A Real-Time Server Based Approach for Safe and Timely Intersection Crossings

Abstract

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion.While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technologyagnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10-50%. (GENERAL AUDIENCE ABSTRACT)Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to congestion that blocks other intersection upstream, which disrupt traffic flows across the entire network. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This work presents a technologyagnostic adaptive approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide theoretical bounds to guarantee the performance of our approach in terms of the travel delays that may incur on the vehicles in the system, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed which uses robots to mimic human driving behaviour in an urban environment. Comparisons with widely deployed and state-of-the-art traffic control techniques show that our ...

Work-in-Progress: Worst-Case Response Time of Intersection Management Protocols

“…The literature on the worst-case analysis of IMs is relatively scarce, with examples in [5]- [8]. In [5], Oza and Chantem provide bounds on worst-case vehicle waiting times to show the reliability and safety of their adaptive real-time serverbased management against pre-timed approaches. The work was further analyzed in the presence of an emergency response vehicle using a preemption strategy [6].…”

“…We call this the Worst-Case Response Time (WCRT) of the IM, following the similar concept used in real-time computing systems. The WCRT is an indicator of IM reliable performance as needed for safety-critical or mission-critical traffic [5], [6] and collision avoidance [7], [8].…”

Worst-Case Response Time of Mixed Vehicles at Complex Intersections

“…Other less common metrics also relevant for characterizing the performance of such IM strategies are those related to worst-case scenarios [4], [12], [13], [14], [15], [16], [17]. Considering the worst-case situation that can emerge from particular traffic patterns provides useful information for city planners and traffic management entities.…”

“…It equips them with an estimate of the worst service a given intersection can provide, particularly maximum waiting time and queue size. Knowing the maximum queue size allows tuning the IM configuration to prevent queue spillback at neighboring intersections [12]. In turn, knowing the maximum waiting time is imperative for safety-critical or mission-critical traffic [4], [11], [12], [13], [14], [15], [16], [17].…”

Secure Traffic Lights: Replay Attack Detection for Model-based Smart Traffic Controllers

“…Many traffic control strategies have been proposed that rely on sensors, on-road detectors, cameras and/or other road-side units (RSUs) to measure real-time traffic flow characteristics such as queue lengths, vehicle speeds, traffic density, etc [27]. The measured data is then communicated to a traffic controller that adaptively manages the traffic flow as per the deployed control strategy [23,30]. However, most of these techniques are deployed on top of the legacy traffic infrastructure [3] which was built without security in mind.…”