In this paper we introduce a mesoscopic Train Following Model which accurately captures train interactions and predicts delays based on spacing between consecutive trains. The Train Following Model is applied recursively block by block estimating train trajectories given initial conditions (i.e. the trajectory of an initial train and dispatching headways of following trains from the terminal station). We validate the proposed model using data from the Red Line of the Massachusetts Bay Transportation Authority (MBTA). The results indicate that it accurately represents train operations under both normal and disrupted conditions. Based on the model developed, the impacts of factors such as service frequency, headway variations, passenger demand, and initial train delays on line performance (i.e. line throughput and train knock-on delays) are explored. The proposed Train Following Model is generic and can be developed based on readily available historical train tracking data. It is not as resource intensive as micro simulation models, while it can efficiently address the drawbacks of macro-scale analytical models and complex discrete algebraic models. The proposed model can be used to predict system performance either off-line or in real-time.

The paper develops a data-driven approach for the inference of passenger itineraries in urban heavy rail systems, the Passenger Itinerary Inference Model (PIIM). In light of increasing demand and crowding, the model can be used to assess the impact of near capacity operations on customers, evaluate system performance, and understand passenger behavior when choosing alternative routes. PIIM uses data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems and is applicable to general urban rail networks with entry and exit fare transactions. PIIM consists of three main modules: the left behind model, the route fractions inference model, and the inference model. The left behind model estimates the probability of any passenger being left behind by station and time interval. The route fractions inference model estimates the route choice fractions given the left behind probabilities. The inference model maps each pair of fare transaction records (entry and exit) to a set of feasible itineraries and infers the train(s) boarded by passengers based on the left behind probabilities and the route choice fractions. Synthetic data using AFC and AVL data from a major, congested urban rail system were generated to validate the model. The results show that the model accurately predicts the itineraries used by passengers, the left behind probabilities, route choice fractions, train loads, and other performance metrics of interest. PIIM is also used with actual data to illustrate its applicability.

In this paper, we introduce a train following model which can efficiently capture the effects of train interactions in an urban rail line. The train following model is based on the estimation of induced delay for a train closely following a lead train, and is derived from empirical analysis based on historical track circuit data. Based on an analysis of sequential train delays, a mesoscopic train state prediction model is developed which can be used to predict the behavior of the system with respect to changes in initial conditions (e.g. scheduled headway, headway variation or dwell time) and disruptions. The performance analysis using this train following model is richer and more accurate than that from analytical macroscopic models while not being as time- and resource- intensive as a detailed simulation model. The development and potential application of this model is demonstrated for the Massachusetts Bay Transportation Authority (MBTA) Red Line. The model can be used for both real-time control strategy evaluation and offline operational strategy analyses.

Monitoring rail transit system performance is important for effective operations planning. The number of times passengers are denied boarding is becoming a key measure of the impact of near-capacity operations on customers and is fundamental for calculating other performance metrics, such as expected waiting time for service. This paper reviews existing methods and proposes a denied boarding probability distribution inference method for closed Automated Fare Collection (AFC) urban rail systems. Using AFC (tap-in and tap-out) and Automated Vehicle Location (AVL) data, the method relaxes some of the limitations of existing approaches. The problem is modeled using a mixture distribution framework that incorporates a priori structural information. It is data-driven and requires neither observations of denied boardings, nor assumptions about access/egress time distributions. Also, for comparison purposes, the paper presents an event-based deterministic transit assignment model with explicit capacity constraints. While the network assignment works at the network level and requires train capacity, the mixture model works at the station level, requires no external parameters, and can be easily applied to any station and for any time period. A case study illustrates the application of the proposed methods using actual data and compares the results against existing methods, and also survey data. The results demonstrate the mixture model’s robustness and applicability for monitoring denied boarding.

Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs. In this paper, a Bayesian inference method is proposed to analyze the AFC repeated boarding records of passengers in the presence of real-time bus arrival information. A continuous representation of boarding strategies is introduced that can capture the behavior of passengers if they extend their waiting times to board a preferred route that is due shortly. The proposed method is tested in a case study on the Western Avenue corridor in Chicago, IL. The case study demonstrates the possibility of making confident inferences (95%) for thousands of the corridor passengers. The case study also confirms intuitive correlation of the inferred strategies with variables such as travel distance, egress distance, time of day, and availability of countdown timers at the stop. Potential biases of the inference sample and possible applications in service planning are discussed.

Previous research has combined automated fare-collection (AFC) and automated vehicle-location (AVL) data to infer the times and locations of passenger origins, interchanges (transfers), and destinations on multimodal transit networks. The resultant origin-interchange-destination flows (and the origin-destination (OD) matrices that comprise those flows), however, represent only a sample of total ridership, as they contain only those journeys made using the AFC payment method that have been successfully recorded or inferred. This paper presents a method for scaling passenger-journey flows (i.e., linked-trip flows) using additional information from passenger counts at each station gate and bus farebox, thereby estimating the flows of non-AFC passengers and of AFC passengers whose journeys were not successfully inferred. The proposed method is applied to a hypothetical test network and to AFC and AVL data from London's multimodal public transit network. Because London requires AFC transactions upon both entry and exit for rail trips, a rail-only OD matrix is extracted from the estimated multi modal linked-trip flows, and is compared to a rail OD matrix generated using the iterative proportional fitting method.

