DOI: 10.17863/cam.85434
title: Datasets supporting PhD dissertation 'Autonomous goods vehicles: implications for fleet operating models'

description: This repository includes datasets and programmes supporting the PhD dissertation 'Autonomous goods vehicles: implications for fleet operating models'. The contents are described as follows: Chp 3: Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles Folder contains all sets of VRP analyses described in the PhD. Analyses include full datasets, radial and random data subsets for 'Customer Deliveries' and 'Intermediate Distribution' case studies. Each folder consists of the following files: DistMatrix.txt: This is a matrix of distances between each destination in the analysis in kilometres FVOT.txt: This a small table listing the different values of FVOT (Freight Value of Time) LocDemand.txt: This is a table listing the reference numbers of the destinations in the dataset, the quantity of cargo required to be delivered to each destination and the coordinates of the destination. To preserve commercial confidentiality, coordinates have been removed for the uploaded datasets. In the 'Intermediate Distribution' cases, cargo loads are created in multiples of 12 cages with dummy location numbers used to differentiate between each multiple of 12 cages. VehParams.txt: This is a table of vehicle parameters including hourly driver and vehicle cost (in £/hr), mass capacity of the vehicle (in tonnes), volume capacity (in units of cargo, e.g. crates for LGVs and cages for rigid/tractor-trailer), mass per unit volume, fuel consumption when the vehicle is empty and full respectively. LWFMVRP.py: This is the Python program which runs the optimization analysis using Gurobi commercial solver. Output.xlsx: This is the excel output of the analysis and includes a summary as well as output matrices for the best solution discovered by the analysis. Chp 4: Opportunities for off peak deployment Folder contains all sets of VSP analyses described in the PhD. Analyses include the multi-destination case study as well as 30 journeys of the 'High congestion' example and of the 'Low congestion' example. Analyses are labelled as per the PhD. Each folder consists of the following files: Aik.txt: This is a matrix of one-way journey duration in hours for each destination for each hour of the day (Tk=0:23) Cdik.txt: This is a matrix of return journey driver time cost in £ for each destination for each hour of the day (Tk=0:23) Di.txt: This is a list of 'latest delivery' restrictions for each destination. Fc.txt: This is a matrix of return journey fuel consumption in litres for each destination for each hour of the day (Tk=0:23) Pik.txt: This is a matrix of return journey duration in hours for each destination for each hour of the day (Tk=0:23) Ri.txt: This is a list of 'earliest arrival' restrictions for delivery for each destination. Tk.txt: This is a list of the hours of the day for which the data pertains to (Tk=0:23) TDFSVSP.py: This is the Python program which runs the optimization analysis using Gurobi Commercial solver. Output.xlsx: This is the excel output of the analysis and includes a summary as well as output matrices for the best solution discovered by the analysis. Chp 5: Opportunities for integrated operating model changes Folder contains all sets of VSP analyses described in the PhD. Analyses include the multi-destination case study as well as 30 journeys of the 'High congestion' example and of the 'Low congestion' example. Analyses are labelled as per the PhD. RB=Baseline speed strategy. S1: Speed strategy 1 (80km/h max). S2: Speed strategy 2 (70km/h max). Free: Speed strategy is modelled as a decision variable. Each folder, other than the analyses denoted 'Free' consists of the following files: Aik.txt: This is a matrix of one-way journey duration in hours for each destination for each hour of the day (Tk=0:23) Cdik.txt: This is a matrix of return journey driver time cost in £ for each destination for each hour of the day (Tk=0:23) Di.txt: This is a list of 'latest delivery' restrictions for each destination. Fc.txt: This is a matrix of return journey fuel consumption in litres for each destination for each hour of the day (Tk=0:23) Pik.txt: This is a matrix of return journey duration in hours for each destination for each hour of the day (Tk=0:23) Ri.txt: This is a list of 'earliest arrival' restrictions for delivery for each destination. Tk.txt: This is a list of the hours of the day for which the data pertains to (Tk=0:23) TDFSVSP.py: This is the Python program which runs the optimization analysis using Gurobi Commercial solver. Output.xlsx: This is the excel output of the analysis and includes a summary as well as output matrices for the best solution discovered by the analysis. In the folders denoted 'Free', the above files aik, cdik, fc and pik end with numbers 0, 1 or 2. These correspond to the respective speed strategies (base, S1 and S2, as described above)