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Improving Transit Service Quality and Headway Regularity with Real-Time Control
Abstract and Figures
Disrupted transit operations are often caused by stochastic variations of passenger demand at stations and traffic conditions on service routes, which increase passenger wait times and thus discourage passengers from using the transit system. Efficient, real-time operational control is desirable to maintain headway regularity and reduce the negative effects of service disturbance. A real-time headway control model is proposed to maintain desired headways for pairs of successive vehicles by minimizing total headway variance for all stations in an advanced public transportation system environment, such as an automatic train control system and an automatic vehicle location system. A vehicle's departure time can be adjusted on the basis of its optimal arrival time at the next station, while considering the maximum attainable operating speeds and the headways to its leading and following vehicles. The proposed real-time control model is tested by simulating a high-frequency light rail transit route in Newark, New Jersey. The simulation results demonstrate that the average passenger wait time is significantly reduced after applying the control model.
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... Recently, there has been a growing importance placed on guiding cities towards low-carbon, energy-saving, and sustainable development through meticulous rail transit planning [17][18][19]. Experience of leading countries and regional countries with relatively close conditions shows that these have all developed and are planning to build a multi-purpose urban transportation system with types such as free bicycle loans, buses, bus In addition, the Medium Capacity Rail System (MCS) system has been introduced with outstanding advantages that can meet the traffic capacity range that bus rapid transit -BRT (from 5,400 passengers per hour directionp/h/d to 10,800 (p/h/d) or LRT (from 18,000 p/h/d to 24,000 p/h/d) cannot meet the requirement or MRT (from 18,000 p/h/d to larger) with too expensive construction costs [23][24][25][26]. In this study, MCS is the proposed solution in the case study on the PVD route with a surveyed traffic flow of 29,595 motorcycles during peak hours in direction [8][9][10]13]. ...
This paper delves into the study and proposition of a Medium Capacity Rail System (MCS) model to tackle urban traffic issues, both in the immediate future and, more specifically, the public transportation challenges along the Pham Van Dong (PVD) route. Drawing from statistical surveys and an in-depth analysis of motorcycle traffic patterns at intersections during near-peak and peak hours in the morning and afternoon along PVD's main artery in the northeastern part of Ho Chi Minh City (HCMC), Vietnam. Moreover, this research explores the relatively new concept of MCS, which have gained traction in recent years due to their distinct advantages over existing systems. The findings in this article underscore the practicality of the proposal and provide a blueprint for sustainable, integrated urban transport development, improve traffic flow, reduce congestion, reduce environmental pollution and enhance a complete and modern urban railway network for Ho Chi Minh City or other similar global scenarios.
... Abkowitz et al. [5] addressed a headway control strategy that can be implemented on high-frequency routes, and developed an algorithm for solving the headway variation model, while Ning et al. [6] developed an integrated real-time control method for optimising the train headway by adjusting the station arrival times of the trains, effectively reducing the average passenger waiting time and energy consumption. Ding et al. [7] proposed a real-time headway control model that maintains headway regularity by minimising the total headway variance, the simulation results demonstrated that the average passenger waiting time was significantly reduced by this control model. Zhao et al. [8] presented a dynamic headway system for positive train control based on active communications, which can be integrated in a dynamic dispatching model to improve track capacity and reduce the total travel time. ...
This paper proposes an optimisation model for an urban rail transit line timetable considering headway coordination between the mainline and the depot during the transition period. The model accounts for the tracking operation scenario of trains inserted from the depot onto the mainline and related train operation constraints. The optimisation objectives are the number of trains inserted, maximum train capacity rate and average headway deviation. Second-generation non-dominated sorting genetic algorithm is designed to solve the model. A case study shows that optimisation achieves a total of 25 trains inserted, a maximum train capacity rate of 0.975 and an average headway deviation of 9.5 s, resulting in significant improvements in train operations and passenger satisfaction. Compared with the current train timetable before optimisation, the average dwell time and the maximum train capacity rate at various stations have been reduced after optimisation. The proposed model and approach can be used for train timetabling optimisation and managing the operations of urban rail transit lines.
... Recently, there has been a growing importance placed on guiding cities towards low-carbon, energy-saving, and sustainable development through meticulous rail transit planning [17][18][19]. Experience of leading countries and regional countries with relatively close conditions shows that these have all developed and are planning to build a multi-purpose urban transportation system with types such as free bicycle loans, buses, bus In addition, the Medium Capacity Rail System (MCS) system has been introduced with outstanding advantages that can meet the traffic capacity range that bus rapid transit -BRT (from 5,400 passengers per hour directionp/h/d to 10,800 (p/h/d) or LRT (from 18,000 p/h/d to 24,000 p/h/d) cannot meet the requirement or MRT (from 18,000 p/h/d to larger) with too expensive construction costs [23][24][25][26]. In this study, MCS is the proposed solution in the case study on the PVD route with a surveyed traffic flow of 29,595 motorcycles during peak hours in direction [8][9][10]13]. ...
