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Purpose – Freeway networks are designed to higher standards and are safer infrastructures as compared to other road types, if properly designed. On the other hand, these facilities are driven at very high speeds and therefore speed and design consistency are essential for achieving safe infrastructure designs. This chapter describes the criteria for speed and design consistency and looks at new tools and criteria for improving freeway safety in new and in existing infrastructures.

Methodology – This chapter describes the criteria to evaluate if there are speed, design and human factors inconsistencies, as well as potential solutions for tackling local deficiencies and speeding issues. As one of the critical issues in freeway safety is represented by run-off-road crashes, a specific section in the chapter is devoted to newly developed design and assessment tools for improving roadside safety. The potential implications of Intelligent Transportation System (ITS) technologies on freeways design and management are also presented.

Findings and Social Implications – The important crash reduction trends observed in the decade 2001–2010 are now slowing down and new actions are required to be coupled with more traditional design checks. The full implementation of cooperative ITS systems is expected to have a very important impact on road safety, but in the short term several safety improvements can be realised: section speed enforcement techniques and high-friction wearing courses have been proven to be extremely effective, as have perceptual measures accounting for human factors principles.

Ramp meters in the Twin Cities were turned off for 8 weeks in the Fall of 2000. This paper analyzes traffic data collected in this experiment on travel time variability with and without ramp metering for several representative freeways during the afternoon peak period. Travel time variability is generally reduced with metering. However, it is found that ramp meters are particularly helpful for long trips relative to short trips. The annual benefits from reducing travel time variability with meters are estimated to be $33.1 million, compared to the annual ramp metering costs of $2.6 million in the Twin Cities metro area. Thus, the impact on travel time variability should be captured in future ramp metering benefit/cost analysis.

Freeways in Australia play a significant role in connecting distant communities, shifting freight and strengthening the country’s economy. To meet the growing needs of present and future generations, delivering a socially sustainable road infrastructure that creates generational benefits is essential. However, the existing literature reveals the lack of comprehensive indicators to assess the social sustainability performance of freeway projects. Therefore, this paper aims to identify a critical set of system-specific indicators to evaluate the life cycle social footprint of Australian freeways.

This study conducted 31 interview questionnaire surveys with actively engaged stakeholders involved in various freeway projects around Australia. The data collected was analysed using fuzzy set theory and other statistical approaches.

The study identified 42 critical indicators for assessing the social sustainability performance throughout the life cycle of freeways in the Australian context. For example, stakeholder involvement, reduction of casualty rate due to road accidents, fair remuneration to project workforce and improved accessibility to required services.

The context-specific opinions extracted from the industry experts and the comprehensive set of critical indicators identified would ensure that all the vital aspects of social sustainability are considered throughout the life cycle of Australian freeways in the future, assisting the decision-makers in enhancing the project’s social sustainability performance.

The linguistic explanations associated with the ratings given by the industry experts provide greater insight into the context of the life cycle social sustainability assessment of Australian freeways exclusively.

On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors embedded in intelligent freeways. A multi‐paradigm intelligent system approach is employed to solve the complicated and chaotic pattern recognition problem using neural networks, fuzzy logic, and wavelets. Wavelet‐based de‐noising and feature extraction techniques are employed to eliminate undesirable fluctuations in observed data from traffic sensors. The result is reliable algorithms with high incident detection and very low false alarm rates.