Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

Li, Peiqing; Jiang, Linshu; Zhang, Shunfeng; Xi, Jiang

doi:10.3390/su142013328

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Li et al introduce a flexible ODT and develope a public transit scheduling model to meet public transit travel needs. The results show that the flexible ODT service can reduce operating costs by 9.5% and running time by 9% [10]. Nourbakhsh and Ouyang compare the performance of flexible route transit and traditional transit for different demand levels and found that the former usually has the lowest system cost when the demands mild [11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…where ω is the unit time value of travelers before boarding the bus (Unit: RMB/h), including the walking time to the stop and the waiting time at the stop; T walk and T wait represent the walking time and waiting time for all travelers who board at both normal stops and on-demand stops (Unit: h); t walk oi and t walk os denote the walking time (Unit: h) from the origin o to the nearest normal stop i and to the nearest on-demand stop s, respectively, as shown in Equations ( 5) and ( 6); t wait idw and t wait sdw represent the average waiting time (Unit: h) of travelers at the normal stop i and the on-demand stop s, respectively, during the time window w, as shown in Equations ( 7) and ( 8); and λ odwi and λ odws are, respectively, the average number of travelers waiting at normal stop I and on-demand stop s who travel from origin o to destination d in time window w, as shown in Equations ( 9) and (10).…”

Section: Upper Model: Optimization Of On-demand Servicementioning

confidence: 99%

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Optimizing On-Demand Bus Services for Remote Areas

Yang

Feng

2023

Sustainability

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This study proposes on-demand bus services for remote areas with low transit demand, incorporating travelers’ willingness to pay and values of time. To jointly optimize the on-demand service of overlapping bus routes, we construct a bi-level model. The upper-level model (UM) optimizes bus departure frequency in different time windows and ticket prices of on-demand services to minimize the total generalized cost, subject to travelers’ willingness to pay for on-demand services. The lower-level model (LM) calculates the probability of travelers choosing on-demand stops. A numerical analysis based on Meishan Island data in Ningbo indicates that with on-demand bus services, the total generalized cost incurred by buses and travelers can be reduced by 30.36% and 15.35% during rush and off-rush hours, respectively. Additionally, the waiting time at an on-demand bus stop is only 4.3 min during rush hours and 6.8 min during off-rush hours.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Upper Model: Optimization Of On-demand Servicementioning

confidence: 99%

Optimizing On-Demand Bus Services for Remote Areas

Yang

Feng

2023

Sustainability

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“…For high-dimensional data, this algorithm may reduce the clustering accuracy to some extent. However, DBSCAN does not require a predetermined number of clusters [41,42]. In the clustering of urban nodes, due to the small number and dimension of nodes, the precision reduction will hardly be affected.…”

Section: Research On Dbscanmentioning

confidence: 99%

Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China

2023

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In order to optimize the network layout of urban agglomerations, improve the comprehensive benefits of transportation networks and promote the sustainable development of urban agglomerations, this paper studies the main trunk line selection model of the Beijing–Tianjin–Hebei high-speed railway (HSR). Firstly, the characteristics of cities in urban agglomeration are analyzed, and the economic capacity, transportation capacity, passenger turnover and network characteristics of urban nodes are selected as evaluation indexes. A node importance model and a line urgency model were established to obtain the value of the importance of urban nodes and the urgency of each line in the urban agglomeration. Secondly, the DBSCAN is used to cluster the city nodes, and the city nodes are divided into four grades. With the goal of maximizing the urgency of the lines and considering the constraints of the urban node level, the optimization model of the Beijing–Tianjin–Hebei backbone network selection is constructed. The backbone lines of the Beijing–Tianjin–Hebei urban agglomeration are obtained, and the selection results of backbone lines are analyzed, which lays a foundation for the design and optimization of the HSR operation scheme in urban agglomeration. The planned backbone network can basically realize the commuting between the important urban nodes in the Beijing–Tianjin–Hebei urban agglomeration to achieve the goal of driving and alleviating the operation of the branch line. It can accelerate the development of the internal traffic of the urban agglomeration. In addition, it has certain practical significance and practical value.

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“…Compared to GA, simulated annealing (SA) [30,31] has a global search superiority by which fewer results are trapped in the local optimum. SA is derived from the annealing process of solid materials in physics starting with an initial temperature and followed by a temperature decrease.…”

Section: Simulated Annealingmentioning

confidence: 99%

Shuttle Bus Timetable Adjustment in Response to Behind-Schedule Commuter Railway Disturbance

2022

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Shuttle bus connection is a valid technique to handle unplanned problems and promote sustainable transportation. The study describes tools that facilitate the shuttle bus timetable adjustment responding to a disturbance resulting from behind-schedule trains on a commuter railway. This behind-schedule disturbance is divided in four stages allowing for different delay ranges. The problem and its solution involve different elements, such as shuttle bus route selection, stop location, and timetable adjustment. We propose a nonlinear integer programming model, in which the objective function is based on the waiting, travelling, and walking costs for passengers as well as the operation cost of the route chosen. Vehicle capacity constraints and precise passengers’ waiting times are considered. A genetic algorithm and a simulated annealing algorithm combined with a priori decomposition are used to derive an efficient solution. A case study of a shuttle bus serving the Jinshan Railway in Shanghai, China, is tested to validate that, compared to the no-planning timetable, the total cost of the optimized timetable is reduced by 7.6%, especially including a dramatic reduction in the cost of passenger waiting time by 49.1%.

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Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

Cited by 7 publications

References 41 publications

Optimizing On-Demand Bus Services for Remote Areas

Optimizing On-Demand Bus Services for Remote Areas

Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China

Shuttle Bus Timetable Adjustment in Response to Behind-Schedule Commuter Railway Disturbance

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