Advanced Traveler Information Systems (ATIS) have the potential to improve the travel experience of individuals and, consequently, enhance the transportation system performance. ATIS will provide information that assists the traveler in selecting his destination, departure time, pre-trip route, enroute diversion, and trip chaining. One of the direct benefits of ATIS is to provide warnings on incident blockages which may encourage drivers to divert from incident routes, leading to shorter queues, fewer abrupt deceleration and safer travel conditions.
To achieve the level of safety and efficiencies promised by autonomous vehicles (AVs), understanding of interactions between human driven vehicles and AVs is crucial. The limited access to publicly available AV data in the field has been the main source of challenge to explore these questions. Using recently released annotated AV data released by Waymo, we investigate interactions between AVs with Human-driven manual vehicles (MVs) in a public road environment. A scalable methodology is presented to study interactions between AVs and MVs. This research reports two main findings (a) AVs tend to be more conservative than MVs at higher speeds on arterials and at lower speeds on freeways (b) No statistical differences in the mean reaction times between MVs and AVs, however, MVs following MVs were found to have statistically significantly lower variance in reaction times. These findings demonstrate the broader impacts of AVs on traffic flow and capacity.
Truck accident rates on major truck routes were found to be higher than rates for passenger cars both before and after the 55 mph speed limit. However, the accident rates for trucks and cars were less and trending to be similar under the 55 On the other hand, truck and passenger car rates were similar under the 55 mph limit.The increase in ratio between truck and passenger car accident rates was also significant for this highway class.
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