One of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the $k$ -nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed $k$ -nearest neighbor model is an effective approach in predicting the short-term traffic condition.

The prediction of bus arrival time is important for passengers who want to determine their departure time and reduce anxiety at bus stops that lack timetables. The random forests based on the near neighbor (RFNN) method is proposed in this article to predict bus travel time, which has been calibrated and validated with real-world data. A case study with two bus routes is conducted, and the proposed RFNN is compared with four methods: linear regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and classic random forest (RF). The results indicate that the proposed model achieves high accuracy. That is, one bus route has the results of 13.65 mean absolute error (MAE), 6.90% mean absolute percentage error (MAPE), 26.37 root mean squared error (RMSE) and 13.77 (MAE), 7.58% (MAPE), 29.01 (RMSE), respectively. RFNN has a longer computation time of 44,301 seconds for a data set with 14,182 data. The proposed method can be optimized by the technology of parallel computing and can be applied to real-time prediction.

As inter-vehicle communication and automatic driving technology continue to develop, but are not yet popular, regular vehicles, connected vehicles and connected autonomous vehicles (CAVs) will coexist on the road for a long time. This mixed traffic environment highlights the need to theoretically analyze the impacts of some connected and autonomous technologies (i.e., accurate detection technology, inter-vehicle communication technology, data storage technology and inter-vehicle cooperation technology) on the stable operation of heterogeneous traffic. According to the characteristics of the vehicles equipped with different technologies, this paper extends the corresponding car-following models based on the optimal velocity model. Through these analytical models, these connected and autonomous technologies are quantified and the linear stability analyses are conducted. Numerical simulation shows that the inter-vehicle communication between three vehicles, and two previous time-step data storage or two future time-step inter-vehicle cooperation are sufficient to stabilize the mixed traffic. As CAV penetration rates increase, the stability of heterogeneous traffic is improved. Furthermore, the stability of heterogeneous traffic is weakened when the size of the largest single fleet increases. These theoretical results can serve as a quantitative tool for scholars and vehicle designers before drawing any qualitative conclusions of related technologies on heterogeneous fleet stability to avoid wasting resources such as data storage capacity and inter-vehicle communication ranges.