Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this paper, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

Traffic incident management in metropolitan areas is crucial for the recovery of road systems from accidents as well as the mobility and safety of the community. With the continuous improvement in computation and communication technologies, connected and automated vehicles (CAVs) exhibit the potential to relieve incident-induced traffic degradation. To understand the benefits of CAVs on traffic incidents, this paper models the impacts of CAVs with joint consideration of microscopic CAV driving behaviors and macroscopic traffic assignment in mixed traffic environment comprising both CAVs and human-driven vehicles (HDVs). Firstly, a generic traffic assignment model with mixed traffic is proposed to analyze the mixed traffic process from the macroscopic perspective. Then, we incorporate the traffic assignment model with bottleneck delays and incident effects from the microscopic perspective, to model the dynamic road system with incident effects in mixed traffic environment. Furthermore, cooperating with the mixed traffic assignment model, dynamic signal control policies are presented according to different incident severities, and the conditions for equilibrium existence, uniqueness and stability of the road system are derived. The analytical results indicate that road system stability with incident effects is closely related to the incident severity, signal control policy as well as penetration rate and spatial distribution of CAVs. Finally, simulation results are conducted to demonstrate the effectiveness of our proposed incident management policy in improving the recovery rate and system stability of road networks.

Air-ground Integrated Mobility (AIM), as a disruptive mode of travel, has the tremendous potential to alleviate ground traffic congestion issues substantially. However, the primary challenge in achieving this leapfrog development lies in ensuring driving safety. Receiving collision warnings in time within a limited distance can significantly reduce collision risks, which is crucial for ensuring driving safety in AIM. However, due to challenges in aerial network coverage, ensuring the communication quality of aerial Personal Aerial Vehicles (PAVs) remains difficult, thereby affecting the effective transmission of messages. Furthermore, the integration of ground Connected and Automated Vehicles (CAVs) with aerial PAVs in AIM results in significant differences in user resource requirements. Given the complexity of the AIM environment and the high mobility of PAVs, it is challenging to rapidly and accurately capture user communication quality. Therefore, addressing the differential resource requirements of users in this environment is particularly challenging. To this end, we propose a personalized resource allocation strategy assisted by a Channel Knowledge Map (CKM) in AIM. This strategy aims to meet the personalized resource requirements of users while maintaining the maximum Perception Response Time (PRT), thereby ensuring driving safety. Specifically, the CKM in AIM is constructed to obtain channel states through environment-aware communication. Next, a 3D collision warning system is designed to analyze rigorously the maximum PRT of vehicles under different motion states in avoiding collisions. On this basis, with the help of CKM, the channel knowledge of the user’s location is obtained to quantify the communication and computing resources required by each user to maintain the maximum PRT. Finally, we establish the PRT-driven resource optimization problem and employ Deep Reinforcement Learning (DRL) to seek the optimal resource allocation strategy. Simulation results indicate that the proposed method effectively enhances safety and resource utilization in AIM under resource constraints and uneven distribution.

Identifying the root cause of congestion and taking appropriate strategies to improve traffic network performance are important goals of Advanced Traffic Management Systems (ATMS). On many occasions, the causes of congestion are not necessarily attributable to road infrastructures themselves. Instead, signal control strategies at intersections are very often the major contributors of congestion. In lieu of this, in this paper, a root cause identification method is developed with consideration of the impact from both road infrastructure and traffic signal control. Firstly, we differentiate congestion effects between road segments and intersections to attribute the causes of congestion to road infrastructure and signal control respectively. Then, we construct causal congestion trees to model congestion propagation and quantify congestion costs for each road segment and intersection in the whole road network. A Markov model is utilized to capture congestion spatio-temporal correlation among multiple road segments and intersections simultaneously, with which the most critical root cause can be located. Furthermore, a gradient boosting decision tree based method is presented to predict the root cause of congestion according to traffic flows, signal control strategies and road topology in traffic networks. Finally, simulations based on Simulation of Urban Mobility (SUMO) validate the effectiveness of our proposed method in identifying and predicting the congestion root cause. Experiments are further conducted using inductive loop detector data to identify the root cause for the road network of Taipei.

With the development of autonomous driving technology, it is foreseeable that connected and automated vehicles (CAVs) will be fully popularized in people’s lives. During this process, transportation systems are expected to evolve into the era of mixed-autonomy, where CAVs and human-driven vehicles (HDVs) coexist in road networks and share available road resources. To materialize the much-anticipated potential of CAVs, a thorough understanding of CAVs’ effects on transportation systems is indispensable. On the one hand, attributing to advanced sensing, communication, and computation capabilities, CAVs provide opportunities to enhance mixed traffic safety, improve energy savings and suppress shockwave spread. On the other hand, due to advantages in large-scale information and cloud-computing resources, CAVs have the ability to occupy more road resources compared with HDVs, resulting in a reduction in the travel efficiency of HDVs, and even of the entire transportation systems. In this paper, by clarifying the key differences between HDVs and CAVs, we comprehensively review the potential impacts of CAVs when they are appearing on road networks coexisting with HDVs. It can be regarded as the first-of-its-kind paper that systematically overviews the impacts of CAVs in the era of mixed autonomy on both positive and negative emotions. Specifically, the main focuses of this paper are: (1) what are the key differences between CAVs and HDVs? (2) what are the positive impacts of CAVs’ appearance on mixed traffic systems? (3) will the introduction of CAVs cause some negative effects simultaneously? (4) what kinds of strategies should be employed to relieve these negative effects? Hopefully, this paper can not only call for an objective attitude towards the introduction of CAVs, but also provide foresighted advice to address possible challenges during the popularization of CAVs, so as to create a cooperative, safe, and efficient mixed traffic ecosystem.

Traffic incidents are recognized as a key contributor to non-recurrent congestion, which causes many negative effects in economy, environment, health and lifestyle. In this article, we investigate an incident management policy considering both signal control and route choice, which presents a real-time systematic effort to provide a rapid recovery from an incident and mitigate incident-related congestion according to different incident effects. Firstly, we introduce a route choice method on a multiple-route urban road network with consideration of bottleneck delays. Then, we analyze the route travel costs under incident effects and give the equilibrium existence condition after the occurrence of an incident. Furthermore, combining with the route choice method, a novel traffic signal control policy is proposed and the condition for equilibrium existence is given with the consideration of dynamic signal control and route choice simultaneously. Sufficient conditions for the dynamic road system to be stable are also derived and validated by using Lyapunov stability theorem. The analytical results indicate that opposite signal control policies should be applied in road networks under different incident circumstances and the proposed control policy can achieve the improved recovery rate and system stability than existing control policies in terms of dynamic incident effects in road networks. Finally, numerical results have been conducted to demonstrate the effectiveness of our proposed incident control policy and confirm the conditions for road system stability when different incident circumstances had been identified.