关键词: 交通系统工程, 行程时间可靠性, 实时估计方法, 城市道路网络, 数据驱动

Abstract: Urban road networks are often affected by periodic or stochastic perturbations, leading to traffic issues such as supply chain disruptions or personal travel costs increase. As a probabilistic expression of system risk, network reliability is defined as the probability of the road network providing satisfactory service levels under random disturbances. Travel time reliability is an important indicator for measuring the reliability of the road network, which can be represented by the distribution of vehicle travel times. Existing studies mostly rely on simulation or historical data, and the estimation of network travel time reliability has not yet been achieved. This study proposes a method framework for estimating the threshold, expectation, and variance of road network travel time reliability using cross-sectional data as input, and the proposed real-time estimation method for the expectation and variance of the probability distribution of vehicle delay travel time ratios. These methods reduce the constraints on data volume and data types, providing theoretical support for evaluating the performance of traffic control in different regions from the reliability perspective.
Firstly, the statistical definition of real-time network travel time reliability is clarified, and its mathematical expression quantization index is given: the probability that the vehicle delay travel time ratio in the road network is less than a given threshold within a differential time period. On this basis, a quantitative model for network travel time reliability is established, and a framework for estimating the threshold, expectation, and variance of network travel time reliability using cross-sectional data as input is proposed. Then, the delay travel time ratio threshold is determined based on the percentile of the probability distribution of the delay travel time ratio. The BPR function is used to estimate the expectation of the delay travel time ratio. The variance of the travel time ratio is estimated based on the relationship between the difference between the ideal and actual total travel distance of vehicles in the macroscopic fundamental diagram and the traffic flow density distribution of the road network. Combined with practical application requirements, reliability estimation steps are provided. Finally, a microscopic simulation network is used as an example to verify the effectiveness of the proposed real-time reliability estimation method.
The estimation method described in this study makes average absolute errors of 0.0568, 0.0617, and 0.0759 when the signal cycle of the road network is 60s, 90s, and 120s, respectively, with an estimated reliability error of less than 10%. The estimation results are better when the network traffic flow is in a non-saturated state. Based on the application of this method in Qingpu District of Shanghai and Binjiang District of Hangzhou, a reliability error below 20% can meet the daily usage requirements of traffic management departments. This proves that dynamic estimation of network travel time reliability can be achieved by obtaining only partial cross-sectional traffic detection data within the network, providing a new solution for estimating road network travel time reliability.
This study can provide traffic information that reflects the current regional travel reliability for traffic participants: the higher the network travel time reliability, the greater the probability of completing the trip as planned in that area. For traffic managers, the research results can support the evaluation of traffic control effectiveness at the reliability level, providing theoretical support for the performance evaluation of traffic control in different regions. This study has not yet analyzed the actual impact of the delay travel time ratio threshold. To address this limitation, crowdsourced vehicle trajectory data will be collected to conduct an empirical sensitivity analysis in the future.

Key words: traffic system engineering, travel time reliability, real-time estimation method, urban road network, data driven