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Bus bunching

About: Bus bunching is a research topic. Over the lifetime, 228 publications have been published within this topic receiving 6846 citations. The topic is also known as: clumping & convoying.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors present a comprehensive review of the literature on transit network planning problems and real-time control strategies suitable for bus transport systems, emphasizing recent studies as well as works not addressed in previous reviews.
Abstract: The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

476 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of determining the optimal strategy (dispatch or hold) for a system of m vehicles is formulated as a dynamic programming problem and analyzed in detail for m = 1 and m = 2.
Abstract: Vehicles load passengers at a single service point and, after traversing some route, return for another trip. The travel times of successive trips are independent identically distributed random variables with a known distribution function. After a vehicle returns to the service point, one has the option of holding it, or dispatching it immediately. Passengers arrive at a uniform rate and the objective is to minimize the average wait per passenger. 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 = 1 and m = 2. For m = 1, the optimal strategy will hold a vehicle if it returns within less than about half the mean trip time. For m = 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 C4/3(T).

410 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose an adaptive control scheme to mitigate the problem of short headway on busy lines by dynamically determining bus holding times at a route's control points based on real-time headway information.
Abstract: Bus schedules cannot be easily maintained on busy lines with short headways: experience shows that buses offering this type of service usually arrive irregularly at their stops, often in bunches. Although transit agencies build slack into their schedules to alleviate this problem - if necessary holding buses at control points to stay on schedule - their attempts often fail because practical amounts of slack cannot prevent large localized disruptions from spreading system-wide. This paper systematically analyzes an adaptive control scheme to mitigate this problem. The proposed scheme dynamically determines bus holding times at a route's control points based on real-time headway information. The method requires less slack than the conventional, schedule-based approach to produce headways within a given tolerance. This allows buses to travel faster than with the conventional approach, reducing in-vehicle passenger delay and increasing bus productivity.

383 citations

Journal ArticleDOI
TL;DR: This paper formulates the holding problem as a deterministic quadratic program in a rolling horizon scheme, and develops an efficient solution algorithm to solve it, and tests the algorithm and the impact of the resulting holding policies.
Abstract: Holding is one of the most commonly used real-time control strategies in transit operations. Given a transit network and its operations plan, the holding problem is to decide at a given time at a control station, which vehicle is to be held and for how long, such that the total passenger cost along the route is minimized over a time period. Previous research on the holding problem has always assumed no real-time information available. Such an assumption not only poses great difficulties in solving the problem, but also limits practical applications in a real-time, dynamic operations environment. In this paper we formulate the holding problem as a deterministic quadratic program in a rolling horizon scheme, and develop an efficient solution algorithm to solve it. Using headway data collected by an automated system, we tested the algorithm and evaluated the impact of the resulting holding policies. Important and interesting properties of the holding solution, obtained from both theoretical and computational analyses, are presented.

277 citations

Journal ArticleDOI
TL;DR: This paper proposes an adaptive control scheme that adjusts a bus cruising speed in real-time based on both, its front and rear spacings much as if successive bus pairs were connected by springs, and is shown to yield regular headways with faster bus travel than existing control methods.
Abstract: Schedule-based or headway-based control schemes to reduce bus bunching are not resilient because they cannot prevent buses from losing ground to the buses they follow when disruptions increase the gaps separating them beyond a critical value. This critical gap problem can be avoided, however, if buses at the leading end of such gaps are given information to cooperate with the ones behind by slowing down. This paper builds on this idea. It proposes an adaptive control scheme that adjusts a bus cruising speed in real-time based on both its front and rear spacings, much as if successive bus pairs were connected by springs. The scheme is shown to yield regular headways with faster bus travel than existing control methods. Its simple and decentralized logic automatically compensates for traffic disruptions and inaccurate bus driver actions. Its hardware and data requirements are minimal.

265 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202123
202028
201923
201823
201712
201622