arXiv subjects

Zuduo Zheng

Publications and source records attributed to Zuduo Zheng.

7 recordsLinked to original sources

arXiv2025

Central-Upwind Scheme for the Phase-Transition Traffic Flow Model

Phase-transition models are an important family of non-equilibrium continuum traffic flow models, offering properties like replicating complex traffic phenomena, maintaining anisotropy, and promising potentials for accommodating automated vehicles. However, their complex mathematical characteristics such as discontinuous solution domains, pose numerical challenges and limit their exploration in traffic flow theory. This paper focuses on developing a robust and accurate numerical method for phase-transition traffic flow models: We propose a second-order semi-discrete central-upwind scheme specifically designed for discontinuous phase-transition models. This novel scheme incorporates the projection onto appropriate flow domains, ensuring enhanced handling of discontinuities and maintaining physical consistency and accuracy. We demonstrate the efficacy of the proposed scheme through extensive and challenging numerical tests, showcasing their potential to facilitate further research and application in phase-transition traffic flow modeling. The ability of phase-transition models to embed the ``time-gap'' -- a crucial element in automated traffic control -- as a conserved variable aligns seamlessly with the control logic of automated vehicles, presenting significant potential for future applications, and the proposed numerical scheme now substantially facilitates exploring such potentials.

math.NA↗

arXiv2025

Evaluating Electric Micro-Mobility Related Mode Choice Stated Preferences: A Latent Class Choice Approach

This paper examines mode choice behaviour regarding Electric Micro-Mobility (EMM) among car and public transport users using a Latent Class Choice Modelling (LCCM) approach. Utilizing stated preference survey data from 1,671 Brisbane residents, the study identifies distinct user segments with heterogeneous sensitivities to EMM attributes. Results reveal significant potential for EMM adoption, notably among segments inclined toward multimodal trips or personal micromobility, driven primarily by weather conditions, travel time, and cost. Conversely, resistant segments characterized by demographic factors such as age, gender, income, and lack of EMM experience highlight challenges in shifting mode choices. Further analyses, including class profiles, elasticities, and geographical distribution, are conducted to generate comprehensive insights that inform policy decisions. These insights support targeted investments in micromobility infrastructure and integrated pricing strategies, thereby enhancing EMM adoption and promoting sustainable urban mobility.

physics.soc-ph↗

arXiv2023

Continuum modeling of freeway traffic flows: State-of-the-art, challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles (CAVs) are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling. While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives (e.g., traffic efficiency and safety), there are uncertainties about the flow dynamics of CAVs in real-world traffic. The uncertainties are especially amplified for mixed traffic flows, consisting of CAVs and human-driven vehicles, where the implications can be significant from the continuum-modeling perspective, which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations. This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs. We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework. Wherever applicable, we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature. The paper then discusses some major problems inherent to continuum modeling of real-world (mixed) CAV traffic flows modeling by distinguishing between two major research directions: (a) modeling for explaining purposes, where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest, and (b) modeling for practical purposes, in which the focus is on the reliable predictions for operation and control. The paper proposes some potential solutions in each research direction and recommends some future research topics.

math.AP↗

arXiv2022

Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches

This paper addresses the problem of estimating link flows in a road network by combining limited traffic volume and vehicle trajectory data. While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. Vehicle trajectory data collected from vehicle tracking sensors are also incorporated these days. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole population, where the exact sampling rate is unknown and may vary over space and time. This study proposes a novel generative modelling framework, where we formulate the link-to-link movements of a vehicle as a sequential decision-making problem using the Markov Decision Process framework and train an agent to make sequential decisions to generate realistic synthetic vehicle trajectories. We use Reinforcement Learning (RL)-based methods to find the best behaviour of the agent, based on which synthetic population vehicle trajectories can be generated to estimate link flows across the whole network. To ensure the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data, two methods based on Inverse Reinforcement Learning and Constrained Reinforcement Learning are proposed. The proposed generative modelling framework solved by either of these RL-based methods is validated by solving the link flow estimation problem in a real road network. Additionally, we perform comprehensive experiments to compare the performance with two existing methods. The results show that the proposed framework has higher estimation accuracy and robustness under realistic scenarios where certain behavioural assumptions about drivers are not met or the network coverage and penetration rate of trajectory data are low.

cs.LG↗

arXiv2021

A Generative Car-following Model Conditioned On Driving Styles

Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is able to generate realistic human CF behaviors for any given observed or even unobserved driving style. Specifically, the ability of accurately capturing human CF behaviors is ensured by designing and calibrating an Intelligent Driver Model (IDM) with time-varying parameters. The reason behind is that such time-varying parameters can express both the inter-driver heterogeneity, i.e., diverse driving styles of different drivers, and the intra-driver heterogeneity, i.e., changing driving styles of the same driver. The ability of generating realistic human CF behaviors of any given observed driving style is achieved by applying a neural process (NP) based model. The ability of inferring CF behaviors of unobserved driving styles is supported by exploring the relationship between the calibrated time-varying IDM parameters and an intermediate variable of NP. To demonstrate the effectiveness of our proposed models, we conduct extensive experiments and comparisons, including CF model parameter calibration, CF behavior prediction, and trajectory simulation for different driving styles.

cs.AI↗

arXiv2021

Reasons, Challenges and Some Tools for Doing Reproducible Research in Transportation Research

This paper introduces reproducible research, and explains its importance, benefits and challenges. Some important tools for conducting reproducible research in Transportation Research are also introduced. Moreover, the source code for generating this paper has been designed in a way so that it can be used as a template for researchers to write their future journal papers as dynamic and reproducible documents.

cs.DL↗

arXiv2020

A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although many DLC decision-making models have been studied in traffic engineering and autonomous driving, the impact of human factors, which is an integral part of current and future traffic flow, is largely ignored in the existing literature. In autonomous driving, the ignorance of human factors of surrounding vehicles will lead to poor interaction between the ego vehicle and the surrounding vehicles, thus, a high risk of accidents. The human factors are also a crucial part to simulate a human-like traffic flow in the traffic engineering area. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers' decision-making maneuvers to the greatest extent by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model almost follows the human decision-making maneuvers, which can achieve 98.66% prediction accuracy with respect to human drivers' decisions against the ground truth. Besides, the lane-change impact analysis results demonstrate that our model even performs better than human drivers in terms of improving the safety and speed of traffic.

cs.RO↗