Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions

Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.

Signal Timing Optimization Model for Intersections in Traffic Incidents

The intersection control and management can alleviate the traffic congestion caused by traffic incidents. Therefore, it becomes essential to develop a signal optimization method for intersections influenced by traffic incidents, which will be beneficial to prevent congestion spreading. In this paper, the proposed model is capable of maximizing the intersection throughput by comprehensively considering the queue length as the penalty value. The headway of leaving vehicles is assumed to follow the Cowan’s M3 headway distribution, where formulas for queue length can be derived based on gap acceptance theory. To satisfy the conditions for efficiently identifying feasible solutions in a short time, a heuristic algorithm (simulated annealing algorithm) is employed to solve the model. The numerical results can validate that the proposed method can solve the problem more efficiently and alleviate the intersection congestion caused by the incidents more desirably. When the incident occurs away from the intersection stop line, the impacts on intersection throughput will be gradually weakened. The proposed method is capable of improving the signalized intersection throughput while preventing the congestion from spreading to the upstream intersection.

Queue-Based Headway Distribution Models at Signal Controlled Intersection under Mixed Traffic

Headway of vehicles during platoon dispersion at signalized intersection is one of the critical microscopic traffic characteristics in traffic flow theory. The distribution of the discharge headways of vehicles also has a significant impact on the traffic generation process in most of the microsimulation approaches. However, few studies have investigated the vehicle discharge headway for interrupted flow at signalized intersections under mixed traffic conditions. The present study uses data collected from 20 intersections in six cities for comprehensive analysis of discharge headway. A box-and-whiskers plot is generated for discharge headway to quantify its reasonable profile. The diagram shows that headway of vehicles decreases with the queue dispersion. A stable headway can be observed after the fifth vehicle position of a queue, giving a saturation headway of 2.05 s per vehicle. Six types of continuous distribution are tested to model the discharge headway distribution. A statistical investigation is also performed to verify the best-fitted model for each vehicle position in a queue. The ranking of a best-fitted distribution is done for each vehicle position as per the statistical significance. This study demonstrates the discharge headway characteristics and distribution at each vehicle position, which can be useful for traffic flow analysis and especially for improving microsimulation models.

Research on Mandatory Lane-Changing Behavior in Highway Weaving Sections

As the accident-prone sections and bottlenecks, highway weaving sections will become more complicated when it comes to the mixed-traffic environments with connected and automated vehicles (CAVs) and human-driven vehicles (HVs). In order to make CAVs accurately identify the driving behavior of manual-human vehicles to avoid traffic accidents caused by lane changing, it is necessary to analyze the characteristics of the mandatory lane-changing (MCL) process in the weaving area. An analytical MCL method based on the driver’s psychological characteristics is proposed in this study. Firstly, the driver’s MLC pressure concept was proposed by leading in the distance of the off-ramp. Then, the lane-changing intention was quantified by considering the driver’s MLC pressure and tendentiousness. Finally, based on the lane-changing intention and the headway distribution of the target lane, an MLC positions probability density model was proposed to describe the distribution characteristics of the lane-changing position. Through the NGSIM data verification, the lane-changing analysis models can objectively describe the vehicle lane-changing characteristics in the actual scenarios. Compared with the traditional lane-changing model, the proposed models are more interpretable and in line with the driving intention. The results show significant improvements in the lane-changing safe recognition of CAVs in heterogeneous traffic flow (both CAVs and HVs) in the future.