Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling

2022 ◽  
Vol 92 ◽  
pp. 101730
Author(s):  
Yantao Huang ◽  
Kara M. Kockelman ◽  
Venu Garikapati
Keyword(s):  
Automated Vehicle ◽  
Last Mile ◽  
Vehicle Fleet ◽  
Fleet Operations

scholarly journals Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Zhang ◽  
Lei Shi ◽  
Jing Chen ◽  
Xuefeng Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Initial Period ◽  
Pso Algorithm ◽  
Automated Vehicles ◽  
Routing Problem ◽  
Automated Vehicle ◽  
Last Mile ◽  
The Cost

The application of automated vehicles in logistics can efficiently reduce the cost of logistics and reduce the potential risks in the last mile. Considering the path restriction in the initial stage of the application of automated vehicles in logistics, the conventional model for a vehicle routing problem (VRP) is modified. Thus, the automated vehicle routing problem with time windows (AVRPTW) model considering path interruption is established. Additionally, an improved particle swarm optimisation (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Four automated vehicles are designated to execute all delivery tasks required by 25 stores. Capacities of all of the automated vehicles are almost fully utilised. It is of considerable significance for the promotion of automated vehicles in last-mile situations to develop such research into real problems arising in the initial period.

Utilizing Automated Report Generation and Data Acquisition Tools to Guide Fuel Cell Vehicle Fleet Operations

2008 ◽  
Author(s):  
Tim McGuire ◽  
Taylor Roche ◽  
Andreas Weinberger ◽  
Peter Friebe ◽  
Juergen Friedrich
Keyword(s):  
Fuel Cell ◽  
Data Acquisition ◽  
Fuel Cell Vehicle ◽  
Report Generation ◽  
Vehicle Fleet ◽  
Fleet Operations

scholarly journals Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Pei Nen Esther Chee ◽  
Yusak O. Susilo ◽  
Yiik Diew Wong ◽  
Anna Pernestål
Keyword(s):  
Service Quality ◽  
Willingness To Pay ◽  
Latent Variables ◽  
Structural Equation ◽  
Travel Demand ◽  
Ride Comfort ◽  
Quality Attributes ◽  
Quality Attribute ◽  
Automated Vehicle ◽  
Last Mile

Abstract Introduction Travel demand and travel satisfaction of a transport service are affected by user perceptions of the service quality attributes, and such perceptions should be included in studying user willingness-to-pay (WTP) for automated vehicle (AV) services. This study applied structural equation modelling with service quality attribute perceptions as latent variables affecting WTP. Objectives We investigated how WTP AV services are affected by socio-demographic characteristics, knowledge and experiences with AV, existing travel modes and particularly, perceptions of the associated service quality attributes. The AV services are: 1) on-demand personalised AV (PAV) service, 2) demand responsive shared AV (SAV) service, and 3) first−/last-mile automated bus (AB) service. Methods The data were collected from 584 potential users of a first−/last-mile AB service trial operated in Kista, Stockholm. Results Results show people hold different expectations towards each type of AV service. These expectations act as the minimum requirements for people to pay for the AV services. Respondents are found to be willing to pay more for PAV service if it is safe, provides good ride comfort, and is competitively priced relative to the price travelling by metro and train over a same distance. Other than service quality attribute perceptions, income level, existing travel modes for daily trips, familiarity with automated driving technology and AB ride experience are important factors affecting WTP for the AV services. Conclusion The developed model can be applied to understand expectations of potential users towards a new AV service, and to identify user groups who are willing to pay the service. New AV services can thus be designed sensibly according to users’ actual needs.

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