基于LightGBM模型的公交线路串车状态识别方法

同1条线路上相邻公交车辆由于受到道路等因素的影响，其实际车头时距与发车间隔相比显著缩短，导致相邻车辆在较短的时间内到达同1个公交站点即引发公交线路“串车运行”问题（即相邻公交车辆在实际运行中的车头时距与发车间隔相比显著缩短的现象）。辨识线路的串车状态（串车运行和非串车运行）是进一步提升城市公交车辆运营的稳定性的关键。提出了基于贝叶斯参数优化的LightGBM模型，并将其用以识别公交线路串车状态。从站点、运行、乘客、时间和天气这5个角度初步选取20个影响线路串车状态的关键因素，并采用Spearman相关性检验和方差膨胀因子诊断多重共线性。建立二元Logit模型进行影响因素分析。提取显著的影响因子，构建LightGBM模型用以识别线路串车状态，并分别采用贝叶斯优化与随机搜索优化对模型中用以确定模型属性和训练过程的超参数进行寻优。以西安市公交车辆行车数据为例进行模型的应用验证，对比2种参数寻优方法（贝叶斯优化与随机搜索优化）的效率，并将提出的LightGBM模型与XGBoost、随机森林（RF）、决策树模型（DT）和AdaBoost模型的识别精度进行对比。研究表明:上车乘客数、信号灯数量、近距商业区数量、近距内主路上行驶的距离（即车辆在近距离范围内在主道路上行驶过的距离）和拥堵延时指数对线路串车状态有显著影响。LightGBM模型的参数采用贝叶斯优化比采用随机搜索优化的准确率提高了1.31%。采用贝叶斯算法优化参数的LightGBM模型比采用随机搜索算法优化的准确率提高了1.31%。所提出的经贝叶斯优化的LightGBM模型正确识别公交线路串车状态（包括串车运行和非串车运行）的比率为82.89%，识别性能优于对比模型。 

Identification of Bunching State of Bus Lines Based on a LightGBM Model

Actual headways of adjacent buses of a same line can be significantly shortened, compared with the departing intervals, due to the influences of road situations and other factors, resulting in adjacent buses arriving at the same bus station in a relatively short period of time. This is called "bus bunching" in the transit industry. Identification of the bunching state of bus lines(i.e., bunching or non-bunching)is a key step to improve the operation of the urban public transit system. A LightGBM model with its parameters optimized by a Bayesian algorithm is proposed and applied to identify the bunching state. First, 20 factors related to the following five aspects including bus stops, operation, passengers, time, and weather, which potentially influence the bus bunching state, are selected. Spearman correlation test and variance inflation factor are used to diagnose their multi-collinearity. Then, a binary Logit model is developed to identify the significant impact factors, based on which the LightGBM model is developed to identify the bus bunching state. The super parameters of the LightGBM model(which are used to determine model attributes and training process)are optimized by a Bayesian optimization and a random search optimization, respectively. Finally, bus operation data from the City of Xi'an, China is used to verify the proposed model. The efficiency of the above two parameter optimization methods(i.e., Bayesian and random search)are compared, and the identification accuracy of the proposed LightGBM model is compared with XGBoost, Random Forest(RF), Decision Tree(DT)and AdaBoost models. Study results show that: first, the following factors, including number of passengers, number of signal lights, number of business districts within a short range, driving length on the main road within a short-range and traffic congestion index have a significant impact on the bus bunching state; second, the accuracy of the LightGBM model with its parameters optimized with the Bayesian method is 1.31%higher than that model with its parameters optimized by the random search method; third, the accuracy of the proposed Bayesian optimized LightGBM model for identifying the two bus bunching states(i.e., bunching or non-bunching)reaches 82.89%, which is found to be better than the above competing models.