Adjacent closely spaced intersections with characteristics of short link distance and high pedestrian flow are primarily located in high-density urban areas. To address the problems of queue overflow, poor traffic operation, and high pedestrian travel delays, a pedestrian-motor vehicle signal optimization method for adjacent closely spaced intersections was proposed in this paper. First, the traffic flow entering the closely spaced intersections is divided into nonarterial and arterial flow categories to establish a delay model of pedestrian crossing. Then, a pedestrian crossing delay model based on pedestrian demand is constructed according to pedestrian crossing time and a space diagram. An optimization model for pedestrians and vehicles at adjacent closely spaced intersections is established, and an artificial intelligence algorithm is used to optimize this model. Finally, a selected case intersection is optimized. The results show that compared with a traditional single optimization method, vehicle delay decreased about 4%, 13.8%, 17.1%, and 25.9% and total pedestrian delay decreased by 3%, 15%, 25%, and 31%, respectively, for the four proposed scenarios.

The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S‐TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T‐TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059.

To evaluate the operation of an urban transportation system by accurately analyzing the reliability of a road network, with the aim of reducing the substantial fluctuation of travel time, a method for dynamically estimating the reliability of road network travel time is proposed. First, the definition of travel time reliability is given by referring to system reliability theory: the possibility that all travelers in the road network reach their destination within a predetermined time. The travel time reliability is numerically expressed as the probability that the ratio of delay to travel time (RODT) is less than a certain value. Then, actual data are used to prove that the RODT of vehicles in the road network obeys the normal distribution, based on which a data-driven method of travel time reliability estimation is proposed. The travel time reliability of a real-world network is estimated based on the trajectory. Finally, the variation in travel time reliability under different road network capacities is studied, and the accuracy of the estimated travel time reliability under different trajectory data penetration rates is analyzed. The dynamic estimation method of travel time reliability proposed in this paper supports better understanding of the operation efficiency of urban road traffic systems, to help better evaluate the performance of road network systems and provide a basis for road network reliability optimization.

The performance of intersections has been considered a key factor in measuring the efficiency of urban road systems. In this paper, a reliability model for a two‐phase signalized intersection is proposed on the basis of presenting a concept of traffic function reliability (TFR). First, classic cumulative curves are created to derive delay formulas. Then, a model for traffic function reliability is proposed based on the quantitative relationship between the random traffic flows, signal timing, and queue lengths. Finally, the delay threshold of the intersection is determined by referring to the level of service. A numerical simulation has been created to clarify the proposed mechanism of TFR. The results show that the saturation and the green time ratio have a dramatical influence on TFR. Under different saturation levels, the sensitivity of TFR to changes in green time ratio gradually weakened. When the green signal ratio increases above a certain value, TFR remains nearly constant. A microscopic simulation verified the applicability of the proposed model. The results show that the accuracy of the model is close to 90% in the case of low saturation. This method provides road authorities useful insights to understand travel time variability.

Intersections are a vital component of the urban road network. Their use is essential for traffic efficiency and operational stability. In this paper, a dynamic control method for two-phase signal control intersection is proposed by using the intercycle flow standard deviation. This technique can dynamically assign weights to different control goals of traffic efficiency and operational stability. For intersections with large flow fluctuation amplitudes, greater weight is given to the control goal of operational stability to reduce the sensitivity of the signal control on flow fluctuation. For intersections with a small flow fluctuation amplitude, greater weight is given to the control goal of traffic efficiency to maximize the intersection throughput. Compared with the plan selection and pretimed control method, the numerical results can validate that this method has better control performance under different flow fluctuations amplitude. The fluctuation range length is significantly optimized, especially in the cases of the low and medium flow saturation conditions.

Ensuring the reliable operation of all nodes in a road network is often challenging due to the influence of managed resources and other dynamic factors. This study proposes a method for identifying critical nodes based on multi-attribute decision-making, aimed at enhancing traffic efficiency and reliability. By utilizing dynamic traffic flow data and real-time ranking of node criticality, an adaptive signal optimization approach was developed to establish a collaborative control method for road network signals. First, a quantitative analysis was conducted to evaluate the impact of road network topology, traffic volume, and travel time reliability, enabling a comprehensive ranking of critical nodes. Subsequently, based on real-time traffic flow and critical node rankings, a signal collaborative control method was established to optimize travel time reliability while mitigating congestion and resource inefficiencies. Case analysis revealed that nodes with higher OD (origin–destination) pairs do not necessarily exhibit high traffic flow or criticality, underscoring the importance of targeted signal control strategies. The results demonstrate that the proposed optimization method effectively improves the dynamic reliability and operational efficiency of road networks while contributing to sustainable transportation by enhancing adaptability to traffic fluctuations. This study provides theoretical and practical references for advancing sustainable traffic management and supporting the transition to smarter transportation systems.