0. Lin, T. and N.H.M. Wilson. Dwell Time Relationships for Light Rail Systems. In Transportation Research Record: Journal of the Transportation Research Board, No, Transportation Research Board of the National Academies, Washington, D.C.,, pp... Dueker, K.J., T.J. Kimpel, J.G. Strathman, and S. Callas. Determinants of Dwell Time. In Journal of Public Transportation, Vol., No., 00, pp. 0.. Milkovitz, M.N. Modeling the factors affecting bus stop dwell time. In Transportation Research Record: Journal of the Transportation Research Board, No 0, Transportation Research Board of the National Academies, Washington, D.C., 00, pp. 0.
Gordon 0 0 0. Fricker, J.D. Bus dwell time analysis using on-board video. 0 th Annual Meeting of the Transportation Research Board, Washington, D.C., 00.. Iseki, H., A. Demisch, B.D. Taylor, and A.C. Yoh. Evaluating the Costs and Benefits of Transit Smart Cards. California PATH Research Report UCB-ITS-PRR-00-. University of California Berkeley, 00.. Transit Capacity and Quality of Service Manual, rd Edition. Transportation Research Board of the National Academies, Washington, D.C., 0. p.. Barry, J.J., R. Newhouser, A. Rahbee, and S. Sayeda. Origin and Destination Estimation in New York City with Automated Fare System Data. In Transportation Research Record: Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D. C., 00, pp... Cui, A. Bus Passenger Origin-Destination Matrix Estimation Using Automated Data Collection Systems. MS thesis. Massachusetts Institute of Technology, Cambridge, 00. Trépanier, M., N. Tranchant, and R. Chapleau. Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System. In Journal of Intelligent Transportation Systems, Vol., No., 00, pp.. 0. Chu, K.K.A., and R. Chapleau. Enriching Archived Smart Card Transaction Data for Transit Demand Modeling. In Transportation Research Record: Journal of the Transportation Research Board, No. 0, Transportation Research Board of the National Academies, Washington, D. C., 00, pp... Wang, W., J.P. Attanucci, and N.H.M. Wilson. Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems. In Journal of Public Transportation, Vol., No., 0, pp. 0.. Munizaga, M., and C. Palma. Estimation of a Disaggregate Multimodal Public Transport Origin-Destination Matrix from Passive Smartcard Data from Santiago, Chile. In Transport Research Part C: Emerging Technologies, Vol., 0, pp... Gordon, J.B., H.N. Koutsopoulos, N.H.M. Wilson, and J.P. Attanucci. 0. Automated inference of linked passenger journeys in London using fare-transaction and vehiclelocation data. In Transportation Research Record: Journal of the Transportation Research Board, No., Transportation Research Board of the National Academies, Washington, D. C., pp... Sánchez-Martínez, G.E. Inference of Public Transportation Trip Destinations using Fare Transaction and Vehicle Location Data: a Dynamic Programming Approach. In process for Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D. C., 0.
Gordon 0 0. Ma, X. and Y. Wang. Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data. In Journal of Transportation Engineering, Vol 0, Issue, 0.. Fabian, J.J. and G.E. Sánchez-Martínez. An Algorithm for the Processing and Cleansing of Automatic Vehicle Location Data from Multiple Sources. Presented at the nd International Workshop on Automated Data Collection Systems, Boston, MA, 0.. Dumas, R. Analyzing Transit Equity Using Automatically Collected Data. Master s Thesis, Massachusetts Institute of Technology, 0.. Zografos, K.G., and H.S. Levinson. Passenger Service Times for a No-Fare Bus System. In Transportation Research Record: Journal of the Transportation Research Board, No 0, Transportation Research Board of the National Research Council, Washington, D.C.,, pp... Trépanier, M., C. Morencey, and B. Agard. Calculation of Transit Performance Measures Using Smartcard Data. In Journal of Public Transportation, Vol, No., 00, pp.. 0. Federal Highway Administration. Travel Time Reliability: Making It There on Time, All the Time. 00. Washington, DC. <http://ops.fhwa.dot.gov/publications/tt_reliability>. Accessed November 0.. Maltzan, D. Using Real-Time Data to Improve Reliability on High-Frequency Transit Service. MS thesis, Massachusetts Institute of Technology, 0.. Massachusetts Executive Office for Administration and Finance. 00 Environmental Justice Populations: Boston Metro. 00. www.mass.gov/anf/docs/itd/services/massgis/boston-metro-ej-00-map.pdf. Accessed July 0