Arab Future Cities Dubai 2019

16-17 September 2019
Jumeirah Emirates Towers Hotel
Dubai, United Arab Emirates

#AFCSDXB

Future of UAE mobility: Preparing infrastructure for next-gen vehicles

Posted On June 27, 2019

Future of UAE mobility: Preparing infrastructure for next-gen vehicles

Dubai has one of the most progressive autonomous vehicles strategy in place. It aims to transform 25 per cent of total transportation in Dubai to autonomous mode by 2030. Expectations are that autonomous mobility will create a revenue of Dh22 billion as it will reduce transportation cost by 44 per cent, saving Dh99 million a year and reduce environmental pollution by 12 per cent with a saving of up to Dh1.5 billion a year. The strategy aims to reduce accidents by 12 per cent, which is equivalent to Dh2 billion every year.

Self-driving cars have the potential in the future to reduce deaths and injuries from car crashes, particularly those that result from driver distraction. It will reduce traffic congestion which will result in a reduction of CO2 emissions as well. Even a small percentage of autonomous vehicles (AVs) on the road can reduce the total fuel consumption by up to 40 per cent. AVs could potentially supplement public transport and cut travel time for commuters. It will also reduce demands for street and lot parking.

But what are the challenges in getting there? Even though autonomous driving offers a solution for many current traffic issues we need to resolve a lot of uncertainties. Introducing CAVs on our roads, where they will first coexist with human-driven vehicles, will potentially result in many traffic issues. It is important to establish scenarios and prepare infrastructure for autonomous cars and connected vehicles (CAVs), focusing on creating transport modelling software. Industry, governments and research are pushing the use of CAVs in real traffic to their full potential where benefits include, removing driver error to increase safety, smoothing vehicle flow to reduce emissions and reducing congestion across our road networks. The use of traffic modelling and simulation can assist decision makers by quantifying the impact of increasing levels of CAVs, helping to identify what effect this will have on future transport infrastructure.

A case in point is the research project CoEXist funded by the Horizon 2020 framework programme of the European Commission. It focuses on the interaction between semi-automated and conventional vehicles in the transitional period to fully autonomous vehicle fleets. For 18 months our PTV colleagues worked closely together with project partners from Vedecom, Renault, TASS International and the University of Stuttgart. Microscopic simulation software PTV Vissim and the macroscopic modelling software PTV Visum were used for simulations. Field data was collected in a real traffic environment. Furthermore, the data and the car-following behaviour of the automated vehicles were analysed and the modelling software was made AV ready. Now the CoEXist team will tackle the automation-readiness of road infrastructure and authorities to increase the capacity of local authorities and other urban mobility stakeholders to get ready for the transition towards a shared road network.

We believe that virtual testbed simulations provide a clear vision on planning and facilitating the execution of autonomous vehicles by inserting the vehicles in simulations in millions of miles to understand its behaviour. It is possible to build entire road networks virtually, in which a self-driving car is surrounded by many other vehicles and pedestrians. It is important to test variations of different scenarios to prepare for the adoption of autonomous vehicles for our day-to-day commuting.

 

Source: Khaleej Times

View All »


Register Today!

16-17 September 2019

Jumeirah Emirates Towers Hotel
Dubai, United Arab Emirates

Articles

 

Follow us on

Contact Us

Expotrade Middle East FZ-LLC
Level 10, 1002 Aurora Tower, Dubai Media City
PO Box 500686 Dubai, U.A.E.
Tel: +9714-4542135 Fax: +9714-4542136
Email: info@expotrade-me.com

Want more information? Let us know, we can help.

(Page Load time: 0.7592 Sec)