2013 Session: 421

• Forecasting Mobile Ticketing Utilization for Commuter Rail
  Abstract: Several commuter rail systems are beginning to accept mobile payments, in which tickets are purchased and validated on smartphones. Mobile payments may improve the rider experience while reducing costs and simplifying the fare collection process for rail operators. Before investing in this new ticketing technology, rail operators want to understand rider demand for mobile tickets. To assess the potential adoption of mobile payments, stated preference data from an onboard survey on two MBTA Commuter Rail lines (Worcester and Newburyport/Rockport) in the greater Boston area were analyzed. Binary logit was then used to forecast adoption on all commuter rail lines. Based on this model, 26% of Commuter Rail riders in Boston are very likely to adopt mobile ticketing.
  Authors: Brakewood, Candace; Rojas, Francisca; Robin, Joshua K; Sion, Jake; Jordan, Samuel; Block-Schachter, David
  Authors: Brakewood, Candace; Rojas, Francisca; Robin, Joshua K; Sion, Jake; Jordan, Samuel; Block-Schachter, David
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1244
• Mining Smart Card Data for Transit Riders’ Travel Patterns
  Abstract: To mitigate congestion caused by the increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. With a better understanding of the travel patterns and regularity (the “magnitude” level of travel pattern) of transit riders, transit authorities can evaluate the current transit services to adjust marketing strategies, keep loyal customers and improve transit performance. However, it is fairly challenging to identify travel pattern for each individual transit rider in a large dataset. Therefore, this paper proposes an efficient and effective data-mining approach that models the travel patterns of transit riders using the smart card data collected in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. Based on the identified trip chains, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities. The rough-set-based algorithm is compared with other classification algorithms, including Naïve Bayes Classifier, C4.5 Decision Tree, K-Nearest Neighbor (KNN) and three-hidden-layers Neural Network. The results indicate that the proposed rough-set-based algorithm outperforms other prevailing data-mining algorithms in terms of accuracy and efficiency.
  Authors: Ma, Xiaolei; Wu, Yao-Jan; Wang, Yinhai; Chen, Feng; Liu, Jianfeng
  Authors: Ma, Xiaolei; Wu, Yao-Jan; Wang, Yinhai; Chen, Feng; Liu, Jianfeng
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-3460
• Analysis of Evening Contraflow Fare on the London Underground
  Abstract: ABSTRACTThis paper studies the impacts of the peak contraflow fare which was introduced by Transport for London (TfL) on the London Underground at the beginning of January 2011. Passengers travelling into central London during a weekday evening peak period using Pay-As-You-Go on Oystercard, London’s public transport smartcard, are charged an off-peak fare instead of a peak fare. The majority of commuters leave the city centre during the peak period and accordingly the main reason for the introduction of the fare was to fill spare capacity on trains travelling into the city centre. Given the fare was so recently introduced, this research is the first study of the fare’s impact. No similar fare structure was found to exist elsewhere during the course of this research and the results of this paper are therefore important for both TfL and transport network operators worldwide. Data collected from a 5% sample of Oystercards was used to conduct the study.No statistically significant change was found between the number of passengers travelling into the centre of London during the evening peak period before and after the introduction of the fare. TfL are therefore making a loss in revenue in comparison with previous years where passengers would have been charged a peak fare. Two primary reasons were suggested to explain these results. Firstly, passengers take time to respond to fare changes. Secondly, TfL’s promotion of the fare seems very low.
  Authors: Rooney, Lydia; Majumdar, Arnab
  Authors: Rooney, Lydia; Majumdar, Arnab
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-4612
• Cost and Optimal Fare Estimation for Urban Bus Transit System of Santiago, Chile
  Abstract: In 2007 the city of Santiago, Chile implemented a new urban transit system (Transantiago), which integrated the Metro system with a redesigned bus network. The city was divided in several regions and each region was tendered and awarded to the highest bidder that fulfilled the requirements. After a very complicated start, the Government was forced to give subsidies to bus operators. Since subsidies were not contemplated in the original plan for Transantiago, it has been a long discussion between the Government, opposition politicians, and the public agencies involved whether there should be a subsidy for Transantiago and how much it should be. In this paper, we attempt to estimate the cost function of the operators and the budget-balance fare of the system, in order to contribute technically to this discussion. Our database is from several sources and includes public data from the bus operators. We estimate the cost function through a Cobb-Douglas function and we define an aggregate demand model. Our results show that there are economies of density. Once the cost function is estimated, we estimate a budget-balance fare using Ramsey pricing. Our results show that this fare is higher than the actual bus fare, suggesting that subsidies are justified. Nevertheless, we estimate that for the current (December 2011) fare the subsidy should be 40% lower that the one determined by the Government. On the other hand, we estimate that for such level of subsidy the optimal fare should be only 50% of the current fare. Further research should consider the different levels of efficiency in the industry and the externalities generated by private car and public transport trips such as accidents, pollution, congestion, and noise in order to have a broader picture for the decision-makers.
  Authors: Batarce, Marco; Galilea, Patricia
  Authors: Batarce, Marco; Galilea, Patricia
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-4814
• Objectives for Setting Transfer Time Windows and Other Considerations for Transit Fare Policy
  Abstract: Transfers allow customers to board connecting services without the requirement to pay an additional fare to reach their destinations. As transfers are a core component of fare policy, it is important to select an appropriate transfer time window, defined as the time between the first and final boardings in a journey. The transfer time window selected is important to satisfy both revenue and customer needs.Different approaches can be utilized to derive transfer time windows. For example, a transfer time window can be derived by finding the trip possible with the longest duration on the transit network and setting a transfer time window that allows this trip to be taken on a single fare. Alternatively, a transfer time window can be set so that the time spent on the system is proportional to the fare paid. Unfortunately, both approaches yield relatively long transfer time windows if extreme travel scenarios are considered. To address this concern, an alternative approach was developed to consider more common trips. This analysis found that a transfer time window can be better derived by selecting commonly accessed destinations on the periphery of the transit network, calculating transfer time windows for each origin-destination pair for a weekday during the midday, and then taking the 85th percentile of the transfer time windows required for the origin-destination pairs identified in the sample. The transfer time window should then be rounded to the next 15- or 30-minute increment for communication purposes. Additional considerations when setting a transfer time window include revenue impacts, forward compatibility, congestion and delays, and enforcement. Finally, transfer time windows should be reviewed periodically as the transit system evolves to manage any revenue or customer risks due to changes in network design and travel patterns.
  Authors: Hui, William
  Authors: Hui, William
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1377
  Practice-Ready: Yes
• Can BART Do Better? Sketch Modeling Alternate Fare Structures to Manage Demand
  Abstract:

