Investigating Drivers' Responses to Advisory Messages in a Connected Vehicle Environment
Hayat, Md Tanveer, Civil Engineering - School of Engineering and Applied Science, University of Virginia
Smith, Brian, Department of Civil Engineering, University of Virginia
Freeway congestion is one of the most severe problems of the transportation system. Congestion has resulted in the loss of billions of dollars in terms of delays and fuel consumption, among others. Among the many contributing factors, merging conflicts in freeway ramp areas have been identified as one of the major causes of congestion. Though different ramp management strategies have been implemented over the years, each of these strategies has been partially successful due to their limited real-time traffic data collection and information dissemination capabilities.
To address the limitations of current ramp management strategies, the Connected Vehicle (CV) initiative takes advantage of advances in wireless communication, sensors, in-vehicle computer and GPS technologies, in addition to providing a unique opportunity to collect and exchange real time individual vehicular data. The Freeway Merge Assistance System (FMAS) is a connected-vehicle enabled prototype traffic management approach to ensure smoother merging by early identification and dynamic notification of merging opportunities through advisory messages. This is one of the first prototype applications that fully utilizes the capabilities provided by connected vehicle technology to enable a more cooperative driving environment between vehicles and the infrastructure. However, the effectiveness of this application entirely depends on actual drivers’ response behavior to these new generation of in-vehicle personalized advisories. Numerous studies have investigated drivers’ responses to safety alerts, automated braking and situational awareness alerts. As the objectives and benefits of safety systems are fundamentally different from mobility applications, drivers may demonstrated varied response behavior between these two systems. Therefore, proper understanding of drivers’ response behavior under CV-based mobility application is a must. This provided the fundamental motivation for this dissertation to evaluate the freeway merge management system from the perspective of driver behavior.
To understand the variability of drivers’ responses under diverse traffic conditions, in this research, a field experiments with 68 naïve test subjects was conducted with instrumented vehicles in a controlled environment. To simulate diverse traffic conditions for the participants, a set of nine scenarios were developed with three different gap sizes (small, medium and large) for each of the three FMAS algorithms (Variable Speed Limit, Lane Changing Advisory and Merging Control Algorithm). The three gaps sizes represented three different traffic conditions- free-flow, medium congestion, high congestion. The collected compliance data indicated that drivers feel more comfortable following the advisories when large and medium gaps are available, which represent low and medium traffic conditions respectively. Though the small gap size scenarios resulted in the lowest compliance rates, this is still meaningful in that “some” drivers are still willing to follow the advisory even in a high volume traffic condition. No significant difference was found between the compliance rates of male and female drivers. However, older driver group demonstrated lower advisory compliance rate (63%) than the younger driver group (84%).
The data on perception-reaction times show that perception-reaction time increases as the available gap size decreases. An estimated 0.64 sec difference in average perception-reaction time was observed from a large gap case (3.77 sec) and small gap case (4.41 sec). This increase in perception-reaction time can be attributed to drivers becoming more cautious in making decision under relatively congested situation. Therefore, in the system design the variability of perception-reaction time for diverse traffic conditions should be considered. Similar to compliance rate data, no significant difference was found in perception-reaction time between male and female drivers. On the other hand, older drivers were found having significantly higher perception-reaction times with a significant difference of 1.57 sec when compared with the youngest group of drivers. This relatively slow perception-reaction can be attributed to age-induced cognition and motor skill loss. However, actual lane changing time does not change much regardless of the traffic condition, gender and age; this indicates once a driver initiates a lane change, the required time to complete lane change is independent of the traffic condition.
Another significant finding from the field testing was that drivers demonstrated better responses in terms of both compliance and perception-reaction times with a direct advisory messages, which gives clear and specific instruction. On the other hand, an indirect advisory message, which indirectly stimulate a driving action were found to be relatively less effective and efficient. The compliance data from field test show that direct advisories such as Merging control algorithm (84.8%) and Lane Changing advisory (84.3%) have higher compliance rates than the Variable Speed limit (63%) which provided indirect instructions to the participants. Perception reaction time was reduced by 1.30 sec (from 4.76 sec of variable speed limit to 3.46 sec of merging control) by providing most direct advisories. It is therefore recommended that developing and implementing an application that provides more direct advisory messages is desirable.
In conclusion, the actual drivers’ response data collected and presented in this research is one of the very first studies that directly investigates driver behavior in a cooperative CV mobility application. Given the significance of proper understanding of drivers’ behavior in developing, evaluating, and deploying connected vehicle mobility applications, continuous effort should be made to gather actual drivers’ behavior data which provides valuable insight in drivers’ decision making process under connected-vehicle environment.
PHD (Doctor of Philosophy)
Connected Vehicle, Advisory Messages, Driver Response, Driver Behavior
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