Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

Frei, Charlotte; Hyland, Michael; Mahmassani, Hani S.

doi:10.1016/j.trc.2016.12.017

Cited by 120 publications

(64 citation statements)

References 19 publications

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“…When < , there will be idle time at the downstream checkpoint. For this condition, the expected walking time is equal to the result from (8). The expected waiting of the type I, type II, and flag request type III passengers can be derived by (9).…”

Section: At the Expected Demand Levelmentioning

confidence: 99%

“…Most of these studies revealed that flexible transit services are promising operating policies for shaping new travel patterns in low-demand areas and passengers are generally willing to use these innovative transit systems [7,8]. However, how to choose the most appropriate policy in a certain service area, especially between route deviation policy and point deviation policy, remains a challenge for transit operators.…”

Section: Introductionmentioning

confidence: 99%

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A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services

Zheng

Qiu

2018

Journal of Advanced Transportation

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Flexible transit services, which bring together the characteristics of fixed-route transit and demand-responsive transit, have been proven to be cost-efficient in low-density residential areas. In this paper, a methodology is proposed to assist planners in making better decisions when choosing between route deviation policy and point deviation policy, which are two promising types of flexible transit services. A user cost function is developed to measure the service quality of the transit systems, and analytical models are constructed to compare the system performance under both expected and unexpected demand levels. Based on the experiments for various scenarios over a real-life transit example, the critical demands, which represent the switching point between the two competing service policies, have been derived. Our findings show that point deviation policy is more efficient at low-demand levels, while route deviation policy is a better choice at low-to-moderate demand levels. At unexpectedly high demand levels, route deviation policy is better able to accommodate rejected passengers than point deviation policy.

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Section: At the Expected Demand Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services

Zheng

Qiu

2018

Journal of Advanced Transportation

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“…Constraint (10) indicates that any demand can only be served by one bus. Constraint (11) indicates that a shuttle bus can only serve a passenger at a departure time of a target station. Constraint (12) guarantees all demand serviced equal to the known amount of reservation required.…”

Section: Model Representationmentioning

confidence: 99%

“…Alonso-Mora et al [10] focused on dynamic high-capacity carpooling and designed many-to-one, flexible bus demand response strategies based on vehicle-demand analysis. Motivated by emerging transportation technologies and business modes, researchers devoted their efforts to a wide range of innovative mobility studies of the "many-to-one" mode, such as flexing service schedules for demand-adaptive hybrid transit [11], flexible mobility on demand transportation systems [12,13], roundtrip car-sharing systems [14], and autonomous vehicle-sharing and reservation systems [15]. All the flexible bus connection modes involved in the above research studies are in the form of "many-to-one", that is, multiple flexible stations corresponding to one target station.…”

Section: Introductionmentioning

confidence: 99%

Flexible Bus Route Optimization for Multitarget Stations

Sun

Huang

Chen

et al. 2020

Mathematical Problems in Engineering

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is paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. e model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles' running time and the passengers' travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. e results show that the total number of vehicles needed to complete the service is 17-21, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passenger's dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.

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“…The mentioned uncertainties lead to increasing the variability of the travel time and diminishing service reliability. Some published transfer models consider uncertainties of travel demand: authors of the paper [8] propose demand-oriented train timetabling models aiming to decrease passenger waiting times, authors of [9] assess the demand for an adaptive transit service on the example of Chicago region, in the paper [10], its authors have developed a predictive control scheme for a hybrid model with actuation via bus speeds, which can regularize headways and improve bus service quality, etc.…”

Section: Introductionmentioning

confidence: 99%

Modeling demand for passenger transfers in the bounds of public transport network

Naumov¹

2018

EasyChair Preprints

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The passenger transfers optimization is one of the main directions to enhance the quality of public transport services. Modelling a demand for services of transfer nodes is quite a complex issue due to the stochastic nature of the transport process and the random nature of demand for trips of urban residents. However, this procedure is an essential stage for any problem related to optimization in transfer nodes, such as timetables scheduling for public transport lines, designing the public transport network or estimating the parameters of a given transfer node. The proposed model is based on the developed approach to perform the routing assignment stage in the classical four-stage urban planning procedure. Demand for trips is generated for each stop of a public transport system on the grounds of the stochastic variable of the time interval between passengers arrival to the respective stop. After defining of the destination stop, the route for the passenger’s trip is determined with the use of Dijkstra’s algorithm within the frame of a public transport network which is presented as a graph model with stops for vertices and route segments for edges. Transfer nodes are defined in the model as such graph vertices which are common for at least two lines of the public transport system. The author presents a class library implemented with the use of the Python programming language. On the basis of this library, the model for simulations of demand for transfers within the given public transport system was developed. The proposed approach to the demand modeling and the developed software were used for simulations of demand for transfers within the bounds of the public transport system of Bochnia (Poland). The brief results of these simulations are presented in the paper.

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Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

Cited by 120 publications

References 19 publications

A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services

A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services

Flexible Bus Route Optimization for Multitarget Stations

Modeling demand for passenger transfers in the bounds of public transport network

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