Percolation phenomenon in connected vehicle network through a multi-agent approach: Mobility benefits and market penetration

2017 ◽  
Vol 85 ◽  
pp. 312-333 ◽  
Author(s):  
Alireza Mostafizi ◽  
Shangjia Dong ◽  
Haizhong Wang
Keyword(s):  
Connected Vehicle ◽  
Market Penetration ◽  
Multi Agent ◽  
Vehicle Network ◽  
Percolation Phenomenon

INFLUENCE OF PERSONAL COMMUNICATION NETWORKS ON INNOVATION ADOPTION — USING MULTI-AGENT SIMULATIONS TO PROJECT THE LAUNCH OF AN INNOVATIVE MEDICAL DEVICE

2013 ◽  
Vol 10 (05) ◽  
pp. 1340021 ◽  
Author(s):  
CHRISTIAN BARROT ◽  
JAN KUHLMANN ◽  
ANDREA POPA
Keyword(s):  
Medical Device ◽  
Interpersonal Communication ◽  
Communication Networks ◽  
Personal Communication ◽  
Market Penetration ◽  
Modeling Framework ◽  
Communication Processes ◽  
The Social ◽  
The Us ◽  
Multi Agent

Adoption processes are often heavily influenced by interpersonal communication. Marketing managers are increasingly trying to use these relationships to foster the market penetration of their products. In an empirical study of the US market for an innovative medical device, we survey the social network of (mostly chief) anesthetists from 151 hospitals. We confirm the influences from personal communication on individual adoption decisions through hazard regressions. We then use a multi-agent modeling framework trying to identify what seeding strategies would have been optimal to achieve a fast market penetration, i.e. which and how many anesthetists should be selected to initiate personal communication processes.

Sequential Optimization of an Emergency Response Vehicle’s Intra-Link Movement in a Partially Connected Vehicle Environment

2021 ◽  
pp. 036119812110179
Author(s):  
Gaby Joe Hannoun ◽  
Pamela Murray-Tuite ◽  
Kevin Heaslip ◽  
Thidapat Chantem
Keyword(s):  
Emergency Response ◽  
Lateral Position ◽  
Automated System ◽  
Sequential Optimization ◽  
Connected Vehicles ◽  
Linear Programs ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Short Term ◽  
New Approach

This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.

scholarly journals Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates

2021 ◽  
Vol 159 ◽  
pp. 106234
Author(s):  
Guiming Xiao ◽  
Jaeyoung Lee ◽  
Qianshan Jiang ◽  
Helai Huang ◽  
Mohamed Abdel-Aty ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Vehicle Technologies

Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment

2020 ◽  
Vol 2674 (11) ◽  
pp. 120-133 ◽  
Author(s):  
Xiaoyu Guo ◽  
Yongxin Peng ◽  
Sruthi Ashraf ◽  
Mark W. Burris
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Information Exchange ◽  
Traffic Management ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Performance Measurements ◽  
Time Saving ◽  
Travel Time Savings ◽  
Time Savings

Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.

scholarly journals Detector-Free Optimization of Traffic Signal Offsets with Connected Vehicle Data

2017 ◽  
Vol 2620 (1) ◽  
pp. 54-68 ◽  
Author(s):  
Christopher M. Day ◽  
Howell Li ◽  
Lucy M. Richardson ◽  
James Howard ◽  
Tom Platte ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Goodness Of Fit ◽  
Sampling Period ◽  
Signalized Intersections ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Trajectory Data ◽  
Vehicle Data ◽  
Low Levels ◽  
Vehicle Trajectory

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

Safety Evaluation of the Advanced Stop Assist System in Connected Vehicle Environment

2018 ◽  
Vol 2672 (22) ◽  
pp. 47-57 ◽  
Author(s):  
Gwamaka Njobelo ◽  
Thobias Sando ◽  
Soheil Sajjadi ◽  
Enock Mtoi ◽  
Eren Erman Ozguven ◽  
...  
Keyword(s):  
Safety Evaluation ◽  
Signalized Intersections ◽  
Simulation Software ◽  
Sensitivity Analyses ◽  
Dominant Factor ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Test Bed ◽  
Dilemma Zone ◽  
Advisory Speed

Although traffic signals are installed to reduce the overall number of collisions at intersections, certain types, in particular, rear-end collisions are increasing due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposes and evaluates an Advanced Stop Assist System (ASAS) at signalized intersections by using Vehicle-to-Infrastructure (V2I) communication. The proposed system utilizes communication data, received from roadside equipment, to provide approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking at a yellow or red signal. A simulation test bed was modeled using VISSIM, a microscopic simulation software, to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that a penetration rate of at least 60% is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections.

Safety Impacts of Queue Warning in a Connected Vehicle Environment

2017 ◽  
Vol 2621 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Samaneh Khazraeian ◽  
Mohammed Hadi ◽  
Yan Xiao
Keyword(s):  
Traffic Safety ◽  
Assessment Model ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Warning Systems ◽  
Safety Measures ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Message Signs ◽  
Surrogate Safety Measures

Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.

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