Improving Traffic Safety and Efficiency by Adaptive Signal Control Systems Based on Deep Reinforcement Learning

As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist\u27s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety

Evaluation and Augmentation of Traffic Data from Private Sector and Bluetooth Detection System on Arterials

Traffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE, one of the private sector data vendors, and BDS of arterial corridors in Orlando, Florida. The results showed that the accuracy and reliability of BDS data are better than private sector data, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In addition, another preliminary study aiming at improving the quality of private sector data was also demonstrated. Information about bimodal traffic flow extracted by a finite mixture model from historical BDS is employed to augment real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the studied corridor except for segments highly influenced by traffic from or to the expressway ramps

Development of Crash Modification Factors (CMFs) for Utah Intersections

5H086 40H22-8288This research project aims at developing state-specific CMFs at intersections in Utah to quantify the safety impact of the countermeasures. Considering the research needs, data availability, and suggestions from the Technical Advisory Committee, the CMFs of four left-turn-phasing-related treatments, namely converting both-roadway permissive to one-roadway permissive-protected left-turn phasing, converting one-roadway to both-roadway permissive-protected left-turn phasing, converting both-roadway permissive-protected to one-roadway protected left-turn phasing, and converting one-roadway to both-roadway protected left-turn phasing are developed using the cross-sectional study. The results revealed that the conversion from permissive to permissive-protected left-turn signals showed no improvement in safety. However, converting permissive-protected signals to protected signals did result in a reduction of left-turn-related crashes. The CMF estimates for converting to protected left-turn phasing align with findings from other states, but CMFs for converting to permissive-protected phasing are higher than those seen in other states. A survey of other state DOT practices of permissive-protected left-turn phasing indicates that Utah typically uses a shorter protected portion of the permissive-protected phasing compared to other states which may be a possible reason for why Utah\u2019s CMFs are higher

Impact of Connected Vehicle Technology on Traffic Safety under Different Highway Geometric Designs

Connected and automated vehicle (CAV) driving features can impact traffic safety in many aspects owing to their improved driving behavior. On the other hand, road geometric design elements are mainly based on human reactions and behavior, which might affect safety depending on road layout and the parties involved. However, automation and connectivity can convey more data about the driving environment that will reduce confronting unexpected driving conditions and driving load on drivers. Therefore, the risk of crashes due to roadway geometries will be reduced. The main objective of this study is to focus on the performance of the traffic flow, including CAVs with different geometric designs addressing the potential crash spots. This study aims to determine the efficacy of CAVs on traffic network safety quantitively and qualitatively. For this purpose, multiple scenarios with different geometric features are designed and simulated. Simulations include varied CAV shares in traffic composition and employ driving features of CAVs. Using the surrogate safety assessment model (SSAM), simulation results are evaluated for potential conflicts. Crash severity, frequency, and classification are studied to determine the safety effects of CAVs on potential crash hot spots. Results indicated that higher penetration rates of CAVs could improve the safety performance of traffic networks in multiple cases by reducing deceleration rates, cooperative lane changing, and adjusted speed in required situations. However, due to the interaction of CAVs and HDVs in a signalized intersection, safety performance might not benefit from CAV presence