With the increasing number of automated vehicles (AVs) being tested and operating on roads, external Human–Machine Interfaces (eHMIs) are proposed to facilitate interactions between AVs and other road users. Considering the need to protect vulnerable road users, this paper addresses the issue by providing research evidence on various designs of eHMIs. Ninety participants took part in this experiment. Six sets of eHMI prototypes—Text, Arrowed (Dynamic), Text and Symbol, Symbol only, Tick and Cross and Traffic Lights, including two sub-designs (Cross and Do Not Cross)—were designed. The results showed that 65.1% of participants agreed that external communication would have a positive effect on pedestrians’ crossing decisions. Among all the prototypes, Text, and Text and Symbol, eHMIs were the most widely accepted. In particular, for elderly people and those unfamiliar with traffic rules, Text, and Text and Symbol, eHMIs would lead to faster comprehension. The results confirmed that 68.5% of participants would feel safer crossing if the eHMI had the following features: ‘Green’, ‘Text’, ‘Symbol’, or ‘Dynamic’. These features are suggested in the design of future systems. This research concluded that eHMIs have a positive effect on V2X communication and that textual eHMIs were clear to pedestrians.

The emergence of automated vehicles (AVs) is expected to have a huge impact on traffic safety and environmental improvement. In order to promote the sustainable development of AVs, it is urgent to study the public’s acceptance of and willingness-to-pay for automated vehicles and their influencing factors. Based on a questionnaire survey and descriptive research, this paper investigates the public’s general views on AVs. A psychological model considering technical trust (TT), perceived benefit (PB), perceived risk (PR), and perceived ease of use (PU) was constructed to study the factors that influence the public’s acceptance of and willingness-to-pay for AVs. Logistic regression models based on demographic factors such as monthly income (MI) and driving experience (DE) and psychological factors were established to predict end-users’ acceptance and willingness-to-pay. The accuracy of the two models is 93.2% and 87.9%, respectively. Based on the results, the following policies can be put forward to promote the development of AVs: (1) more information to enhance TT; (2) pricing and easy maintenance based on PU; (3) education and training based on TT and PB; and (4) personalized sales based on DE and MI.

Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report a pressure-sensitive and self-powered material named triboelectric nano-generators (TENGs). The generated voltage data of TENGs, which is associated with the pedal action, can be collected easily and stored sequentially. According to the characteristics of the voltage data, we have employed a hybrid machine learning method. After collecting signals from TENGs and driving simulator simultaneously, an unsupervised Gaussian mixture model is used to cluster the pedal events automatically using data from simulator. Then, multi-feature candidates of the voltage data from TENGs are extracted and ranked. A supervised random forest model that treats voltage data of TENGs as input data is trained and tested. Results show that data from TENGs can have a high accuracy of more than 90% using the random forest algorithm. The evaluating results demonstrate the accuracy of the proposed data-driven hybrid learning algorithm for recognition of driver’s pedal action intensity. Furthermore, technical and economic characteristics of TENGs and some common sensors are compared and discussed. This work may demonstrate the feasibility of using these data-driven methods on the detection of driver’s pedal action intensity.

Driving behaviours have been proved sensitive to the cultural background. This study aims to investigate the heterogeneity of car‐following behaviour between and within Chinese and German driver groups. A driving simulator‐based experiment was designed and conducted. Participants were asked to drive through two scenarios: (i) Leading vehicle with high speed; braking after a specific distance. (ii) Leading vehicle with a frequently changing speed; braking after a specific distance. Totally 41 participants’ (20 Chinese; 21 German) car‐following behaviours were recorded. Desired safety margin (DSM) model was used to conclude each participant's driving character in car following. Heterogeneity between and within participant groups was investigated through statistical analysis of five driving behaviour parameters. Results showed that Chinese participants had a higher degree of heterogeneity for acceleration sensitivity parameter in Scenario 2. German participants had a higher value of the lower limit of DSM in Scenario 1. The authors also found that four out of five driving behaviour parameters were observed sensitive to the scenario. This study may provide support for researches on the traffic flows’ cross‐cultural difference and the technique transferring from developed country to developing country.

The global closure policy to limit the spread of the new coronavirus (COVID-19) in 2020 was based on public safety and health considerations. In the implementation of arrangements to prevent the epidemic, the function of the transportation system as a basis for securing cities has been severely affected. After summarizing the domestic and international literature on epidemic policies and travel, this study analyzes the changes of the spatial and temporal distribution characteristics of people’s travel and the impacts in the context of the two epidemic phases in Beijing and abroad. During the epidemic, traffic volume into and out of Beijing showed a downward trend. In our study, we found that total travel volume in Beijing during the Spring Festival in 2020 was down by about 70% year-on-year, the distribution of daily traffic trips during the day was not affected by the outbreak, and six urban areas in the center of Beijing experienced greater declines in travel volume compared to other urban areas. The conclusions of the study can provide a reference for the sustainability and recovery of urban areas and formulation of policies in the subsequent pandemic era in terms of the relationship between public travel and epidemic control.

Speed bumps are extensively used to regulate speed and improve traffic safety. Speed bumps can also affect traffic characteristics significantly. Aiming to explore the impact of speed bumps on traffic flow, we propose a simulating method with improved cellular automata model based on the NaSch model. The developed model incorporates the aspects of three diverse driving behaviors: (1) type of drivers (cautious or rash), (2) decelerating range, and (3) passing speed. Numerical results show that traffic flow state experience phase transitions from free flow to stable flow to traffic jams due to the installation of speed bumps. During the stable flow, a speed bump can cause small-scale traffic jams in the upstream. The influence of speed bumps enlarges significantly as cautious drivers increase and the passing speed declines, but the expansion of decelerating range has a less significant effect. The results hope to enlighten sensible applications of speed bumps.