Traffic simulation is a cost-effective way to test the deployment of Cooperative Adaptive Cruise Control (CACC) vehicles in a large-scale transportation network. By using a previously developed microscopic simulation testbed, this paper examines the impacts of four managed lane strategies for the near-term deployment of CACC vehicles under mixed traffic conditions. Network-wide performance measures are investigated from the perspectives of mobility, safety, equity, and environmental impacts. In addition, the platoon formation performance of CACC vehicles is evaluated with platoon-orientated measures, such as the percentage of platooned CACC vehicles, average platoon depth, and vehicle-hour-platooned that is proposed in this paper under the imperfect DSRC communication environment. Moreover, managed lane score matrices are developed to incorporate heterogeneous categories of performance measures, aiming to provide a more comprehensive picture for stakeholders. The results show that mixing CACC traffic along with non-CACC traffic across all travel lanes is an acceptable option when the market penetration (MP) is lower than 30% for roadways where a managed lane is absent. Providing CACC with priority access to an existing managed lane, if available, is also a good strategy for improving the overall traffic performance when the MP is lower than 40%. When the MP reaches above 40%, a dedicated lane for CACC vehicles is recommended, as it provides greater opportunity for CACC vehicles to form platoons. The facilitation of homogeneous CACC traffic flow could make further improvements possible in the future.
Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads. Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs). This paper aims to quantify such impacts by studying the high-resolution vehicle trajectory data that are obtained from microscopic simulation. Two platoon clustering strategies for CACC-an ad hoc coordination strategy and a local coordination strategy-are implemented. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. According to the two-sample Kolmogorov-Smirnov test, however, the distributions of the hard braking events (as a potential safety impact) for HVs change significantly under local coordination strategy. For both of the clustering strategy, CAVs increase the average lane change frequency for HVs. The break-even point for average lane change frequency between the two strategies is observed at 30% MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change frequency following a monotonically increasing pattern in response to MPR, and it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction state of the car-following model for HVs is analyzed. It is revealed that the composition of the interaction state could be influenced by CAVs as well. One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs.
Automated longitudinal control technology has been tested through cooperative adaptive cruise control (CACC), which is envisioned to improve highway mobility drastically by forming a vehicle platoon with short headway while maintaining stable traffic flow under disturbances. Compared with previous research efforts with the pseudomultiobjective optimization process, this paper proposes an automated longitudinal control framework based on multiobjective optimization (MOOP) for CACC by taking into consideration four optimization objectives: mobility, safety, driver comfort, and fuel consumption. Of the target time headways that have been tested, the proposed CACC platoon control method achieved the best performance with 0.9- and 0.6-s target time headways. Compared with a non-optimization-based CACC, the MOOP CACC achieved 98%, 93%, 42%, and 33% objective value reductions of time headway deviation, unsafe condition, jitter, and instantaneous fuel consumption, respectively. In comparison with a single-objective-optimization-based approach, which optimized only one of the four proposed objectives, it was shown that the MOOP-based CACC maintained a good balance between all of the objective functions and achieved Pareto optimality for the entire platoon.
This paper presents a low-cost and energy-saving urban mobility monitoring system based on wireless sensor networks (WSNs). The primary components of the proposed sensor unit are a Bluetooth sensor and a Zigbee transceiver. Within the WSN, the Bluetooth sensor captures the MAC addresses of Bluetooth units equipped in mobile devices and car navigation systems. The Zigbee transceiver transmits the collected MAC addresses to a data center without any major communications infrastructures (e.g., fiber optics and 3G/4G network). A total of seven prototype sensor units have been deployed on roadway segments in Newark, New Jersey, for a proof of concept (POC) test. The results of the POC test show that the performance of the proposed sensor unit appears promising, resulting in 2% of data drop rates and an improved Bluetooth capturing rate.
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