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Some recommendations for safer truck traffic operations were presented based on the results obtained. The outcomes of this study could be effectively utilized to support the development of various traffic operations strategies and policies for truck traffic safety. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within VISSIM and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50-60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. investigated as 18.15% and 15.31%, respectively. It is noted that the key to the success is not about the prediction accuracy but the correct identification of an approach with the highest CTT. The prediction accuracies to identify the highest CTT of the SKF and AKF were 89.6% and 92.1%, respectively. In addition, there are a few factors affecting the travel-time prediction accuracy. These include communication errors, vehicle types, MPR, etc. This paper only considered MPR in evaluating the performance of the Kalman filter because it is generally understood that communication errors are important for safety critical applications (i.e., not critical for travel-time estimation) and the vehicle mix on this corridor has less than 2% trucks. In addition, the Kalman filter used in this paper predicted quite well even without considering vehicle types.information to the signal head. Note that step 5 is not available to consider for indoor experiments. Thus, this study analyzed the CTR algorithm in the HILS configuration implementing step 1 through step 4. RESULTSThe effectiveness of the CTR algorithm over the actuated signal algorithm is summarized in Table III in terms of MPRs, volume scenarios, communication types, and Kalman filter algorithms compared. It is noted that the existing actuated signal control is considered to be up to date as the Northern Virginia traffic engineers well maintain the ti...

Adverse weather conditions are a critical external factor that threaten traffic safety on freeways. Variable speed limit (VSL) operations can be used as a traffic management tool to harmonize vehicle speed and reduce the likelihood of crashes during adverse weather conditions. This paper proposes a novel VSL operations strategy based on the prediction of weather and traffic conditions, referred to as a proactive VSL operations strategy. The k–nearest neighbors ( k-NN) statistic was used to predict weather and traffic conditions. VSL operations logic, which used visibility distances and safe stopping distances obtained from road weather information systems and average speeds obtained by vehicle detection systems, was developed. The proposed methodology is focused on the restricted visibility distance resulting from foggy weather conditions on freeways. A microscopic traffic simulator, VISSIM, was used to simulate a freeway traffic stream and collect vehicle-maneuvering data. An external application program interface, the VISSIM COM interface, was used to implement the proposed VSL operations strategy. A surrogate safety assessment model was used to derive indirect safety measures to evaluate the effectiveness of the proposed methodology. Total conflicts in the proposed VSL algorithm were reduced in comparison with a system lacking VSL by 19.10% and 27.27% under moderately and severely foggy weather conditions, respectively. These outcomes are expected to be of great use in the development of more effective VSL systems to enhance freeway safety during adverse weather conditions.