  How can transit agencies explore of fare policies for congestion management quickly and cheaply? This research develops an elasticity-based sketch-planning model, and applies it to the Bay Area Rapid Transit (BART) system. The model predicts that BART could increase revenue significantly with a small decrease, or even increase, in ridership by introducing peak period and direction pricing on trips to San Francisco.BART provided ridership data by origin-destination pair in 15-minute intervals for nine weekdays in 2011, and elasticity values for commute (-0.15) and non-commute trips (-0.30). The model forecast new ridership after fare changes using elasticity. A 1000-iteration Monte Carlo simulation demonstrated that the findings of the Excel-based model are robust.Several new fare structures were developed, based on International transit systems. For each fare structure, the model also determined ridership in a revenue-neutral case where new revenue subsidized off-peak trips. The best performing alternative (existing fares plus a $1.00 peak period surcharge and $1.00 Transbay peak direction surcharge) increases weekday revenue by 19.5% but loses 2.5% of ridership. By introducing off-peak discounts, BART ridership would increase 4.9% without during uncongested times.The model indicates that BART could meet its revenue and mode shift goals with a more complex fare structure. If implemented, care should be taken to reduce impact on lower income households with inflexible transit demands.

  Authors: Schabas, Matthew; Miller, Ruth
  Authors: Schabas, Matthew; Miller, Ruth
  Year: 2013
  Document Type: Paper
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1378
• Forecasting Mobile Ticketing Utilization for Commuter Rail
  Authors: Brakewood, Candace
  Authors: Brakewood, Candace
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1244
• Mining Smart Card Data for Transit Riders' Travel Patterns
  Authors: Ma, Xiaolei
  Authors: Ma, Xiaolei
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-3460
• Analysis of Evening Contraflow Fare on the London Underground
  Authors: Rooney, Lydia
  Authors: Rooney, Lydia
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-4612
• Cost and Optimal Fare Estimation for Urban Bus Transit System of Santiago, Chile
  Authors: Galilea, Patricia
  Authors: Galilea, Patricia
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-4814
• Can BART Do Better? Sketch Modeling Alternate Fare Structures to Manage Demand
  Authors: Miller, Ruth
  Authors: Miller, Ruth
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1378
• Objectives for Setting Transfer Time Windows and Other Considerations for Transit Fare Policy
  Authors: Hui, William
  Authors: Hui, William
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1377
• Can BART Do Better? Sketch Modeling Alternate Fare Structures to Manage Demand
  Authors: Schabas, Matthew
  Authors: Schabas, Matthew
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Policy; Public Transportation
  Session: 421
  Paper Number: 13-1378