Electric vehicles (EVs) endow great potentials for future transportation systems, while efficient charge scheduling strategies are crucial for improving profits and mass adoption of EVs. Two critical and open issues concerning EV charging are how to minimize the total charging cost (Objective 1) and how to minimize the peak load (Objective 2). Although extensive efforts have been made to model EV charging problems, little information is available about model properties and efficient algorithms for dynamic charging problems. This paper aims to fill these gaps. For Objective 1, we demonstrate that the greedy-choice property applies, which means that a globally optimal solution can be achieved by making locally optimal greedy choices, whereas it does not apply to Objective 2. We propose a non-myopic charging strategy accounting for future demands to achieve global optimality for Objective 2. The problem is addressed by a heuristic algorithm combining a multi-commodity network flow model with customized bisection search algorithm in a rolling horizon framework. To expedite the solution efficiency, we derive the upper bound and lower bound in the bisection search based on the relationship between charging volume and parking time. We also explore the impact of demand levels and peak arrival ratios on the system performance. Results show that with prediction, the peak load can converge to a globally optimal solution, and that an optimal look-ahead time exists beyond which any prediction is ineffective. The proposed algorithm outperforms the state-of-the-art algorithms, and is robust to the variations of demand and peak arrival ratios.

Accurate bus passenger flow prediction contributes to informed decisions and full utilization of transit supply. Passenger flow is affected by an extensive range of attributes featuring travel environment, which can be collected through multi-source information. A successful prediction model should not only fully utilize the latent knowledge hidden in multi-source data, but also address the resulting multicollinearity issue. Based on this principle, we propose a novel scaled stacking gradient boosting decision tree (SS-GBDT) model to predict bus passenger flow with multi-source datasets. SS-GBDT includes two modules: the prior feature-generation module and the subsequent GBDT-prediction module. The prior module entails a couple of basic models with similar performance, which generates several enhanced features of multi-source data by stacking process. Particularly, we devise a scaled stacking method by introducing a quasi-attention based mechanism (precision-based scaling and time-based scaling). Taking the newly generated features as input, the prediction module forecasts the passenger flow using GBDT model with stacked data, thereby enhancing the prediction performance. The proposed model is tested in two real-life bus lines in Guangzhou, China. Results show that SS-GBDT not only presents superiority in both prediction accuracy and stability, but can also better handle the multicollinearity issue with multi-source data. It can also prioritize the influential factors on passenger flow prediction. The prediction model is flexible and scalable, which enables the integration of various influential factors in the presence of big data.

This paper explores the joint effect of economic development, demographic characteristics and the road network on regional road safety. Although extensive efforts have been undertaken to model and predict the safety effects of different influential factors using statistical regression or machine learning models, little evidence is provided on the relative importance of explanatory variables by accounting for their mutual interactions and non-linear effects on traffic accidents. We present an innovative gradient boosting decision tree (GBDT) model to explore the joint effects of these comprehensive factors on four traffic accident indicators (i.e., the number of traffic accidents, injuries, deaths, and the economic loss). A total of 27 elaborated influential factors associated with the economic, demographic and road network conditions in Zhongshan, China for the period of 2000-2016 are collected. The results show that, compared to other traditional machine learning methods, the GBDT not only presents a higher prediction accuracy, but can also better handle the multicollinearity between the explanatory variables; more importantly, it can rank the influential factors on traffic accident prediction. The results also show that there are both similarities and differences in the key influential factors for the four traffic accident indicators. In particular, we also investigate the partial effects of the key influential factors. Based on the key findings, we highlight the practical insights for planning practice.