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In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts. [ABSTRACT FROM AUTHOR]

There have been enormous efforts to implement automated vehicle-based mobility (AVM) by considering smart infrastructure such as cooperative intelligent transportation system. However, there is lack of consideration on economical approach for an optimal deployment strategy of the AVM service and smart infrastructure. Furthermore, the influence of travel demand in service area has been ignored. We develop a new framework for maximizing the profit of connected and automated vehicle-based mobility (CAV-M) service using cost modeling and metaheuristic optimization algorithm. The proposed framework extracts an optimal sub-network, which is selected by a set of optimal links in the service area, and identifies an optimal construction strategy for the smart infrastructure depending on given operational design domain and travel demand. Based on service network analyses with varying demand patterns and volumes, we observe that the optimal sub-network varies with the combination of trip demand patterns and volumes. It is also found that the benefit of deploying the smart infrastructure is obtainable only when there are sufficient travel demands. Furthermore, the optimal sub-network is always superior to raw network in terms of economical profit, which suggests the proposed framework has great potential to prioritize road links in the target area for the CAV-M service. [ABSTRACT FROM AUTHOR]

A previously developed real-time forward collision warning system (RCWS) using a multi-layer perceptron neural network (MLPNN) with a single hidden layer aims to be implemented with in-vehicle sensor and smartphone under cloud-based communication environment. However, several issues exist concerning the communication delay between the smartphone and the cloud server, especially when uploading massive traffic information to the cloud server simultaneously. In order to mitigate the impact of the delay, this research proposes two modified RCWSs using an advanced feed-forward neural network (F2N2). One of them involves MLPNN with two hidden layers and the other includes radial basis function network. The modified RCWSs are evaluated by the real-time warning accuracy under different market penetration rates (MPRs) and delays. The evaluation shows that the warning performances of each RCWS increase when the MPR increases or the delay decreases overall. In addition, the modified RCWSs outperform the original one in all conditions. Furthermore, the performance gap between the modified RCWSs increases as the MPR decreases and the delay increases. These findings suggest that the advanced F2N2 model can be an effective alternative for uprating the performance of the RCWS, particularly under a large delay with low MPR. [ABSTRACT FROM AUTHOR]

Surrogate Safety Measure (SSM) is one of the most widely used methods for identifying future threats, such as rear-end collision. Various SSMs have been proposed for the application of Advanced Driver Assistance Systems (ADAS), including Forward Collision Warning System (FCWS) and Emergency Braking System (EBS). The existing SSMs have been mainly used for assessing criticality of a certain traffic situation or detecting critical actions, such as severe braking maneuvers and jerking before an accident. The ADAS shows different warning signals or movements from drivers’ driving behaviours depending on the SSM employed in the system, which may lead to low reliability and low satisfaction. In order to explore the characteristics of existing SSMs in terms of human driving behaviours, this study analyzes collision risks estimated by three different SSMs, including Time-To-Collision (TTC), Stopping Headway Distance (SHD), and Deceleration-based Surrogate Safety Measure (DSSM), based on two different car-following theories, such as action point model and asymmetric driving behaviour model. The results show that the estimated collision risks of the TTC and SHD only partially match the pattern of human driving behaviour. Furthermore, the TTC and SHD overestimate the collision risk in deceleration process, particularly when the subject vehicle is faster than its preceding vehicle. On the other hand, the DSSM shows well-matched results to the pattern of the human driving behaviour. It well represents the collision risk even when the preceding vehicle moves faster than the follower one. Moreover, unlike other SSMs, the DSSM shows a balanced performance to estimate the collision risk in both deceleration and acceleration phase. These research findings suggest that the DSSM has a great potential to enhance the driver’s compliance to the ADAS, since it can reflect how the driver perceives the collision risks according to the driving behaviours in the car-following situation. [ABSTRACT FROM AUTHOR]

Ideal configuration or layout of highways should resemble the actual demands for the roads represented by Origin-Destination (OD) information. It would be beneficial if existing highways can be evaluated for their configurational fitness against the current demands, and newly planned highways can carefully be designed in terms of their layouts and topologies that would reflect the demands. Analysis techniques used for complex networks in the matured field of network theory can be applied for the highway layout health monitoring against the current OD information. This paper proposes a methodology of measuring the fitness of existing highways by comparing their structural configuration against conceptual OD networks using well-established techniques in network theory for complex networks. In the first phase, this paper conducts an empirical analysis and finds that both structural highway network and OD network follow the “power law” distribution as they are weighted by capacity and traffic volume respectively. It is also found that the power law coefficient of the OD network dynamically changes throughout the day and week. In the second phase, a noble methodology of weighting and measuring the health, of structural highway networks against OD networks by means of comparing their power law coefficients is proposed. It is found that the proposed method is effective at detecting deviations from ideal structural configurations associated with actual demands. [ABSTRACT FROM AUTHOR]

The existing rear-end collision warning systems (CWS) that involve the variable perception–reaction time (PRT) have some negative effects on the collision warning performance due to the poor adaptive capability for the influence of different PRTs. To deal with the related problems, several studies have been conducted based on nonparametric approaches. However, the previous nonparametric methods are of doubtful validity with different PRTs. Moreover, there is a lack of consideration for the criterion to split the real-time data into training and testing sets in terms of enhancing the algorithm performance. In this paper, we propose multilayer perceptron neural-network-based rear-end collision warning algorithm (MCWA) to develop a real-time CWS without any influence of human PRTs. Through a sensitivity analysis, the optimal criterion for splitting real-time data into training and prediction is found in terms of a tradeoff between training time and algorithm accuracy. Comparison study demonstrates that the proposed algorithm outperforms other previous algorithms for predicting the potential rear-end collision by detecting severe deceleration in advance. [ABSTRACT FROM PUBLISHER]

Various mobility services have been proposed based on the integration of automated vehicle (AV) and road infrastructure. Service providers need to identify a set of road sections for ensuring the driving safety of an AV-based mobility service. The main objective of this research is to analyze the safety performance of AVs on the road geometrical features present during this type of mobility service. To achieve the research goal, a mobility service is classified by a combination of six road types, including expressway, bus rapid transit (BRT) lane, principal arterial road, minor arterial road, collector road, and local road. With any given road type, a field test dataset is collected and analyzed to assess the safety performance of the AV-based mobility service with respect to road geometry. Furthermore, the safety performances of each road section are explored by using a historical dataset for human-driven vehicle-involved accident cases. The result reveals that most of the dangerous occurrences in both AV and human-driven vehicles show similar patterns. However, contrasting results are also observed in crest vertical curve sections, where the AV shows a lower risk of dangerous events than that of a human-driven vehicle. The findings can be used as primary data for optimizing the physical and digital infrastructure needed to implement efficient and safe AV-based mobility services in the future. [ABSTRACT FROM AUTHOR]