Aims and Scope
Data Fusion of Non-destructive Testing Methods for Bridge Deck Condition AssessmentSalvatore Cafiso, Alessandro Di Graziano, Dimitrios G. Goulias and Giuseppina Pappalardo
The Open Transportation Journal, 2022; 16: e187444782211210.
Electronic Publication Date: December 29, 2022
Population vs. Intersection Densities: An Assessment of a Correlation Using Spatial Comparison and Regression Analysis in Yaoundé, CameroonEdouard Bengono Essola and Chunho Yeom
The Open Transportation Journal, 2022; 16: e187444782211180.
Electronic Publication Date: December 29, 2022
Evaluation of Back of Queue at Signalized Intersections in Erbil City Using Different SoftwareDilveen H. Omar and Salar K. Hussein
The Open Transportation Journal, 2022; 16: e187444782210250.
Electronic Publication Date: December 21, 2022
Estimation of Cross Classified Value of Travel Time Using Binary Logit Model on Egyptian RoadsHeba Mahmoud Bakry, Yusra M.H. Elgohary, Aya Farag and Ibrahim M.I. Ramadan
The Open Transportation Journal, 2022; 16: e187444782209140.
Electronic Publication Date: December 20, 2022
Horizontal Collaboration among SMEs through a Supply and Distribution CooperativeMehmet Soysal, Sedat Belbağ and Sibel Erişkan
The Open Transportation Journal, 2022; 16: e187444782208101.
Electronic Publication Date: November 17, 2022
Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based ApproachPeter Hogeveen, Maarten Steinbuch, Geert Verbong, Auke Hoekstra
Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society.
Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles.
To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand.
An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type.
The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles.
Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.
May 17, 2021
- December 31, 2020
Bridge Safety Analysis Based on the Function of Exceptional Vehicle Transit SpeedSeptember 30, 2019
Travel-time Prediction Using K-nearest Neighbor Method with Distance Metric of Correlation CoefficientJanuary 18, 2018
Effect of Traffic Flow, Proportion of Motorcycle, Speed, Lane Width, and the Availabilities of Median and Shoulder on Motorcycle Accidents at Urban Roads in IndonesiaJanuary 22, 2018
A Simulation Model to Determine the Capacity of a Y-Type Waterway Intersection for a Real SeaportJanuary 29, 2018
Effectiveness of Experimental Left-Turn Sign Usage in Terms of Crashes and Analyzing Severity of Left Turn Crashes in AlaskaJanuary 29, 2018
Collisions Between Pedestrians and Reversing Vehicles in Public Settings in France