Comparing demand responsive and conventional public transport in a low demand context

Kashani, Zahra Navidi; Ronald, Nicole; Winter, Stephan

doi:10.1109/percomw.2016.7457089

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Tese fully fexible systems typically run using smaller vehicles, and routes are created according to passengers' requests in a ridesharing format, with fxed or variable fares according to the travelled distance and service availability. Some papers compare these fully fexible systems with other systems or traditional bus systems using simulations [10][11][12]. Other papers focus on developing guidelines for the operation and classifcation of these systems [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of a Semiflexible Demand-Responsive Feeder System in Suburban Areas Using a Memetic Algorithm

Vieira

Sörensen

Vansteenwegen

2023

Journal of Advanced Transportation

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Traditional bus operations in suburban areas are inefficient due to their fixed routes and timetables. Since suburban operations deal with low demand spread in a large area, the service stays underused for most off-peak hours. In order to render the operation profitable and increase the number of passengers on each bus, operators reduce the frequency of the service, which results in an increase of passenger waiting time for the service. As a solution to this problem, this paper introduces a demand-responsive public bus system that aims to adjust routes and timetables of a semiflexible system to the demand for transportation. The operation still offers a reliable service like the traditional system but aims to reduce the passenger travel time. A memetic algorithm is developed to optimize this demand-responsive system. For a network with 25 bus stops served hourly by three lines and with an average demand of 20 requests per hour, the memetic algorithm is demonstrated to reduce the passenger waiting time with almost 50% in comparison with a traditional system operating in the same network with fixed routes and timetable.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of a Semiflexible Demand-Responsive Feeder System in Suburban Areas Using a Memetic Algorithm

Vieira

Sörensen

Vansteenwegen

2023

Journal of Advanced Transportation

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“…In regions with low demand, demandresponsive service performs well than the conventional transit operations. Kashani et al (2016) simulate the conventional public transport (CPT) system and the demand responsive transport (DRT) system, in a grid network to compare the user performance and operator cost considering various demand levels. The objective is to determine the threshold between the operation types and to analyze the service quality.…”

Section: Integration and Coordination Of Transit Operationsmentioning

confidence: 99%

A Planning Model for Flexible-Route Bus Operations with Financial Constraints

Han

Kim

2022

KSCE J Civ Eng

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In suburban areas where the demand is not high enough, the fixed-route buses usually operate with infrequent services, which increase the cost of waiting transit services for fixedroute service users. For those low demand regions, flexible-route bus operations with doorstep services, which operate with flexible routes, flexible schedules, and smaller vehicles, can be a feasible alternative. This study formulates the social welfare for flexible-route bus operations under various financial constraints, seeking to jointly optimize the headway, fare, and zone size. Elastic demand functions are constructed as a part of problem formulations. Unlike many previous studies, this study does not presume the shape of the zone, so that the flexible-route bus services can be designed according to the geographical conditions in the regions. Moreover, the amount of subsidy for financially constrained policy is calculated based on the actual demand although many previous studies used the potential demand to calculate the subsidy amount. Numerical analyses are explored to evaluate and compare system-wide welfares for different financial perspectives, i.e., no financial constraint, break-even policy, and subsidized policy. The solution optimality for constrained nonlinear optimization problems is verified using analytical optimization or the Karush-Kuhn-Tucker (KKT) conditions. It is found that the welfare of subsidized transit operations is located between the welfare amounts of unconstrained and break-even cases; this finding of welfare variations over subsidy amounts can be a helpful resource for transportation mobility analysts in determining the appropriate service quality and the corresponding amount of subsidy. Sensitivity analyses are presented to explore the system-wide welfare with respect to critical input parameters, including a comprehensive analysis of the unit cost of bus operations, which offers implications toward Mobility-as-a-Service (MaaS) planning.

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“…( 1) of comfort perception, so the algorithm implement-ed while assigning request to any vehicle consider the load on it and try to distribute it by applying criteria mentioned in section 5. [1,6,7,22] have put their focus on solving the BFM problem by having a Dynamic Routing algorithm of public transport vehicles. The major emphasis was put on routing the vehicle as per the passenger flow request which could be taken into the system by various ways.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Parivahan: Passenger Demand Triggered Bus (Vahan) Routing in Intelligent Public Transport System

Ladhai¹,

Chaubey²

2022

INDJCSE

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Daily commuters using the Public Transport System (PTS) have a different experience in ComfortPerception (CP) depending on the time of day. The pick-time CP value is not good, compared to rest time. This problem raised in PTS is due to the fixed-route strategy. The issue here is addressed by implementing demand based dynamic routing approach using the SUMO tool noting service time, request allocated per vehicle, average waiting time, and average travel time over variously categorized network topology. It was observed that dynamic routing reduces the travel time of passengers from 5.3mins to under 5mins. This reduction here is achieved with a little overhead of computational time as low as 14.37ms/request. The result shows that the ratio of average waiting time to average travel time is up to 0.5 for smaller network and decreases with an increase in network size and well connected-network.

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Comparing demand responsive and conventional public transport in a low demand context

Cited by 12 publications

References 16 publications

Optimization of a Semiflexible Demand-Responsive Feeder System in Suburban Areas Using a Memetic Algorithm

Optimization of a Semiflexible Demand-Responsive Feeder System in Suburban Areas Using a Memetic Algorithm

A Planning Model for Flexible-Route Bus Operations with Financial Constraints

Parivahan: Passenger Demand Triggered Bus (Vahan) Routing in Intelligent Public Transport System

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