Traffic incidents have negative impacts on traffic flow and the gross domestic product of most countries. In addition, they may result in fatalities and injuries. Thus, efficient incident detection systems have a vital role in restoring normal traffic conditions on the roads and saving lives and properties. Researchers have realized the importance of Automatic Incident Detection (AID) systems and conducted several studies to develop AID systems to quickly detect traffic incidents with an acceptable performance level. An incident detection system mainly consists of two modules: a data collection module and a data processing module. The performance of AID systems is assessed using three performance measures; Detection Rate (DR), False Alarm Rate (FAR) and Mean Time to Detect (MTTD). Based on data processing and incident detection algorithms, AID can be categorized into four categories: comparative, statistical, artificial intelligence-based and video–image processing algorithms. The aim of this paper is to investigate and summarize the existing AID systems by assessing their performance, strengths, limitations and their corresponding data collection and data processing techniques. This is useful in highlighting the shortcomings of these systems and providing potential solutions that future research should focus on. The literature is sought through an extensive review of the existing refereed publications using the Google Scholar search engine and Scopus database. The methodology adopted for this research is a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study can serve as a reference for researchers who are interested in developing new AID systems.

The construction sector is a crucial contributor to the national and global economy. Therefore, improving the efficiency and effectiveness of construction projects can have a significant impact on gross domestic product (GDP). However, managing construction projects can be challenging due to the uncertainties and complexities involved. The three primary interrelated constraints of construction projects, namely, time, scope, and cost, require effective management to ensure successful completion. To optimize the time and cost of construction projects, various optimization models and techniques have been proposed in the literature. This paper presents a systematic review of the time-cost optimization models in construction management and proposes some future work to improve the solution of the considered problem. The review categorizes the existing models into three categories: exact models, approximate models, and hybrid algorithm models. The exact models provide optimal solutions but require a lot of computational time and may not be efficient in solving multi-objective and large-scale problems. The approximate models provide near-optimal solutions and reduce computational effort but may not be efficient in solving large-scale projects. The hybrid algorithm models combine the good properties of different algorithms to provide high-quality and efficient solutions. The purpose of this paper is accomplished through a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The paper analyzes the contribution, advantages, and limitations of each category and provides recommendations for future work. Based on the review, several recommendations for future work are provided, including the development of hybrid models that combine different optimization techniques, the incorporation of risk management into optimization models, and the use of advanced data analytics techniques to improve the accuracy of optimization models. Overall, this paper provides an up-to-date comprehensive review of the time-cost optimization models used in construction management and offers valuable insights for researchers and practitioners in this field. The findings of this review can be used to guide future research and improve the effectiveness of optimization models for construction projects.

Traffic incidents have adverse effects on traffic operations, safety, and the economy. Efficient Automatic Incident Detection (AID) systems are crucial for timely and accurate incident detection. This paper develops a realistic AID model using the Random Forest (RF), which is a machine learning technique. The model is trained and tested on simulated data from VISSIM traffic simulation software. The model considers the variations in four critical factors: congestion levels, incident severity, incident location, and detector distance. Comparative evaluation with existing AID models, in the literature, demonstrates the superiority of the developed model, exhibiting higher Detection Rate (DR), lower Mean Time to Detect (MTTD), and lower False Alarm Rate (FAR). During training, the RF model achieved a DR of 96.97%, MTTD of 1.05 min, and FAR of 0.62%. During testing, it achieved a DR of 100%, MTTD of 1.17 min, and FAR of 0.862%. Findings indicate that detecting minor incidents during low traffic volumes is challenging. FAR decreases with the increase in Demand to Capacity ratio (D/C), while MTTD increases with D/C. Higher incident severity leads to lower MTTD values, while greater distance between an incident and upstream detector has the opposite effect. The FAR is inversely proportional to the incident’s location from the upstream detector, while being directly proportional to the distance between detectors. Larger detector spacings result in longer detection times.

This study presents a novel, machine-learning-based Automatic Incident Detection (AID) system for freeways. Through a comprehensive analysis of existing AID systems, the paper identifies their limitations and key performance metrics. VISSIM, a traffic simulation software, is employed to generate diverse, realistic traffic data incorporating factors significantly impacting AID performance. The developed system utilizes an Artificial Neural Network (ANN) trained via RapidMiner software. The ANN is designed to learn and differentiate normal and incident traffic patterns. Training yields a Detection Rate (DR) of 95.6%, a False Alarm Rate (FAR) of 1.01%, and a Mean Time to Detection (MTTD) of 0.89 min. Testing demonstrates continued effectiveness with a DR of 100%, a FAR of 1.29%, and a MTTD of 1.6 min. Furthermore, a sensitivity analysis is conducted to assess the influence of individual factors on system performance. Based on these findings, recommendations for enhancing AID systems are provided, promoting improved traffic safety and incident management. This research empowers transportation authorities with valuable insights to implement effective incident detection strategies, ultimately contributing to safer and more efficient freeways.

Autonomous vehicles (AVs) are smart driving technology that is expected to alter the perception of transportation. The purpose of this study is to evaluate the impacts of AVs on freeway traffic performance at different percentages of AVs ranging from 0% to 100% and at two different undersaturated traffic volume levels with demand to capacity ratios of 0.6 and 0.8. The well-known VISSIM software was used to develop a microsimulation model to evaluate different scenarios that represent different market penetration rates of AVs and different demand to capacity ratios. The results showed that the minimum improvement was at 5% AVs and 0.6 demand to capacity ratio and the maximum improvement was achieved at 100% AVs and 0.8 demand to capacity ratio. The increase in the average speed ranges from about 5% to about 15%, the reduction in travel time ranges from about 1% to about 12% and the delay reduction is about 18% to about 97%. The improvement in traffic performance when AVs market penetration rates increases from 0% to 100% is attributed to the fact that the conventional vehicles (CVs) are replaced by AVs that can travel with higher constant speed and with a smaller time headway. Statistical t-test was carried out to examine the statistical significance of the difference between scenarios’ average speeds and between the average speed of both AVs and CVs. The test revealed that there average speed values of AVs are significantly higher than CVs values for all AVs market penetration rates at demand to capacity ratios of 0.6 and 0.8. Because at these demand to capacity ratios the congestion is low. Thus, AVs can travel freely with speeds significantly higher than CVs. Keywords: Autonomous vehicles, Freeway performance, Conventional vehicles, Average speed, Travel time, Delay