As an effective method, Traffic Conflict Technology (TCT) is widely applied to estimate the safety level of some risky areas, especially for the merging areas in the urban roads. Most of researchers prefer to just exploit the promising safety assessment models using realistic traffic data to predict the numbers of conflicts. There are a few types of research focusing on how to predict traffic conflict assessment indexes precisely in merging areas. Despite some related studies have realized this critical dilemma, significant lane-change characteristics are usually ignored and it is worthwhile devoting much effort to this. Hence, a modified Post Encroachment Time (PET) model is proposed in this study, to figure out lane-change characteristics as well as accurately forecast traffic safety of the merging area. Unlike other conventional methods, such as Time to Collision (TTC) or PET models, the proposed model not only fully takes the lane-change characteristics of merging vehicles into consideration, but also it explores the safety requirements in the process of lane changing in details. Moreover, the calculation formula of the modified PET model is gradually deduced by the trajectory of merging behavior and velocity formula. Besides, for the sake of pursuing a high validity, this paper exceptionally adopts two crucial compositions of PET, and eventually gives a unified calculation formula. In order to determine an appropriate threshold, traffic conflict data collected from Guangyuan Road in Guangzhou are analyzed. The results clearly prove that PET < 0.7 means a serious conflict, 0.7. PET <1.31 means a general conflict, 1.31. PET < 2.25 means a slight conflict, and PET. 2.25 means a potential conflict. Finally, 50 groups of PET data and a comparative experiment are collected to demonstrate the effectiveness and reliability of the modified PET model.

Emergency rescue plays a key role in accident remediation and prevention. It has been the most critical factor to control the negative impacts of accident deterioration, which can save more lives and reduce property loss in time. As an essential component, emergency rescue path planning can effectively shorten the travelling time and improve the robustness of the rescue path. However, there still exist various uncertainties that may make a great impact on selecting the rescue path, which is less successful and still requires further research. To address the problem of low rescue efficiency, a co-evolutionary optimization algorithm (CEOA) is proposed in this study. Meanwhile, this study presents how the sub-path weight function co-evolves with the future traffic environment dynamics using the evolution mechanism, considering the complex vehicle running characteristics in the urban roads. Three sets of simulation experiments are conducted to test the comprehensive performance of CEOA under various scenarios. Experimental results show that the proposed CEOA is superior to traditional and emerging path optimization methods in terms of the travelling time and its stability, such as on-line re-optimization (OLRO) and co-evolutionary path optimization (CEPO). The proposed CEOA integrates the advanced advantages of regular re-optimization and co-evolutionary optimization, and opens the door to develop new path optimization technology. The findings provide powerful technology support and a theoretical basis for emergency rescue management improvement.

The severe impact of traffic accidents, along with a large number of deaths and disabilities, necessitates further improvements in rescue path optimization. To make the emergency rescue more efficient and furthermore ensure health care in life-saving and mitigating traffic congestion as soon as possible, a methodology for rescue vehicle path optimization, timing co-evolutionary path optimization (TCEPO), is proposed to optimize the rescue path. Distinguishing from conventional online re-optimization (OLRO) and co-evolutionary path optimization (CEPO), in TCEPO, each optimization process co-evolves with future traffic environment that keeps changing over time, and the best path will be modified timely based on the predicted routing environmental dynamics (PRED) and recent traffic data. Besides, for better computation efficiency, this research reports an improved ripple spreading algorithm (RSA) as a realization of TCEPO to resolve the optimality problem. The modeling and solutions of TCEPO are discussed in detail to illustrate the applications in emergency rescue path optimization. In order to compare the performance of three methods (OLRO, CEPO and TCEPO), the same optimization tasks and scenarios are presented, and numerical simulation is carried out 100 times. Experimental results clearly prove that the proposed TCEPO possesses stronger robustness and is about 17.65% to 40.02% shorter than CEPO, as well as about 26.34% to 38.47% shorter than OLRO in terms of the travelling time under the PRED with various uncertainties. These advantages have a great impact on raising efficiency and reliability of emergency rescue, which can help rescue vehicles reach the destination as quickly as possible and save more lives.

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

The safety of signalized intersections is of great concern. To allow for an effective evaluation measure on the safety level of intersections, traffic conflict analysis methods are commonly used. However, the existing literature has mainly focused on the statistical prediction of conflicts by using surrogate measurements, among which the spatial-temporal characteristics of the potential conflicts have been less addressed. In addition, most of the relevant studies rely on precise trajectory data, and the results could be limited to engineering applications when real-time/comprehensive trajectory data are not available. To address these issues, this study proposes a SICP (signalized intersection conflict probability) model to predict a straight-left traffic flow conflict with a spatial-temporal distribution in the heat map, which could effectively evaluate the traffic safety of the existing or prebuilt signalized intersections on urban roads. Firstly, the impact of vehicle movement characteristics on traffic conflict at signalized intersections was considered by incorporating the vehicle movement trajectory. Secondly, the signal phase was categorized in several stages (each phase contains switching and nonswitching stages); then, a vehicle-vehicle conflicts probability prediction model was established by integrating both horizontal and vertical arrival probability. Finally, to validate the performance of the proposed model, the measured data were collected from the intersection of Wushan road and Yuehan road in Tianhe District, Guangzhou, China. SSAM（Surrogate Safety Assessment Model）traffic conflict simulation was used to analyze the traffic conflict in the actual data and compared to the SICP model. A case study was conducted to reveal the evolution mechanism of the conflict risk coefficient at the signalized intersection and to estimate the safety status under the various security optimization strategies. The experimental results verified the effectiveness of the SICP model, indicating that the proposed model is effective in evaluating the safety level of existing or prebuilt signalized intersections.