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Transportation Research Part C

ISSN: 0968-090X

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Datasets associated with articles published in Transportation Research Part C

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1970
2024
1970 2024
9 results
  • Data for: Evaluation of Ride-Sourcing Search Frictions and Driver Productivity: A Spatial Denoising Approach
    This dataset contains information about ride-sourcing rides from 06-02-2016 through 04-13-2017 provided by Ride Austin through the website https://data.world/ride-austin/ride-austin-june-6-april-13
    • Dataset
  • Data for: Effects of on-demand ridesourcing on vehicle ownership, fuel consumption, vehicle miles traveled, and emissions per capita in U.S. states
    The attached files include the R code that executes the analysis in the paper and the subset of the data used in the paper that is public. With the public data only, the code will execute some of the analysis fully and produce error messages where non-public data are needed. Proprietary data used in the analysis may be purchased from IHS/Polk (https://ishmarkit.com/products/products/automotive-market-data-analysis.html) and Ward's Automotive (https://subscribers.wardsintelligence.com/data-center) to run the full analysis.
    • Dataset
  • Data underlying the study of the ride acceptance behaviour and relocation strategies of ride-sourcing drivers, CriticalMaaS WP3.2 WP3.3-PA
    This dataset is part of the CriticalMaaS project within the Department of Transport and Planning, Faculty of Civil Engineering. The data was collected through a stated preference survey designed to study the ride acceptance behaviour and relocation strategies of ride-sourcing drivers. The target group was Uber and Lyft drivers operating in the US.
    • Dataset
  • Data underlying the study of the ride acceptance behaviour and relocation strategies of ride-sourcing drivers, CriticalMaaS WP3.2 WP3.3-PA
    This dataset is part of the CriticalMaaS project within the Department of Transport and Planning, Faculty of Civil Engineering. The data was collected through a stated preference survey designed to study the ride acceptance behaviour and relocation strategies of ride-sourcing drivers. The target group was Uber and Lyft drivers operating in the US.
    • Dataset
  • Matlab codes for Probabilistic Field Approach for Motorway Driving Risk Assessment
    This package contains scripts used in the article: `Mullakkal-Babu, Freddy A., et al. "Probabilistic field approach for motorway driving risk assessment." Transportation research part C: emerging technologies 118 (2020): 102716.` All the folders contain a `README.md` file with steps to use and detailed description of the folder contents. The generic description of the folder contents are as follows: ## Figures1-2+ This folder contains scripts used to create `Figure.1` and `Figure.2`. + Please follow the README file in the folder. ## singleStepPDRF+ This folder contains scripts for estimate single-step PDRF for a given trajectory pair. Here the trajectory pair is specified in .xls format+ The scripts were used to create `Figure.5`, `Figure.6` and `Figure.7`+ Please follow the README file in the folder. ## multiStepPDRF+ This folder contains scripts for estimate multi-step PDRF+ The scripts were used to create `Figure.3` and `Figure.12`. + Please follow the README file in the folder. ## SimulationBasedValidationStudy+ This folder contains scripts for estimate single-step PDRF for a given trajectory pair involved in a cut-in event. These scipts can be used to generate variations of cut-in encounters and study the risk of these encounters as expressed by singleStepPDRF+ The scripts were used to create `Figure.8` and `Figure.9`.
    • Software/Code
  • Data supporting 'Simulation of electric vehicle driver behaviour in road transport and electric power networks'
    The research considers an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure. 2 datasets are provided containing the input and output (result) data of the model presented in this publication. The file "input_data.xlsx" contains the data from the modelling of the EV agent characteristics, as described in the paper. Data regarding the power consumption, discharge characteristics, battery voltage, battery current, battery soc and charger power are presented in separate tabs. Power consumption (measured in kW) was calculated using the battery model described in the paper, and is split into 4 categories: P aerodynamics, P drivetrain, P tires and P ancillary. P aerodynamics are the vehicle power losses due to the outside air friction, P drivetrain are the vehicle power losses due to the operation of the motor, P tires are the vehicle losses due to the vehicle weight and the rolling drag, while P ancillary reflects all the other electrical load of the vehicle (lights etc). Each category is given as a function of vehicle speed (in km/hour). The Discharge Characteristics are the characteristic discharge curves calculated with the battery model described in the paper. The data include information about the terminal voltage of the battery (in Volts) as a function of the discharge level (in %) for 4 different discharge rates (0.2C, 0.5C, 1C and 2C). The battery voltage, battery current, battery soc and charger power show the calculated values of battery voltage (in Volts), battery current (in amperes), battery soc (in %) and charger power (in kW) as a function of time (in hours) for a full charging cycle using a Home and a Public Charger respectively. The above data were used as inputs to the multi-agent simulation model described in the paper. The file "output_data.xlsx" contains the result data from the multi-agent simulation model, as described in the paper. Data regarding the home charging demand, public charging demand and traffic distribution are presented in separate tabs. The Home charging demand data is the aggregated power demand (in MW) of the simulated network as a function of time (in minutes). Three cases are presented: the base case (without EV agents) and 2 simulation case studies (considering Home Chargers and 2 different types of EV agents). The Public charging demand data is the aggregated charging energy (in kWh) from Public Chargers in the different nodes of the simulated road network for two different simulation study cases (with different types of EV agents). The Traffic distribution data contain information about the average hourly density (in vehicles/hour) for every road of the simulated road network in two different simulation study cases (with different types of EV agents). The number of EVs on the road A1_2 are also provided (per minute) for two different simulation study cases (with different types of EV agents).
    • Dataset
  • New York City Hourly Traffic Estimates (2010-2013)
    This dataset contains hourly traffic estimates (speeds) for individual links of the New York City road network for the years 2010-2013, estimated from New York City Taxis.
    • Dataset
  • New York City Taxi Trip Data (2010-2013)
    This dataset contains records of four years of taxi operations in New York City and includes 697,622,444 trips. Each trip records the pickup and drop-off dates, times, and coordinates, as well as the metered distance reported by the taximeter. The trip data also includes fields such as the taxi medallion number, fare amount, and tip amount. The dataset was obtained through a Freedom of Information Law request from the New York City Taxi and Limousine Commission. The files in this dataset are optimized for use with the ‘decompress.py’ script included in this dataset. This file has additional documentation and contact information that may be of help if you run into trouble accessing the content of the zip files.
    • Dataset
  • A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
    This dataset contains and describes the data used in Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. Transportation Research Part C: Emerging Technologies, 44, 146-170. doi:10.1016/j.trc.2014.03.015 Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).
    • Dataset