Microscopic traffic simulation based evaluation of highly automated driving on highways

Author(s):  
Mohammad Bahram ◽  
Zahra Ghandeharioun ◽  
Peter Zahn ◽  
Mathias Baur ◽  
Werner Huber ◽  
...  
2019 ◽  
Vol 11 (5) ◽  
pp. 1276 ◽  
Author(s):  
Clélia Lopez ◽  
Chuan-Lin Zhao ◽  
Stéphane Magniol ◽  
Nicolas Chiabaut ◽  
Ludovic Leclercq

This paper investigated economic truck parking behavior to implement comprehensive Freight Loading Zone (FLZ) policies. We assumed that the delivery trucks can only park on FLZ. The proposed contribution is to quantify the cruising for parking time of trucks. We used a microscopic traffic simulation based on a Manhattan network and the real network of Lyon (France). This paper explored the relationship between the searching time, the parking probabilities and the region’s parking density. Based on research results, an application to last mile cost function is proposed.


2019 ◽  
Vol 14 (3) ◽  
pp. 223
Author(s):  
Bernhard Werth ◽  
Erik Pitzer ◽  
Christian Backfrieder ◽  
Gerald Ostermayer ◽  
Michael Affenzeller

2011 ◽  
Vol 187 ◽  
pp. 677-682 ◽  
Author(s):  
Hao Wang ◽  
Der Horng Lee ◽  
Ruey Cheu

This paper presents a study of taxi booking service in Singapore using a microscopic traffic simulation model embedded with a link-to-link shortest path algorithm. A novel trip-chaining strategy for taxi booking based on a customized algorithm of Pickup and Delivery Problem with Time Window (PDPTW) was proposed. The idea is to chain several bookings with demand time points which are spread out within a reasonable period of time, and with each pick-up point coinciding with or being within close proximity to the previous drop-off location. Based on the simulation results, the proposed system has the potential to improve the taxi booking service currently operating in Singapore.


Author(s):  
Xuan Fang ◽  
Tamás Tettamanti

It is believed that autonomous vehicles will replace conventional human drive vehicles in the next decades due to the emerging autonomous driving technology, which will definitely bring a massive transformation in the road transport sector. Due to the high complexity of traffic systems, efficient traffic simulation models for the assessment of this disruptive change are critical. The objective of this paper is to justify that the common practice of microscopic traffic simulation needs thorough revision and modification when it is applied with the presence of autonomous vehicles in order to get realistic results. Two high-fidelity traffic simulators (SUMO and VISSIM) were applied to show the sensitivity of microscopic simulation to automated vehicle’s behavior. Two traffic evaluation indicators (average travel time and average speed) were selected to quantitatively evaluate the macro-traffic performance of changes in driving behavior parameters (gap acceptance) caused by emerging autonomous driving technologies under different traffic demand conditions.


10.29007/cqps ◽  
2019 ◽  
Author(s):  
Thomas Weber ◽  
Patrick Driesch ◽  
Dieter Schramm

The introduction of highly automated driving functions is one of the main research and development efforts in the automotive industry worldwide. In the early stages of the development process, suppliers and manufacturers often wonder whether and to what extend the potential of the systems under development can be estimated in a cheap and timely manner. In the context of a current research project, a sensor system for the detection of the road surface condition is to be developed and it is to be investigated how such a system can be used to improve higher level driving functions. This paper presents how road surface conditions are introduced in various elements of the microscopic traffic simulation such as the actual network, the network editor, a device for detection, and an adaptation of the standard Krauß car following model. It is also shown how the adaptations can subsequently affect traffic scenarios. Furthermore, a summary is given how this preliminary work integrates into the larger scope of using SUMO as a tool in the process of analyzing the effectiveness of a road surface condition sensor.


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