Simulation Evaluation of Route-Based Control of Bus Operations

Chandrasekar, P.; Cheu, Ruey Long; Chin, Hoong Chor

doi:10.1061/(asce)0733-947x(2002)128:6(519)

Cited by 40 publications

(19 citation statements)

References 7 publications

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“…The impacts of design and operational measures are either analyzed by conducting a beforeafter comparison of public transport performance indicators or by simulating public transport operations and investigating the expected effects. Simulation studies were often used to study the effects of real-time control strategies such as public transport signal priority (Chandrasekar et al 2002), stop skipping (Sun and Hickman, 2005), holding (Cats et al 2011) and short-turning (Tirachini et al, 2011). Performance indicators such as headway variability, passenger waiting times and on-board delays were compared for alternative set-ups and control strategy design based on simplified line representation.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the impacts and benefits of public transport design and operational measures

Oshyani

Cats

2016

Transport Policy

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Design and operational measures are designed and implemented to improve public transport performance and level-of-service. In the case of urban bus systems, priority, operational and control measures are aimed to elevate bus services to buses with high level of service (BHLS). Even though there is an explosive growth in design and operational measures implementation and growing research interest in investigating their impact on performance indicators, there is lack of a systematic evaluation of their benefits. We present an evaluation framework and a detail sequence of steps for quantifying the impacts of public transport design and operational measures. The effects of service performance on travel times and costs are assessed by accounting for relations between reliability and waiting times, crowding and perceived travel times, and vehicle scheduling and operational costs. The evaluation integrates the implications of reliability on generalized passenger travel costs and operational costs. We deploy the proposed evaluation framework to a field experiment in Stockholm where a series of measures were implemented on the busiest bus line. The results suggest that the total passenger and operator benefits amount to 36.8 million Swedish crowns on an annual basis. The overall assessment of the impacts of design and operational measures enables the comparison of different implementations, assess their effectiveness, prioritize alternative measures and provide a sound basis for motivating investments.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating the impacts and benefits of public transport design and operational measures

Oshyani

Cats

2016

Transport Policy

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“…At present, there are two kinds of transit priority control strategies at signalized intersections: (I) signal control-the signal phase responds to bus priority request; (II) speed control-buses actively adapt to signal phase, which is mainly realized by optimizing the speed guidance. Compared with the signal control approach, speed control is a control strategy that has less impact on passengers and is easier to accept [1]. It can also reduce stops, fuel consumption, and emissions [2,3].…”

Section: Introductionmentioning

confidence: 99%

Two-Way Cooperative Priority Control of Bus Transit with Stop Capacity Constraint

et al. 2020

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Signal priority control and speed guidance are effective ways to reduce the delay of buses at intersections. Previous work generally focused on the optimization strategy at the intersection area, without simultaneously considering the influence on adjacent downstream bus stops. This probably leads to the size of the passed bus platoon exceeding the capacity of berths and queuing, which in turn causes additional delay to the overall bus travel time. Focusing on this problem, this paper proposes a two-way cooperative control strategy that constrains the size of the upstream platoon. Besides this, to avoid bus bunching, no more than two buses from the same route can be admitted in the same platoon. Based on these principles, we modeled how to make buses pass without stopping by simultaneously considering the signal control and speed guidance. Finally, the effectiveness was validated by simulation in Verkehr in Städten Simulation (VISSIM, German for “Traffic in cities—simulation”), a microscopic traffic simulator. The results show that compared to the existing methods, which only use signal control, the cooperative strategy reduces the total delay at the intersection and the downstream stop. It alleviates the queuing phenomenon at the downstream bus stop greatly, and the bus arrivals tend to be more uniform, which helps improve the reliability and sustainability of bus services.

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“…The microscopic models are the most used for public transport simulation. These models are included in the simulation of the operations at public transport stops [14,15], the public transport system's reliability [16,17], bus priority methods [18,19], exclusive bus lanes [20,21], bus routes [22], transfer organization of public transportation terminals [23], multimodal systems [24], and traffic networks [25].…”

Section: Introductionmentioning

confidence: 99%

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

2019

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There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The processing times for these type of simulations increase in an unmanageable way since all the relevant variables that are required to simulate consistently and reliably the system behavior must be included. In this paper, we present a new simulation model for public transport routes’ simulation called Masivo. It runs the public transport stops’ operations in OpenCL work items concurrently, using a multi-core high performance platform. The performance results of Masivo show a speed-up factor of 10.2 compared with the simulator model running with one compute unit and a speed-up factor of 278 times faster than the validation simulator. The real-time factor achieved was 3050 times faster than the 10 h simulated duration, for a public transport system of 300 stops, 2400 buses, and 456,997 passengers.

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Simulation Evaluation of Route-Based Control of Bus Operations

Cited by 40 publications

References 7 publications

Evaluating the impacts and benefits of public transport design and operational measures

Evaluating the impacts and benefits of public transport design and operational measures

Two-Way Cooperative Priority Control of Bus Transit with Stop Capacity Constraint

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

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