Chengchuan An

• Ph. D.
• PostDoc Position at Southeast University

This paper delves into the robust optimal longitudinal control of connected and automated vehicle (CAV) platoons under parameter uncertainties and external disturbances. String stability not only can contribute to disturbance attenuation throughout a vehicle platoon, but also helps to enhance traffic capacity. However, the existing achievements lac...

Signal retiming is the most cost-efficient measure to reduce vehicle delay and alleviate congestion on urban roads. Previous studies have explored the potential of using connected vehicle data for signal retiming specifically under the current low-penetration environment, which will significantly reduce the cost and increase the productivity of sig...

The acquisition of real-time and accurate traffic arrival information is of vital importance for proactive traffic control systems, especially in partially connected vehicle environments. License plate recognition (LPR) data that record both vehicle departures and identities are proven to be desirable in reconstructing lane-based arrival curves in...

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using lowpenetration vehicle trajectory data. Leveraging traffic w...

Rapid advancements in traffic monitoring and sensing technologies have permitted the multiplex and democratized gathering of numerous traffic data (e.g. speed, volume), depicting identical traffic dynamics from various but complementary views. Incomplete values are ubiquitous in these data, which undermines their utility in subsequent applications....

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using low-penetration vehicle trajectory data. Leveraging traffic...

The acquisition of real-time and accurate traffic arrival information is of vital importance for proactive traffic control systems, especially in partially connected vehicle environments. License plate recognition (LPR) data that record both vehicle departures and identities are proven to be desirable in reconstructing lane-based arrival curves in...

Link progression speed is a key characteristic of urban traffic flow and is essential to developing effective signal coordination schemes. Typically, it is empirically determined as a fixed value regardless of its stochastic nature. Previous studies have proposed both analytical and simulation models to investigate the vehicle progression delay cau...

Short-term traffic flow prediction plays a critical role in Intelligent Transportation System (ITS), and has attracted continuous attention. Previous studies have focused on improving the prediction accuracy of mean traffic flow. Due to the dynamics and propagation of traffic system, reliable traffic control and induction measures have been conside...

The partition of a long urban arterial is necessary for efficient and reliable traffic signal coordination. Previous studies have utilized fixed detector data or aggregated vehicle trajectory data to measure the correlation between adjacent intersections for an arterial partition. However, the appropriate partition of a long urban arterial remains...

Accurate and reliable traffic state identification is crucial to developing responsive and proactive traffic management applications. In this study, the problem of vehicle discharge state identification at signalized intersections is investigated, which focuses on the vehicle discharge process during the green interval. Instead of using detailed ve...

Vehicle trajectory data derived from automatic vehicle location (AVL) and automatic vehicle identification (AVI) systems provide critical support for intelligent transportation systems. However, the field-obtained vehicle trajectories are usually incomplete due to sensor malfunction or communication issues. To recover the incomplete data, the exist...

Traffic data chronically suffer from missing and corruption, leading to accuracy and utility reduction in subsequent Intelligent Transportation System (ITS) applications. Noticing the inherent low-rank property of traffic data, numerous studies formulated missing traffic data recovery as a low-rank tensor completion (LRTC) problem. Due to the non-c...

To partition an urban network into several subareas (i.e., subarea partition) is a vital step for regional coordinated signal control. The correlation between intersections must be analyzed for achieving reliable subarea partition results. However, because of the incompleteness of spatial–temporal information in traffic data, previous studies merel...

Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when netw...

Real-time and accurate queue length information is crucial to developing effective queue management applications in modern traffic control systems to alleviate traffic congestion. A Random Forests (RF) based real-time queue length estimation method is proposed using the vehicle Global Position System (GPS) trajectory and License Plate Recognition (...

Understanding the traffic arrival process and its patterns is of vital importance for delay and queue analysis at intersections. The installation of advance loop detectors to sense vehicle arrivals could be costly and biased. Utilizing sampled vehicle trajectory data to reconstruct the traffic arrival flow might suffer from small sample sizes. Lice...

Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully consider...

Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their nois...

Traffic demand is the basis for urban traffic planning and management. Most studies focus on exploring the temporal-spatial pattern of traffic demand. Few studies have investigated traffic demand structure with field data, such as the ratio of commuting demand. The availability of automatic license plate recognition (ALPR) data provides the opportu...

A common way to estimate dynamic O-D flows is to establish and solve a bilevel optimization model. Though numerous efforts have been devoted to effectively and efficiently solving the model, challenges still exist because of the interdependence of jointly solving the upper-level O-D estimation and lower-level traffic assignment problems and the non...

Macroscopic Fundamental Diagram (MFD) reveals the relationship between network accumulation and flow at the macroscopic level. The network traffic flow state analysis is a fundamental problem for the MFD-based applications. Theoretical and experimental investigations have provided insights into the dynamics and characters of traffic flow states. Al...

Many analytical procedures, technical methods, and tools have been developed to facilitate manual inspection of traffic congestion and support the decision-making process for traffic authorities. However, lacking an automatic mechanism, it would be a time-consuming and labour-intensive process for day-today and location-by-location analyses. This s...

Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated b...

Bus systems play an important role in urban public transport, and bus schedules are essential to bus companies and passengers. Historically bus timetables have generally been set manually, which is inefficient. In this paper a new model aiming to help bus companies with bus schedule will be developed. In short, the number of passengers at each stat...

Many analytical procedures, technical methods and tools have been developed to facilitate manual inspection of traffic congestion and support the decision-making process for traffic authorities. However, lacking an automatic mechanism, this remains a time-consuming and labor-intensive process for day-to-day and location-by-location analyses. This p...

Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposit...

Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposit...

Shockwave profiles describe the traffic flow dynamics and contain comprehensive information of signal performance on arterial. Hence, the construction of shockwave profiles could be of great help to achieve more responsive and effective traffic signal operations. Previous work has made significant efforts to derive performance measures, such as que...

Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated b...

Because of the popularity and necessity of taxicabs, taxicab-related research has received increasing attention over the past decade. However, few studies have highlighted the value of taxicabs as an important component of public transportation systems, and the measurement and evaluation of taxicab systems have been largely missing in the previous...

Travel time reliability (TTR) is an important performance indicator for transportation systems. TTR can be generally categorized as either segment based or origin–destination (O-D) based. A primary difference between the two TTR estimations is that route information is implied in segment-based TTR estimations. Segment-based TTR estimations have bee...

Reliable communication in a traffic signal network is essential for responsive traffic management. However, communication malfunctions often go unnoticed by transportation agencies because of unfamiliarity with the potential problems and inefficiencies that can arise from poor communication quality. Therefore, this paper focuses on the critical eff...

As multiple traffic data sources have become available recently, a new opportunity has been provided for improving the accuracy of short-term travel time forecasting by fusing different but valid data sources. However, previous studies seldom quantified and integrated the reliability of data sources into model development to achieve the potential p...