Innovative analyses of origin–destination (OD) data derived from automatic fare collection and automatic vehicle location systems in public transport networks enable planners to gain new insights into how passengers travel in the network and the quality of service provided, and can even inform decisions about network improvements. Particularly in large, complex networks, systematic, data-driven approaches to network evaluation and planning are essential. New methodologies are needed to transform OD data into informative metrics and planning recommendations. This paper proposes a framework for this process and applies it to London’s public transport network. Though there are many ways to improve public transport networks, this paper focuses on the addition of new bus routes to reduce circuity. The proposed framework includes three steps that combine OD-level analysis with spatial aggregation methodologies for the identification of corridors for new bus services. First, bus stops and rail stations were clustered into geographic zones. Second, a subset of zonal OD pairs with circuitous service were identified as candidates for improvement through new bus routes, based on performance standards established with user-defined parameters. Third, an algorithm that clusters OD pairs into corridors was applied to identify promising corridors for new bus services. This paper discusses corridors identified for new services in the London case study.

High-frequency transit systems are essential for the socioeconomic and environmental well-being of large and dense cities. The planning and control of their operations are important determinants of service quality. Although headway and optimization-based control strategies generally outperform schedule-adherence strategies, high-frequency operations are mostly planned with schedules, in part because operators must observe resource constraints (neglected by most control strategies) while planning and delivering service. This research develops a schedule-free paradigm for high-frequency transit operations, in which trip sequences and departure times are optimized in real-time, employing stop-skipping strategies and utilizing real-time information to maximize service quality while satisfying operator resource constraints. Following a discussion of possible methodological approaches, a simple methodology is applied to operate a simulated transit service without schedules. Results demonstrate the feasibility of the new paradigm.

With subway systems around the world experiencing increasing demand, measures such as passengers left behind are becoming increasingly important. This paper proposes a methodology for inferring the probability distribution of the number of times a passenger is left behind at stations in congested metro systems using automated data. Maximum likelihood estimation (MLE) and Bayesian inference methods are used to estimate the left behind probability mass function (LBPMF) for a given station and time period. The model is applied using actual and synthetic data. The results show that the model is able to estimate the probability of being left behind fairly accurately.

Planners must understand how public transportation systems are used in order to make strategic decisions. Smart card transaction data provides vast, detailed records of network usage. Combined with other automatically collected data sources, established inference methodologies can convert smart card transactions into complete linked journeys made by individuals within the public transport network. However, for large, multi-modal public transport networks it can be challenging to summarize the journey records meaningfully. This paper develops a method for categorizing origin–destination (OD) pairs by public transport mode or combination of used modes. By aggregating across OD pairs, this categorization scheme summarizes the multi-modal aspects of public transport network usage. The methodology can also be applied to subsets of data filtered by time of day or geography. The categorization results can inform performance analysis of OD pairs, allowing planners to make comparisons between pairs served by different combinations of modes. London Oyster card data is analyzed to illustrate how the OD pair categorization can characterize a network, allowing planners to quickly assess the roles of different modes, and perform OD pair analysis in a multi-modal public transport network.

The growing adoption of automated data collection systems in the transit industry, such as automated fare-collection (AFC) and automated vehicle location (AVL), is providing operators with extensive data about the state of the system and its usage by passengers. The paper proposes a framework for using automated data to support the various functions, both planning and real time, and demonstrates its use with two examples of recent developments: a) using AFC and AVL data from metro systems with entry and exit transactions to assign passengers to the itinerary they actually used on a particular day; and b) a predictive decision support platform with real time prediction of passenger demand in terms of station arrivals and OD flows, and its use for platform and train crowding prediction.

The paper presents a methodology for assigning passengers to individual trains using: (i) fare transaction records from Automatic Fare Collection (AFC) systems and (ii) Automatic Vehicle Location (AVL) data from train tracking systems. The proposed Passenger-to-Train Assignment Model (PTAM) is probabilistic and links each fare transaction to a set of feasible train itineraries. The method estimates the probability of the passenger boarding each feasible train, and the probability distribution of the number of trains a passenger is unable to board due to capacity constraints. The access/egress time distributions are important inputs to the model. The paper also suggests a maximum likelihood approach to estimate these distributions from AFC and AVL data. The methodology is applied in a case study with data from a major, congested, subway system during peak hours. Based on actual AFC and train tracking data, synthetic data was generated to validate the model. The results, both in terms of the trains passengers are assigned to and train loads, are similar to the "true" observations from the synthetic data. The probability of a passenger being left behind (due to capacity constraints) in the actual system is also estimated by time of day and compared with survey data collected by the agency at the same station. The left behind probabilities can be accurately estimated from the assignment results. Furthermore, it is shown that the PTAM output can also be used to estimate crowding metrics at transfer stations. (C) 2017 Elsevier Ltd. All rights reserved.

