The electric vehicle (EV) is a kind of innovation helping to address the issue of climate change and conventional energy consumption, compared to internal combustion engine vehicles. Electric vehicle sharing is a new way to promote the market penetration of electric vehicles due to its convenience and economy. Aiming to provide a more profound understanding of the influential factors in the acceptance of electric vehicle sharing, a structural equation model is proposed based on the theory of planned behavior as the policy environment has been added as prepositive variable. The data about the travelers’ perspective of electric vehicle sharing are acquired from questionnaires in Beijing. The results indicate that the perceived behavioral control is the primary factor with positive contributions to EV-sharing acceptance. Subjective norm, ranking second, is also proven to exert a significant positive effect on EV-sharing acceptance. The results also reveal the insignificant relationship between the attitude towards behavior and sharing acceptance, which is consistent with relevant research. Moreover, the significant positive effects of policy support on attitude and subjective norm are demonstrated. Finally, strategies to promote electric vehicle sharing are proposed, including providing more accessible resources (charging facility, service station), improving social pressure (free trial, sharing atmosphere), and strengthening policy support (financial support, legal guarantee). This study can give a better understanding of the acceptance of EV sharing and the strategy to promoting EV sharing in urban traffic.

On-street parking is an important component of the parking system. Because of its occupancy of roadway resources, it can significantly impact traffic performance and safety. The aim of this paper is to give a quantitative analysis of the influence of on-street parking on travel time. The travel time data of the motor vehicles moving in the road sections with on-street parking are gathered by observers. A proportional hazard-based duration model is proposed to analyze the influential factors related to on-street parking, including effective lane width, the number of parking maneuvers, and occupancy. The results show that on-street parking has a significant impact on the travel time of vehicles. In addition, various factors can modify travel time in different degrees, and the model can be used to estimate the travel time under assumed conditions. It is hoped that this paper will help to improve the planning and management of on-street parking.

Apart from public transit, urban citizens nowadays seek for the personalized travel mode more frequently to satisfy their ever-increasing travel demand. Tailored taxi, based on mobile internet technology, such as Uber in America and Didi-taxi in China, characterizing by its high quality service, is now rapidly expanding its market penetration worldwide. The new emerging tailored taxi challenges the conventional taxi industry. This study concentrates on the personalized travel choices between taxi and tailored taxi. The personalized travel choice is determined by personal characteristic and trip characteristic. Using stated preference technique, a questionnaire is design to acquire data on travel preference. Then the binary logit model is proposed to describe the preferences of traveler's personalized travel behavior. Next, the sensitivity analysis is performed to discuss the influence of different preference factors and individual's characteristics. Finally, the general features of taxi and tailored taxi users are described and the differential development strategies are proposed.

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.

Vehicle trajectory prediction is crucial for the safe driving of the intelligent and connected vehicle, but the existing researches suffer from inadequate interaction modeling, especially lacking modeling for dynamic interaction. To solve this problem, we propose a vehicle trajectory prediction model SDT-ATT based on multivariate interaction modeling. Firstly, a hierarchical extracting module is constructed based on the bi-directional long short-term memory network (Bi-LSTM) to extract the contexts of the target vehicle parameters, neighbor vehicle spatial parameters and neighbor vehicle dynamic parameters, respectively. Secondly, the temporal, spatial and dynamic interactions of vehicles are modeled and learned based on the multi-head attention (MHA) mechanism, then fused to obtain the multivariate interaction features. Next, a direct multi-step prediction module is constructed based on long short-term memory network (LSTM), which directly outputs the future coordinates of the vehicle in the target prediction horizons. Finally, the vehicle trajectory prediction model SDT-ATT proposed in this paper is validated based on the public naturalistic driving datasets I-80 and US-101. The data results show that compared with the CS-LSTM model, the SDT-ATT has 9.3% lower ADE average value, 8.5% lower FDE average value and 8.7% lower RMSE average value, and the inference speed of the SDT-ATT model meets the real-time requirement.

Hundreds of new features and functionalities have been introduced as in-vehicle systems (IVS) mature. However, it remains unclear whether these novel designs have appropriately addressed driver preferences and requirements, especially when factors such as geographical or cultural differences are considered. An empirical study was conducted to determine cultural differences between young Chinese and German drivers with respect to (a) preferences for 18 selected IVS and (b) behavioral effects in six secondary driving tasks. Data from 232 Chinese and 94 German drivers were collected through an online questionnaire and the results indicate that young Chinese drivers value most of the selected IVS designs more significantly than the Germans do, except in categories such as radio, navigation and autonomous emergency braking. In addition, rotary with a display screen is the most preferred interaction modality for both groups. As for behavioral effects when performing secondary tasks, young Chinese drivers are more likely to engage in safety-related scenarios while the Germans in efficiency-related scenarios. An ordinal logistic regression analysis suggested a strong correlation between secondary tasks (looking up navigation, dialing the phone and connecting Bluetooth) and behavioral degradation for young Chinese drivers, whereas the six secondary tasks seem to affect German drivers minimally. Based on the preference analysis and attitudes to behavioral impacts, implications for the design of IVS are discussed to better satisfy needs from drivers of different cultural backgrounds.