Travel Time Minimization at Roundabouts for Connected and Automated Vehicles

2020 ◽  
Author(s):  
Ovidiu Pauca ◽  
Constantin F. Caruntu
Keyword(s):  
Travel Time ◽  
Automated Vehicles ◽  
Time Minimization

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

2021 ◽  
pp. 036119812199458
Author(s):  
Slobodan Gutesa ◽  
Joyoung Lee ◽  
Dejan Besenski
Keyword(s):  
Travel Time ◽  
Information Exchange ◽  
Fixed Time ◽  
Optimization Method ◽  
Time Signal ◽  
Market Penetration ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Concept Evaluation ◽  
Intersection Management

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles

2019 ◽  
Vol 46 (6) ◽  
pp. 2103-2116 ◽  
Author(s):  
Jingya Gao ◽  
Andisheh Ranjbari ◽  
Don MacKenzie
Keyword(s):  
Travel Time ◽  
Automated Vehicles

scholarly journals A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Haigen Min ◽  
Yukun Fang ◽  
Runmin Wang ◽  
Xiaochi Li ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Game Theory ◽  
Travel Time ◽  
Fuel Consumption ◽  
Transportation Network ◽  
Control Algorithms ◽  
Traffic Density ◽  
Automated Vehicles ◽  
Network Efficiency ◽  
Total Travel Time ◽  
The Cost

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.

scholarly journals An inventory–routing problem with the objective of travel time minimization

2014 ◽  
Vol 236 (3) ◽  
pp. 936-945 ◽  
Author(s):  
Kunpeng Li ◽  
Bin Chen ◽  
Appa Iyer Sivakumar ◽  
Yong Wu
Keyword(s):  
Travel Time ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem ◽  
Time Minimization

scholarly journals Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

2020 ◽  
Vol 1 ◽  
Author(s):  
Lama Alfaseeh ◽  
Bilal Farooq
Keyword(s):  
Travel Time ◽  
Short Term Memory ◽  
Ghg Emissions ◽  
Automated Vehicles ◽  
Multi Objective ◽  
Proactive Routing ◽  
Routing Strategy ◽  
Average Travel Time ◽  
Information And Communication ◽  
Single Objective

This study exploited the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing to develop proactive multi-objective eco-routing strategies for travel time and Greenhouse Gas (GHG) emissions reduction on urban road networks. For a robust application, several GHG costing approaches were examined. The predictive models for link level traffic and emission states were developed using the long short-term memory (LSTM) deep network with exogenous predictors. It was found that proactive routing strategies outperformed the reactive strategies regardless of the routing objective. Whether reactive or proactive, the multi-objective routing, with travel time and GHG minimization, outperformed the single objective routing strategies. Using a proactive multi-objective (travel time and GHG) routing strategy, we observed a reduction in average travel time (17%), average vehicle kilometer traveled (22%), total GHG (18%), and total nitrogen oxide (20%) when compared with the reactive single-objective (travel time).

Energy and Mobility Impacts of System Optimal Dynamic Traffic Assignment for a Mixed Traffic of Legacy and Automated Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 389-406
Author(s):  
H. M. Abdul Aziz
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation Networks ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Dynamic Traffic ◽  
System Optimal ◽  
Optimal Dynamic

This research develops a system optimal dynamic traffic assignment (DTA) model for mixed traffic of human drivers and automated vehicles (AVs) and investigates network level mobility and energy impacts for different market shares of AVs. A methodology based on vehicle-specific-energy is proposed to estimate the energy consumption from the embedded spatial-queuing traffic flow model within the DTA formulation. Results with a test network indicate that potential travel time and energy consumption reductions are possible with increased AV market share in transportation networks. Results also report a decrease in travel time as high as 49% and energy consumption as high as 28% at the system level. The developed DTA model will be able to assist in transportation planning and the investment decision process by estimating the mobility and energy impacts in future transportation networks with mixed traffic of human drivers and AVs.

Sign in / Sign up

Export Citation Format

Share Document