arXiv subjects

Ran Tu

Publications and source records attributed to Ran Tu.

6 recordsLinked to original sources

arXiv2024

Modeling Battery Electric Vehicle Users' Charging Decisions in Scenarios with Both Time-Related and Distance-Related Anxiety

As one of the most promising alternatives to internal combustion engine vehicles, battery electric vehicles (BEVs) have become increasingly prevalent in recent years. However, range anxiety is still a major concern among BEV users or potential users in recent years. The social-psychological factors were found to be associated with range anxiety, but how the charging decisions are affected by range anxiety is still unclear. Thus, in our study, through an online questionnaire issued in mainland China, we collected 230 participants' charging decisions in 60 range-anxiety-inducing scenarios in which both distance-related, and time-related anxiety co-existed. Then, an interpretable machine learning (ML) approach with the Shapley Additive Explanations method was used to model BEV users' charging decisions in these scenarios. To further explore users' decision-making mechanisms, a Bayesian-Network-regression mixed approach was used to model the inner topological structure among the factors influencing users' decisions. We find that both time-related and distance-related factors can affect users' charging decisions, but the influence of waiting time is softer compared to the BEV range. Users' charging decisions can also be moderated by users' psychological states (i.e., range anxiety level and trust in range estimation system), individual differences (i.e., age and personality), and BEV using experience (i.e., driving mileage, display mileage and range estimation cycle of range estimation system), of which, the range anxiety level is more directly related with users' charging decisions. Findings from this study can provide insights into the optimization of charge station distribution and customization of the charging recommendation system.

cs.HC↗

arXiv2023

Range Anxiety Among Battery Electric Vehicle Users: Both Distance and Waiting Time Matter

Range anxiety is a major concern of battery electric vehicles (BEVs) users or potential users. Previous work has explored the influential factors of distance-related range anxiety. However, time-related range anxiety has rarely been explored. The time cost when charging or waiting to charge the BEVs can negatively impact BEV users' experience. As a preliminary attempt, this survey study investigated time-related anxiety by observing BEV users' charging decisions in scenarios when both battery level and time cost are of concern. We collected and analyzed responses from 217 BEV users in mainland China. The results revealed that time-related anxiety exists and could affect users' charging decisions. Further, users' charging decisions can be a result of the trade-off between distance-related and time-related anxiety, and can be moderated by several external factors (e.g., regions and individual differences). The findings can support the optimization of charge station distribution and EV charge recommendation algorithms.

cs.CY↗

arXiv2023

Influential Factors of Users' Trust in the Range Estimation Systems of Battery Electric Vehicles -- A Survey Study in China

Although the rapid development of battery technology has greatly increased the range of battery electric vehicle (BEV), the range anxiety is still a major concern of BEV users or potential users. Previous work has proposed a framework explaining the influential factors of range anxiety and users' trust toward the range estimation system (RES) of BEV has been identified as a leading factor of range anxiety. The trust in RES may further influence BEV users' charging decisions. However, the formation of trust in RES of BEVs has not yet explored. In this work, a questionnaire has been designed to investigate BEV users' trust in RES and further explore the influential factors of BEV users' charging decision. In total, 152 samples collected from the BEV users in mainland China have been analyzed. The BEV users' gender, driving area, knowledge of BEV or RES, system usability and trust in battery system of smartphones have been identified as influential factors of RES in BEVs, supporting the three-layer framework in automation-related trust (i.e., dispositional trust, situational trust and learned trust). A connection between smartphone charging behaviors and BEV charging behaviors has also been observed. The results from this study can provide insights on the design of RES in BEVs in order to alleviate range anxiety among users. The results can also inform the design of strategies (e.g., advertising, training and in-vehicle HMI design) that can facilitate more rational charging decisions among BEV users.

cs.CY↗

arXiv2022

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Ecodriving guidance includes courses or suggestions for human drivers to improve driving behaviour, reducing energy use and emissions. This paper presents a systematic review of existing eco-driving guidance studies and identifies challenges to tackle in the future. A standard agreement on the guidance design has not been reached, leading to difficulties in designing and implementing eco-driving guidance for human drivers. Both static and dynamic guidance systems have a great variety of guidance results. In addition, the influencing factors, such as the suggestion content, the displaying methods, and drivers socio-demographic characteristics, have opposite effects on the guidance result across studies, while the reason has not been revealed. Drivers motivation to practice eco behaviour, especially long-term, is overlooked. Besides, the relationship between users acceptance and system effectiveness is still unclear. Adaptive driving suggestions based on drivers habits can improve the effectiveness, while this field is under investigation.

cs.RO↗

arXiv2020

Greenhouse Gas Emission Prediction on Road Network using Deep Sequence Learning

Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS). We develop a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and the GHG ER of previous time steps. In particular, various specifications of the long-short term memory (LSTM) networks with exogenous variables are examined and compared with clustering and the autoregressive integrated moving average (ARIMA) model with exogenous variables. The downtown Toronto road network is used as the case study and highly detailed data are synthesized using a calibrated traffic microsimulation and MOVES. It is found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performs the best while adopting 2 hidden layers and when the hyper-parameters are systematically tuned. Adopting a 30 second updating interval improves slightly the correlation between true and predicted GHG ERs, but contributes negatively to the prediction accuracy as reflected on the increased root mean square error (RMSE) value. Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way to non-myopic eco-routing on large-scale road networks {to alleviate the adverse impact on the global warming

eess.SP↗

arXiv2019

A multi-layered blockchain framework for smart mobility data-markets

Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control.

cs.CY↗