Showing papers by "Xiaolei Ma published in 2019"

Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction

Abstract: Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and transferring from station to station. An increasing number of deep learning algorithms are being utilized to forecast metro ridership due to the development of computational intelligence. However, limited efforts have been exerted to consider spatiotemporal features, which are important in forecasting ridership through deep learning methods, in large-scale metro networks. To fill this gap, this paper proposes a parallel architecture comprising convolutional neural network (CNN) and bi-directional long short-term memory network (BLSTM) to extract spatial and temporal features, respectively. Metro ridership data are transformed into ridership images and time series. Spatial features can be learned from ridership image data by using CNN, which demonstrates favorable performance in video detection. Time series data are input into the BLSTM which considers the historical and future impacts of ridership in temporal feature extraction. The two networks are concatenated in parallel and prevented from interfering with each other. Joint spatiotemporal features are fed into a fully connected network for metro ridership prediction. The Beijing metro network is used to demonstrate the efficiency of the proposed algorithm. The proposed model outperforms traditional statistical models, deep learning architectures, and sequential structures, and is suitable for ridership prediction in large-scale metro networks. Metro authorities can thus effectively allocate limited resources to overcrowded areas for service improvement.

Impacts of free-floating bikesharing system on public transit ridership

Abstract: The development of bike sharing system (BSS) has changed travelers’ commuting and lifestyle in recent years. Whether BSSs are complementary or competitive to public transit remains controversial. This study uses a propensity score matching-based difference-in-difference (DID) method to evaluate the impact of free-floating BSS on bus ridership in Chengdu, China. The transaction data of bus service and BSS and the neighboring points of interest are investigated. Results indicate that, (a) on the bus route level, each shared bike results in a 4.23 increment and a 0.56 reduction in daily bus ridership on weekdays and weekends, respectively; (b) on the bus stop level, the increment in shared bikes significantly negatively impacts bus ridership on weekends; (c) on the route level, regarding the time of day, each unit increment of shared bike significantly increases bus ridership on weekdays by 0.54, 0.34, and 0.15 during a.m. peak, p.m. peak, and off peak, respectively; and (d) on the bus stop level, the relationship between shared bikes and bus ridership is insignificant on weekdays. This study reveals that the demand pattern of commuters strongly impacts the relationship between shared bike and public transportation.

A predictive headway-based bus-holding strategy with dynamic control point selection: A cooperative game theory approach

Abstract: Bus holding is a widely used control method to regularize bus headways and reduce bus bunching. The method works in such a way by delaying buses at control points if their departure times or headways deviate from the planned ones. However, it may result in reduced bus commercial speeds and increased passenger onboard travel time. To avoid this problem, researchers have suggested that control points be spaced cautiously along the route such that only a few are needed. This study proposes a predictive headway-based bus holding strategy with dynamic control point importance ranking and selection based on the cooperative game theory. The framework considers not only individual control points’ impact but also the collective group control effects. Specifically, the proposed framework consists of two components: a performance model and a cooperative game model. The performance model predicts headway performances of all running buses when different control point combinations are in effect. Dynamic bus running times and passenger demands are reflected in the model. Then, these headway performances are passed to the cooperative game model with control points being players and improvements in headway performances compared with that under no holding control being the utility function. The game is solved by Myerson value, a concept that extends Shapley value used for the normal cooperative game and considers the cooperation structure and potential worth of coalitions. We use Myerson value to rank the importance of control points on regularizing headways, as it measures the average marginal utility contribution of a control point to all possible coalitions that exclude that point. We prove that Myerson value lies in the Ω-core of the game and thus satisfies allocation efficiency, individual and coalition rationality. The proposed framework is applied to target headway control and two-way-looking self-equalizing headway control. Simulation results show that the framework can significantly reduce passenger waiting time and bus headway variation.

Spatial Interpolation of Missing Annual Average Daily Traffic Data Using Copula-Based Model

Abstract: Accurate estimation of traffic counts [(i.e., annual average daily traffic (AADT)] is essential to transportation agencies for traffic demand forecasting, emission evaluation, pavement design, and project prioritization. Traditional AADT estimation methods rely on either temporal data imputation techniques based on historical records or kriging-based spatial interpolation approaches. However, Kriging method utilizes the correlation function as the sole descriptor of spatial dependency, posing limitations to yield accurate interpolation results for unstable AADTs under complex traffic patterns due to diverse road functions or land uses. This study proposed a copula-based model that combines spatial dependency and marginal distribution for missing AADT interpolation to weaken the limitation of Kriging method. Thus, the proposed model not only can describe the spatial dependency but also is robust to outliers. AADT data collected from the California state highway network were used to evaluate the effectiveness of spatial copula models with varying missing data rates. Four road segments with regular and recreational traffic patterns were selected to compare with existing kriging-based approaches. Results suggested that the spatial copulas yielded significantly higher accuracy rates than kriging did for irregular travel patterns with high missing data rates. Spatial copula models hold a great potential to improve the performance of large-scale transportation network-wide data imputation for planning and operational usages.

Agent-Based Simulation Optimization Model for Road Surface Maintenance Scheme

Abstract: Road surface maintenance is a difficult problem in traffic management due to the interference with traffic. Considering the irrational road surface maintenance scheme in practice, a bilevel...

Modeling travel time volatility using copula-based Monte Carlo simulation method for probabilistic traffic prediction

Abstract: Travel time volatility (TTV) is introduced in this study to depict the variation in travel time. TTV can provide travelers with understandable information, such as probabilistic travel time estimat...

Analyzing the Spatial and Temporal Characteristics of Subway Passenger Flow Based on Smart Card Data

Abstract: Passenger flow is a core feature of rail transportation stations, and its station-level fluctuation is strongly influenced by its surrounding land-use types. This study develops a sequential K-means clustering algorithm that utilizes smart card data to categorize Beijing subway stations. The temporal characteristics of daily inbound and outbound subway passenger flows are considered in the clustering. The stations are divided into 10 groups that are classified under three categories: employment-oriented, dual-peak, and residence-oriented stations. We analyze how these categories differ in terms of station-level passenger flow. In addition, a station-level buffer area calculation method is used to estimate the land-use density around each subway station. Considering the spatial nonstationarity of passenger flow, we employ a geographically weighted regression (GWR) model to determine the correlation effect between peak-hour passenger flow and land-use density. We then analyze the spatial distribution of the correlation coefficients. Results demonstrate that most residents commute via rail transportation, and the passenger flows for the different categories of stations exhibit distinct characteristics of residences and workplaces. The findings of this study provide insightful information and theoretical foundation for rail transportation network design and operation management.