Showing papers by "Xiaolei Ma published in 2020"

Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction

Abstract: Network-wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. With the rise of artificial intelligence, many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance, given the volume and variety of traffic data has been greatly increased. Considering that traffic status on a road segment is highly influenced by the upstream/downstream segments and nearby bottlenecks in the traffic network, extracting well-localized features from these neighboring segments is essential for a traffic prediction model. Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks, the lack of flexibility in the local-feature extraction process is still a big issue. Classical wavelet transform can detect sudden changes and peaks in temporal signals. Analogously, when extending to the graph/spectral domain, graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features. In this study, to capture the complex spatial-temporal dependencies in network-wide traffic data, we learn the traffic network as a graph and propose a graph wavelet gated recurrent (GWGR) neural network. The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model. A gated recurrent structure is employed to learn temporal dependencies in the sequence data. Comparing to baseline models, the proposed model can achieve state-of-the-art prediction performance and training efficiency on two real-world datasets. In addition, experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR.

Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach

Abstract: It is a common practice for transit lines with fluctuating passenger demands to use demand-driven bus scheduling to reduce passenger waiting time and avoid bus overcrowding. However, current literature on the demand-driven bus scheduling generally assumes fixed bus capacity and exclusively optimizes bus dispatch headways. With the advent of connected and autonomous vehicle technology and the introduction of autonomous minibus/shuttle, the joint design of bus capacity and dispatch headway holds promises to further improving the system efficiency while reducing operating and passenger costs. This paper formulates this problem as an integer nonlinear programming model for transit systems operating with mixed human-driven and autonomous buses. In such mixed operating environment, the model simultaneously considers: (1) dynamic capacity design of autonomous bus, i.e., autonomous buses with varying capacity can be obtained by assembling and/or dissembling multiple autonomous minibuses; (2) trajectory control of autonomous bus, i.e., autonomous bus can dynamically adjust its running time as a function of its forward and backward headways; and (3) stop-level passenger boarding and alighting behavior. The objective of the model is designed to balance the trade-off between the operating costs of dispatching different types of bus and the costs of increased passenger waiting time due to inadequate bus dispatching. The model is solved using a dynamic programming approach. We show that the proposed model is effective in reducing passenger waiting time and total operating cost. Sensitivity analysis is further conducted to explore the impact of miscellaneous factors on optimal dispatching decisions, such as penetration rate of autonomous bus, bus running time variation, and passenger demand level.

Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

Abstract: The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and control. Traditional methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks, where traffic states exhibit high degrees of dynamics and complexity. Recently, advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven methods. However, limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic characteristics. To this end, this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic state. After matrix factorization, cluster analysis is conducted based on the obtained low-dimensional representative matrices, which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network congestion. The applicability and effectiveness of the proposed approach are examined in a road network of Beijing, China. Results show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

Measuring the Spatial Spillover Effects of Multimodel Transit System in Beijing: A Structural Spatial Vector Autoregressive Approach

Abstract: Changes in local transit passenger flow may cause a spatial spillover effect across the involved regions and affect traffic patterns in other regions. To identify the affected areas and the traffic patterns, this study develops an enhanced spatial vector autoregressive (SpVAR) model to investigate relations in public transport systems in the case of sudden large passenger flow impact. The proposed model captures the interacted correlation within different transit models in separated regions. Three representative commuting regions in Beijing, namely, Zhongguancun, Guomao, and Huilongguan, are employed for empirical study. Results confirm the existence of spatial spillover effect in the commuter regions and reveal heterogeneous effects of multimodal transit system on regions with different distances.

Examining regional mobility patterns of public transit and automobile users based on the smart card and mobile Internet data: a case study of Chengdu, China

Abstract: Understanding human travel patterns with relation to land uses and other land characteristics is a crucial topic in urban studies and offers guidance to better design, plan and manage diverse transportation systems and infrastructures. However, previous studies on such patterns still have some limitations. First, current studies mainly focus on mobility pattern analysis on stop and route levels. Rare studies have investigated user mobility on region levels to exhibit a macroscopic perspective on human activities. Second, the works usually examine one specific transportation mode and fail to compare regional mobility among multiple modes. This study proposes a comprehensive methodology framework to uncover regional travel patterns and regularities. Regional travel patterns are analysed using the non-negative matrix factorisation method. The travel regularity is examined using the Lempel–Ziv algorithm. A case study of Chengdu, China, is conducted to validate the proposed framework. Results can support several applications that are beneficial for urban transportation planning.

Mining Truck Platooning Patterns Through Massive Trajectory Data

Abstract: Truck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements. Properly planning platoons and evaluating the potential of truck platooning are crucial to trucking companies and transportation authorities. This study proposes a series of data mining approaches to learn spontaneous truck platooning patterns from massive trajectories. An enhanced map matching algorithm is developed to identify truck headings by using digital map data, followed by an adaptive spatial clustering algorithm to detect instantaneous co-moving truck sets. These sets are then aggregated to find the network-wide maximum platoon duration and size through frequent itemset mining for computational efficiency. We leverage real GPS data collected from truck fleeting systems in Liaoning Province, China, to evaluate platooning performance and successfully extract spatiotemporal platooning patterns. Results show that approximately 36% spontaneous truck platoons can be coordinated by speed adjustment without changing routes and schedules. The average platooning distance and duration ratios for these platooned trucks are 9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel consumption. We also distinguish the optimal platooning periods and space headways for national freeways and trunk roads, and prioritize the road segments with high possibilities of truck platooning. The derived results are reproducible, providing useful policy implications and operational strategies for large-scale truck platoon planning and roadside infrastructure construction.