Monitoring the service quality of high-frequency bus transit is important both to agencies running their own operations and those contracting out, where performance measures can be used to assess contract penalties or bonuses. The availability of automatically collected vehicle movement and demand data enables detecting changes in running times and demand, which may present opportunities to improve service quality and fleet utilization. This research develops a framework to maximize service performance in a set of high-frequency bus routes, given their planned headways and a total fleet size constraint. Using automatically collected data and simulation modelling to evaluate the performance of each route with varying fleet sizes, a greedy algorithm adjusts allocation toward optimality. A simplified case study involving morning peak service on nine bus routes in Boston demonstrates the feasibility and potential benefits of the approach. A potential application is automated detection of routes operating with insufficient or excessive resources.

Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at a lower operating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. We propose a new formulation that allows a minimal frequency restriction on any line that is operated and minimizes passenger inconvenience cost, which includes transfers and frequency-dependent waiting time costs. This model is applied to a shuttle design problem based on a real-world case study of the Massachusetts Bay Transportation Authority network of Boston, Massachusetts. The results show that additional shuttle routes can reduce passenger delay compared to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations.

Access distance to public transport is an important metric for planning, modeling, and evaluating public transport networks and is often used in policy goals and statements. However, accurately measuring access (and egress) distance can be difficult. Estimates often rely either on aggregate inferences based on census data or on small samples of disaggregate data from travel diary surveys. When smart cards used for fare payment are also registered with home address information, they represent a new data source that can be used to infer access distances for a large sample of users, at a disaggregate level and at low cost, compared with travel diary surveys. This paper demonstrates the inference of access distance from smart card fare and transaction data for a large sample of London public transport journeys and compares the inferred access distributions to data from the London Travel Demand Survey, a travel diary survey. Possible instances of false inferences are considered and measures to eliminate false inferences are discussed. This access distance inference methodology allows for the analysis of variation in access distance across the network, and examples of this type of analysis are presented.

Transit operations are subject to disruptions caused by events. Some events cause unpredictable disruptions (e.g., signal failures in rail transit and traffic accidents along a bus route operating in mixed traffic). Other events, such as concerts or sport contests, cause foreseen disruptions. In some cases, dynamic functions of running times and demand can be used to predict and model disruptions. Past research has explored the use of operations control to respond to disruptions after service deteriorates, assuming constant running times and demand. The proposed framework enables anticipatory control strategies by dynamically modeling expected changes in running times and demand during events. A holding optimization model formulated to capture dynamics is applied to a simulated transit system experiencing disruptions induced by an unforeseen event and a foreseen event. Controlling operations with an awareness of events has little effect in the unforeseen event case but significantly improves performance in the foreseen event case. Erroneous estimates of the time an event will occur can lead to counterproductive control policies.

Bus bunching affects transit operations by increasing passenger waiting time and variability. To tackle this phenomenon, a wide range of control strategies has been proposed. However, none of them have considered station and interstation control together. In this study station and interstation control were tackled to determine the optimal vehicle control strategy for various stops and traffic lights in a single service transit corridor. The strategy minimized the total time that users must devote to making a trip, taking into account delays for transit and general traffic users. Based on a high-frequency, capacity-constrained, and unscheduled service (no timetable) for which real-time information about bus position (GPS) and bus load (automated passenger counter) is available, this study focused on strategies for traffic signal priority in the form of green extension considered together with holding buses at stops and limiting passenger boarding at stops. The decisions on transit signal priority were made according to a rolling horizon scheme in which effects over the whole corridor were considered in every single decision. The proposed strategy was evaluated in a simulated environment under different operational conditions. Results showed that the proposed control strategy achieves reductions in the excess delay for transit users close to 61.4% compared with no control, while general traffic increases only by 1.5%.

This paper explores ridership increases in response to incrementally upgraded bus services in U.S. and Canadian cities. Current guidelines for developing bus rapid transit (BRT) corridors reveal a tension between comprehensive implementation of full-fledged corridors on the one hand and incremental, flexible development on the other. A review of the literature discusses this tension, various BRT elements, and the impact of these elements on performance and ridership. A methodology for comparing high-productivity bus corridors in different contexts using general transit feed specification (GTFS) data and a spatial database framework is described. Longitudinal and cross-sectional sketch models, with corridors as the unit of analysis, offer some insights into the relative impact of BRT features and external factors. Current data limitations allow for suggestive, if not definitive, results. Dedicated lanes and signal priority were positively correlated with increased ridership in some models tested, even when decreased travel time was controlled for and, suggesting that they may have had important perception and reliability benefits beyond improved speeds. While BRT can be a promising mode for a range of contexts, this analysis suggests that service frequency and reliability improvements are the common foundation for successful projects. Building political momentum for sustained improvements in bus networks is a challenge; the use of emerging data sources, such as GTFS, to compare incremental BRT projects allows for a better understanding of projects that can help meet this challenge.