Proactive Traffic Merging Strategies for Sensor-Enabled Cars Ziyuan Wang, Lars Kulik and Kotagiri Ramamohanarao Department of Computer Science and Software Engineering The University of Melbourne, Australia VANET 2007, September, 2007

Outline Introduction Problem Statement Progress So Far Future Directions

Traffic Congestion Some facts on traffic congestion Total amount of delay: 3.7 billion hours in 2003 Wasted fuel: 2.3 billion gallons lost Congestion cost: $63 billion Source: Texas Transportation Institute, 2005 Urban Mobility Report.

Major Causes of Congestion Bottlenecks: Intersections of on-ramps and main roads Blockage due to obstacles “slinky type” effect Source: Federal Highway Administration. Traffic Congestion and Reliability: Linking Solutions to Problems - Executive Summary.

Emergence of VANETs Sensor-Enabled Cars Spatial information Dedicated Short-Range Communications (DSRC) Vehicle-to-Vehicle (V2V) Vehicle-to-Roadside (V2R) Vehicular Ad hoc Networks (VANETs) Safety: less accidents Efficiency: higher road utility Position Speed Acceleration Deceleration

Problem Statement Goal How Applications Optimize traffic throughput Proactive traffic merging algorithms Technology available: sensor-enabled cars + VANETs Applications Intersections at the ramp and the main road of highways (Highway merge assistant) Lane changing when there are obstacles on the way

Existing Approaches Traffic signal timing Ramp metering Fixed Traffic-responsive Ramp metering Real-time information Automation Fully: Platoon (tightly grouped cars) Partial: Adaptive Cruise Control (ACC) Limitations Adaptive Flexible Robust Traffic conditions are highly variable and unpredictable

Contributions Proposed proactive traffic merging algorithms that aim to use the current road facilities efficiently Designed a controlled simulation environment intended to test various traffic merging strategies Investigated what criteria are significant to evaluate the performance of traffic merging algorithms

Proactive Merging Algorithm B X Y Regular Proactive Highway bottleneck Regular strategy Local decision Distance-based Velocity-based Regular means from normal traffic laws. There are different various version of laws, whatever law u follow, give ways to one stream, and always making decision at the merging point. The figure shows a scenario where ramp cars merge onto a main road. In both cases we assume the same initial configuration (middle figure). In the upper figure we assume a priority-based merging algorithm where a car does not adapt its speed before it arrives at the merging section. This requires car x to slow down considerably in order to merge onto the main road. In the lower figure, we assume that car x adapts its speed before its arrival at the merging section and can merge immediately when it arrives at this section. This leads to a smaller impact on both traffic streams as the merging car has the same speed as the cars on the main road when it merges.

Outline of Our Algorithms Comparisons of the proactive merging algorithms Strategy Information Right of Way Assumption Distance-based Position The car that is closest to the merging point Velocity does not vary much Velocity-based Velocity The car that arrives to the merging point first Acceleration does not vary much

Outline of Our Algorithms Sliding decision point Adjust speed appropriately Input {c, d, e} {x, y} Merging strategy Output {c, d, x, e, y} {c, x, d, y, e} {x, c, d, y, e} Regular Distance Velocity

Evaluation Metrics Delay Throughput Flow The time to fill up the main road with a certain number of cars from the ramp Throughput The number of cars that complete merging over a period of time Flow The product of density and velocity

Simulation Intelligent Driver Model (IDM) Microscopic traffic model Parameter Value Maximum velocity Safe time headway Maximum acceleration Maximum deceleration 100 km/h 1.5 s 1m/s^2 3m/s^2 Intelligent Driver Model (IDM) Microscopic traffic model Safety distance Exit ramp

Experiments and Results Experiment settings Light Medium Heavy Unit Main road Ramp 5 10 15 3.6 -- 7.2 cars/km ∞

Experiments and Results

Summary Traffic merging strategies benefit from sensor-enabled cars Proactive merging algorithm outperforms regular strategy in terms of throughput and delay Achieved at the cost of slightly lower velocity Findings so far We must manage complex tradeoffs among factors, such as velocity, throughput, and latency. The traffic flow increases at the beginning as the ramp cars merge, and decreases as the average velocity decreases. In order to maintain a relatively high traffic flow as well as a large number of cars get into the loop, we need to find a balance between the velocity and the number of cars merge into the loop. Now I’m talking the immediate future work focuses on robustness.

Robustness of Algorithms Human factors Imperfect information Sensor accuracy Unreliable communication medium Studies* show only 50-60% of cars in range will receive a car’s broadcast Penetration rates Initially, only a small number of sensor-enabled cars Despite the human factors we talked before, some other factors of importance are involved in this issue. Considering those factors will also enable a more realistic model. * Source: J. Yin, T. EIBatt, and S. Habermas, Performance evaluation of safety applications over DSRC vehicular ad hoc networks, VANET 2004

Higher Degree of Realism Obstacles Blocking Traffic patterns Different distributions Multiple lanes Lane-changing Heterogeneity Different types of vehicles We will address some other factors Why Poisson

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