This paper delves into the study and proposition of a Medium Capacity Rail System (MCS) model to tackle urban traffic issues, both in the immediate future and, more specifically, the public transportation challenges along the Pham Van Dong (PVD) route. Drawing from statistical surveys and an in-depth analysis of motorcycle traffic patterns at intersections during near-peak and peak hours in the morning and afternoon along PVD’s main artery in the northeastern part of Ho Chi Minh City (HCMC), Vietnam. Moreover, this research explores the relatively new concept of MCS, which have gained traction in recent years due to their distinct advantages over existing systems. The findings in this article underscore the practicality of the proposal and provide a blueprint for sustainable, integrated urban transport development, improve traffic flow, reduce congestion, reduce environmental pollution and enhance a complete and modern urban railway network for Ho Chi Minh City or other similar global scenarios.
... There have been plenty of studies on control strategies to maintain the preplanned service quality of transit services. One of the most common strategies is holding control to maintain the reliability of headways on transit services (Osuna and Newell 1972;Turnquist 1981;Abkowitz and Lepofsky 1990;Wirasinghe and Liu 1995;Ding and Chien 2001;Zhao et al. 2003;Nesheli and Ceder 2014). In an inclusive analytical investigation of vehicle holding strategy, Hickman (2001) presented a stochastic holding model at a given control station. ...
The time required to transfer between different transit lines is a critical component of passenger travel time. Although transit agencies attempt to design well-coordinated timetables among intersecting lines at the scheduling stage, an operational control method is necessary to maintain the coordinated transfers that may occasionally be disrupted due to unexpected delays of public transit (PT) vehicles. One possible and practical approach is Connection Protection (CP), which involves holding a transit unit in order to wait for another transit unit that is planned to provide a coordinated transfer but has been delayed. This study develops a CP model to apply holding control to a receiving vehicle trip for the purpose of protecting the scheduled connection against delay of a feeder trip. The study incorporates the probabilistic nature of transit operations in formulating the cost function of the model, and accordingly makes more robust decisions for controlling the PT system. The developed model is evaluated by means of a sensitivity analysis. The results show that the model improves transfer efficiency and reduces the waiting times of affected passengers while minimizing delays to other passengers.
Providing real-time crowding information in urban railways would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial because of the unavailability of ground-truth crowding data, particularly for the direct impact of crowding on passengers (e.g., denied boarding on platforms). This paper proposes a data-driven method for real-time denied boarding prediction in urban railway systems using automated fare collection (AFC) and automated vehicle location (AVL) data. It predicts the denied boarding probability distribution or its derived metrics as a function of explanatory variables, including demand, operations, and incident-related factors. The method is validated through a case study covering 18 months on Hong Kong Mass Transit Railways. The results highlight the model’s accurate and robust performance in predicting denied boarding on platforms using purely AFC and AVL data (e.g., an average of 6%–7% error) under both recurrent and non-recurrent situations, in which transfer demand-related factors contribute most to the prediction. The prediction model outputs can support proactive operations control and management, as well as provision of customer information without relying on expensive and time-consuming data collection efforts. Customer information, for example, can include the average number of trains to wait before boarding or the average waiting time on the platform.
The stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes.
The evolution of headways (time intervals between consecutive vehicles) is an important performance indicator of public transportation systems. In this paper, we develop a data processing system that processes the expected arrival time data provided by the corresponding APIs of Madrid and London bus public transport systems with the aim of modeling and supervising their evolution along the time to detect their operational anomalies. By employing only estimates of the time headways between buses, we construct joint statistical modeling along the time of these headways within a stochastic process framework. Then, this model is employed to design a deviation detection procedure that provides a simple and adjustable anomaly detection scheme, as well as a complementary index for assessing the overall Quality of Service (QoS) provided by the operator. The proposed adjustable system can be easily tuned by managers or operators for online anomaly detection, which is an important asset to increase the efficiency of bus transportation services.
