Vehicle trajectory usually suffers from a large number of outliers and observation noises. This paper proposes a novel framework for reconstructing vehicle trajectories. The framework integrates the wavelet transform, Lagrange interpolation and Kalman filtering. The wavelet transform based on waveform decomposition in the time and frequency domain is used to identify the abnormal frequency of a trajectory. Lagrange interpolation is used to estimate the value of data points after outliers are removed. This framework improves computation efficiency in data segmentation. The Kalman filter uses normal and predicted data to obtain reasonable results, and the algorithm makes an optimal estimation that has a better denoising effect. The proposed framework is compared with a baseline framework on the trajectory data in the NGSIM dataset. The experimental results showed that the proposed framework can achieve a 45.76% lower root mean square error, 26.43% higher signal-to-noise ratio and 25.58% higher Pearson correlation coefficient.

Freeway-diverging areas are prone to low traffic efficiency, congestion, and frequent accidents. Because of the fluctuation of the surrounding traffic flow distribution, the individual decision-making of vehicles in diverging areas is typically unable to plan a departure trajectory that balances safety and efficiency well. Consequently, it is critical that vehicles in freeway-diverging regions develop a lane-changing driving strategy that strives to improve both the safety and efficiency of divergence areas. For CAV leaving the diverging area, this study suggested a full-time horizon optimum solution. Since it is a dynamic strategy, an MPC system based on rolling time horizon optimization was constructed as the primary algorithm of the strategy. A simulation experiment was created to verify the viability of the proposed methodology based on a mixed-flow environment. The results show that, in comparison with the feasible strategies exiting to off-ramp, the proposed strategy can take over 60% reduction in lost time traveling through a diverging area under the premise of safety and comfort without playing a negative impact on the surrounding traffic flow. Thus, the MPC system designed for the subject vehicle is capable of performing an optimal driving strategy in diverging areas within the full-time and space horizon.

According to the principle of urban transport integration and sustainable development, in this work, we study the level of connection between urban rail transit and regular bus transport, construct an evaluation indicator system according to the characteristics of the connection system, use the entropy weighting method (EWM) to calculate the weights of the indicators to determine the influence of each indicator on the level of connection, and construct a TOPSIS comprehensive evaluation model, which can make an overall evaluation of objects subject to multiple factors to analyze the level of connection between rail transit stations. Finally, the system of evaluation indicators and the analysis of the level of connection are applied to an example of a rail transit station in operation in Wuxi city, and the problems of connection and interchange in the case station are analyzed. We find that 57.5% of rail stations in Wuxi have low connectivity and that interchange information service and average transfer time are the most influential factors. This study defines and quantifies eight key indicators that influence the level of rail-transit connectivity to quantify and grade the connectivity of different stations, and selects the city of Wuxi as a case study for validation. Our research provides theoretical support and practical guidance for improving rail transit interchange capacity and the sustainable development of public transport.

This study deals with the problem of road side unit (RSU) location optimization for origin-destination (OD) demand estimation. With the point-to-point measurement provided by RSUs in connected vehicle environment, the errors of OD demand estimation come from two sources: 1) the lack of enough path flow information; and 2) the vehicle-to-RSU (V2R) communication delay. However, increasing the amount of path flow information collected by RSUs results in the increase of V2R communication delay encountered by each collected data packet. Moreover, it is difficult to find a global optimal solution by formulating the problem as a single objective program. To address the investigated problem, this study proposes a novel framework consisting of solving a bi-objective RSU location optimization problem and an OD demand estimation problem. This RSU location optimization problem is formulated as a bi-objective nonlinear binary integer program to balance the maximization of the amount of path flow information and the minimization of V2R communication delay. The OD demand estimation problem is formulated as a least square estimator to identify the RSU location scheme with the smallest OD demand estimation error, among the Pareto optimal solutions to the bi-objective program. An efficient $\varepsilon$ -constraint method is developed to generate the Pareto optimal solutions. The numerical example demonstrates that the proposed framework achieves 6.95 lower root-mean-square error of OD demand estimation, compared with the baseline framework.

This paper presents a shockwave-based method for estimating queue length associated with presignals for bus priority. Both queue length at presignals and main signals are estimated by the proposed method. In this study, we first analyze traffic operations with the implementation of presignals for bus priority using shockwave theory. On this basis, queue length at presignals and main signals are estimated for eight cases in which a bus arrives at different times during a traffic signal cycle. Detailed comparisons of queue length estimated by shockwave theory and that obtained by a cellular-automaton-based simulation are made from the shape of the fundamental diagram (triangular and concave), areas (upstream of the presignal and between the presignal and the main signal), bus arrival scenarios (bus is not present and bus is present), and bus arrival times (during the red presignal and during the green presignal). Simulation experiments show that the proposed shockwave-based method is effective in estimating queue length associated with presignals for bus priority in all cases. The efforts are made to provide the basis for evaluating the performance of intersections with presignals for bus priority implemented and designing sorting area for presignals for bus priority.

This study looks into how psychological and socioeconomic factors interact to affect people’s propensity to purchase autonomous vehicles (AVs). Inspired by the Technology Acceptance Model, six psychological variables—social influence, convenience, perceived utility, perceived ease of use, perceived risk, and usage attitude—are proposed. Twenty-two measurement variables are introduced because it is difficult to measure these latent factors directly. To understand the link between the latent variables and calculate their factor scores, a structural equation model is created. The latent variables, along with observable socioeconomic attributes, are included as explanatory variables in a mixed logit model to estimate the purchase likelihood for AVs on different levels. A stated preference survey is conducted for data collection. We obtained 302 effective samples. The experiment results demonstrate that perceived usefulness has the most significant positive impact on purchase likelihood, followed by social influence and perceived ease of use. However, perceived risk has a significant negative impact on the purchase likelihood. Individuals with less driving experience and those without a motor vehicle driving license are more inclined to adopt autonomous vehicles. Additionally, there is a substantial correlation between the frequency of car use and the propensity to support the deployment of autonomous vehicles.