A multiclass cell transmission model for shared human and autonomous vehicle roads

Chen

Journal of Advanced Transportation

et al. 2018

The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers' natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies.

Section: Introductionmentioning

confidence: 99%

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Chen

Journal of Advanced Transportation

et al. 2018

Journal of Advanced Transportation

“…More specifically, autonomous vehicles (AVs) have negligible reaction time delays as compared to regular vehicles (RVs) and are able to run with much less spacing or headway with possibly a broad spectrum of speed. It is therefore expected that traffic capacity of roads with AVs would be increased [6][7][8][9]. AVs are also able to access the running status of neighboring vehicles so as to make more informed and smarter decisions on their lanechange maneuvers.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Liu

Guo

Taplin

et al. 2017

The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.

“…In micro-simulation, detailed movements and behaviors of the individual vehicles are taken into account in any analysis. The early and basic features of automation (Levels 1 and 2) have prompted some scholars to include AV technologies into micro-simulations [70][71][72][73]. As noted above, the connectedness of AVs has been investigated in signal control policies which resulted in significant reductions in delay [46][47][48].…”

Section: Av Navigation Model 14mentioning

confidence: 99%

“…As noted above, the connectedness of AVs has been investigated in signal control policies which resulted in significant reductions in delay [46][47][48]. Other studies tend to extend the reach of the existing dynamic traffic assignment models to somehow include AVs [71,74,75]. Despite current computational technologies and current modeling knowledge, the scale of micro-simulation is limited to a portion of a city and not the entire city.…”

Section: Av Navigation Model 14mentioning

confidence: 99%

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

et al. 2016

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connectedvehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.