Urban rail transit (URT) develops rapidly in modern cities, and its energy efficiency attracts extensive attention. The utilization of regenerative energy (URE) is an important method for energy-efficient operation of URT. Regenerative braking is an energy recovery mechanism that slows down a moving train by converting its kinetic energy into electric energy. The electric energy can be utilized for other trains to accelerate in a cooperative way. To take full advantage of the regenerative energy, an energy calculation method which considers regenerative braking power to optimize the timetable is proposed in this paper. First, four operating modes of URE are defined and an integer programming model is formulated. Second, a branch and bound algorithm is designed to solve the optimal timetable in different scenarios. Third, the model is evaluated based on the operation data from the Yanfang Line, Beijing Metro, China. For peak hours, the results illustrate that the proposed method can significantly improve URE by 73.7% compared with the original timetable. Also, URE can be improved by 46.3% for off-peak hours. Finally, the comparison between the proposed method and the method based on the kinetic energy theorem is given. The simulation results illustrate that the proposed method could increase URE by 29.7% and 9.9% for peak and off-peak hours scenarios, respectively, in comparison with the method based on the kinetic energy theorem.
An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process.
Evaluating bus transit performance periodically is a key step to improve service quality and system efficiency. An extenics-based model is developed with the real-world data. The performance indices employed for the evaluation are identified. The dependent function applied for measuring the correlation between the bus transit and the system performance is formulated. The hybrid weights associated with the indices are determined by subjective weights through analytic hierarchy process (AHP) and objective weights through the entropy method. The proposed extenics model is applied for evaluating a bus transit system of a medium-sized city in China. The model outcomes are informative, while the suggestions corresponding to the identified weakness are concluded for future planning and operation.
A simulation model is developed of a single-direction bus route, including explicit simulation of traffic signals. Using a hypothetical situation, several strategies are tested for the control of bus headways in real-time. These are: holding points, skipping stops, selective application of bus priority signalization, and reducing dispatch uncertainty.
This report documents work performed under FTA`s Advanced Public Transportation Systems (APTS) Program, a program structured to undertake research and development of innovative applications of advanced navigation, information, and communication technologies that most benefit public transportation. This report is the latest in a series of State-of-the-Art reports, the last of which was published in January 1994. It contains the results of an investigation of the extent of adoption of advanced technology in the provision of public transportation service in North America. It focused on some of the most innovative or comprehensive implementations, categorized under four types of services/technologies: Fleet Management, Traveler Information, Electronic Fare Payment, and Transportation Demand Management.
Four major classes of strategies for improving reliability of bus transit service are analyzed: vehicle-holding strategies, reduction of the number of stops made by each bus, signal preemption, and provision of exclusive right-of-way.
This paper describes simple tests that can be used to identify situations for which control is potentially effective. This analysis provides transit operators with a simple screening model to evaluate potential effectiveness of controls.
The problem of determining the optimal strategy (dispatch or hold) for a system of m vehicles is formulated as a dynamic programming problem. It is analyzed in detail for m equals 1 and m equals 2. For m equals 1, the optimal strategy will hold a vehicle if it returns within less than about half the mean trip time. For m equals 2, and for a small coefficient of variation of trip time C(T), the optimal strategy will control the vehicles so as to retain nearly equally spaced dispatch times,% within a range of time proportional to C//4///3(T).
The corridor simulator (CORSIM)-based microscopic program, which can simulate realistic bus operations, has been enhanced with some newly developed features; these are validated. Transit operations-related data for New Jersey Transit Bus Route 39 were collected to assess the reliability of the enhanced CORSIM program. On the basis of the mean average percentage error and the root mean square error, this study demonstrated that simulation output can represent realistic bus operations, such as the disruptions of transit headways caused by ridership fluctuations at bus stops and delays at intersections. The proposed program provides a potential basis for evaluating advanced transit management and control strategies and real-time transit traveler information systems in the advent of advanced public transportation systems.
Headway control strategies have been proposed as methods for correcting transit service irregularities and thereby reducing passenger wait times at stops. This paper addresses a particular strategy which can be implemented on high frequency routes (headways under 10–12 minutes), in which buses are held at a control stop to a threshold headway. An algorithm is developed which yields the optimal control stop location and optimal threshold headway with respect to a system wait function. The specification of the wait function is based on the development of several empirical models, including a headway variation model and an average delay time model at control stops.
A conclusion is reached that the headway variation does not increase linearly along a route, a common assumption made in many previous studies. Furthermore, the location of the optimal control stop and threshold value are sensitive to the passenger boarding profile, as expected. The algorithm itself appears to have practical application to conventional transit operations.
In high frequency transit operations, randomness and incidents often result in highly irregular headways which can significantly decrease service quality. Deadheading is one commonly used real-time operations control strategy that can improve service quality in such situations. When a vehicle is deadheaded, it runs empty from a terminal skipping a number of stations, typically in order to reduce expected large headways at later stations. The real-time deadheading problem is to determine at dispatching time which vehicles to deadhead and how many stations to skip in order to minimize the total passenger cost in the system. This paper formulates this problem, optimally solves a simplified version of the general formulation, and demonstrates that the solutions of the simpler problem are good approximations to the solutions of the more general problem.