Demand prediction for on-demand food delivery (ODFD) is of great importance to the operation and transportation resource utilization of ODFD platforms. This paper addresses short-term ODFD demand prediction using an end-to-end deep learning architecture. The problem is formulated as a spatial–temporal prediction. The proposed model is composed of convolutional long short-term memory (ConvLSTM), and convolutional neural network (CNN) units with encoder–decoder structure. Specifically, long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. The convolution unit is responsible for capturing spatial attributes, while the LSTM part is adopted to learn temporal attributes. Additionally, an attentional model is designed and integrated to enhance the prediction performance by addressing the spatial variation in demand. The proposed approach is compared to several baseline models using a historical ODFD dataset from Shenzhen, China. Results indicate that the proposed model obtains the highest prediction accuracy by capturing both spatial and temporal correlations with attention information focusing on different parts of the input series.

To mitigate potential safety hazards during expressway operation, safety evaluation plays a crucial role. This study develops a fuzzy comprehensive evaluation model to assess the safety of expressway operations. First, an expressway operation safety risk assessment index system consisting of four criteria-layer indicators and 10 sub-criteria layer indicators is established. Then, the comprehensive weight of each evaluation index is calculated based on the combined AHP and entropy methods. Finally, the expressway safety level is obtained by converting qualitative indicators into fuzzy sets and defining corresponding membership functions to describe their degree of membership. The evaluation model is applied to the Jinliwen Expressway in a mountainous area in Zhejiang Province, China. The results show that the method could quantitatively and comprehensively evaluate the safety status of expressways and identify the weak links of operational safety management, which can effectively improve the safety level of expressway operations. The research findings will assist decision-makers in gaining a better understanding of the safety issues of expressways and, thus, using corresponding measures to enhance the safety level of the expressway.

This study presents a robust deep reinforcement learning (RL) approach for real-time, network-wide holding control with transfer synchronization, considering stochastic passenger demand and vehicle running time during daily operations. The problem is formulated within a multi-agent RL framework where each active trip is considered as an agent, which not only interacts with the environment but also with other agents in the considered transit network. A specific learning procedure is developed to learn robust policies by introducing $maxmin$ optimization into the learning objective. The agents are trained via deep deterministic policy gradient algorithm (DDPG) using an extended actor-critic framework with a joint action approximator. The effectiveness of the proposed approach is evaluated in a simulator, which is calibrated using data collected from a transit network in Twin Cities Minnesota, USA. The learned policy is compared with no control, rule-based control and the rolling horizon optimization control (RHOC). Computational results suggest that RL approach can significantly reduce the online computation time by about 50% compared with RHOC. In terms of policy performance, under deterministic scenario, the average waiting time of RL approach is 1.3% higher than the theoretical lower bound of average waiting time; under stochastic scenarios, RL approach could reduce as much as 18% average waiting time than RHOC, and the performance relative to RHOC improves when the level of system uncertainty increases. Evaluation under disrupted environment also suggests that the proposed RL method is more robust against short term uncertainties. The promising results in terms of both online computational efficiency and solution effectiveness suggest that the proposed RL method is a valid candidate for real-time transit control when the dynamics cannot be modeled perfectly with system uncertainties, as is the case for the network-wide transfer synchronization problem.

Knowledge of vacant taxis’ passenger-searching behavior is of great social and economic interest to multiple applications, particularly in planning and operating on-demand mobility services. The inherent stochasticity and dynamic nature in both passenger demand and drivers’ decision-making pose challenges in capturing vacant taxis movements. This study proposes a novel deep clustering framework to comprehensively uncover passenger-searching strategies from extensive trajectory data. Specifically, multiple features from vacant searching trips are extracted and analogously defined as multi-channel images, where each channel corresponds to a specific feature. A novel deep image clustering approach is then proposed, integrating a feature representation module utilizing convolutional neural networks (ConvNets), a self-expression module for affinity learning, a spectral clustering module, and a classification module for self-supervision. An effective training procedure is also presented for the proposed deep clustering framework. Experiments demonstrate the effectiveness of the proposed approach against benchmark methods. Based on the clustering results, common and specific passenger searching strategies are further revealed. Specifically, our findings highlight the importance of individual’s contextual experience in explaining searching behavior and operational efficiency. Moreover, drivers cruising without clear searching strategy often exhibit lower performance, and some drivers may gamble with peers to increase their chances of picking up passengers. These results deliver important justifications for future studies and provide managerial implications to improve on-demand mobility.