Sensitivity Analyses over the Service Area for Mobility Allowance Shuttle Transit (MAST) Services

Quadrifoglio, Luca; Dessouky, Maged

doi:10.1007/978-3-540-73312-6_22

Cited by 20 publications

(13 citation statements)

References 34 publications

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“…By doing this, all passengers can reach the transfer station before their expected arrival time. Then, we calculate the latest visiting time of each passenger in a backward way (see (22)), and do the same for the latest start time (LST P,m ) of feeder bus (see (23)). Equations (24)- (26) ensure that the feeder bus to collect passengers after their earliest pick-up time.…”

Section: A the Common Time Window Of Passengers Sharing One Feeder Busmentioning

confidence: 99%

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Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

2020

IEEE Access

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This study concentrates on the routing and scheduling problem of Demand Responsive Connector to build feeder plans for people traveling from and to transit hub. An in-depth analysis on the characteristics of feeder services was implemented to inspire the compatibility-based algorithm design. With the goal of reducing operating cost and passenger inconvenience, the proposed algorithm took several factors critical to the real-word operation into consideration, such as double time window assurance (the time constraints at the beginning and end of passenger travels), the flexibility of feeder plans, and the number of vehicles. Our method was validated on numerical instances of 400, 600, 800 and 1000 passengers. Simulation results show that the compatibility-based algorithm can effectively reduce the number of vehicles with acceptable increase of passengers' inconvenience, and can improve the algorithm efficiency considerably. In addition, the setting of flexible time window of shutter plan can hold some elasticity for feeder services. Sensitivity analysis was conducted to help service providers evaluate the trade-off between the operation cost and level of service. INDEX TERMS Compatibility-based approach, demand responsive connector, routing and scheduling.

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Section: A the Common Time Window Of Passengers Sharing One Feeder Busmentioning

confidence: 99%

“…Some researches contributed to the pre-evaluation in the planning level of DRC. Quadrifoglio et al and Li et al concentrated on the optimal zone design of DRC operation [22]- [24]. The service area with several transit stations was divided into zones and each zone was served by dedicated feeder buses.…”

Section: Introductionmentioning

confidence: 99%

Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

2020

IEEE Access

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“…Requirements under "Many-to-Many" Demand Mode. e flexible bus system is a demand-based transportation system, which can collect the travel needs of individual passengers and provide personalized transportation service [16,17]. Compared with "many-to-one" bus feeder mode flexible bus system studied in Pan et al [18] and Pan [19], the "many-to-many" mode studied in this paper has many merits and more flexibility.…”

Section: Flexible Bus Systemmentioning

confidence: 99%

“…Constraints (15) and (16) indicate that the time of the passenger at the last demand station of the shuttle bus arriving at the target station should be equal to the time that the vehicle reached the demand point and the travel time between the two demand points. Constraint (17) ensures that the time of vehicle arriving at the target station is not later than the departure time.…”

Section: Model Representationmentioning

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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“…T 1 and T 4 are related to the land use, surrounding road network density and bus stop location. Regarding the distance between mini-bus stops and destinations that cannot be directly measured, additionally existing studies have studied the service area of mini-bus in a rectangular area [24,25]. Therefore, GIS is adopted to establish the Cartesian coordinate system.…”

Section: Optimization Model 321 Analysis Of Travel Processmentioning

confidence: 99%

Optimization of urban mini-bus stop spacing: a case study of Shanghai (China)

et al. 2017

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Preliminary communicationIn recent years, the mini-bus service has gained a rapid development, while the reasonable stop spacing is significant to improve residents' travel efficiency. In order to locate the mini-bus stops practically, this paper firstly proposes a mathematical model based on Voronoi Diagrams to minimize residents' total travel time. Improved Wilson-Han-Powell Sequential Quadratic Programming (SQP) technique is employed to solve the model. Based on Geographic Information Systems (GIS), the Cartesian coordinate system is used to calculate the walking distance between mini-bus stops and origins or destinations, which cannot be directly measured. Secondly, based on the actual situation, this paper proposes modification methods for adjusting mini-bus stops. The validity and usefulness of the methodology is tested through applying it to a real case in Shanghai (China). The calculation results suggest that the model deals well with the mini-bus stop spacing problem. Keywords: mini-bus service; modification methods; optimization model; Sequential Quadratic Programming; stop spacing; Voronoi DiagramsOptimizacija razmaka između stajališta gradskog mini-autobusa: studija slučaja Šangaj (Kina) Prethodno priopćenje U posljednjih se nekoliko godina služba mini-autobusa brzo razvija, a odgovarajući razmak između stajališta značajno poboljšava učinkovitost putovanja stanovnika. Kako bi mini-bus stajališta bila praktično locirana, u ovom se radu najprije predlaže matematički model temeljen na Voronoi dijagramima u svrhu smanjenja ukupnog vremena putovanja putnika. Za rješenje tog modela primijenjena je poboljšana tehnika Wilson-Han-Powell sekvencionalno kvadratno programiranje (SQP). Na temelju geografskih informacijskih sustava (GIS) korišten je Kartezijanski koordinatni sustav za izračunavanje pješačke udaljenosti između mini-bus stanica i početnih ili odredišta, koja se ne mogu izravno izmjeriti. Drugo, u skladu sa stvarnim stanjem, u radu se predlažu metode modifikacije za podešavanje stajališta mini-autobusa. Valjanost i korisnost metodologije ispitana je njenom primjenom na stvarnom slučaju u Šangaju (Kina). Rezultati proračuna pokazuje da se modelom dobro rješava problem određivanja razmaka između stajališta mini autobusa.Ključne riječi: metode modifikacije; model optimizacije; sekvencionalno kvadratno programiranje; služba mini-autobusa; razmak između stajališta; Voronoi dijagrami

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Sensitivity Analyses over the Service Area for Mobility Allowance Shuttle Transit (MAST) Services

Cited by 20 publications

References 34 publications

Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

Flexible Bus Route Optimization for Multitarget Stations

Optimization of urban mini-bus stop spacing: a case study of Shanghai (China)

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